/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "test_precomp.hpp" #ifdef HAVE_CUDA using namespace cvtest; ////////////////////////////////////////////////////////////////////////////// // GEMM #ifdef HAVE_CUBLAS CV_FLAGS(GemmFlags, 0, cv::GEMM_1_T, cv::GEMM_2_T, cv::GEMM_3_T); #define ALL_GEMM_FLAGS testing::Values(GemmFlags(0), GemmFlags(cv::GEMM_1_T), GemmFlags(cv::GEMM_2_T), GemmFlags(cv::GEMM_3_T), GemmFlags(cv::GEMM_1_T | cv::GEMM_2_T), GemmFlags(cv::GEMM_1_T | cv::GEMM_3_T), GemmFlags(cv::GEMM_1_T | cv::GEMM_2_T | cv::GEMM_3_T)) PARAM_TEST_CASE(GEMM, cv::cuda::DeviceInfo, cv::Size, MatType, GemmFlags, UseRoi) { cv::cuda::DeviceInfo devInfo; cv::Size size; int type; int flags; bool useRoi; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); type = GET_PARAM(2); flags = GET_PARAM(3); useRoi = GET_PARAM(4); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(GEMM, Accuracy) { cv::Mat src1 = randomMat(size, type, -10.0, 10.0); cv::Mat src2 = randomMat(size, type, -10.0, 10.0); cv::Mat src3 = randomMat(size, type, -10.0, 10.0); double alpha = randomDouble(-10.0, 10.0); double beta = randomDouble(-10.0, 10.0); if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE)) { try { cv::cuda::GpuMat dst; cv::cuda::gemm(loadMat(src1), loadMat(src2), alpha, loadMat(src3), beta, dst, flags); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code); } } else if (type == CV_64FC2 && flags != 0) { try { cv::cuda::GpuMat dst; cv::cuda::gemm(loadMat(src1), loadMat(src2), alpha, loadMat(src3), beta, dst, flags); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsNotImplemented, e.code); } } else { cv::cuda::GpuMat dst = createMat(size, type, useRoi); cv::cuda::gemm(loadMat(src1, useRoi), loadMat(src2, useRoi), alpha, loadMat(src3, useRoi), beta, dst, flags); cv::Mat dst_gold; cv::gemm(src1, src2, alpha, src3, beta, dst_gold, flags); EXPECT_MAT_NEAR(dst_gold, dst, CV_MAT_DEPTH(type) == CV_32F ? 1e-1 : 1e-10); } } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, GEMM, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, testing::Values(MatType(CV_32FC1), MatType(CV_32FC2), MatType(CV_64FC1), MatType(CV_64FC2)), ALL_GEMM_FLAGS, WHOLE_SUBMAT)); //////////////////////////////////////////////////////////////////////////// // MulSpectrums CV_FLAGS(DftFlags, 0, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::DFT_REAL_OUTPUT) PARAM_TEST_CASE(MulSpectrums, cv::cuda::DeviceInfo, cv::Size, DftFlags) { cv::cuda::DeviceInfo devInfo; cv::Size size; int flag; cv::Mat a, b; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); flag = GET_PARAM(2); cv::cuda::setDevice(devInfo.deviceID()); a = randomMat(size, CV_32FC2); b = randomMat(size, CV_32FC2); } }; CUDA_TEST_P(MulSpectrums, Simple) { cv::cuda::GpuMat c; cv::cuda::mulSpectrums(loadMat(a), loadMat(b), c, flag, false); cv::Mat c_gold; cv::mulSpectrums(a, b, c_gold, flag, false); EXPECT_MAT_NEAR(c_gold, c, 1e-2); } CUDA_TEST_P(MulSpectrums, Scaled) { float scale = 1.f / size.area(); cv::cuda::GpuMat c; cv::cuda::mulAndScaleSpectrums(loadMat(a), loadMat(b), c, flag, scale, false); cv::Mat c_gold; cv::mulSpectrums(a, b, c_gold, flag, false); c_gold.convertTo(c_gold, c_gold.type(), scale); EXPECT_MAT_NEAR(c_gold, c, 