mirror of
https://github.com/opencv/opencv.git
synced 2024-11-25 11:40:44 +08:00
cdc10defa3
Conflicts: modules/cuda/test/test_objdetect.cpp modules/gpu/perf/perf_core.cpp modules/gpu/perf/perf_video.cpp modules/gpu/test/test_core.cpp
255 lines
7.4 KiB
C++
255 lines
7.4 KiB
C++
/*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 "perf_precomp.hpp"
|
|
|
|
using namespace std;
|
|
using namespace testing;
|
|
using namespace perf;
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// GEMM
|
|
|
|
#ifdef HAVE_CUBLAS
|
|
|
|
CV_FLAGS(GemmFlags, 0, cv::GEMM_1_T, cv::GEMM_2_T, cv::GEMM_3_T)
|
|
#define ALL_GEMM_FLAGS 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))
|
|
|
|
DEF_PARAM_TEST(Sz_Type_Flags, cv::Size, MatType, GemmFlags);
|
|
|
|
PERF_TEST_P(Sz_Type_Flags, GEMM,
|
|
Combine(Values(cv::Size(512, 512), cv::Size(1024, 1024)),
|
|
Values(CV_32FC1, CV_32FC2, CV_64FC1),
|
|
ALL_GEMM_FLAGS))
|
|
{
|
|
const cv::Size size = GET_PARAM(0);
|
|
const int type = GET_PARAM(1);
|
|
const int flags = GET_PARAM(2);
|
|
|
|
cv::Mat src1(size, type);
|
|
declare.in(src1, WARMUP_RNG);
|
|
|
|
cv::Mat src2(size, type);
|
|
declare.in(src2, WARMUP_RNG);
|
|
|
|
cv::Mat src3(size, type);
|
|
declare.in(src3, WARMUP_RNG);
|
|
|
|
if (PERF_RUN_CUDA())
|
|
{
|
|
declare.time(5.0);
|
|
|
|
const cv::cuda::GpuMat d_src1(src1);
|
|
const cv::cuda::GpuMat d_src2(src2);
|
|
const cv::cuda::GpuMat d_src3(src3);
|
|
cv::cuda::GpuMat dst;
|
|
|
|
TEST_CYCLE() cv::cuda::gemm(d_src1, d_src2, 1.0, d_src3, 1.0, dst, flags);
|
|
|
|
CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
|
|
}
|
|
else
|
|
{
|
|
declare.time(50.0);
|
|
|
|
cv::Mat dst;
|
|
|
|
TEST_CYCLE() cv::gemm(src1, src2, 1.0, src3, 1.0, dst, flags);
|
|
|
|
CPU_SANITY_CHECK(dst);
|
|
}
|
|
}
|
|
|
|
#endif
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// MulSpectrums
|
|
|
|
CV_FLAGS(DftFlags, 0, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::DFT_REAL_OUTPUT)
|
|
|
|
DEF_PARAM_TEST(Sz_Flags, cv::Size, DftFlags);
|
|
|
|
PERF_TEST_P(Sz_Flags, MulSpectrums,
|
|
Combine(CUDA_TYPICAL_MAT_SIZES,
|
|
Values(0, DftFlags(cv::DFT_ROWS))))
|
|
{
|
|
const cv::Size size = GET_PARAM(0);
|
|
const int flag = GET_PARAM(1);
|
|
|
|
cv::Mat a(size, CV_32FC2);
|
|
cv::Mat b(size, CV_32FC2);
|
|
declare.in(a, b, WARMUP_RNG);
|
|
|
|
if (PERF_RUN_CUDA())
|
|
{
|
|
const cv::cuda::GpuMat d_a(a);
|
|
const cv::cuda::GpuMat d_b(b);
|
|
cv::cuda::GpuMat dst;
|
|
|
|
TEST_CYCLE() cv::cuda::mulSpectrums(d_a, d_b, dst, flag);
|
|
|
|
CUDA_SANITY_CHECK(dst);
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
TEST_CYCLE() cv::mulSpectrums(a, b, dst, flag);
