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提交 48183f10 编写于 作者: A Alexey Spizhevoy
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optimized memory requirements for gpu::minMax's buffers, added support of compute capability 1.0

上级 c4654620
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Showing with 265 addition and 139 deletion
modules/gpu/src/arithm.cpp
浏览文件 @ 48183f10
......@@ -490,44 +490,64 @@ Scalar cv::gpu::sum(const GpuMat& src)
////////////////////////////////////////////////////////////////////////
// minMax
namespace cv { namespace gpu { namespace mathfunc {
namespace cv { namespace gpu { namespace mathfunc { namespace minmax {
void get_buf_size_required(int elem_size, int& b1cols, int& b1rows,
int& b2cols, int& b2rows);
template <typename T>
void min_max_caller(const DevMem2D src, double* minval, double* maxval);
}}}
void min_max_caller(const DevMem2D src, double* minval, double* maxval,
unsigned char* minval_buf, unsigned char* maxval_buf);
template <typename T>
void min_max_caller_2steps(const DevMem2D src, double* minval, double* maxval,
unsigned char* minval_buf, unsigned char* maxval_buf);
}}}}
void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal)
{
GpuMat src_ = src.reshape(1);
using namespace mathfunc::minmax;
double maxVal_;
if (!maxVal)
maxVal = &maxVal_;
if (!maxVal) maxVal = &maxVal_;
GpuMat src_ = src.reshape(1);
// Allocate GPU buffers
Size b1size, b2size;
get_buf_size_required(src.elemSize(), b1size.width, b1size.height, b2size.width, b2size.height);
GpuMat b1(b1size, CV_8U), b2(b2size, CV_8U);
int major, minor;
getComputeCapability(getDevice(), major, minor);
switch (src_.type())
if (major >= 1 && minor >= 1)
{
case CV_8U:
mathfunc::min_max_caller<unsigned char>(src_, minVal, maxVal);
break;
case CV_8S:
mathfunc::min_max_caller<signed char>(src_, minVal, maxVal);
break;
case CV_16U:
mathfunc::min_max_caller<unsigned short>(src_, minVal, maxVal);
break;
case CV_16S:
mathfunc::min_max_caller<signed short>(src_, minVal, maxVal);
break;
case CV_32S:
mathfunc::min_max_caller<int>(src_, minVal, maxVal);
break;
case CV_32F:
mathfunc::min_max_caller<float>(src_, minVal, maxVal);
break;
case CV_64F:
mathfunc::min_max_caller<double>(src_, minVal, maxVal);
break;
default:
CV_Error(CV_StsBadArg, "Unsupported type");
switch (src_.type())
{
case CV_8U: min_max_caller<unsigned char>(src_, minVal, maxVal, b1.data, b2.data); break;
case CV_8S: min_max_caller<signed char>(src_, minVal, maxVal, b1.data, b2.data); break;
case CV_16U: min_max_caller<unsigned short>(src_, minVal, maxVal, b1.data, b2.data); break;
case CV_16S: min_max_caller<signed short>(src_, minVal, maxVal, b1.data, b2.data); break;
case CV_32S: min_max_caller<int>(src_, minVal, maxVal, b1.data, b2.data); break;
case CV_32F: min_max_caller<float>(src_, minVal, maxVal, b1.data, b2.data); break;
case CV_64F: min_max_caller<double>(src_, minVal, maxVal, b1.data, b2.data); break;
default: CV_Error(CV_StsBadArg, "Unsupported type");
}
}
else
{
switch (src_.type())
{
case CV_8U: min_max_caller_2steps<unsigned char>(src_, minVal, maxVal, b1.data, b2.data); break;
case CV_8S: min_max_caller_2steps<signed char>(src_, minVal, maxVal, b1.data, b2.data); break;
