Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

optimized memory requirements for gpu::minMax's buffers, added support of compute capability 1.0

Browse Source
pull/13383/head
Alexey Spizhevoy 14 years ago
parent c46546203f
commit 48183f10e7
3 changed files with 265 additions and 139 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 106
      modules/gpu/src/arithm.cpp
  2. 269
      modules/gpu/src/cuda/mathfunc.cu
  3. 29
      tests/gpu/src/arithm.cpp

106
modules/gpu/src/arithm.cpp
Unescape View File

@ -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");

269
modules/gpu/src/cuda/mathfunc.cu
Unescape View File

@ -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
}}}

29
tests/gpu/src/arithm.cpp
Unescape View File

@ -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);

Write Preview
Loading…
Cancel
Save