1
0
mirror of https://github.com/opencv/opencv_contrib.git synced 2025-10-18 08:44:11 +08:00
Files
opencv_contrib/modules/cudev/test/test_nd.cu
Alexander Smorkalov 542e22aeff Merge branch 4.x
2024-04-01 11:33:48 +03:00

245 lines
7.9 KiB
Plaintext

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test { namespace {
template <typename ElemType>
class GpuMatNDTest : public ::testing::Test
{
public:
using MatType = Mat_<ElemType>;
using CnType = typename Mat_<ElemType>::channel_type;
static constexpr int cn = DataType<ElemType>::channels;
using SizeArray = GpuMatND::SizeArray;
static MatType RandomMat(const SizeArray& size)
{
const auto dims = static_cast<int>(size.size());
MatType ret(dims, size.data());
for (ElemType& elem : ret)
for (int i = 0; i < cn; ++i)
elem[i] = cv::randu<CnType>();
return ret;
}
static std::vector<Range> RandomRange(const SizeArray& size)
{
const auto dims = static_cast<int>(size.size());
std::vector<Range> ret;
const auto margin = cv::randu<int>() & 0x1 + 1; // 1 or 2
for (int s : size)
if (s > margin * 2)
ret.emplace_back(margin, s-margin);
else
ret.push_back(Range::all());
return ret;
}
static std::vector<Range> RandomRange2D(const SizeArray& size)
{
const auto dims = static_cast<int>(size.size());
std::vector<Range> ret = RandomRange(size);
for (int i = 0; i < dims - 2; ++i)
{
const auto start = cv::randu<unsigned int>() % size[i];
ret[i] = Range(static_cast<int>(start), static_cast<int>(start) + 1);
}
return ret;
}
static void doTest1(const SizeArray& size)
{
const MatType gold = RandomMat(size);
MatType dst;
GpuMatND gmat;
// simple upload, download test for GpuMatND
gmat.upload(gold);
gmat.download(dst);
EXPECT_TRUE(std::equal(gold.begin(), gold.end(), dst.begin()));
}
static void doTest2(const SizeArray& size)
{
const MatType gold = RandomMat(size);
const std::vector<Range> ranges = RandomRange(size);
const MatType goldSub = gold(ranges);
MatType dst;
GpuMatND gmat;
// upload partial mat, download it, and compare
gmat.upload(goldSub);
gmat.download(dst);
EXPECT_TRUE(std::equal(goldSub.begin(), goldSub.end(), dst.begin()));
// upload full mat, extract partial mat from it, download it, and compare
gmat.upload(gold);
gmat = gmat(ranges);
gmat.download(dst);
EXPECT_TRUE(std::equal(goldSub.begin(), goldSub.end(), dst.begin()));
}
static void doTest3(const SizeArray& size)
{
if (std::is_same<CnType, hfloat>::value) // GpuMat::convertTo is not implemented for CV_16F
return;
const MatType gold = RandomMat(size);
const std::vector<Range> ranges = RandomRange2D(size);
MatType dst;
GpuMatND gmat;
// Test GpuMatND to GpuMat conversion:
// extract a 2D-plane and set its elements in the extracted region to 1
// compare the values of the full mat between Mat and GpuMatND
gmat.upload(gold);
GpuMat plane = gmat(ranges).createGpuMatHeader();
EXPECT_TRUE(!plane.refcount); // plane points to externally allocated memory(a part of gmat)
const GpuMat dummy = plane.clone();
EXPECT_TRUE(dummy.refcount); // dummy is clone()-ed from plane, so it manages its memory
// currently, plane(GpuMat) points to a sub-matrix of gmat(GpuMatND)
// in this case, dummy and plane have same size and type,
// so plane does not get reallocated inside convertTo,
// so this convertTo sets a sub-matrix region of gmat to 1
dummy.convertTo(plane, -1, 0, 1);
EXPECT_TRUE(!plane.refcount); // plane still points to externally allocated memory(a part of gmat)
gmat.download(dst);
// set a sub-matrix region of gold to 1