|
|
|
|
CPU_SANITY_CHECK(dst);
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// MulAndScaleSpectrums
|
|
|
|
PERF_TEST_P(Sz, MulAndScaleSpectrums,
|
|
CUDA_TYPICAL_MAT_SIZES)
|
|
{
|
|
const cv::Size size = GetParam();
|
|
|
|
const float scale = 1.f / size.area();
|
|
|
|
cv::Mat src1(size, CV_32FC2);
|
|
cv::Mat src2(size, CV_32FC2);
|
|
declare.in(src1,src2, WARMUP_RNG);
|
|
|
|
if (PERF_RUN_CUDA())
|
|
{
|
|
const cv::cuda::GpuMat d_src1(src1);
|
|
const cv::cuda::GpuMat d_src2(src2);
|
|
cv::cuda::GpuMat dst;
|
|
|
|
TEST_CYCLE() cv::cuda::mulAndScaleSpectrums(d_src1, d_src2, dst, cv::DFT_ROWS, scale, false);
|
|
|
|
CUDA_SANITY_CHECK(dst);
|
|
}
|
|
else
|
|
{
|
|
FAIL_NO_CPU();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// Dft
|
|
|
|
PERF_TEST_P(Sz_Flags, Dft,
|
|
Combine(CUDA_TYPICAL_MAT_SIZES,
|
|
Values(0, DftFlags(cv::DFT_ROWS), DftFlags(cv::DFT_INVERSE))))
|
|
{
|
|
declare.time(10.0);
|
|
|
|
const cv::Size size = GET_PARAM(0);
|
|
const int flag = GET_PARAM(1);
|
|
|
|
cv::Mat src(size, CV_32FC2);
|
|
declare.in(src, WARMUP_RNG);
|
|
|
|
if (PERF_RUN_CUDA())
|
|
{
|
|
const cv::cuda::GpuMat d_src(src);
|
|
cv::cuda::GpuMat dst;
|
|
|
|
TEST_CYCLE() cv::cuda::dft(d_src, dst, size, flag);
|
|
|
|
CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
TEST_CYCLE() cv::dft(src, dst, flag);
|
|
|
|
CPU_SANITY_CHECK(dst);
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// Convolve
|
|
|
|
DEF_PARAM_TEST(Sz_KernelSz_Ccorr, cv::Size, int, bool);
|
|
|
|
PERF_TEST_P(Sz_KernelSz_Ccorr, Convolve,
|
|
Combine(CUDA_TYPICAL_MAT_SIZES,
|
|
Values(17, 27, 32, 64),
|
|
Bool()))
|
|
{
|
|
declare.time(10.0);
|
|
|
|
const cv::Size size = GET_PARAM(0);
|
|
const int templ_size = GET_PARAM(1);
|
|
const bool ccorr = GET_PARAM(2);
|
|
|
|
const cv::Mat image(size, CV_32FC1);
|
|
const cv::Mat templ(templ_size, templ_size, CV_32FC1);
|
|
declare.in(image, templ, WARMUP_RNG);
|
|
|
|
if (PERF_RUN_CUDA())
|
|
{
|
|
cv::cuda::GpuMat d_image = cv::cuda::createContinuous(size, CV_32FC1);
|
|
d_image.upload(image);
|
|
|
|
cv::cuda::GpuMat d_templ = cv::cuda::createContinuous(templ_size, templ_size, CV_32FC1);
|
|
d_templ.upload(templ);
|
|
|
|
cv::Ptr<cv::cuda::Convolution> convolution = cv::cuda::createConvolution();
|
|
|
|
cv::cuda::GpuMat dst;
|
|
|
|
TEST_CYCLE() convolution->convolve(d_image, d_templ, dst, ccorr);
|
|
|
|
CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
|
|
}
|
|
else
|
|
{
|
|
if (ccorr)
|
|
FAIL_NO_CPU();
|
|
|
|
cv::Mat dst;
|
|
|
|
TEST_CYCLE() cv::filter2D(image, dst, image.depth(), templ);
|
|
|
|
CPU_SANITY_CHECK(dst);
|
|
}
|
|
}
|