test_ml.cpp 10.2 KB
Newer Older
wester committed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309
///////////////////////////////////////////////////////////////////////////////////////
//
//  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) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
//    Jin Ma,        jin@multicorewareinc.com
//    Xiaopeng Fu,   fuxiaopeng2222@163.com
//    Erping Pang,   pang_er_ping@163.com
// 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 "test_precomp.hpp"

#ifdef HAVE_OPENCL

using namespace cv;
using namespace cv::ocl;
using namespace cvtest;
using namespace testing;

///////K-NEAREST NEIGHBOR//////////////////////////

static void genTrainData(cv::RNG& rng, Mat& trainData, int trainDataRow, int trainDataCol,
                         Mat& trainLabel = Mat().setTo(Scalar::all(0)), int nClasses = 0)
{
    cv::Size size(trainDataCol, trainDataRow);
    trainData = randomMat(rng, size, CV_32FC1, 1.0, 1000.0, false);
    if(nClasses != 0)
    {
        cv::Size size1(trainDataRow, 1);
        trainLabel = randomMat(rng, size1, CV_8UC1, 0, nClasses - 1, false);
        trainLabel.convertTo(trainLabel, CV_32FC1);
    }
}

PARAM_TEST_CASE(KNN, int, Size, int, bool)
{
    int k;
    int trainDataCol;
    int testDataRow;
    int nClass;
    bool regression;
    virtual void SetUp()
    {
        k = GET_PARAM(0);
        nClass = GET_PARAM(2);
        trainDataCol = GET_PARAM(1).width;
        testDataRow = GET_PARAM(1).height;
        regression = GET_PARAM(3);
    }
};

OCL_TEST_P(KNN, Accuracy)
{
    Mat trainData, trainLabels;
    const int trainDataRow = 500;
    genTrainData(rng, trainData, trainDataRow, trainDataCol, trainLabels, nClass);

    Mat testData, testLabels;
    genTrainData(rng, testData, testDataRow, trainDataCol);

    KNearestNeighbour knn_ocl;
    CvKNearest knn_cpu;
    Mat best_label_cpu;
    oclMat best_label_ocl;

    /*ocl k-Nearest_Neighbor start*/
    oclMat trainData_ocl;
    trainData_ocl.upload(trainData);
    Mat simpleIdx;
    knn_ocl.train(trainData, trainLabels, simpleIdx, regression);

    oclMat testdata;
    testdata.upload(testData);
    knn_ocl.find_nearest(testdata, k, best_label_ocl);
    /*ocl k-Nearest_Neighbor end*/

    /*cpu k-Nearest_Neighbor start*/
    knn_cpu.train(trainData, trainLabels, simpleIdx, regression);
    knn_cpu.find_nearest(testData, k, &best_label_cpu);
    /*cpu k-Nearest_Neighbor end*/
    if(regression)
    {
        EXPECT_MAT_SIMILAR(Mat(best_label_ocl), best_label_cpu, 1e-5);
    }
    else
    {
        EXPECT_MAT_NEAR(Mat(best_label_ocl), best_label_cpu, 0.0);
    }
}

INSTANTIATE_TEST_CASE_P(OCL_ML, KNN, Combine(Values(6, 5), Values(Size(200, 400), Size(300, 600)),
    Values(4, 3), Values(false, true)));

////////////////////////////////SVM/////////////////////////////////////////////////

PARAM_TEST_CASE(SVM_OCL, int, int, int)
{
    cv::Size size;
    int kernel_type;
    int svm_type;
    Mat src, labels, samples, labels_predict;
    int K;

    virtual void SetUp()
    {

        kernel_type = GET_PARAM(0);
        svm_type = GET_PARAM(1);
        K = GET_PARAM(2);
        cv::Size sz = cv::Size(MWIDTH, MHEIGHT);
        src.create(sz, CV_32FC1);
        labels.create(1, sz.height, CV_32SC1);
        int row_idx = 0;
        const int max_number = sz.height / K - 1;
        CV_Assert(K <= sz.height);
        for(int i = 0; i < K; i++ )
        {
            Mat center_row_header = src.row(row_idx);
            center_row_header.setTo(0);
            int nchannel = center_row_header.channels();
            for(int j = 0; j < nchannel; j++)
            {
                center_row_header.at<float>(0, i * nchannel + j) = 500.0;
            }
            labels.at<int>(0, row_idx) = i;
            for(int j = 0; (j < max_number) ||
                    (i == K - 1 && j < max_number + sz.height % K); j ++)
            {
                Mat cur_row_header = src.row(row_idx + 1 + j);
                center_row_header.copyTo(cur_row_header);
                Mat tmpmat = randomMat(cur_row_header.size(), cur_row_header.type(), 1, 100, false);
                cur_row_header += tmpmat;
                labels.at<int>(0, row_idx + 1 + j) = i;
            }
            row_idx += 1 + max_number;
        }
        labels.convertTo(labels, CV_32FC1);
        cv::Size test_size = cv::Size(MWIDTH, 100);
        samples.create(test_size, CV_32FC1);
        labels_predict.create(1, test_size.height, CV_32SC1);
        const int max_number_test = test_size.height / K - 1;
        row_idx = 0;
        for(int i = 0; i < K; i++ )
        {
            Mat center_row_header = samples.row(row_idx);
            center_row_header.setTo(0);
            int nchannel = center_row_header.channels();
            for(int j = 0; j < nchannel; j++)
            {
                center_row_header.at<float>(0, i * nchannel + j) = 500.0;
            }
            labels_predict.at<int>(0, row_idx) = i;
            for(int j = 0; (j < max_number_test) ||
                    (i == K - 1 && j < max_number_test + test_size.height % K); j ++)
            {
                Mat cur_row_header = samples.row(row_idx + 1 + j);
                center_row_header.copyTo(cur_row_header);
                Mat tmpmat = randomMat(cur_row_header.size(), cur_row_header.type(), 1, 100, false);
                cur_row_header += tmpmat;
                labels_predict.at<int>(0, row_idx + 1 + j) = i;
            }
            row_idx += 1 + max_number_test;
        }
        labels_predict.convertTo(labels_predict, CV_32FC1);
    }
};

