310 lines
10 KiB
C++
310 lines
10 KiB
C++
///////////////////////////////////////////////////////////////////////////////////////
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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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// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
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// Copyright (C) 2010-2012, Advanced Micro Devices, 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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// @Authors
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// Jin Ma, jin@multicorewareinc.com
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// Xiaopeng Fu, fuxiaopeng2222@163.com
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// Erping Pang, pang_er_ping@163.com
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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_OPENCL
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using namespace cv;
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using namespace cv::ocl;
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using namespace cvtest;
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using namespace testing;
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///////K-NEAREST NEIGHBOR//////////////////////////
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static void genTrainData(cv::RNG& rng, Mat& trainData, int trainDataRow, int trainDataCol,
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Mat& trainLabel = Mat().setTo(Scalar::all(0)), int nClasses = 0)
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{
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cv::Size size(trainDataCol, trainDataRow);
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trainData = randomMat(rng, size, CV_32FC1, 1.0, 1000.0, false);
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if(nClasses != 0)
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{
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cv::Size size1(trainDataRow, 1);
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trainLabel = randomMat(rng, size1, CV_8UC1, 0, nClasses - 1, false);
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trainLabel.convertTo(trainLabel, CV_32FC1);
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}
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}
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PARAM_TEST_CASE(KNN, int, Size, int, bool)
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{
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int k;
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int trainDataCol;
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int testDataRow;
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int nClass;
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bool regression;
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virtual void SetUp()
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{
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k = GET_PARAM(0);
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nClass = GET_PARAM(2);
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trainDataCol = GET_PARAM(1).width;
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testDataRow = GET_PARAM(1).height;
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regression = GET_PARAM(3);
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}
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};
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OCL_TEST_P(KNN, Accuracy)
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{
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Mat trainData, trainLabels;
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const int trainDataRow = 500;
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genTrainData(rng, trainData, trainDataRow, trainDataCol, trainLabels, nClass);
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Mat testData, testLabels;
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genTrainData(rng, testData, testDataRow, trainDataCol);
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KNearestNeighbour knn_ocl;
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CvKNearest knn_cpu;
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Mat best_label_cpu;
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oclMat best_label_ocl;
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/*ocl k-Nearest_Neighbor start*/
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oclMat trainData_ocl;
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trainData_ocl.upload(trainData);
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Mat simpleIdx;
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knn_ocl.train(trainData, trainLabels, simpleIdx, regression);
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oclMat testdata;
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testdata.upload(testData);
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knn_ocl.find_nearest(testdata, k, best_label_ocl);
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/*ocl k-Nearest_Neighbor end*/
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/*cpu k-Nearest_Neighbor start*/
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knn_cpu.train(trainData, trainLabels, simpleIdx, regression);
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knn_cpu.find_nearest(testData, k, &best_label_cpu);
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/*cpu k-Nearest_Neighbor end*/
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if(regression)
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{
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EXPECT_MAT_SIMILAR(Mat(best_label_ocl), best_label_cpu, 1e-5);
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}
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else
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{
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EXPECT_MAT_NEAR(Mat(best_label_ocl), best_label_cpu, 0.0);
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}
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}
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INSTANTIATE_TEST_CASE_P(OCL_ML, KNN, Combine(Values(6, 5), Values(Size(200, 400), Size(300, 600)),
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Values(4, 3), Values(false, true)));
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////////////////////////////////SVM/////////////////////////////////////////////////
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PARAM_TEST_CASE(SVM_OCL, int, int, int)
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{
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cv::Size size;
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int kernel_type;
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int svm_type;
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Mat src, labels, samples, labels_predict;
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int K;
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virtual void SetUp()
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{
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kernel_type = GET_PARAM(0);
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svm_type = GET_PARAM(1);
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K = GET_PARAM(2);
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cv::Size size = cv::Size(MWIDTH, MHEIGHT);
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src.create(size, CV_32FC1);
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labels.create(1, size.height, CV_32SC1);
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int row_idx = 0;
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const int max_number = size.height / K - 1;
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CV_Assert(K <= size.height);
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for(int i = 0; i < K; i++ )
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{
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Mat center_row_header = src.row(row_idx);
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center_row_header.setTo(0);
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int nchannel = center_row_header.channels();
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for(int j = 0; j < nchannel; j++)
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{
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center_row_header.at<float>(0, i * nchannel + j) = 500.0;
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}
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labels.at<int>(0, row_idx) = i;
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for(int j = 0; (j < max_number) ||
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(i == K - 1 && j < max_number + size.height % K); j ++)
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{
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Mat cur_row_header = src.row(row_idx + 1 + j);
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center_row_header.copyTo(cur_row_header);
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Mat tmpmat = randomMat(cur_row_header.size(), cur_row_header.type(), 1, 100, false);
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cur_row_header += tmpmat;
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labels.at<int>(0, row_idx + 1 + j) = i;
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}
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row_idx += 1 + max_number;
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}