case CV_16U: min_max_caller_2steps<unsigned short>(src_, minVal, maxVal, b1.data, b2.data); break;
case CV_16S: min_max_caller_2steps<signed short>(src_, minVal, maxVal, b1.data, b2.data); break;
case CV_32S: min_max_caller_2steps<int>(src_, minVal, maxVal, b1.data, b2.data); break;
case CV_32F: min_max_caller_2steps<float>(src_, minVal, maxVal, b1.data, b2.data); break;
default: CV_Error(CV_StsBadArg, "Unsupported type");
}
}
}
......@@ -535,14 +555,18 @@ void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal)
////////////////////////////////////////////////////////////////////////
// minMaxLoc
namespace cv { namespace gpu { namespace mathfunc {
namespace cv { namespace gpu { namespace mathfunc { namespace minmaxloc {
template <typename T>
void min_max_loc_caller(const DevMem2D src, double* minval, double* maxval, int* minlocx, int* minlocy,
int* maxlocx, int* maxlocy);
}}}
void min_max_loc_caller(const DevMem2D src, double* minval, double* maxval,
int* minlocx, int* minlocy, int* maxlocx, int* maxlocy);
}}}}
void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc)
{
using namespace mathfunc::minmaxloc;
CV_Assert(src.channels() == 1);
double maxVal_;
......@@ -557,25 +581,25 @@ void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point
switch (src.type())
{
case CV_8U:
mathfunc::min_max_loc_caller<unsigned char>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
min_max_loc_caller<unsigned char>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
break;
case CV_8S:
mathfunc::min_max_loc_caller<signed char>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
min_max_loc_caller<signed char>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
break;
case CV_16U:
mathfunc::min_max_loc_caller<unsigned short>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
min_max_loc_caller<unsigned short>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
break;
case CV_16S:
mathfunc::min_max_loc_caller<signed short>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
min_max_loc_caller<signed short>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
break;
case CV_32S:
mathfunc::min_max_loc_caller<int>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
min_max_loc_caller<int>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
break;
case CV_32F:
mathfunc::min_max_loc_caller<float>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
min_max_loc_caller<float>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
break;
case CV_64F:
mathfunc::min_max_loc_caller<double>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
min_max_loc_caller<double>(src, minVal, maxVal, &minLoc->x, &minLoc->y, &maxLoc->x, &maxLoc->y);
break;
default:
CV_Error(CV_StsBadArg, "Unsupported type");
......
modules/gpu/src/cuda/mathfunc.cu
浏览文件 @ 48183f10
......@@ -42,8 +42,10 @@
#include "cuda_shared.hpp"
#include "transform.hpp"
#include "limits_gpu.hpp"
using namespace cv::gpu;
using namespace cv::gpu::device;
#ifndef CV_PI
#define CV_PI 3.1415926535897932384626433832795f
......@@ -399,8 +401,8 @@ namespace cv { namespace gpu { namespace mathfunc
//////////////////////////////////////////////////////////////////////////////
// Min max
enum { MIN, MAX };
// To avoid shared banck confilict we convert reach value into value of
