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added tests for gpu::sum, it supports all data types, but single channel images only

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This commit is contained in:
Alexey Spizhevoy 2010-12-13 12:00:58 +00:00
parent 442cd75c32
commit 3997514b7c
5 changed files with 133 additions and 109 deletions
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9
modules/gpu/include/opencv2/gpu/gpu.hpp
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@ -421,9 +421,12 @@ namespace cv
CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode);
//! computes sum of array elements
//! supports CV_8UC1, CV_8UC4 types
//! disabled until fix crash
CV_EXPORTS Scalar sum(const GpuMat& m);
//! supports only single channel images
CV_EXPORTS Scalar sum(const GpuMat& src);
//! computes sum of array elements
//! supports only single channel images
CV_EXPORTS Scalar sum(const GpuMat& src, GpuMat& buf);
//! finds global minimum and maximum array elements and returns their values
CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal=0, const GpuMat& mask=GpuMat());

61
modules/gpu/src/arithm.cpp
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@ -65,6 +65,7 @@ double cv::gpu::norm(const GpuMat&, int) { throw_nogpu(); return 0.0; }
double cv::gpu::norm(const GpuMat&, const GpuMat&, int) { throw_nogpu(); return 0.0; }
void cv::gpu::flip(const GpuMat&, GpuMat&, int) { throw_nogpu(); }
Scalar cv::gpu::sum(const GpuMat&) { throw_nogpu(); return Scalar(); }
Scalar cv::gpu::sum(const GpuMat&, GpuMat&) { throw_nogpu(); return Scalar(); }
void cv::gpu::minMax(const GpuMat&, double*, double*, const GpuMat&) { throw_nogpu(); }
void cv::gpu::minMax(const GpuMat&, double*, double*, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::minMaxLoc(const GpuMat&, double*, double*, Point*, Point*, const GpuMat&) { throw_nogpu(); }
@ -480,36 +481,50 @@ void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode)
////////////////////////////////////////////////////////////////////////
// sum
Scalar cv::gpu::sum(const GpuMat& src)
namespace cv { namespace gpu { namespace mathfunc
{
CV_Assert(!"disabled until fix crash");
template <typename T>
void sum_caller(const DevMem2D src, PtrStep buf, double* sum);
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC4);
template <typename T>
void sum_multipass_caller(const DevMem2D src, PtrStep buf, double* sum);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar res;
int bufsz;
if (src.type() == CV_8UC1)
namespace sum
{
nppiReductionGetBufferHostSize_8u_C1R(sz, &bufsz);
GpuMat buf(1, bufsz, CV_32S);
nppSafeCall( nppiSum_8u_C1R(src.ptr<Npp8u>(), src.step, sz, buf.ptr<Npp32s>(), res.val) );
void get_buf_size_required(int cols, int rows, int& bufcols, int& bufrows);
}
else
{
nppiReductionGetBufferHostSize_8u_C4R(sz, &bufsz);
GpuMat buf(1, bufsz, CV_32S);
}}}
nppSafeCall( nppiSum_8u_C4R(src.ptr<Npp8u>(), src.step, sz, buf.ptr<Npp32s>(), res.val) );
}
Scalar cv::gpu::sum(const GpuMat& src)
{
GpuMat buf;
return sum(src, buf);
}
return res;
Scalar cv::gpu::sum(const GpuMat& src, GpuMat& buf)
{
using namespace mathfunc;
CV_Assert(src.channels() == 1);
typedef void (*Caller)(const DevMem2D, PtrStep, double*);
static const Caller callers[2][7] =
{ { sum_multipass_caller<unsigned char>, sum_multipass_caller<char>,
sum_multipass_caller<unsigned short>, sum_multipass_caller<short>,
sum_multipass_caller<int>, sum_multipass_caller<float>, 0 },
{ sum_caller<unsigned char>, sum_caller<char>,
sum_caller<unsigned short>, sum_caller<short>,
