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259 lines
7.5 KiB
C++
259 lines
7.5 KiB
C++
/*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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using namespace std;
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using namespace testing;
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using namespace perf;
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namespace { // workaround conflict with DftFlags
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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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