// appropriate type (32 bits minimum)
template <typename T> struct MinMaxTypeTraits {};
template <> struct MinMaxTypeTraits<unsigned char> { typedef int best_type; };
template <> struct MinMaxTypeTraits<signed char> { typedef int best_type; };
......@@ -410,129 +412,208 @@ namespace cv { namespace gpu { namespace mathfunc
template <> struct MinMaxTypeTraits<float> { typedef float best_type; };
template <> struct MinMaxTypeTraits<double> { typedef double best_type; };
template <typename T, int op> struct Opt {};
template <typename T>
struct Opt<T, MIN>
// Available optimization operations
enum { OP_MIN, OP_MAX };
namespace minmax
{
static __device__ void call(unsigned int tid, unsigned int offset, volatile T* optval)
{
optval[tid] = min(optval[tid], optval[tid + offset]);
}
};
__constant__ int ctwidth;
__constant__ int ctheight;
static const unsigned int czero = 0;
// Estimates good thread configuration
// - threads variable satisfies to threads.x * threads.y == 256
void estimate_thread_cfg(dim3& threads, dim3& grid)
{
threads = dim3(64, 4);
grid = dim3(6, 5);
}
// Returns required buffer sizes
void get_buf_size_required(int elem_size, int& b1cols, int& b1rows, int& b2cols, int& b2rows)
{
dim3 threads, grid;
estimate_thread_cfg(threads, grid);
b1cols = grid.x * grid.y * elem_size; b1rows = 1;
b2cols = grid.x * grid.y * elem_size; b2rows = 1;
}
// Estimates device constants which are used in the kernels using specified thread configuration
void estimate_kernel_consts(int cols, int rows, const dim3& threads, const dim3& grid)
{
int twidth = divUp(divUp(cols, grid.x), threads.x);
int theight = divUp(divUp(rows, grid.y), threads.y);
cudaSafeCall(cudaMemcpyToSymbol(ctwidth, &twidth, sizeof(ctwidth)));
cudaSafeCall(cudaMemcpyToSymbol(ctheight, &theight, sizeof(ctheight)));
}
// Does min and max in shared memory
template <typename T>
struct Opt<T, MAX>
__device__ void merge(unsigned int tid, unsigned int offset, volatile T* minval, volatile T* maxval)
{
static __device__ void call(unsigned int tid, unsigned int offset, volatile T* optval)
{
optval[tid] = max(optval[tid], optval[tid + offset]);
}
};
minval[tid] = min(minval[tid], minval[tid + offset]);
maxval[tid] = max(maxval[tid], maxval[tid + offset]);
}
// Global counter of blocks finished its work
__device__ unsigned int blocks_finished;
template <int nthreads, int op, typename T>
__global__ void opt_kernel(int cols, int rows, const PtrStep src, PtrStep optval)
template <int nthreads, typename T>
__global__ void min_max_kernel(int cols, int rows, const PtrStep src, T* minval, T* maxval)
{
typedef typename MinMaxTypeTraits<T>::best_type best_type;
__shared__ best_type soptval[nthreads];
__shared__ best_type sminval[nthreads];
__shared__ best_type smaxval[nthreads];
unsigned int x0 = blockIdx.x * blockDim.x;
unsigned int y0 = blockIdx.y * blockDim.y;
unsigned int x0 = blockIdx.x * blockDim.x * ctwidth + threadIdx.x;
unsigned int y0 = blockIdx.y * blockDim.y * ctheight + threadIdx.y;
unsigned int tid = threadIdx.y * blockDim.x + threadIdx.x;
if (x0 + threadIdx.x < cols && y0 + threadIdx.y < rows)
soptval[tid] = ((const T*)src.ptr(y0 + threadIdx.y))[x0 + threadIdx.x];
else