Mat plane_ = gold(ranges);
const Mat dummy_ = plane_.clone();
dummy_.convertTo(plane_, -1, 0, 1);
EXPECT_TRUE(std::equal(gold.begin(), gold.end(), dst.begin()));
}
static void doTest4(const SizeArray& size)
{
if (std::is_same<CnType, hfloat>::value) // GpuMat::convertTo is not implemented for CV_16F
return;
const MatType gold = RandomMat(size);
const std::vector<Range> ranges = RandomRange2D(size);
MatType dst;
GpuMatND gmat;
// Test handling external memory
gmat.upload(gold);
const GpuMatND external(gmat.size, gmat.type(), gmat.getDevicePtr(), {gmat.step.begin(), gmat.step.end() - 1});
// set a sub-matrix region of external to 2
GpuMat plane = external(ranges).createGpuMatHeader();
const GpuMat dummy = plane.clone();
dummy.convertTo(plane, -1, 0, 2);
external.download(dst);
// set a sub-matrix region of gold to 2
Mat plane_ = gold(ranges);
const Mat dummy_ = plane_.clone();
dummy_.convertTo(plane_, -1, 0, 2);
EXPECT_TRUE(std::equal(gold.begin(), gold.end(), dst.begin()));
}
static void doTest5(const SizeArray& size)
{
if (std::is_same<CnType, hfloat>::value) // GpuMat::convertTo is not implemented for CV_16F
return;
const MatType gold = RandomMat(size);
const std::vector<Range> ranges = RandomRange(size);
MatType goldSub = gold(ranges);
MatType dst;
GpuMatND gmat;
// Upload a sub-mat, set a sub-region of the sub-mat to 3, download, and compare
gmat.upload(goldSub);
const std::vector<Range> rangesInRanges = RandomRange2D(gmat.size);
GpuMat plane = gmat(rangesInRanges).createGpuMatHeader();
const GpuMat dummy = plane.clone();
dummy.convertTo(plane, -1, 0, 3);
gmat.download(dst);
Mat plane_ = goldSub(rangesInRanges);
const Mat dummy_ = plane_.clone();
dummy_.convertTo(plane_, -1, 0, 3);
EXPECT_TRUE(std::equal(goldSub.begin(), goldSub.end(), dst.begin()));
}
};
using ElemTypes = ::testing::Types<
Vec<uchar, 1>, Vec<uchar, 2>, Vec<uchar, 3>, Vec<uchar, 4>, // CV_8U
Vec<schar, 1>, Vec<schar, 2>, Vec<schar, 3>, Vec<schar, 4>, // CV_8S
Vec<ushort, 1>, Vec<ushort, 2>, Vec<ushort, 3>, Vec<ushort, 4>, // CV_16U
Vec<short, 1>, Vec<short, 2>, Vec<short, 3>, Vec<short, 4>, // CV_16S
Vec<int, 1>, Vec<int, 2>, Vec<int, 3>, Vec<int, 4>, // CV_32S
Vec<float, 1>, Vec<float, 2>, Vec<float, 3>, Vec<float, 4>, // CV_32F
Vec<double, 1>, Vec<double, 2>, Vec<double, 3>, Vec<double, 4>, //CV_64F
Vec<hfloat, 1>, Vec<hfloat, 2>, Vec<hfloat, 3>, Vec<hfloat, 4> // CV_16F
>;
using SizeArray = GpuMatND::SizeArray;
#define DIFFERENT_SIZES_ND std::vector<SizeArray>{ \
SizeArray{2, 1}, SizeArray{3, 2, 1}, SizeArray{1, 3, 2, 1}, SizeArray{2, 1, 3, 2, 1}, SizeArray{3, 2, 1, 3, 2, 1}, \
SizeArray{1}, SizeArray{1, 1}, SizeArray{1, 1, 1}, SizeArray{1, 1, 1, 1}, \
SizeArray{4}, SizeArray{4, 4}, SizeArray{4, 4, 4}, SizeArray{4, 4, 4, 4}, \
SizeArray{11}, SizeArray{13, 11}, SizeArray{17, 13, 11}, SizeArray{19, 17, 13, 11}}
TYPED_TEST_CASE(GpuMatNDTest, ElemTypes);
TYPED_TEST(GpuMatNDTest, Test1)
{
for (auto& size : DIFFERENT_SIZES_ND)
GpuMatNDTest<TypeParam>::doTest1(size);
}
TYPED_TEST(GpuMatNDTest, Test2)
{
for (auto& size : DIFFERENT_SIZES_ND)
GpuMatNDTest<TypeParam>::doTest2(size);
}
TYPED_TEST(GpuMatNDTest, Test3)
{
for (auto& size : DIFFERENT_SIZES_ND)
GpuMatNDTest<TypeParam>::doTest3(size);
}
TYPED_TEST(GpuMatNDTest, Test4)
{
for (auto& size : DIFFERENT_SIZES_ND)
GpuMatNDTest<TypeParam>::doTest4(size);
}
TYPED_TEST(GpuMatNDTest, Test5)
{
for (auto& size : DIFFERENT_SIZES_ND)
GpuMatNDTest<TypeParam>::doTest5(size);
}
}} // namespace