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Merge pull request #25450 from savuor:rv/svd_perf
Perf tests for SVD and solve() created #25450 fixes #25336 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Rostislav Vasilikhin
GitHub
parent
8af76698ed
commit
357b9abaef
@ -36,6 +36,234 @@ PERF_TEST_P(VectorLength, phase64f, testing::Values(128, 1000, 128*1024, 512*102
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SANITY_CHECK(angle, 5e-5);
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}
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// generates random vectors, performs Gram-Schmidt orthogonalization on them
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Mat randomOrtho(int rows, int ftype, RNG& rng)
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{
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Mat result(rows, rows, ftype);
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rng.fill(result, RNG::UNIFORM, cv::Scalar(-1), cv::Scalar(1));
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for (int i = 0; i < rows; i++)
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{
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Mat v = result.row(i);
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for (int j = 0; j < i; j++)
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{
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Mat p = result.row(j);
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v -= p.dot(v) * p;
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}
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v = v * (1. / cv::norm(v));
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}
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return result;
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}
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template<typename FType>
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Mat buildRandomMat(int rows, int cols, RNG& rng, int rank, bool symmetrical)
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{
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int mtype = cv::traits::Depth<FType>::value;
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Mat u = randomOrtho(rows, mtype, rng);
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Mat v = randomOrtho(cols, mtype, rng);
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Mat s(rows, cols, mtype, Scalar(0));
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std::vector<FType> singVals(rank);
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rng.fill(singVals, RNG::UNIFORM, Scalar(0), Scalar(10));
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std::sort(singVals.begin(), singVals.end());
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auto singIter = singVals.rbegin();
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for (int i = 0; i < rank; i++)
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{
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s.at<FType>(i, i) = *singIter++;
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}
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if (symmetrical)
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return u * s * u.t();
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else
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return u * s * v.t();
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}
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Mat buildRandomMat(int rows, int cols, int mtype, RNG& rng, int rank, bool symmetrical)
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{
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if (mtype == CV_32F)
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{
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return buildRandomMat<float>(rows, cols, rng, rank, symmetrical);
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}
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else if (mtype == CV_64F)
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{
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return buildRandomMat<double>(rows, cols, rng, rank, symmetrical);
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}
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else
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{
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CV_Error(cv::Error::StsBadArg, "This type is not supported");
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}
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}
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CV_ENUM(SolveDecompEnum, DECOMP_LU, DECOMP_SVD, DECOMP_EIG, DECOMP_CHOLESKY, DECOMP_QR)
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enum RankMatrixOptions
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{
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RANK_HALF, RANK_MINUS_1, RANK_FULL
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};
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CV_ENUM(RankEnum, RANK_HALF, RANK_MINUS_1, RANK_FULL)
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enum SolutionsOptions
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{
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NO_SOLUTIONS, ONE_SOLUTION, MANY_SOLUTIONS
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};
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CV_ENUM(SolutionsEnum, NO_SOLUTIONS, ONE_SOLUTION, MANY_SOLUTIONS)
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typedef perf::TestBaseWithParam<std::tuple<int, RankEnum, MatDepth, SolveDecompEnum, bool, SolutionsEnum>> SolveTest;
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PERF_TEST_P(SolveTest, randomMat, ::testing::Combine(
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::testing::Values(31, 64, 100),
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::testing::Values(RANK_HALF, RANK_MINUS_1, RANK_FULL),
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::testing::Values(CV_32F, CV_64F),
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::testing::Values(DECOMP_LU, DECOMP_SVD, DECOMP_EIG, DECOMP_CHOLESKY, DECOMP_QR),
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::testing::Bool(), // normal
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::testing::Values(NO_SOLUTIONS, ONE_SOLUTION, MANY_SOLUTIONS)
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))
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{
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auto t = GetParam();
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int size = std::get<0>(t);
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auto rankEnum = std::get<1>(t);
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int mtype = std::get<2>(t);
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int method = std::get<3>(t);
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bool normal = std::get<4>(t);
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auto solutions = std::get<5>(t);
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bool symmetrical = (method == DECOMP_CHOLESKY || method == DECOMP_LU);
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if (normal)
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{
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method |= DECOMP_NORMAL;
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}
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int rank = size;
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switch (rankEnum)
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{
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case RANK_HALF: rank /= 2; break;
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case RANK_MINUS_1: rank -= 1; break;
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default: break;
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}
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RNG& rng = theRNG();