soptval[tid] = ((const T*)src.ptr(y0))[x0];
T val;
T mymin = numeric_limits_gpu<T>::max();
T mymax = numeric_limits_gpu<T>::min();
for (unsigned int y = 0; y < ctheight && y0 + y * blockDim.y < rows; ++y)
{
const T* ptr = (const T*)src.ptr(y0 + y * blockDim.y);
for (unsigned int x = 0; x < ctwidth && x0 + x * blockDim.x < cols; ++x)
{
val = ptr[x0 + x * blockDim.x];
mymin = min(mymin, val);
mymax = max(mymax, val);
}
}
sminval[tid] = mymin;
smaxval[tid] = mymax;
__syncthreads();
if (nthreads >= 512) if (tid < 256) { Opt<best_type, op>::call(tid, 256, soptval); __syncthreads(); }
if (nthreads >= 256) if (tid < 128) { Opt<best_type, op>::call(tid, 128, soptval); __syncthreads(); }
if (nthreads >= 128) if (tid < 64) { Opt<best_type, op>::call(tid, 64, soptval); __syncthreads(); }
if (nthreads >= 512) if (tid < 256) { merge(tid, 256, sminval, smaxval); __syncthreads(); }
if (nthreads >= 256) if (tid < 128) { merge(tid, 128, sminval, smaxval); __syncthreads(); }
if (nthreads >= 128) if (tid < 64) { merge(tid, 64, sminval, smaxval); __syncthreads(); }
if (tid < 32)
{
if (nthreads >= 64) Opt<best_type, op>::call(tid, 32, soptval);
if (nthreads >= 32) Opt<best_type, op>::call(tid, 16, soptval);
if (nthreads >= 16) Opt<best_type, op>::call(tid, 8, soptval);
if (nthreads >= 8) Opt<best_type, op>::call(tid, 4, soptval);
if (nthreads >= 4) Opt<best_type, op>::call(tid, 2, soptval);
if (nthreads >= 2) Opt<best_type, op>::call(tid, 1, soptval);
if (nthreads >= 64) merge(tid, 32, sminval, smaxval);
if (nthreads >= 32) merge(tid, 16, sminval, smaxval);
if (nthreads >= 16) merge(tid, 8, sminval, smaxval);
if (nthreads >= 8) merge(tid, 4, sminval, smaxval);
if (nthreads >= 4) merge(tid, 2, sminval, smaxval);
if (nthreads >= 2) merge(tid, 1, sminval, smaxval);
}
if (tid == 0) ((T*)optval.ptr(blockIdx.y))[blockIdx.x] = (T)soptval[0];
__syncthreads();
if (tid == 0)
{
minval[blockIdx.y * gridDim.x + blockIdx.x] = (T)sminval[0];
maxval[blockIdx.y * gridDim.x + blockIdx.x] = (T)smaxval[0];
}
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 110
// Process partial results in the first thread of the last block
if ((gridDim.x > 1 || gridDim.y > 1) && tid == 0)
{
__threadfence();
if (atomicInc(&blocks_finished, gridDim.x * gridDim.y) == gridDim.x * gridDim.y - 1)
{
mymin = numeric_limits_gpu<T>::max();
mymax = numeric_limits_gpu<T>::min();
for (unsigned int i = 0; i < gridDim.x * gridDim.y; ++i)
{
mymin = min(mymin, minval[i]);
mymax = max(mymax, maxval[i]);
}
minval[0] = mymin;
maxval[0] = mymax;
}
}
#endif
}
// This kernel will be used only when compute capability is 1.0
template <typename T>
__global__ void min_max_kernel_2ndstep(T* minval, T* maxval, int size)
{
T val;
T mymin = numeric_limits_gpu<T>::max();
T mymax = numeric_limits_gpu<T>::min();
for (unsigned int i = 0; i < size; ++i)
{
val = minval[i]; if (val < mymin) mymin = val;
val = maxval[i]; if (val > mymax) mymax = val;
}
minval[0] = mymin;
maxval[0] = mymax;
}
template <typename T>
void min_max_caller(const DevMem2D src, double* minval, double* maxval)
void min_max_caller(const DevMem2D src, double* minval, double* maxval,
unsigned char* minval_buf, unsigned char* maxval_buf)
{
dim3 threads(32, 8);
dim3 threads, grid;
estimate_thread_cfg(threads, grid);
