perf_ml.cpp 7.48 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
/*M///////////////////////////////////////////////////////////////////////////////////////
//
//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
//  By downloading, copying, installing or using the software you agree to this license.
//  If you do not agree to this license, do not download, install,
//  copy or use the software.
//
//
//                           License Agreement
//                For Open Source Computer Vision Library
//
// Copyright (C) 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
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
//   * Redistribution's of source code must retain the above copyright notice,
//     this list of conditions and the following disclaimer.
//
//   * Redistribution's in binary form must reproduce the above copyright notice,
//     this list of conditions and the following disclaimer in the documentation
//     and/or other materials provided with the distribution.
//
//   * The name of the copyright holders may not be used to endorse or promote products
//     derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors as is and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
using namespace perf;
using namespace std;
using namespace cv::ocl;
using namespace cv;
using std::tr1::tuple;
using std::tr1::get;
////////////////////////////////// K-NEAREST NEIGHBOR ////////////////////////////////////
static void genData(Mat& trainData, Size size, Mat& trainLabel = Mat().setTo(Scalar::all(0)), int nClasses = 0)
{
    trainData.create(size, CV_32FC1);
    randu(trainData, 1.0, 100.0);

    if (nClasses != 0)
    {
        trainLabel.create(size.height, 1, CV_8UC1);
        randu(trainLabel, 0, nClasses - 1);
        trainLabel.convertTo(trainLabel, CV_32FC1);
    }
}

typedef tuple<int> KNNParamType;
typedef TestBaseWithParam<KNNParamType> KNNFixture;

PERF_TEST_P(KNNFixture, KNN,
            testing::Values(1000, 2000, 4000))
{
    KNNParamType params = GetParam();
    const int rows = get<0>(params);
    int columns = 100;
    int k = rows/250;

    Mat trainData, trainLabels;
    Size size(columns, rows);
    genData(trainData, size, trainLabels, 3);

    Mat testData;
    genData(testData, size);
    Mat best_label;

    if (RUN_PLAIN_IMPL)
    {
        TEST_CYCLE()
        {
            CvKNearest knn_cpu;
            knn_cpu.train(trainData, trainLabels);
            knn_cpu.find_nearest(testData, k, &best_label);
        }
    }
    else if (RUN_OCL_IMPL)
    {
        cv::ocl::oclMat best_label_ocl;
        cv::ocl::oclMat testdata;
        testdata.upload(testData);

        OCL_TEST_CYCLE()
        {
            cv::ocl::KNearestNeighbour knn_ocl;
            knn_ocl.train(trainData, trainLabels);
            knn_ocl.find_nearest(testdata, k, best_label_ocl);
        }
        best_label_ocl.download(best_label);
    }
    else
        OCL_PERF_ELSE
    SANITY_CHECK(best_label);
}


typedef TestBaseWithParam<tuple<int> > SVMFixture;

// code is based on: samples\cpp\tutorial_code\ml\non_linear_svms\non_linear_svms.cpp
PERF_TEST_P(SVMFixture, DISABLED_SVM,
            testing::Values(50, 100))
{

    const int NTRAINING_SAMPLES = get<0>(GetParam()); // Number of training samples per class

    #define FRAC_LINEAR_SEP     0.9f // Fraction of samples which compose the linear separable part

    const int WIDTH = 512, HEIGHT = 512;

    Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32FC1);
    Mat labels   (2*NTRAINING_SAMPLES, 1, CV_32FC1);

    RNG rng(100); // Random value generation class

    // Set up the linearly separable part of the training data
    int nLinearSamples = (int) (FRAC_LINEAR_SEP * NTRAINING_SAMPLES);

    // Generate random points for the class 1
    Mat trainClass = trainData.rowRange(0, nLinearSamples);
    // The x coordinate of the points is in [0, 0.4)
    Mat c = trainClass.colRange(0, 1);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * WIDTH));
    // The y coordinate of the points is in [0, 1)
    c = trainClass.colRange(1,2);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));

    // Generate random points for the class 2
    trainClass = trainData.rowRange(2*NTRAINING_SAMPLES-nLinearSamples, 2*NTRAINING_SAMPLES);
    // The x coordinate of the points is in [0.6, 1]
    c = trainClass.colRange(0 , 1);
    rng.fill(c, RNG::UNIFORM, Scalar(0.6*WIDTH), Scalar(WIDTH));
    // The y coordinate of the points is in [0, 1)
    c = trainClass.colRange(1,2);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));

    //------------------ Set up the non-linearly separable part of the training data ---------------

    // Generate random points for the classes 1 and 2
    trainClass = trainData.rowRange(  nLinearSamples, 2*NTRAINING_SAMPLES-nLinearSamples);
    // The x coordinate of the points is in [0.4, 0.6)
    c = trainClass.colRange(0,1);
    rng.fill(c, RNG::UNIFORM, Scalar(0.4*WIDTH), Scalar(0.6*WIDTH));
    // The y coordinate of the points is in [0, 1)
    c = trainClass.colRange(1,2);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));

    //------------------------- Set up the labels for the classes ---------------------------------
    labels.rowRange(                0,   NTRAINING_SAMPLES).setTo(1);  // Class 1
    labels.rowRange(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES).setTo(2);  // Class 2

    //------------------------ Set up the support vector machines parameters --------------------
    CvSVMParams params;
    params.svm_type    = SVM::C_SVC;
    params.C           = 0.1;
    params.kernel_type = SVM::LINEAR;
    params.term_crit   = TermCriteria(CV_TERMCRIT_ITER, (int)1e7, 1e-6);

    Mat dst = Mat::zeros(HEIGHT, WIDTH, CV_8UC1);

    Mat samples(WIDTH*HEIGHT, 2, CV_32FC1);
    int k = 0;
    for (int i = 0; i < HEIGHT; ++i)
    {
        for (int j = 0; j < WIDTH; ++j)
        {
            samples.at<float>(k, 0) = (float)i;
            samples.at<float>(k, 0) = (float)j;
            k++;
        }
    }
    Mat results(WIDTH*HEIGHT, 1, CV_32FC1);

    CvMat samples_ = samples;
    CvMat results_ = results;

    if (RUN_PLAIN_IMPL)
    {
        CvSVM svm;
        svm.train(trainData, labels, Mat(), Mat(), params);
        TEST_CYCLE()
        {
            svm.predict(&samples_, &results_);
        }
    }
    else if (RUN_OCL_IMPL)
    {
        CvSVM_OCL svm;
        svm.train(trainData, labels, Mat(), Mat(), params);
        OCL_TEST_CYCLE()
        {
            svm.predict(&samples_, &results_);
        }
    }
    else
        OCL_PERF_ELSE

    SANITY_CHECK_NOTHING();
}