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Open Source Computer Vision Library
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254 lines
7.4 KiB
254 lines
7.4 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. |
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "perf_precomp.hpp" |
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namespace opencv_test { namespace { |
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////////////////////////////////////////////////////////////////////// |
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// GEMM |
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#ifdef HAVE_CUBLAS |
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CV_FLAGS(GemmFlags, 0, cv::GEMM_1_T, cv::GEMM_2_T, cv::GEMM_3_T) |
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#define ALL_GEMM_FLAGS Values(GemmFlags(0), GemmFlags(cv::GEMM_1_T), GemmFlags(cv::GEMM_2_T), GemmFlags(cv::GEMM_3_T), \ |
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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)) |
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DEF_PARAM_TEST(Sz_Type_Flags, cv::Size, MatType, GemmFlags); |
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PERF_TEST_P(Sz_Type_Flags, GEMM, |
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Combine(Values(cv::Size(512, 512), cv::Size(1024, 1024)), |
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Values(CV_32FC1, CV_32FC2, CV_64FC1), |
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ALL_GEMM_FLAGS)) |
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{ |
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const cv::Size size = GET_PARAM(0); |
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const int type = GET_PARAM(1); |
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const int flags = GET_PARAM(2); |
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cv::Mat src1(size, type); |
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declare.in(src1, WARMUP_RNG); |
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cv::Mat src2(size, type); |
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declare.in(src2, WARMUP_RNG); |
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cv::Mat src3(size, type); |
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declare.in(src3, WARMUP_RNG); |
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if (PERF_RUN_CUDA()) |
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{ |
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declare.time(5.0); |
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const cv::cuda::GpuMat d_src1(src1); |
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const cv::cuda::GpuMat d_src2(src2); |
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const cv::cuda::GpuMat d_src3(src3); |
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cv::cuda::GpuMat dst; |
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TEST_CYCLE() cv::cuda::gemm(d_src1, d_src2, 1.0, d_src3, 1.0, dst, flags); |
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CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE); |
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} |
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else |
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{ |
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declare.time(50.0); |
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cv::Mat dst; |
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TEST_CYCLE() cv::gemm(src1, src2, 1.0, src3, 1.0, dst, flags); |
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CPU_SANITY_CHECK(dst); |
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} |
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} |
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#endif |
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////////////////////////////////////////////////////////////////////// |
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// MulSpectrums |
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CV_FLAGS(DftFlags, 0, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::DFT_REAL_OUTPUT) |
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DEF_PARAM_TEST(Sz_Flags, cv::Size, DftFlags); |
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PERF_TEST_P(Sz_Flags, MulSpectrums, |
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Combine(CUDA_TYPICAL_MAT_SIZES, |
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Values(0, DftFlags(cv::DFT_ROWS)))) |
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{ |
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const cv::Size size = GET_PARAM(0); |
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const int flag = GET_PARAM(1); |
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cv::Mat a(size, CV_32FC2); |
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cv::Mat b(size, CV_32FC2); |
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declare.in(a, b, WARMUP_RNG); |
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if (PERF_RUN_CUDA()) |
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{ |
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const cv::cuda::GpuMat d_a(a); |
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const cv::cuda::GpuMat d_b(b); |
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cv::cuda::GpuMat dst; |
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TEST_CYCLE() cv::cuda::mulSpectrums(d_a, d_b, dst, flag); |
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CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE); |
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} |
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else |
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{ |
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cv::Mat dst; |
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TEST_CYCLE() cv::mulSpectrums(a, b, dst, flag); |
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CPU_SANITY_CHECK(dst); |
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} |
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} |
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////////////////////////////////////////////////////////////////////// |
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// MulAndScaleSpectrums |
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PERF_TEST_P(Sz, MulAndScaleSpectrums, |
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CUDA_TYPICAL_MAT_SIZES) |
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{ |
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const cv::Size size = GetParam(); |
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const float scale = 1.f / size.area(); |
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cv::Mat src1(size, CV_32FC2); |
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cv::Mat src2(size, CV_32FC2); |
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declare.in(src1,src2, WARMUP_RNG); |
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if (PERF_RUN_CUDA()) |
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{ |
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const cv::cuda::GpuMat d_src1(src1); |
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const cv::cuda::GpuMat d_src2(src2); |
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cv::cuda::GpuMat dst; |
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TEST_CYCLE() cv::cuda::mulAndScaleSpectrums(d_src1, d_src2, dst, cv::DFT_ROWS, scale, false); |
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CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE); |
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} |
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else |
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{ |
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FAIL_NO_CPU(); |
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} |
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} |
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////////////////////////////////////////////////////////////////////// |
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// Dft |
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PERF_TEST_P(Sz_Flags, Dft, |
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Combine(CUDA_TYPICAL_MAT_SIZES, |
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Values(0, DftFlags(cv::DFT_ROWS), DftFlags(cv::DFT_INVERSE)))) |
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{ |
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declare.time(10.0); |
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const cv::Size size = GET_PARAM(0); |
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const int flag = GET_PARAM(1); |
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cv::Mat src(size, CV_32FC2); |
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declare.in(src, WARMUP_RNG); |
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if (PERF_RUN_CUDA()) |
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{ |
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const cv::cuda::GpuMat d_src(src); |
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cv::cuda::GpuMat dst; |
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TEST_CYCLE() cv::cuda::dft(d_src, dst, size, flag); |
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CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE); |
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} |
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else |
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{ |
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cv::Mat dst; |
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TEST_CYCLE() cv::dft(src, dst, flag); |
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CPU_SANITY_CHECK(dst); |
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} |
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} |
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////////////////////////////////////////////////////////////////////// |
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// Convolve |
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DEF_PARAM_TEST(Sz_KernelSz_Ccorr, cv::Size, int, bool); |
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PERF_TEST_P(Sz_KernelSz_Ccorr, Convolve, |
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Combine(CUDA_TYPICAL_MAT_SIZES, |
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Values(17, 27, 32, 64), |
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Bool())) |
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{ |
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declare.time(10.0); |
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const cv::Size size = GET_PARAM(0); |
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const int templ_size = GET_PARAM(1); |
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const bool ccorr = GET_PARAM(2); |
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const cv::Mat image(size, CV_32FC1); |
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const cv::Mat templ(templ_size, templ_size, CV_32FC1); |
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declare.in(image, templ, WARMUP_RNG); |
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if (PERF_RUN_CUDA()) |
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{ |
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cv::cuda::GpuMat d_image = cv::cuda::createContinuous(size, CV_32FC1); |
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d_image.upload(image); |
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cv::cuda::GpuMat d_templ = cv::cuda::createContinuous(templ_size, templ_size, CV_32FC1); |
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d_templ.upload(templ); |
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cv::Ptr<cv::cuda::Convolution> convolution = cv::cuda::createConvolution(); |
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cv::cuda::GpuMat dst; |
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TEST_CYCLE() convolution->convolve(d_image, d_templ, dst, ccorr); |
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CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE); |
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} |
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else |
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{ |
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if (ccorr) |
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FAIL_NO_CPU(); |
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cv::Mat dst; |
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TEST_CYCLE() cv::filter2D(image, dst, image.depth(), templ); |
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CPU_SANITY_CHECK(dst); |
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} |
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} |
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}} // namespace
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