sum_caller<int>, sum_caller<float>, sum_caller<double> } };
Size bufSize;
sum::get_buf_size_required(src.cols, src.rows, bufSize.width, bufSize.height);
buf.create(bufSize, CV_8U);
Caller caller = callers[hasAtomicsSupport(getDevice())][src.type()];
if (!caller) CV_Error(CV_StsBadArg, "sum: unsupported type");
double result;
caller(src, buf, &result);
return result;
}
////////////////////////////////////////////////////////////////////////

91
modules/gpu/src/cuda/mathfunc.cu
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@ -1419,6 +1419,15 @@ namespace cv { namespace gpu { namespace mathfunc
namespace sum
{
template <typename T> struct SumType {};
template <> struct SumType<unsigned char> { typedef unsigned int R; };
template <> struct SumType<char> { typedef int R; };
template <> struct SumType<unsigned short> { typedef unsigned int R; };
template <> struct SumType<short> { typedef int R; };
template <> struct SumType<int> { typedef int R; };
template <> struct SumType<float> { typedef float R; };
template <> struct SumType<double> { typedef double R; };
__constant__ int ctwidth;
__constant__ int ctheight;
__device__ unsigned int blocks_finished = 0;
@ -1436,12 +1445,11 @@ namespace cv { namespace gpu { namespace mathfunc
}
template <typename T>
void get_buf_size_required(int cols, int rows, int& bufcols, int& bufrows)
{
dim3 threads, grid;
estimate_thread_cfg(cols, rows, threads, grid);
bufcols = grid.x * grid.y * sizeof(T);
bufcols = grid.x * grid.y * sizeof(double);
bufrows = 1;
}
@ -1454,17 +1462,17 @@ namespace cv { namespace gpu { namespace mathfunc
cudaSafeCall(cudaMemcpyToSymbol(ctheight, &theight, sizeof(theight)));
}
template <typename T, int nthreads>
__global__ void sum_kernel(const DevMem2D_<T> src, T* result)
template <typename T, typename R, int nthreads>
__global__ void sum_kernel(const DevMem2D_<T> src, R* result)
{
__shared__ T smem[nthreads];
__shared__ R smem[nthreads];
const int x0 = blockIdx.x * blockDim.x * ctwidth + threadIdx.x;
const int y0 = blockIdx.y * blockDim.y * ctheight + threadIdx.y;
const int tid = threadIdx.y * blockDim.x + threadIdx.x;
const int bid = blockIdx.y * gridDim.x + blockIdx.x;
T sum = 0;
R sum = 0;
for (int y = 0; y < ctheight && y0 + y * blockDim.y < src.rows; ++y)
{
const T* ptr = src.ptr(y0 + y * blockDim.y);
@ -1475,7 +1483,7 @@ namespace cv { namespace gpu { namespace mathfunc
smem[tid] = sum;
__syncthreads();
sum_in_smem<nthreads, T>(smem, tid);
sum_in_smem<nthreads, R>(smem, tid);
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 110
__shared__ bool is_last;
@ -1496,7 +1504,7 @@ namespace cv { namespace gpu { namespace mathfunc
smem[tid] = tid < gridDim.x * gridDim.y ? result[tid] : 0;
__syncthreads();
sum_in_smem<nthreads, T>(smem, tid);
sum_in_smem<nthreads, R>(smem, tid);
if (tid == 0)
{
@ -1510,14 +1518,16 @@ namespace cv { namespace gpu { namespace mathfunc
}
template <typename T, int nthreads>
__global__ void sum_pass2_kernel(T* result, int size)
template <typename T, typename R, int nthreads>
__global__ void sum_pass2_kernel(R* result, int size)
{
__shared__ T smem[nthreads];
__shared__ R smem[nthreads];
int tid = threadIdx.y * blockDim.x + threadIdx.x;
smem[tid] = tid < size ? result[tid] : 0;
sum_in_smem<nthreads, T>(smem, tid);
__syncthreads();
sum_in_smem<nthreads, R>(smem, tid);
if (tid == 0)
result[0] = smem[0];
@ -1527,60 +1537,61 @@ namespace cv { namespace gpu { namespace mathfunc
template <typename T>
T sum_multipass_caller(const DevMem2D_<T> src, PtrStep buf)