1e-2); } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, MulSpectrums, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, testing::Values(DftFlags(0), DftFlags(cv::DFT_ROWS)))); //////////////////////////////////////////////////////////////////////////// // Dft struct Dft : testing::TestWithParam<cv::cuda::DeviceInfo> { cv::cuda::DeviceInfo devInfo; virtual void SetUp() { devInfo = GetParam(); cv::cuda::setDevice(devInfo.deviceID()); } }; namespace { void testC2C(const std::string& hint, int cols, int rows, int flags, bool inplace) { SCOPED_TRACE(hint); cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC2, 0.0, 10.0); cv::Mat b_gold; cv::dft(a, b_gold, flags); cv::cuda::GpuMat d_b; cv::cuda::GpuMat d_b_data; if (inplace) { d_b_data.create(1, a.size().area(), CV_32FC2); d_b = cv::cuda::GpuMat(a.rows, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); } cv::cuda::dft(loadMat(a), d_b, cv::Size(cols, rows), flags); EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr()); ASSERT_EQ(CV_32F, d_b.depth()); ASSERT_EQ(2, d_b.channels()); EXPECT_MAT_NEAR(b_gold, cv::Mat(d_b), rows * cols * 1e-4); } } CUDA_TEST_P(Dft, C2C) { int cols = randomInt(2, 100); int rows = randomInt(2, 100); for (int i = 0; i < 2; ++i) { bool inplace = i != 0; testC2C("no flags", cols, rows, 0, inplace); testC2C("no flags 0 1", cols, rows + 1, 0, inplace); testC2C("no flags 1 0", cols, rows + 1, 0, inplace); testC2C("no flags 1 1", cols + 1, rows, 0, inplace); testC2C("DFT_INVERSE", cols, rows, cv::DFT_INVERSE, inplace); testC2C("DFT_ROWS", cols, rows, cv::DFT_ROWS, inplace); testC2C("single col", 1, rows, 0, inplace); testC2C("single row", cols, 1, 0, inplace); testC2C("single col inversed", 1, rows, cv::DFT_INVERSE, inplace); testC2C("single row inversed", cols, 1, cv::DFT_INVERSE, inplace); testC2C("single row DFT_ROWS", cols, 1, cv::DFT_ROWS, inplace); testC2C("size 1 2", 1, 2, 0, inplace); testC2C("size 2 1", 2, 1, 0, inplace); } } namespace { void testR2CThenC2R(const std::string& hint, int cols, int rows, bool inplace) { SCOPED_TRACE(hint); cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC1, 0.0, 10.0); cv::cuda::GpuMat d_b, d_c; cv::cuda::GpuMat d_b_data, d_c_data; if (inplace) { if (a.cols == 1) { d_b_data.create(1, (a.rows / 2 + 1) * a.cols, CV_32FC2); d_b = cv::cuda::GpuMat(a.rows / 2 + 1, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); } else { d_b_data.create(1, a.rows * (a.cols / 2 + 1), CV_32FC2); d_b = cv::cuda::GpuMat(a.rows, a.cols / 2 + 1, CV_32FC2, d_b_data.ptr(), (a.cols / 2 + 1) * d_b_data.elemSize()); } d_c_data.create(1, a.size().area(), CV_32F); d_c = cv::cuda::GpuMat(a.rows, a.cols, CV_32F, d_c_data.ptr(), a.cols * d_c_data.elemSize()); } cv::cuda::dft(loadMat(a), d_b, cv::Size(cols, rows), 0); cv::cuda::dft(d_b, d_c, cv::Size(cols, rows), cv::DFT_REAL_OUTPUT | cv::DFT_SCALE); EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr()); EXPECT_TRUE(!inplace || d_c.ptr() == d_c_data.ptr()); ASSERT_EQ(CV_32F, d_c.depth()); ASSERT_EQ(1, d_c.channels()); cv::Mat c(d_c); EXPECT_MAT_NEAR(a, c, rows * cols * 1e-5); } } CUDA_TEST_P(Dft, R2CThenC2R) { int cols = randomInt(2, 100); int rows = randomInt(2, 100); testR2CThenC2R("sanity", cols, rows, false); testR2CThenC2R("sanity 0 1", cols, rows + 