434 lines
14 KiB
C++
434 lines
14 KiB
C++
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/*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 "test_precomp.hpp"
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#ifdef HAVE_CUDA
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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 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))
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PARAM_TEST_CASE(GEMM, cv::cuda::DeviceInfo, cv::Size, MatType, GemmFlags, UseRoi)
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{
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cv::cuda::DeviceInfo devInfo;
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cv::Size size;
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int type;
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int flags;
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bool useRoi;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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size = GET_PARAM(1);
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type = GET_PARAM(2);
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flags = GET_PARAM(3);
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useRoi = GET_PARAM(4);
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cv::cuda::setDevice(devInfo.deviceID());
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}
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};
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CUDA_TEST_P(GEMM, Accuracy)
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{
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cv::Mat src1 = randomMat(size, type, -10.0, 10.0);
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cv::Mat src2 = randomMat(size, type, -10.0, 10.0);
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cv::Mat src3 = randomMat(size, type, -10.0, 10.0);
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double alpha = randomDouble(-10.0, 10.0);
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double beta = randomDouble(-10.0, 10.0);
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if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE))
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{
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try
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{
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cv::cuda::GpuMat dst;
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cv::cuda::gemm(loadMat(src1), loadMat(src2), alpha, loadMat(src3), beta, dst, flags);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
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}
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}
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else if (type == CV_64FC2 && flags != 0)
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{
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try
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{
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cv::cuda::GpuMat dst;
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cv::cuda::gemm(loadMat(src1), loadMat(src2), alpha, loadMat(src3), beta, dst, flags);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(cv::Error::StsNotImplemented, e.code);
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}
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}
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else
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{
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cv::cuda::GpuMat dst = createMat(size, type, useRoi);
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cv::cuda::gemm(loadMat(src1, useRoi), loadMat(src2, useRoi), alpha, loadMat(src3, useRoi), beta, dst, flags);
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cv::Mat dst_gold;
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cv::gemm(src1, src2, alpha, src3, beta, dst_gold, flags);
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EXPECT_MAT_NEAR(dst_gold, dst, CV_MAT_DEPTH(type) == CV_32F ? 1e-1 : 1e-10);
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}
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}
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INSTANTIATE_TEST_CASE_P(CUDA_Arithm, GEMM, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES,
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testing::Values(MatType(CV_32FC1), MatType(CV_32FC2), MatType(CV_64FC1), MatType(CV_64FC2)),
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ALL_GEMM_FLAGS,
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WHOLE_SUBMAT));
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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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PARAM_TEST_CASE(MulSpectrums, cv::cuda::DeviceInfo, cv::Size, DftFlags)
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{
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cv::cuda::DeviceInfo devInfo;
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cv::Size size;
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int flag;
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cv::Mat a, b;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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size = GET_PARAM(1);
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flag = GET_PARAM(2);
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cv::cuda::setDevice(devInfo.deviceID());
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a = randomMat(size, CV_32FC2);
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b = randomMat(size, CV_32FC2);
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}
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};
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CUDA_TEST_P(MulSpectrums, Simple)
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{
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cv::cuda::GpuMat c;
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cv::cuda::mulSpectrums(loadMat(a), loadMat(b), c, flag, false);
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cv::Mat c_gold;
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cv::mulSpectrums(a, b, c_gold, flag, false);
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EXPECT_MAT_NEAR(c_gold, c, 1e-2);
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}
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CUDA_TEST_P(MulSpectrums, Scaled)
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{
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float scale = 1.f / size.area();
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cv::cuda::GpuMat c;
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cv::cuda::mulAndScaleSpectrums(loadMat(a), loadMat(b), c, flag, scale, false);
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cv::Mat c_gold;
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cv::mulSpectrums(a, b, c_gold, flag, false);
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c_gold.convertTo(c_gold, c_gold.type(), scale);
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EXPECT_MAT_NEAR(c_gold, c, 1e-2);
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}
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INSTANTIATE_TEST_CASE_P(CUDA_Arithm, MulSpectrums, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES,
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testing::Values(DftFlags(0), DftFlags(cv::DFT_ROWS))));
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////////////////////////////////////////////////////////////////////////////
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// Dft
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struct Dft : testing::TestWithParam<cv::cuda::DeviceInfo>
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{
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cv::cuda::DeviceInfo devInfo;
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virtual void SetUp()
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{
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devInfo = GetParam();
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cv::cuda::setDevice(devInfo.deviceID());
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}
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};
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namespace
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{
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void testC2C(const std::string& hint, int cols, int rows, int flags, bool inplace)
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{
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SCOPED_TRACE(hint);
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cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC2, 0.0, 10.0);
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cv::Mat b_gold;
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cv::dft(a, b_gold, flags);
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cv::cuda::GpuMat d_b;
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cv::cuda::GpuMat d_b_data;
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if (inplace)
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{
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d_b_data.create(1, a.size().area(), CV_32FC2);
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d_b = cv::cuda::GpuMat(a.rows, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize());
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}
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cv::cuda::dft(loadMat(a), d_b, cv::Size(cols, rows), flags);