void sum_multipass_caller(const DevMem2D src, PtrStep buf, double* sum)
{
using namespace sum;
typedef typename SumType<T>::R R;
dim3 threads, grid;
estimate_thread_cfg(src.cols, src.rows, threads, grid);
set_kernel_consts(src.cols, src.rows, threads, grid);
T* buf_ = (T*)buf.ptr(0);
R* buf_ = (R*)buf.ptr(0);
sum_kernel<T, threads_x * threads_y><<<grid, threads>>>(src, buf_);
sum_pass2_kernel<T, threads_x * threads_y><<<1, threads_x * threads_y>>>(
sum_kernel<T, R, threads_x * threads_y><<<grid, threads>>>((const DevMem2D_<T>)src, buf_);
sum_pass2_kernel<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
buf_, grid.x * grid.y);
cudaSafeCall(cudaThreadSynchronize());
T sum;
cudaSafeCall(cudaMemcpy(&sum, buf_, sizeof(T), cudaMemcpyDeviceToHost));
return sum;
R result = 0;
cudaSafeCall(cudaMemcpy(&result, buf_, result, cudaMemcpyDeviceToHost));
sum[0] = result;
}
template unsigned char sum_multipass_caller<unsigned char>(const DevMem2D_<unsigned char>, PtrStep);
template char sum_multipass_caller<char>(const DevMem2D_<char>, PtrStep);
template unsigned short sum_multipass_caller<unsigned short>(const DevMem2D_<unsigned short>, PtrStep);
template short sum_multipass_caller<short>(const DevMem2D_<short>, PtrStep);
template int sum_multipass_caller<int>(const DevMem2D_<int>, PtrStep);
template float sum_multipass_caller<float>(const DevMem2D_<float>, PtrStep);
template void sum_multipass_caller<unsigned char>(const DevMem2D, PtrStep, double*);
template void sum_multipass_caller<char>(const DevMem2D, PtrStep, double*);
template void sum_multipass_caller<unsigned short>(const DevMem2D, PtrStep, double*);
template void sum_multipass_caller<short>(const DevMem2D, PtrStep, double*);
template void sum_multipass_caller<int>(const DevMem2D, PtrStep, double*);
template void sum_multipass_caller<float>(const DevMem2D, PtrStep, double*);
template <typename T>
T sum_caller(const DevMem2D_<T> src, PtrStep buf)
void sum_caller(const DevMem2D src, PtrStep buf, double* sum)
{
using namespace sum;
typedef typename SumType<T>::R R;
dim3 threads, grid;
estimate_thread_cfg(src.cols, src.rows, threads, grid);
set_kernel_consts(src.cols, src.rows, threads, grid);
T* buf_ = (T*)buf.ptr(0);
R* buf_ = (R*)buf.ptr(0);
sum_kernel<T, threads_x * threads_y><<<grid, threads>>>(src, buf_);
sum_kernel<T, R, threads_x * threads_y><<<grid, threads>>>((const DevMem2D_<T>)src, buf_);
cudaSafeCall(cudaThreadSynchronize());
T sum;
cudaSafeCall(cudaMemcpy(&sum, buf_, sizeof(T), cudaMemcpyDeviceToHost));
return sum;
R result = 0;
cudaSafeCall(cudaMemcpy(&result, buf_, sizeof(result), cudaMemcpyDeviceToHost));
sum[0] = result;
}
template unsigned char sum_caller<unsigned char>(const DevMem2D_<unsigned char>, PtrStep);
template char sum_caller<char>(const DevMem2D_<char>, PtrStep);
template unsigned short sum_caller<unsigned short>(const DevMem2D_<unsigned short>, PtrStep);
template short sum_caller<short>(const DevMem2D_<short>, PtrStep);
template int sum_caller<int>(const DevMem2D_<int>, PtrStep);
template float sum_caller<float>(const DevMem2D_<float>, PtrStep);
template double sum_caller<double>(const DevMem2D_<double>, PtrStep);
template void sum_caller<unsigned char>(const DevMem2D, PtrStep, double*);
template void sum_caller<char>(const DevMem2D, PtrStep, double*);
template void sum_caller<unsigned short>(const DevMem2D, PtrStep, double*);
template void sum_caller<short>(const DevMem2D, PtrStep, double*);
template void sum_caller<int>(const DevMem2D, PtrStep, double*);
template void sum_caller<float>(const DevMem2D, PtrStep, double*);
