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#include "perf_precomp.hpp"
namespace opencv_test
{
using namespace perf;
namespace {
typedef perf::TestBaseWithParam<size_t> VectorLength;
PERF_TEST_P(VectorLength, phase32f, testing::Values(128, 1000, 128*1024, 512*1024, 1024*1024))
{
size_t length = GetParam();
vector<float> X(length);
vector<float> Y(length);
vector<float> angle(length);
declare.in(X, Y, WARMUP_RNG).out(angle);
TEST_CYCLE_N(200) cv::phase(X, Y, angle, true);
SANITY_CHECK(angle, 5e-5);
}
PERF_TEST_P(VectorLength, phase64f, testing::Values(128, 1000, 128*1024, 512*1024, 1024*1024))
{
size_t length = GetParam();
vector<double> X(length);
vector<double> Y(length);
vector<double> angle(length);
declare.in(X, Y, WARMUP_RNG).out(angle);
TEST_CYCLE_N(200) cv::phase(X, Y, angle, true);
SANITY_CHECK(angle, 5e-5);
}
// generates random vectors, performs Gram-Schmidt orthogonalization on them
Mat randomOrtho(int rows, int ftype, RNG& rng)
{
Mat result(rows, rows, ftype);
rng.fill(result, RNG::UNIFORM, cv::Scalar(-1), cv::Scalar(1));
for (int i = 0; i < rows; i++)
{
Mat v = result.row(i);
for (int j = 0; j < i; j++)
{
Mat p = result.row(j);
v -= p.dot(v) * p;
}
v = v * (1. / cv::norm(v));
}
return result;
}
template<typename FType>
Mat buildRandomMat(int rows, int cols, RNG& rng, int rank, bool symmetrical)
{
int mtype = cv::traits::Depth<FType>::value;
Mat u = randomOrtho(rows, mtype, rng);
Mat v = randomOrtho(cols, mtype, rng);
Mat s(rows, cols, mtype, Scalar(0));
std::vector<FType> singVals(rank);
rng.fill(singVals, RNG::UNIFORM, Scalar(0), Scalar(10));
std::sort(singVals.begin(), singVals.end());
auto singIter = singVals.rbegin();
for (int i = 0; i < rank; i++)
{
s.at<FType>(i, i) = *singIter++;
}
if (symmetrical)
return u * s * u.t();
else
return u * s * v.t();
}
Mat buildRandomMat(int rows, int cols, int mtype, RNG& rng, int rank, bool symmetrical)
{
if (mtype == CV_32F)
{
return buildRandomMat<float>(rows, cols, rng, rank, symmetrical);
}
else if (mtype == CV_64F)
{
return buildRandomMat<double>(rows, cols, rng, rank, symmetrical);
}
else
{
CV_Error(cv::Error::StsBadArg, "This type is not supported");
}
}
CV_ENUM(SolveDecompEnum, DECOMP_LU, DECOMP_SVD, DECOMP_EIG, DECOMP_CHOLESKY, DECOMP_QR)
enum RankMatrixOptions
{
RANK_HALF, RANK_MINUS_1, RANK_FULL
};
CV_ENUM(RankEnum, RANK_HALF, RANK_MINUS_1, RANK_FULL)
enum SolutionsOptions
{
NO_SOLUTIONS, ONE_SOLUTION, MANY_SOLUTIONS
};
CV_ENUM(SolutionsEnum, NO_SOLUTIONS, ONE_SOLUTION, MANY_SOLUTIONS)
typedef perf::TestBaseWithParam<std::tuple<int, RankEnum, MatDepth, SolveDecompEnum, bool, SolutionsEnum>> SolveTest;
PERF_TEST_P(SolveTest, randomMat, ::testing::Combine(
::testing::Values(31, 64, 100),
::testing::Values(RANK_HALF, RANK_MINUS_1, RANK_FULL),
::testing::Values(CV_32F, CV_64F),
::testing::Values(DECOMP_LU, DECOMP_SVD, DECOMP_EIG, DECOMP_CHOLESKY, DECOMP_QR),