estimate_kernel_consts(src.cols, src.rows, threads, grid);
// Allocate memory for aux. buffers
DevMem2D minval_buf[2];
minval_buf[0].cols = divUp(src.cols, threads.x);
minval_buf[0].rows = divUp(src.rows, threads.y);
minval_buf[1].cols = divUp(minval_buf[0].cols, threads.x);
minval_buf[1].rows = divUp(minval_buf[0].rows, threads.y);
cudaSafeCall(cudaMallocPitch(&minval_buf[0].data, &minval_buf[0].step, minval_buf[0].cols * sizeof(T), minval_buf[0].rows));
cudaSafeCall(cudaMallocPitch(&minval_buf[1].data, &minval_buf[1].step, minval_buf[1].cols * sizeof(T), minval_buf[1].rows));
cudaSafeCall(cudaMemcpyToSymbol(blocks_finished, &czero, sizeof(blocks_finished)));
min_max_kernel<256, T><<<grid, threads>>>(src.cols, src.rows, src, (T*)minval_buf, (T*)maxval_buf);
DevMem2D maxval_buf[2];
maxval_buf[0].cols = divUp(src.cols, threads.x);
maxval_buf[0].rows = divUp(src.rows, threads.y);
maxval_buf[1].cols = divUp(maxval_buf[0].cols, threads.x);
maxval_buf[1].rows = divUp(maxval_buf[0].rows, threads.y);
cudaSafeCall(cudaMallocPitch(&maxval_buf[0].data, &maxval_buf[0].step, maxval_buf[0].cols * sizeof(T), maxval_buf[0].rows));
cudaSafeCall(cudaMallocPitch(&maxval_buf[1].data, &maxval_buf[1].step, maxval_buf[1].cols * sizeof(T), maxval_buf[1].rows));
int curbuf = 0;
dim3 cursize(src.cols, src.rows);
dim3 grid(divUp(cursize.x, threads.x), divUp(cursize.y, threads.y));
cudaSafeCall(cudaThreadSynchronize());
opt_kernel<256, MIN, T><<<grid, threads>>>(cursize.x, cursize.y, src, minval_buf[curbuf]);
opt_kernel<256, MAX, T><<<grid, threads>>>(cursize.x, cursize.y, src, maxval_buf[curbuf]);
cursize = grid;
T minval_, maxval_;
cudaSafeCall(cudaMemcpy(&minval_, minval_buf, sizeof(T), cudaMemcpyDeviceToHost));
cudaSafeCall(cudaMemcpy(&maxval_, maxval_buf, sizeof(T), cudaMemcpyDeviceToHost));
*minval = minval_;
*maxval = maxval_;
}
while (cursize.x > 1 || cursize.y > 1)
{
grid.x = divUp(cursize.x, threads.x);
grid.y = divUp(cursize.y, threads.y);
opt_kernel<256, MIN, T><<<grid, threads>>>(cursize.x, cursize.y, minval_buf[curbuf], minval_buf[1 - curbuf]);
opt_kernel<256, MAX, T><<<grid, threads>>>(cursize.x, cursize.y, maxval_buf[curbuf], maxval_buf[1 - curbuf]);
curbuf = 1 - curbuf;
cursize = grid;
}
template <typename T>
void min_max_caller_2steps(const DevMem2D src, double* minval, double* maxval,
unsigned char* minval_buf, unsigned char* maxval_buf)
{
dim3 threads, grid;
estimate_thread_cfg(threads, grid);
estimate_kernel_consts(src.cols, src.rows, threads, grid);
cudaSafeCall(cudaMemcpyToSymbol(blocks_finished, &czero, sizeof(blocks_finished)));
min_max_kernel<256, T><<<grid, threads>>>(src.cols, src.rows, src, (T*)minval_buf, (T*)maxval_buf);
min_max_kernel_2ndstep<T><<<1, 1>>>((T*)minval_buf, (T*)maxval_buf, grid.x * grid.y);
cudaSafeCall(cudaThreadSynchronize());
// Copy results from device to host
T minval_, maxval_;
cudaSafeCall(cudaMemcpy(&minval_, minval_buf[curbuf].ptr(0), sizeof(T), cudaMemcpyDeviceToHost));
cudaSafeCall(cudaMemcpy(&maxval_, maxval_buf[curbuf].ptr(0), sizeof(T), cudaMemcpyDeviceToHost));
cudaSafeCall(cudaMemcpy(&minval_, minval_buf, sizeof(T), cudaMemcpyDeviceToHost));
cudaSafeCall(cudaMemcpy(&maxval_, maxval_buf, sizeof(T), cudaMemcpyDeviceToHost));