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EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr());
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ASSERT_EQ(CV_32F, d_b.depth());
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ASSERT_EQ(2, d_b.channels());
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EXPECT_MAT_NEAR(b_gold, cv::Mat(d_b), rows * cols * 1e-4);
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}
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}
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CUDA_TEST_P(Dft, C2C)
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{
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int cols = randomInt(2, 100);
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int rows = randomInt(2, 100);
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for (int i = 0; i < 2; ++i)
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{
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bool inplace = i != 0;
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testC2C("no flags", cols, rows, 0, inplace);
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testC2C("no flags 0 1", cols, rows + 1, 0, inplace);
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testC2C("no flags 1 0", cols, rows + 1, 0, inplace);
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testC2C("no flags 1 1", cols + 1, rows, 0, inplace);
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testC2C("DFT_INVERSE", cols, rows, cv::DFT_INVERSE, inplace);
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testC2C("DFT_ROWS", cols, rows, cv::DFT_ROWS, inplace);
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testC2C("single col", 1, rows, 0, inplace);
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testC2C("single row", cols, 1, 0, inplace);
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testC2C("single col inversed", 1, rows, cv::DFT_INVERSE, inplace);
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testC2C("single row inversed", cols, 1, cv::DFT_INVERSE, inplace);
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testC2C("single row DFT_ROWS", cols, 1, cv::DFT_ROWS, inplace);
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testC2C("size 1 2", 1, 2, 0, inplace);
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testC2C("size 2 1", 2, 1, 0, inplace);
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}
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}
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CUDA_TEST_P(Dft, Algorithm)
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{
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int cols = randomInt(2, 100);
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int rows = randomInt(2, 100);
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int flags = 0 | DFT_COMPLEX_INPUT;
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cv::Ptr<cv::cuda::DFT> dft = cv::cuda::createDFT(cv::Size(cols, rows), flags);
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for (int i = 0; i < 5; ++i)
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{
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SCOPED_TRACE("dft algorithm");
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cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC2, 0.0, 10.0);
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cv::cuda::GpuMat d_b;
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cv::cuda::GpuMat d_b_data;
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dft->compute(loadMat(a), d_b);
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cv::Mat b_gold;
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cv::dft(a, b_gold, flags);
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ASSERT_EQ(CV_32F, d_b.depth());
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ASSERT_EQ(2, d_b.channels());
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EXPECT_MAT_NEAR(b_gold, cv::Mat(d_b), rows * cols * 1e-4);
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}
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}
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namespace
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{
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void testR2CThenC2R(const std::string& hint, int cols, int rows, bool inplace)
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{
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SCOPED_TRACE(hint);
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cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC1, 0.0, 10.0);
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cv::cuda::GpuMat d_b, d_c;
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cv::cuda::GpuMat d_b_data, d_c_data;
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if (inplace)
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{
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if (a.cols == 1)
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{
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d_b_data.create(1, (a.rows / 2 + 1) * a.cols, CV_32FC2);
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d_b = cv::cuda::GpuMat(a.rows / 2 + 1, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize());
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}
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else
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{
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d_b_data.create(1, a.rows * (a.cols / 2 + 1), CV_32FC2);
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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());
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}
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d_c_data.create(1, a.size().area(), CV_32F);
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d_c = cv::cuda::GpuMat(a.rows, a.cols, CV_32F, d_c_data.ptr(), a.cols * d_c_data.elemSize());
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}
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cv::cuda::dft(loadMat(a), d_b, cv::Size(cols, rows), 0);
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cv::cuda::dft(d_b, d_c, cv::Size(cols, rows), cv::DFT_REAL_OUTPUT | cv::DFT_SCALE);
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EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr());
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EXPECT_TRUE(!inplace || d_c.ptr() == d_c_data.ptr());
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ASSERT_EQ(CV_32F, d_c.depth());
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ASSERT_EQ(1, d_c.channels());
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cv::Mat c(d_c);
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EXPECT_MAT_NEAR(a, c, rows * cols * 1e-5);
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}
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}
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CUDA_TEST_P(Dft, R2CThenC2R)
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{
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int cols = randomInt(2, 100);
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int rows = randomInt(2, 100);
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testR2CThenC2R("sanity", cols, rows, false);
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testR2CThenC2R("sanity 0 1", cols, rows + 1, false);
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testR2CThenC2R("sanity 1 0", cols + 1, rows, false);
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testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, false);
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testR2CThenC2R("single col", 1, rows, false);
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testR2CThenC2R("single col 1", 1, rows + 1, false);
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testR2CThenC2R("single row", cols, 1, false);
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testR2CThenC2R("single row 1", cols + 1, 1, false);
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testR2CThenC2R("sanity", cols, rows, true);
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testR2CThenC2R("sanity 0 1", cols, rows + 1, true);
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testR2CThenC2R("sanity 1 0", cols + 1, rows, true);
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testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, true);
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testR2CThenC2R("single row", cols, 1, true);
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||
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|
testR2CThenC2R("single row 1", cols + 1, 1, true);
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||
|
|
}
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||
|
|
|
||
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|
INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Dft, ALL_DEVICES);
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||
|
|
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||
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|
////////////////////////////////////////////////////////
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||
|
|
// Convolve
|
||
|
|
|
||
|
|
namespace
|
||
|
|
{
|
||
|
|
void convolveDFT(const cv::Mat& A, const cv::Mat& B, cv::Mat& C, bool ccorr = false)
|
||
|
|
{
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||
|
|
// 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
|
||
|
|
|
||
|
|
}} // namespace
|
||
|
|
|
||
|
|
#endif // HAVE_CUDA
|