opencv/modules/ocl/perf/perf_ml.cpp

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// @Authors
//    Jin Ma, jin@multicorewareinc.com
//    Xiaopeng Fu, fuxiaopeng2222@163.com
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#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();
}