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5ff1fababc
ml: refactored tests * use parametrized tests where appropriate * use stable theRNG in most tests * use modern style with EXPECT_/ASSERT_ checks
108 lines
3.6 KiB
C++
108 lines
3.6 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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void randomFillCategories(const string & filename, Mat & input)
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{
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Mat catMap;
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Mat catCount;
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std::vector<uchar> varTypes;
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FileStorage fs(filename, FileStorage::READ);
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FileNode root = fs.getFirstTopLevelNode();
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root["cat_map"] >> catMap;
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root["cat_count"] >> catCount;
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root["var_type"] >> varTypes;
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int offset = 0;
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int countOffset = 0;
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uint var = 0, varCount = (uint)varTypes.size();
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for (; var < varCount; ++var)
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{
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if (varTypes[var] == ml::VAR_CATEGORICAL)
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{
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int size = catCount.at<int>(0, countOffset);
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for (int row = 0; row < input.rows; ++row)
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{
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int randomChosenIndex = offset + ((uint)cv::theRNG()) % size;
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int value = catMap.at<int>(0, randomChosenIndex);
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input.at<float>(row, var) = (float)value;
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}
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offset += size;
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++countOffset;
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}
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}
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}
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//==================================================================================================
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typedef tuple<string, string> ML_Legacy_Param;
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typedef testing::TestWithParam< ML_Legacy_Param > ML_Legacy_Params;
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TEST_P(ML_Legacy_Params, legacy_load)
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{
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const string modelName = get<0>(GetParam());
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const string dataName = get<1>(GetParam());
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const string filename = findDataFile("legacy/" + modelName + "_" + dataName + ".xml");
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const bool isTree = modelName == CV_BOOST || modelName == CV_DTREE || modelName == CV_RTREES;
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Ptr<StatModel> model;
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if (modelName == CV_BOOST)
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model = Algorithm::load<Boost>(filename);
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else if (modelName == CV_ANN)
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model = Algorithm::load<ANN_MLP>(filename);
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else if (modelName == CV_DTREE)
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model = Algorithm::load<DTrees>(filename);
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else if (modelName == CV_NBAYES)
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model = Algorithm::load<NormalBayesClassifier>(filename);
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else if (modelName == CV_SVM)
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model = Algorithm::load<SVM>(filename);
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else if (modelName == CV_RTREES)
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model = Algorithm::load<RTrees>(filename);
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else if (modelName == CV_SVMSGD)
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model = Algorithm::load<SVMSGD>(filename);
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ASSERT_TRUE(model);
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Mat input = Mat(isTree ? 10 : 1, model->getVarCount(), CV_32F);
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cv::theRNG().fill(input, RNG::UNIFORM, 0, 40);
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if (isTree)
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randomFillCategories(filename, input);
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Mat output;
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EXPECT_NO_THROW(model->predict(input, output, StatModel::RAW_OUTPUT | (isTree ? DTrees::PREDICT_SUM : 0)));
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// just check if no internal assertions or errors thrown
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}
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ML_Legacy_Param param_list[] = {
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ML_Legacy_Param(CV_ANN, "waveform"),
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ML_Legacy_Param(CV_BOOST, "adult"),
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ML_Legacy_Param(CV_BOOST, "1"),
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ML_Legacy_Param(CV_BOOST, "2"),
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ML_Legacy_Param(CV_BOOST, "3"),
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ML_Legacy_Param(CV_DTREE, "abalone"),
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ML_Legacy_Param(CV_DTREE, "mushroom"),
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ML_Legacy_Param(CV_NBAYES, "waveform"),
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ML_Legacy_Param(CV_SVM, "poletelecomm"),
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ML_Legacy_Param(CV_SVM, "waveform"),
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ML_Legacy_Param(CV_RTREES, "waveform"),
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ML_Legacy_Param(CV_SVMSGD, "waveform"),
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};
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INSTANTIATE_TEST_CASE_P(/**/, ML_Legacy_Params, testing::ValuesIn(param_list));
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/*TEST(ML_SVM, throw_exception_when_save_untrained_model)
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{
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Ptr<cv::ml::SVM> svm;
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string filename = tempfile("svm.xml");
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ASSERT_THROW(svm.save(filename.c_str()), Exception);
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remove(filename.c_str());
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}*/
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}} // namespace
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