::testing::Bool(), // normal
::testing::Values(NO_SOLUTIONS, ONE_SOLUTION, MANY_SOLUTIONS)
))
{
auto t = GetParam();
int size = std::get<0>(t);
auto rankEnum = std::get<1>(t);
int mtype = std::get<2>(t);
int method = std::get<3>(t);
bool normal = std::get<4>(t);
auto solutions = std::get<5>(t);
bool symmetrical = (method == DECOMP_CHOLESKY || method == DECOMP_LU);
if (normal)
{
method |= DECOMP_NORMAL;
}
int rank = size;
switch (rankEnum)
{
case RANK_HALF: rank /= 2; break;
case RANK_MINUS_1: rank -= 1; break;
default: break;
}
RNG& rng = theRNG();
Mat A = buildRandomMat(size, size, mtype, rng, rank, symmetrical);
Mat x(size, 1, mtype);
Mat b(size, 1, mtype);
switch (solutions)
{
// no solutions, let's make b random
case NO_SOLUTIONS:
{
rng.fill(b, RNG::UNIFORM, Scalar(-1), Scalar(1));
}
break;
// exactly 1 solution, let's combine b from A and x
case ONE_SOLUTION:
{
rng.fill(x, RNG::UNIFORM, Scalar(-10), Scalar(10));
b = A * x;
}
break;
// infinitely many solutions, let's make b zero
default:
{
b = 0;
}
break;
}
TEST_CYCLE() cv::solve(A, b, x, method);
SANITY_CHECK_NOTHING();
}
typedef perf::TestBaseWithParam<std::tuple<std::tuple<int, int>, RankEnum, MatDepth, bool, bool>> SvdTest;
PERF_TEST_P(SvdTest, decompose, ::testing::Combine(
::testing::Values(std::make_tuple(5, 15), std::make_tuple(32, 32), std::make_tuple(100, 100)),
::testing::Values(RANK_HALF, RANK_MINUS_1, RANK_FULL),
::testing::Values(CV_32F, CV_64F),
::testing::Bool(), // symmetrical
::testing::Bool() // needUV
))
{
auto t = GetParam();
auto rc = std::get<0>(t);
auto rankEnum = std::get<1>(t);
int mtype = std::get<2>(t);
bool symmetrical = std::get<3>(t);
bool needUV = std::get<4>(t);
int rows = std::get<0>(rc);
int cols = std::get<1>(rc);
if (symmetrical)
{
rows = max(rows, cols);
cols = rows;
}
int rank = std::min(rows, cols);
switch (rankEnum)
{
case RANK_HALF: rank /= 2; break;
case RANK_MINUS_1: rank -= 1; break;
default: break;
}
int flags = needUV ? 0 : SVD::NO_UV;
RNG& rng = theRNG();
Mat A = buildRandomMat(rows, cols, mtype, rng, rank, symmetrical);
TEST_CYCLE() cv::SVD svd(A, flags);
SANITY_CHECK_NOTHING();
}
PERF_TEST_P(SvdTest, backSubst, ::testing::Combine(
::testing::Values(std::make_tuple(5, 15), std::make_tuple(32, 32), std::make_tuple(100, 100)),
::testing::Values(RANK_HALF, RANK_MINUS_1, RANK_FULL),
::testing::Values(CV_32F, CV_64F),
// back substitution works the same regardless of source matrix properties
::testing::Values(true),
// back substitution has no sense without u and v
::testing::Values(true) // needUV
))
{
auto t = GetParam();
auto rc = std::get<0>(t);
auto rankEnum = std::get<1>(t);
int mtype = std::get<2>(t);
int rows = std::get<0>(rc);
int cols = std::get<1>(rc);
int rank = std::min(rows, cols);
switch (rankEnum)
{
case RANK_HALF: rank /= 2; break;
case RANK_MINUS_1: rank -= 1; break;
default: break;
}
RNG& rng = theRNG();