OCL_TEST_P(SVM_OCL, Accuracy)
{
    CvSVMParams params;
    params.degree = 0.4;
    params.gamma = 1;
    params.coef0 = 1;
    params.C = 1;
    params.nu = 0.5;
    params.p = 1;
    params.svm_type = svm_type;
    params.kernel_type = kernel_type;

    params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, 0.001);

    CvSVM SVM;
    SVM.train(src, labels, Mat(), Mat(), params);

    cv::ocl::CvSVM_OCL SVM_OCL;
    SVM_OCL.train(src, labels, Mat(), Mat(), params);

    int c = SVM.get_support_vector_count();
    int c1 = SVM_OCL.get_support_vector_count();

    Mat sv(c, MHEIGHT, CV_32FC1);
    Mat sv_ocl(c1, MHEIGHT, CV_32FC1);
    for(int i = 0; i < c; i++)
    {
        const float* v = SVM.get_support_vector(i);

        for(int j = 0; j < MHEIGHT; j++)
        {
            sv.at<float>(i, j) = v[j];
        }
    }
    for(int i = 0; i < c1; i++)
    {
        const float* v_ocl = SVM_OCL.get_support_vector(i);

        for(int j = 0; j < MHEIGHT; j++)
        {
            sv_ocl.at<float>(i, j) = v_ocl[j];
        }
    }
    cv::BFMatcher matcher(cv::NORM_L2);
    std::vector<cv::DMatch> matches;
    matcher.match(sv, sv_ocl, matches);
    int count = 0;

    for(std::vector<cv::DMatch>::iterator itr = matches.begin(); itr != matches.end(); itr++)
    {
        if((*itr).distance < 0.1)
        {
            count ++;
        }
    }
    if(c != 0)
    {
        float matchedRatio = (float)count / c;
        EXPECT_GT(matchedRatio, 0.95);
    }
    if(c != 0)
    {
        CvMat *result = cvCreateMat(1, samples.rows, CV_32FC1);
        CvMat test_samples = samples;

        CvMat *result_ocl = cvCreateMat(1, samples.rows, CV_32FC1);

        SVM.predict(&test_samples, result);

        SVM_OCL.predict(&test_samples, result_ocl);

        int true_resp = 0, true_resp_ocl = 0;
        for (int i = 0; i < samples.rows; i++)
        {
            if (result->data.fl[i] == labels_predict.at<float>(0, i))
            {
                true_resp++;
            }
        }
        float matchedRatio = (float)true_resp / samples.rows;

        for (int i = 0; i < samples.rows; i++)
        {
            if (result_ocl->data.fl[i] == labels_predict.at<float>(0, i))
            {
                true_resp_ocl++;
            }
        }
        float matchedRatio_ocl = (float)true_resp_ocl / samples.rows;

        if(matchedRatio != 0 && true_resp_ocl < true_resp)
        {
            EXPECT_NEAR(matchedRatio_ocl, matchedRatio, 0.03);
        }
    }
}

// TODO FIXIT: CvSVM::EPS_SVR case is crashed inside CPU implementation
// Anonymous enums are not supported well so cast them to 'int'

INSTANTIATE_TEST_CASE_P(OCL_ML, SVM_OCL, testing::Combine(
                            Values((int)CvSVM::LINEAR, (int)CvSVM::POLY, (int)CvSVM::RBF, (int)CvSVM::SIGMOID),
                            Values((int)CvSVM::C_SVC, (int)CvSVM::NU_SVC, (int)CvSVM::ONE_CLASS, (int)CvSVM::NU_SVR),
                            Values(2, 3, 4)
                        ));

#endif // HAVE_OPENCL