1, false); testR2CThenC2R("sanity 1 0", cols + 1, rows, false); testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, false); testR2CThenC2R("single col", 1, rows, false); testR2CThenC2R("single col 1", 1, rows + 1, false); testR2CThenC2R("single row", cols, 1, false); testR2CThenC2R("single row 1", cols + 1, 1, false); testR2CThenC2R("sanity", cols, rows, true); testR2CThenC2R("sanity 0 1", cols, rows + 1, true); testR2CThenC2R("sanity 1 0", cols + 1, rows, true); testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, true); testR2CThenC2R("single row", cols, 1, true); testR2CThenC2R("single row 1", cols + 1, 1, true); } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Dft, ALL_DEVICES); //////////////////////////////////////////////////////// // Convolve namespace { void convolveDFT(const cv::Mat& A, const cv::Mat& B, cv::Mat& C, bool ccorr = false) { // reallocate the output array if needed C.create(std::abs(A.rows - B.rows) + 1, std::abs(A.cols - B.cols) + 1, A.type()); cv::Size dftSize; // compute the size of DFT transform dftSize.width = cv::getOptimalDFTSize(A.cols + B.cols - 1); dftSize.height = cv::getOptimalDFTSize(A.rows + B.rows - 1); // allocate temporary buffers and initialize them with 0s cv::Mat tempA(dftSize, A.type(), cv::Scalar::all(0)); cv::Mat tempB(dftSize, B.type(), cv::Scalar::all(0)); // copy A and B to the top-left corners of tempA and tempB, respectively cv::Mat roiA(tempA, cv::Rect(0, 0, A.cols, A.rows)); A.copyTo(roiA); cv::Mat roiB(tempB, cv::Rect(0, 0, B.cols, B.rows)); B.copyTo(roiB); // now transform the padded A & B in-place; // use "nonzeroRows" hint for faster processing cv::dft(tempA, tempA, 0, A.rows); cv::dft(tempB, tempB, 0, B.rows); // multiply the spectrums; // the function handles packed spectrum representations well cv::mulSpectrums(tempA, tempB, tempA, 0, ccorr); // transform the product back from the frequency domain. // Even though all the result rows will be non-zero, // you need only the first C.rows of them, and thus you // pass nonzeroRows == C.rows cv::dft(tempA, tempA, cv::DFT_INVERSE + cv::DFT_SCALE, C.rows); // now copy the result back to C. tempA(cv::Rect(0, 0, C.cols, C.rows)).copyTo(C); } IMPLEMENT_PARAM_CLASS(KSize, int) IMPLEMENT_PARAM_CLASS(Ccorr, bool) } PARAM_TEST_CASE(Convolve, cv::cuda::DeviceInfo, cv::Size, KSize, Ccorr) { cv::cuda::DeviceInfo devInfo; cv::Size size; int ksize; bool ccorr; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); ksize = GET_PARAM(2); ccorr = GET_PARAM(3); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(Convolve, Accuracy) { cv::Mat src = randomMat(size, CV_32FC1, 0.0, 100.0); cv::Mat kernel = randomMat(cv::Size(ksize, ksize), CV_32FC1, 0.0, 1.0); cv::Ptr<cv::cuda::Convolution> conv = cv::cuda::createConvolution(); cv::cuda::GpuMat dst; conv->convolve(loadMat(src), loadMat(kernel), dst, ccorr); cv::Mat dst_gold; convolveDFT(src, kernel, dst_gold, ccorr); EXPECT_MAT_NEAR(dst, dst_gold, 1e-1); } INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Convolve, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, testing::Values(KSize(3), KSize(7), KSize(11), KSize(17), KSize(19), KSize(23), KSize(45)), testing::Values(Ccorr(false), Ccorr(true)))); #endif // HAVE_CUBLAS #endif // HAVE_CUDA