*minval = minval_;
*maxval = maxval_;
// Release aux. buffers
cudaSafeCall(cudaFree(minval_buf[0].data));
cudaSafeCall(cudaFree(minval_buf[1].data));
cudaSafeCall(cudaFree(maxval_buf[0].data));
cudaSafeCall(cudaFree(maxval_buf[1].data));
}
template void min_max_caller<unsigned char>(const DevMem2D, double*, double*);
template void min_max_caller<signed char>(const DevMem2D, double*, double*);
template void min_max_caller<unsigned short>(const DevMem2D, double*, double*);
template void min_max_caller<signed short>(const DevMem2D, double*, double*);
template void min_max_caller<int>(const DevMem2D, double*, double*);
template void min_max_caller<float>(const DevMem2D, double*, double*);
template void min_max_caller<double>(const DevMem2D, double*, double*);
template void min_max_caller<unsigned char>(const DevMem2D, double*, double*, unsigned char*, unsigned char*);
template void min_max_caller<signed char>(const DevMem2D, double*, double*, unsigned char*, unsigned char*);
template void min_max_caller<unsigned short>(const DevMem2D, double*, double*, unsigned char*, unsigned char*);
template void min_max_caller<signed short>(const DevMem2D, double*, double*, unsigned char*, unsigned char*);
template void min_max_caller<int>(const DevMem2D, double*, double*, unsigned char*, unsigned char*);
template void min_max_caller<float>(const DevMem2D, double*, double*, unsigned char*, unsigned char*);
template void min_max_caller<double>(const DevMem2D, double*, double*, unsigned char*, unsigned char*);
template void min_max_caller_2steps<unsigned char>(const DevMem2D, double*, double*, unsigned char*, unsigned char*);
template void min_max_caller_2steps<signed char>(const DevMem2D, double*, double*, unsigned char*, unsigned char*);
template void min_max_caller_2steps<unsigned short>(const DevMem2D, double*, double*, unsigned char*, unsigned char*);
template void min_max_caller_2steps<signed short>(const DevMem2D, double*, double*, unsigned char*, unsigned char*);
template void min_max_caller_2steps<int>(const DevMem2D, double*, double*, unsigned char*, unsigned char*);
template void min_max_caller_2steps<float>(const DevMem2D, double*, double*, unsigned char*, unsigned char*);
} // namespace minmax
namespace minmaxloc {
template <typename T, int op> struct OptLoc {};
template <typename T>
struct OptLoc<T, MIN>
struct OptLoc<T, OP_MIN>
{
static __device__ void call(unsigned int tid, unsigned int offset, volatile T* optval, volatile unsigned int* optloc)
{
......@@ -546,7 +627,7 @@ namespace cv { namespace gpu { namespace mathfunc
};
template <typename T>
struct OptLoc<T, MAX>
struct OptLoc<T, OP_MAX>
{
static __device__ void call(unsigned int tid, unsigned int offset, volatile T* optval, volatile unsigned int* optloc)
{
......@@ -693,18 +774,18 @@ namespace cv { namespace gpu { namespace mathfunc
dim3 cursize(src.cols, src.rows);
dim3 grid(divUp(cursize.x, threads.x), divUp(cursize.y, threads.y));
opt_loc_init_kernel<256, MIN, T><<<grid, threads>>>(cursize.x, cursize.y, src, minval_buf[curbuf], minloc_buf[curbuf]);
opt_loc_init_kernel<256, MAX, T><<<grid, threads>>>(cursize.x, cursize.y, src, maxval_buf[curbuf], maxloc_buf[curbuf]);