Mat A = buildRandomMat(rows, cols, mtype, rng, rank, /* symmetrical */ false);
cv::SVD svd(A);
// preallocate to not spend time on it during backSubst()
Mat dst(cols, 1, mtype);
Mat rhs(rows, 1, mtype);
rng.fill(rhs, RNG::UNIFORM, Scalar(-10), Scalar(10));
TEST_CYCLE() svd.backSubst(rhs, dst);
SANITY_CHECK_NOTHING();
}
typedef perf::TestBaseWithParam< testing::tuple<int, int, int> > KMeans;
PERF_TEST_P_(KMeans, single_iter)
{
RNG& rng = theRNG();
const int K = testing::get<0>(GetParam());
const int dims = testing::get<1>(GetParam());
const int N = testing::get<2>(GetParam());
const int attempts = 5;
Mat data(N, dims, CV_32F);
rng.fill(data, RNG::UNIFORM, -0.1, 0.1);
const int N0 = K;
Mat data0(N0, dims, CV_32F);
rng.fill(data0, RNG::UNIFORM, -1, 1);
for (int i = 0; i < N; i++)
{
int base = rng.uniform(0, N0);
cv::add(data0.row(base), data.row(i), data.row(i));
}
declare.in(data);
Mat labels, centers;
TEST_CYCLE()
{
kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1, 0),
attempts, KMEANS_PP_CENTERS, centers);
}
SANITY_CHECK_NOTHING();
}
PERF_TEST_P_(KMeans, good)
{
RNG& rng = theRNG();
const int K = testing::get<0>(GetParam());
const int dims = testing::get<1>(GetParam());
const int N = testing::get<2>(GetParam());
const int attempts = 5;
Mat data(N, dims, CV_32F);
rng.fill(data, RNG::UNIFORM, -0.1, 0.1);
const int N0 = K;
Mat data0(N0, dims, CV_32F);
rng.fill(data0, RNG::UNIFORM, -1, 1);
for (int i = 0; i < N; i++)
{
int base = rng.uniform(0, N0);
cv::add(data0.row(base), data.row(i), data.row(i));
}
declare.in(data);
Mat labels, centers;
TEST_CYCLE()
{
kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
attempts, KMEANS_PP_CENTERS, centers);
}
SANITY_CHECK_NOTHING();
}
PERF_TEST_P_(KMeans, with_duplicates)
{
RNG& rng = theRNG();
const int K = testing::get<0>(GetParam());
const int dims = testing::get<1>(GetParam());
const int N = testing::get<2>(GetParam());
const int attempts = 5;
Mat data(N, dims, CV_32F, Scalar::all(0));
const int N0 = std::max(2, K * 2 / 3);
Mat data0(N0, dims, CV_32F);
rng.fill(data0, RNG::UNIFORM, -1, 1);
for (int i = 0; i < N; i++)
{
int base = rng.uniform(0, N0);
data0.row(base).copyTo(data.row(i));
}
declare.in(data);
Mat labels, centers;
TEST_CYCLE()
{
kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
attempts, KMEANS_PP_CENTERS, centers);
}
SANITY_CHECK_NOTHING();
}
INSTANTIATE_TEST_CASE_P(/*nothing*/ , KMeans,
testing::Values(
// K clusters, dims, N points
testing::make_tuple(2, 3, 100000),
testing::make_tuple(4, 3, 500),
testing::make_tuple(4, 3, 1000),
testing::make_tuple(4, 3, 10000),
testing::make_tuple(8, 3, 1000),
testing::make_tuple(8, 16, 1000),
testing::make_tuple(8, 64, 1000),
testing::make_tuple(16, 16, 1000),
testing::make_tuple(16, 32, 1000),
testing::make_tuple(32, 16, 1000),
testing::make_tuple(32, 32, 1000),
testing::make_tuple(100, 2, 1000)
)
);
}
} // namespace
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