cd0e95de16
* remove overloads with explicit buffer, now BufferPool is used * added async versions for all reduce functions
515 lines
13 KiB
C++
515 lines
13 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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//////////////////////////////////////////////////////////////////////
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// Norm
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DEF_PARAM_TEST(Sz_Depth_Norm, cv::Size, MatDepth, NormType);
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PERF_TEST_P(Sz_Depth_Norm, Norm,
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Combine(CUDA_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32S, CV_32F),
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Values(NormType(cv::NORM_INF), NormType(cv::NORM_L1), NormType(cv::NORM_L2))))
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{
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const cv::Size size = GET_PARAM(0);
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const int depth = GET_PARAM(1);
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const int normType = GET_PARAM(2);
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cv::Mat src(size, depth);
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if (depth == CV_8U)
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cv::randu(src, 0, 254);
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else
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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 d_buf;
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double gpu_dst;
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TEST_CYCLE() gpu_dst = cv::cuda::norm(d_src, normType, d_buf);
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SANITY_CHECK(gpu_dst, 1e-6, ERROR_RELATIVE);
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}
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else
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{
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double cpu_dst;
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TEST_CYCLE() cpu_dst = cv::norm(src, normType);
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SANITY_CHECK(cpu_dst, 1e-6, ERROR_RELATIVE);
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}
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}
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//////////////////////////////////////////////////////////////////////
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// NormDiff
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DEF_PARAM_TEST(Sz_Norm, cv::Size, NormType);
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PERF_TEST_P(Sz_Norm, NormDiff,
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Combine(CUDA_TYPICAL_MAT_SIZES,
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Values(NormType(cv::NORM_INF), NormType(cv::NORM_L1), NormType(cv::NORM_L2))))
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{
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const cv::Size size = GET_PARAM(0);
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const int normType = GET_PARAM(1);
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cv::Mat src1(size, CV_8UC1);
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declare.in(src1, WARMUP_RNG);
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cv::Mat src2(size, CV_8UC1);
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declare.in(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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double gpu_dst;
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TEST_CYCLE() gpu_dst = cv::cuda::norm(d_src1, d_src2, normType);
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SANITY_CHECK(gpu_dst);
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}
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else
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{
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double cpu_dst;
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TEST_CYCLE() cpu_dst = cv::norm(src1, src2, normType);
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SANITY_CHECK(cpu_dst);
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}
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}
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//////////////////////////////////////////////////////////////////////
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// Sum
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PERF_TEST_P(Sz_Depth_Cn, Sum,
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Combine(CUDA_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32F),
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CUDA_CHANNELS_1_3_4))
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{
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const cv::Size size = GET_PARAM(0);
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const int depth = GET_PARAM(1);
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const int channels = GET_PARAM(2);
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const int type = CV_MAKE_TYPE(depth, channels);
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cv::Mat src(size, type);
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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::Scalar gpu_dst;
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TEST_CYCLE() gpu_dst = cv::cuda::sum(d_src);
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SANITY_CHECK(gpu_dst, 1e-5, ERROR_RELATIVE);
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}
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else
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{
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cv::Scalar cpu_dst;
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TEST_CYCLE() cpu_dst = cv::sum(src);
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SANITY_CHECK(cpu_dst, 1e-6, ERROR_RELATIVE);
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}
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}
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//////////////////////////////////////////////////////////////////////
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// SumAbs
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PERF_TEST_P(Sz_Depth_Cn, SumAbs,
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Combine(CUDA_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32F),
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CUDA_CHANNELS_1_3_4))
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{
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const cv::Size size = GET_PARAM(0);
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const int depth = GET_PARAM(1);
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const int channels = GET_PARAM(2);
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const int type = CV_MAKE_TYPE(depth, channels);
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cv::Mat src(size, type);
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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::Scalar gpu_dst;
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TEST_CYCLE() gpu_dst = cv::cuda::absSum(d_src);
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SANITY_CHECK(gpu_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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// SumSqr
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PERF_TEST_P(Sz_Depth_Cn, SumSqr,
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Combine(CUDA_TYPICAL_MAT_SIZES,
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Values<MatDepth>(CV_8U, CV_16U, CV_32F),
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CUDA_CHANNELS_1_3_4))
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{
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const cv::Size size = GET_PARAM(0);
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const int depth = GET_PARAM(1);
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const int channels = GET_PARAM(2);
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const int type = CV_MAKE_TYPE(depth, channels);
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cv::Mat src(size, type);
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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::Scalar gpu_dst;
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TEST_CYCLE() gpu_dst = cv::cuda::sqrSum(d_src);
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SANITY_CHECK(gpu_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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// MinMax
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PERF_TEST_P(Sz_Depth, MinMax,
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Combine(CUDA_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32F, CV_64F)))
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{
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const cv::Size size = GET_PARAM(0);
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const int depth = GET_PARAM(1);
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cv::Mat src(size, depth);
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if (depth == CV_8U)
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cv::randu(src, 0, 254);
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else
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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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double gpu_minVal, gpu_maxVal;
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TEST_CYCLE() cv::cuda::minMax(d_src, &gpu_minVal, &gpu_maxVal, cv::cuda::GpuMat());
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SANITY_CHECK(gpu_minVal, 1e-10);
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SANITY_CHECK(gpu_maxVal, 1e-10);
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}
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else
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{
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double cpu_minVal, cpu_maxVal;
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TEST_CYCLE() cv::minMaxLoc(src, &cpu_minVal, &cpu_maxVal);
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SANITY_CHECK(cpu_minVal);
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SANITY_CHECK(cpu_maxVal);
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}
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}
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//////////////////////////////////////////////////////////////////////
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// MinMaxLoc
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PERF_TEST_P(Sz_Depth, MinMaxLoc,
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Combine(CUDA_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32F, CV_64F)))