opt_loc_init_kernel<256, OP_MIN, T><<<grid, threads>>>(cursize.x, cursize.y, src, minval_buf[curbuf], minloc_buf[curbuf]);
opt_loc_init_kernel<256, OP_MAX, T><<<grid, threads>>>(cursize.x, cursize.y, src, maxval_buf[curbuf], maxloc_buf[curbuf]);
cursize = grid;
while (cursize.x > 1 || cursize.y > 1)
{
grid.x = divUp(cursize.x, threads.x);
grid.y = divUp(cursize.y, threads.y);
opt_loc_kernel<256, MIN, T><<<grid, threads>>>(cursize.x, cursize.y, minval_buf[curbuf], minloc_buf[curbuf],
minval_buf[1 - curbuf], minloc_buf[1 - curbuf]);
opt_loc_kernel<256, MAX, T><<<grid, threads>>>(cursize.x, cursize.y, maxval_buf[curbuf], maxloc_buf[curbuf],
maxval_buf[1 - curbuf], maxloc_buf[1 - curbuf]);
opt_loc_kernel<256, OP_MIN, T><<<grid, threads>>>(cursize.x, cursize.y, minval_buf[curbuf], minloc_buf[curbuf],
minval_buf[1 - curbuf], minloc_buf[1 - curbuf]);
opt_loc_kernel<256, OP_MAX, T><<<grid, threads>>>(cursize.x, cursize.y, maxval_buf[curbuf], maxloc_buf[curbuf],
maxval_buf[1 - curbuf], maxloc_buf[1 - curbuf]);
curbuf = 1 - curbuf;
cursize = grid;
}
......@@ -744,4 +825,6 @@ namespace cv { namespace gpu { namespace mathfunc
template void min_max_loc_caller<float>(const DevMem2D, double*, double*, int*, int*, int*, int*);
template void min_max_loc_caller<double>(const DevMem2D, double*, double*, int*, int*, int*, int*);
} // namespace minmaxloc
}}}
tests/gpu/src/arithm.cpp
浏览文件 @ 48183f10
......@@ -678,8 +678,14 @@ struct CV_GpuMinMaxTest: public CvTest
void run(int)
{
int depth_end;
int major, minor;
cv::gpu::getComputeCapability(getDevice(), major, minor);
minor = 0;
if (minor >= 1) depth_end = CV_64F; else depth_end = CV_32F;
for (int cn = 1; cn <= 4; ++cn)
for (int depth = CV_8U; depth <= CV_64F; ++depth)
for (int depth = CV_8U; depth <= depth_end; ++depth)
{
int rows = 1, cols = 3;
test(rows, cols, cn, depth);
......@@ -703,10 +709,11 @@ struct CV_GpuMinMaxTest: public CvTest
}
double minVal, maxVal;
cv::Point minLoc, maxLoc;
Mat src_ = src.reshape(1);
if (depth != CV_8S)
{
cv::Point minLoc, maxLoc;
cv::minMaxLoc(src_, &minVal, &maxVal, &minLoc, &maxLoc);
}
else
......@@ -727,8 +734,16 @@ struct CV_GpuMinMaxTest: public CvTest
cv::Point minLoc_, maxLoc_;
cv::gpu::minMax(cv::gpu::GpuMat(src), &minVal_, &maxVal_);
CHECK(minVal == minVal_, CvTS::FAIL_INVALID_OUTPUT);
CHECK(maxVal == maxVal_, CvTS::FAIL_INVALID_OUTPUT);
if (abs(minVal - minVal_) > 1e-3f)
{
ts->printf(CvTS::CONSOLE, "\nfail: minVal=%f minVal_=%f rows=%d cols=%d depth=%d cn=%d\n", minVal, minVal_, rows, cols, depth, cn);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
}
if (abs(maxVal - maxVal_) > 1e-3f)
{
ts->printf(CvTS::CONSOLE, "\nfail: maxVal=%f maxVal_=%f rows=%d cols=%d depth=%d cn=%d\n", maxVal, maxVal_, rows, cols, depth, cn);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
}
}
};
......@@ -742,7 +757,11 @@ struct CV_GpuMinMaxLocTest: public CvTest
void run(int)
{
for (int depth = CV_8U; depth <= CV_64F; ++depth)
int depth_end;
int major, minor;
cv::gpu::getComputeCapability(getDevice(), major, minor);
if (minor >= 1) depth_end = CV_64F; else depth_end = CV_32F;
for (int depth = CV_8U; depth <= depth_end; ++depth)
{
int rows = 1, cols = 3;
test(rows, cols, depth);
......
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