template void sum_caller<double>(const DevMem2D, PtrStep, double*);
}}}

78
tests/gpu/src/arithm.cpp
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@ -458,29 +458,6 @@ struct CV_GpuNppImageFlipTest : public CV_GpuArithmTest
}
};
////////////////////////////////////////////////////////////////////////////////
// sum
struct CV_GpuNppImageSumTest : public CV_GpuArithmTest
{
CV_GpuNppImageSumTest() : CV_GpuArithmTest( "GPU-NppImageSum", "sum" ) {}
int test( const Mat& mat1, const Mat& )
{
if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4)
{
ts->printf(CvTS::LOG, "\tUnsupported type\t");
return CvTS::OK;
}
Scalar cpures = cv::sum(mat1);
GpuMat gpu1(mat1);
Scalar gpures = cv::gpu::sum(gpu1);
return CheckNorm(cpures, gpures);
}
};
////////////////////////////////////////////////////////////////////////////////
// LUT
struct CV_GpuNppImageLUTTest : public CV_GpuArithmTest
@ -949,27 +926,49 @@ struct CV_GpuCountNonZeroTest: CvTest
}
};
////////////////////////////////////////////////////////////////////////////////
// min/max
struct CV_GpuImageMinMaxTest : public CV_GpuArithmTest
//////////////////////////////////////////////////////////////////////////////
// sum
struct CV_GpuSumTest: CvTest
{
CV_GpuImageMinMaxTest() : CV_GpuArithmTest( "GPU-ImageMinMax", "min/max" ) {}
CV_GpuSumTest(): CvTest("GPU-SumTest", "sum") {}
int test( const Mat& mat1, const Mat& mat2 )
void run(int)
{
cv::Mat cpuMinRes, cpuMaxRes;
cv::min(mat1, mat2, cpuMinRes);
cv::max(mat1, mat2, cpuMaxRes);
try
{
Mat src;
Scalar a, b;
double max_err = 1e-6;
GpuMat gpu1(mat1);
GpuMat gpu2(mat2);
GpuMat gpuMinRes, gpuMaxRes;
cv::gpu::min(gpu1, gpu2, gpuMinRes);
cv::gpu::max(gpu1, gpu2, gpuMaxRes);
int typemax = hasNativeDoubleSupport(getDevice()) ? CV_64F : CV_32F;
for (int type = CV_8U; type <= typemax; ++type)
{
gen(1 + rand() % 1000, 1 + rand() % 1000, type, src);
a = sum(src);
b = sum(GpuMat(src));
if (abs(a[0] - b[0]) > src.size().area() * max_err)
{
ts->printf(CvTS::CONSOLE, "cols: %d, rows: %d, expected: %f, actual: %f\n", src.cols, src.rows, a[0], b[0]);
ts->set_failed_test_info(CvTS::FAIL_INVALID_OUTPUT);
return;
}
}
}
catch (const Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts)) throw;
return;
}
}
void gen(int cols, int rows, int type, Mat& m)
{
m.create(rows, cols, type);
RNG rng;
rng.fill(m, RNG::UNIFORM, Scalar::all(0), Scalar::all(20));
return CheckNorm(cpuMinRes, gpuMinRes) == CvTS::OK && CheckNorm(cpuMaxRes, gpuMaxRes) == CvTS::OK ?
CvTS::OK : CvTS::FAIL_GENERIC;
}
};
@ -992,7 +991,6 @@ CV_GpuNppImageCompareTest CV_GpuNppImageCompare_test;
CV_GpuNppImageMeanStdDevTest CV_GpuNppImageMeanStdDev_test;
CV_GpuNppImageNormTest CV_GpuNppImageNorm_test;
CV_GpuNppImageFlipTest CV_GpuNppImageFlip_test;
CV_GpuNppImageSumTest CV_GpuNppImageSum_test;
CV_GpuNppImageLUTTest CV_GpuNppImageLUT_test;
CV_GpuNppImageExpTest CV_GpuNppImageExp_test;
CV_GpuNppImageLogTest CV_GpuNppImageLog_test;
@ -1003,4 +1001,4 @@ CV_GpuNppImagePolarToCartTest CV_GpuNppImagePolarToCart_test;
CV_GpuMinMaxTest CV_GpuMinMaxTest_test;
CV_GpuMinMaxLocTest CV_GpuMinMaxLocTest_test;
CV_GpuCountNonZeroTest CV_CountNonZero_test;
CV_GpuImageMinMaxTest CV_GpuImageMinMax_test;
CV_GpuSumTest CV_GpuSum_test;

3
tests/gpu/src/gputest_main.cpp
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@ -46,9 +46,6 @@ CvTS test_system("gpu");
const char* blacklist[] =
{
"GPU-AsyncGpuMatOperator", // crash
"GPU-NppImageSum", // crash, probably npp bug
"GPU-NppImageCanny", // NPP_TEXTURE_BIND_ERROR
0
};