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{
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const cv::Size size = GET_PARAM(0);
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const int depth = GET_PARAM(1);
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cv::Mat src(size, depth);
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if (depth == CV_8U)
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cv::randu(src, 0, 254);
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else
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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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double gpu_minVal, gpu_maxVal;
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cv::Point gpu_minLoc, gpu_maxLoc;
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TEST_CYCLE() cv::cuda::minMaxLoc(d_src, &gpu_minVal, &gpu_maxVal, &gpu_minLoc, &gpu_maxLoc);
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SANITY_CHECK(gpu_minVal, 1e-10);
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SANITY_CHECK(gpu_maxVal, 1e-10);
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}
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else
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{
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double cpu_minVal, cpu_maxVal;
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cv::Point cpu_minLoc, cpu_maxLoc;
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TEST_CYCLE() cv::minMaxLoc(src, &cpu_minVal, &cpu_maxVal, &cpu_minLoc, &cpu_maxLoc);
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SANITY_CHECK(cpu_minVal);
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SANITY_CHECK(cpu_maxVal);
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}
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}
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//////////////////////////////////////////////////////////////////////
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// CountNonZero
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PERF_TEST_P(Sz_Depth, CountNonZero,
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Combine(CUDA_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32F, CV_64F)))
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{
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const cv::Size size = GET_PARAM(0);
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const int depth = GET_PARAM(1);
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cv::Mat src(size, depth);
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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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int gpu_dst = 0;
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TEST_CYCLE() gpu_dst = cv::cuda::countNonZero(d_src);
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SANITY_CHECK(gpu_dst);
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}
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else
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{
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int cpu_dst = 0;
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TEST_CYCLE() cpu_dst = cv::countNonZero(src);
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SANITY_CHECK(cpu_dst);
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}
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}
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//////////////////////////////////////////////////////////////////////
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// Reduce
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CV_ENUM(ReduceCode, REDUCE_SUM, REDUCE_AVG, REDUCE_MAX, REDUCE_MIN)
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enum {Rows = 0, Cols = 1};
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CV_ENUM(ReduceDim, Rows, Cols)
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DEF_PARAM_TEST(Sz_Depth_Cn_Code_Dim, cv::Size, MatDepth, MatCn, ReduceCode, ReduceDim);
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PERF_TEST_P(Sz_Depth_Cn_Code_Dim, Reduce,
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Combine(CUDA_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_16S, CV_32F),
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Values(1, 2, 3, 4),
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ReduceCode::all(),
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ReduceDim::all()))
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{
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const cv::Size size = GET_PARAM(0);
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const int depth = GET_PARAM(1);
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const int channels = GET_PARAM(2);
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const int reduceOp = GET_PARAM(3);
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const int dim = GET_PARAM(4);
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const int type = CV_MAKE_TYPE(depth, channels);
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cv::Mat src(size, type);
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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::reduce(d_src, dst, dim, reduceOp, CV_32F);
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CUDA_SANITY_CHECK(dst);
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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::reduce(src, dst, dim, reduceOp, CV_32F);
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CPU_SANITY_CHECK(dst);
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}
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}
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//////////////////////////////////////////////////////////////////////
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// Normalize
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DEF_PARAM_TEST(Sz_Depth_NormType, cv::Size, MatDepth, NormType);
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PERF_TEST_P(Sz_Depth_NormType, Normalize,
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Combine(CUDA_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32F, CV_64F),
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Values(NormType(cv::NORM_INF),
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NormType(cv::NORM_L1),
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NormType(cv::NORM_L2),
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NormType(cv::NORM_MINMAX))))
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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 norm_type = GET_PARAM(2);
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const double alpha = 1;
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const double beta = 0;
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cv::Mat src(size, type);
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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::normalize(d_src, dst, alpha, beta, norm_type, type, cv::cuda::GpuMat());
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CUDA_SANITY_CHECK(dst, 1e-6);
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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::normalize(src, dst, alpha, beta, norm_type, type);
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CPU_SANITY_CHECK(dst);
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}
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}
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//////////////////////////////////////////////////////////////////////
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// MeanStdDev
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PERF_TEST_P(Sz, MeanStdDev,
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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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cv::Mat src(size, CV_8UC1);
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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::Scalar gpu_mean;
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cv::Scalar gpu_stddev;
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TEST_CYCLE() cv::cuda::meanStdDev(d_src, gpu_mean, gpu_stddev);
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SANITY_CHECK(gpu_mean);
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SANITY_CHECK(gpu_stddev);
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}
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else
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{
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cv::Scalar cpu_mean;
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cv::Scalar cpu_stddev;
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TEST_CYCLE() cv::meanStdDev(src, cpu_mean, cpu_stddev);
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SANITY_CHECK(cpu_mean);
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SANITY_CHECK(cpu_stddev);
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}
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}
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//////////////////////////////////////////////////////////////////////
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// Integral
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PERF_TEST_P(Sz, Integral,
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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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cv::Mat src(size, CV_8UC1);
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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::integral(d_src, dst);
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CUDA_SANITY_CHECK(dst);
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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::integral(src, dst);
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CPU_SANITY_CHECK(dst);
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}
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}
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//////////////////////////////////////////////////////////////////////
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// IntegralSqr
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PERF_TEST_P(Sz, IntegralSqr,
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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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cv::Mat src(size, CV_8UC1);
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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::sqrIntegral(d_src, dst);
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CUDA_SANITY_CHECK(dst);
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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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