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labels.convertTo(labels, CV_32FC1);
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cv::Size test_size = cv::Size(MWIDTH, 100);
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samples.create(test_size, CV_32FC1);
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labels_predict.create(1, test_size.height, CV_32SC1);
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const int max_number_test = test_size.height / K - 1;
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row_idx = 0;
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for(int i = 0; i < K; i++ )
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{
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Mat center_row_header = samples.row(row_idx);
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center_row_header.setTo(0);
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int nchannel = center_row_header.channels();
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for(int j = 0; j < nchannel; j++)
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{
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center_row_header.at<float>(0, i * nchannel + j) = 500.0;
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}
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labels_predict.at<int>(0, row_idx) = i;
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for(int j = 0; (j < max_number_test) ||
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(i == K - 1 && j < max_number_test + test_size.height % K); j ++)
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{
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Mat cur_row_header = samples.row(row_idx + 1 + j);
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center_row_header.copyTo(cur_row_header);
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Mat tmpmat = randomMat(cur_row_header.size(), cur_row_header.type(), 1, 100, false);
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cur_row_header += tmpmat;
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labels_predict.at<int>(0, row_idx + 1 + j) = i;
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}
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row_idx += 1 + max_number_test;
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}
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labels_predict.convertTo(labels_predict, CV_32FC1);
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}
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};
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OCL_TEST_P(SVM_OCL, Accuracy)
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{
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CvSVMParams params;
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params.degree = 0.4;
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params.gamma = 1;
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params.coef0 = 1;
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params.C = 1;
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params.nu = 0.5;
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params.p = 1;
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params.svm_type = svm_type;
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params.kernel_type = kernel_type;
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params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, 0.001);
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CvSVM SVM;
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SVM.train(src, labels, Mat(), Mat(), params);
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cv::ocl::CvSVM_OCL SVM_OCL;
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SVM_OCL.train(src, labels, Mat(), Mat(), params);
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int c = SVM.get_support_vector_count();
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int c1 = SVM_OCL.get_support_vector_count();
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Mat sv(c, MHEIGHT, CV_32FC1);
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Mat sv_ocl(c1, MHEIGHT, CV_32FC1);
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for(int i = 0; i < c; i++)
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{
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const float* v = SVM.get_support_vector(i);
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for(int j = 0; j < MHEIGHT; j++)
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{
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sv.at<float>(i, j) = v[j];
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}
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}
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for(int i = 0; i < c1; i++)
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{
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const float* v_ocl = SVM_OCL.get_support_vector(i);
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for(int j = 0; j < MHEIGHT; j++)
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{
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sv_ocl.at<float>(i, j) = v_ocl[j];
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}
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}
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cv::BFMatcher matcher(cv::NORM_L2);
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std::vector<cv::DMatch> matches;
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matcher.match(sv, sv_ocl, matches);
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int count = 0;
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for(std::vector<cv::DMatch>::iterator itr = matches.begin(); itr != matches.end(); itr++)
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{
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if((*itr).distance < 0.1)
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{
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count ++;
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}
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}
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if(c != 0)
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{
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float matchedRatio = (float)count / c;
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EXPECT_GT(matchedRatio, 0.95);
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}
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if(c != 0)
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{
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CvMat *result = cvCreateMat(1, samples.rows, CV_32FC1);
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CvMat test_samples = samples;
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CvMat *result_ocl = cvCreateMat(1, samples.rows, CV_32FC1);
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SVM.predict(&test_samples, result);
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SVM_OCL.predict(&test_samples, result_ocl);
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int true_resp = 0, true_resp_ocl = 0;
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for (int i = 0; i < samples.rows; i++)
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{
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if (result->data.fl[i] == labels_predict.at<float>(0, i))
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{
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true_resp++;
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}
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}
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float matchedRatio = (float)true_resp / samples.rows;
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for (int i = 0; i < samples.rows; i++)
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{
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if (result_ocl->data.fl[i] == labels_predict.at<float>(0, i))
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{
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true_resp_ocl++;
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}
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}
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float matchedRatio_ocl = (float)true_resp_ocl / samples.rows;
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if(matchedRatio != 0 && true_resp_ocl < true_resp)
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{
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EXPECT_NEAR(matchedRatio_ocl, matchedRatio, 0.03);
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}
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}
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}
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// TODO FIXIT: CvSVM::EPS_SVR case is crashed inside CPU implementation
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// Anonymous enums are not supported well so cast them to 'int'
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INSTANTIATE_TEST_CASE_P(OCL_ML, SVM_OCL, testing::Combine(
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Values((int)CvSVM::LINEAR, (int)CvSVM::POLY, (int)CvSVM::RBF, (int)CvSVM::SIGMOID),
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Values((int)CvSVM::C_SVC, (int)CvSVM::NU_SVC, (int)CvSVM::ONE_CLASS, (int)CvSVM::NU_SVR),
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Values(2, 3, 4)
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));
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#endif // HAVE_OPENCL
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