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Mat A = buildRandomMat(size, size, mtype, rng, rank, symmetrical);
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Mat x(size, 1, mtype);
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Mat b(size, 1, mtype);
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switch (solutions)
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{
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// no solutions, let's make b random
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case NO_SOLUTIONS:
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{
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rng.fill(b, RNG::UNIFORM, Scalar(-1), Scalar(1));
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}
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break;
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// exactly 1 solution, let's combine b from A and x
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case ONE_SOLUTION:
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{
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rng.fill(x, RNG::UNIFORM, Scalar(-10), Scalar(10));
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b = A * x;
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}
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break;
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// infinitely many solutions, let's make b zero
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default:
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{
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b = 0;
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}
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break;
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}
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TEST_CYCLE() cv::solve(A, b, x, method);
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SANITY_CHECK_NOTHING();
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}
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typedef perf::TestBaseWithParam<std::tuple<std::tuple<int, int>, RankEnum, MatDepth, bool, bool>> SvdTest;
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PERF_TEST_P(SvdTest, decompose, ::testing::Combine(
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::testing::Values(std::make_tuple(5, 15), std::make_tuple(32, 32), std::make_tuple(100, 100)),
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::testing::Values(RANK_HALF, RANK_MINUS_1, RANK_FULL),
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::testing::Values(CV_32F, CV_64F),
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::testing::Bool(), // symmetrical
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::testing::Bool() // needUV
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))
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{
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auto t = GetParam();
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auto rc = std::get<0>(t);
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auto rankEnum = std::get<1>(t);
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int mtype = std::get<2>(t);
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bool symmetrical = std::get<3>(t);
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bool needUV = std::get<4>(t);
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int rows = std::get<0>(rc);
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int cols = std::get<1>(rc);
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if (symmetrical)
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{
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rows = max(rows, cols);
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cols = rows;
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}
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int rank = std::min(rows, cols);
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switch (rankEnum)
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{
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case RANK_HALF: rank /= 2; break;
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case RANK_MINUS_1: rank -= 1; break;
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default: break;
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}
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int flags = needUV ? 0 : SVD::NO_UV;
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RNG& rng = theRNG();
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Mat A = buildRandomMat(rows, cols, mtype, rng, rank, symmetrical);
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TEST_CYCLE() cv::SVD svd(A, flags);
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SANITY_CHECK_NOTHING();
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}
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PERF_TEST_P(SvdTest, backSubst, ::testing::Combine(
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::testing::Values(std::make_tuple(5, 15), std::make_tuple(32, 32), std::make_tuple(100, 100)),
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::testing::Values(RANK_HALF, RANK_MINUS_1, RANK_FULL),
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::testing::Values(CV_32F, CV_64F),
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// back substitution works the same regardless of source matrix properties
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::testing::Values(true),
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// back substitution has no sense without u and v
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::testing::Values(true) // needUV
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))
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{
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auto t = GetParam();
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auto rc = std::get<0>(t);
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auto rankEnum = std::get<1>(t);
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int mtype = std::get<2>(t);
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int rows = std::get<0>(rc);
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int cols = std::get<1>(rc);
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int rank = std::min(rows, cols);
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switch (rankEnum)
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{
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case RANK_HALF: rank /= 2; break;
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case RANK_MINUS_1: rank -= 1; break;
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default: break;
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}
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RNG& rng = theRNG();
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Mat A = buildRandomMat(rows, cols, mtype, rng, rank, /* symmetrical */ false);
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cv::SVD svd(A);
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// preallocate to not spend time on it during backSubst()
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Mat dst(cols, 1, mtype);
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Mat rhs(rows, 1, mtype);
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rng.fill(rhs, RNG::UNIFORM, Scalar(-10), Scalar(10));
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TEST_CYCLE() svd.backSubst(rhs, dst);
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SANITY_CHECK_NOTHING();
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}
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typedef perf::TestBaseWithParam< testing::tuple<int, int, int> > KMeans;
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PERF_TEST_P_(KMeans, single_iter)
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