/*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. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // 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 Intel Corporation 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" #include #include using namespace cv; using namespace std; CV_SLMLTest::CV_SLMLTest( const char* _modelName ) : CV_MLBaseTest( _modelName ) { validationFN = "slvalidation.xml"; } int CV_SLMLTest::run_test_case( int testCaseIdx ) { int code = cvtest::TS::OK; code = prepare_test_case( testCaseIdx ); if( code == cvtest::TS::OK ) { data.mix_train_and_test_idx(); code = train( testCaseIdx ); if( code == cvtest::TS::OK ) { get_error( testCaseIdx, CV_TEST_ERROR, &test_resps1 ); fname1 = tempfile(".yml.gz"); save( fname1.c_str() ); load( fname1.c_str() ); get_error( testCaseIdx, CV_TEST_ERROR, &test_resps2 ); fname2 = tempfile(".yml.gz"); save( fname2.c_str() ); } else ts->printf( cvtest::TS::LOG, "model can not be trained" ); } return code; } int CV_SLMLTest::validate_test_results( int testCaseIdx ) { int code = cvtest::TS::OK; // 1. compare files FILE *fs1 = fopen(fname1.c_str(), "rb"), *fs2 = fopen(fname2.c_str(), "rb"); size_t sz1 = 0, sz2 = 0; if( !fs1 || !fs2 ) code = cvtest::TS::FAIL_MISSING_TEST_DATA; if( code >= 0 ) { fseek(fs1, 0, SEEK_END); fseek(fs2, 0, SEEK_END); sz1 = ftell(fs1); sz2 = ftell(fs2); fseek(fs1, 0, SEEK_SET); fseek(fs2, 0, SEEK_SET); } if( sz1 != sz2 ) code = cvtest::TS::FAIL_INVALID_OUTPUT; if( code >= 0 ) { const int BUFSZ = 1024; uchar buf1[BUFSZ], buf2[BUFSZ]; for( size_t pos = 0; pos < sz1; ) { size_t r1 = fread(buf1, 1, BUFSZ, fs1); size_t r2 = fread(buf2, 1, BUFSZ, fs2); if( r1 != r2 || memcmp(buf1, buf2, r1) != 0 ) { ts->printf( cvtest::TS::LOG, "in test case %d first (%s) and second (%s) saved files differ in %d-th kb\n", testCaseIdx, fname1.c_str(), fname2.c_str(), (int)pos ); code = cvtest::TS::FAIL_INVALID_OUTPUT; break; } pos += r1; } } if(fs1) fclose(fs1); if(fs2) fclose(fs2); // delete temporary files if( code >= 0 ) { remove( fname1.c_str() ); remove( fname2.c_str() ); } // 2. compare responses CV_Assert( test_resps1.size() == test_resps2.size() ); vector::const_iterator it1 = test_resps1.begin(), it2 = test_resps2.begin(); for( ; it1 != test_resps1.end(); ++it1, ++it2 ) { if( fabs(*it1 - *it2) > FLT_EPSILON ) { ts->printf( cvtest::TS::LOG, "in test case %d responses predicted before saving and after loading is different", testCaseIdx ); code = cvtest::TS::FAIL_INVALID_OUTPUT; } } return code; } TEST(ML_NaiveBayes, save_load) { CV_SLMLTest test( CV_NBAYES ); test.safe_run(); } //CV_SLMLTest lsmlknearest( CV_KNEAREST, "slknearest" ); // does not support save! TEST(ML_SVM, save_load) { CV_SLMLTest test( CV_SVM ); test.safe_run(); } //CV_SLMLTest lsmlem( CV_EM, "slem" ); // does not support save! TEST(ML_ANN, save_load) { CV_SLMLTest test( CV_ANN ); test.safe_run(); } TEST(ML_DTree, save_load) { CV_SLMLTest test( CV_DTREE ); test.safe_run(); } TEST(ML_Boost, save_load) { CV_SLMLTest test( CV_BOOST ); test.safe_run(); } TEST(ML_RTrees, save_load) { CV_SLMLTest test( CV_RTREES ); test.safe_run(); } TEST(ML_ERTrees, save_load) { CV_SLMLTest test( CV_ERTREES ); test.safe_run(); } TEST(ML_SVM, throw_exception_when_save_untrained_model) { SVM svm; string filename = tempfile("svm.xml"); ASSERT_THROW(svm.save(filename.c_str()), Exception); remove(filename.c_str()); } TEST(DISABLED_ML_SVM, linear_save_load) { CvSVM svm1, svm2, svm3; svm1.load("SVM45_X_38-1.xml"); svm2.load("SVM45_X_38-2.xml"); string tname = tempfile("a.xml"); svm2.save(tname.c_str()); svm3.load(tname.c_str()); ASSERT_EQ(svm1.get_var_count(), svm2.get_var_count()); ASSERT_EQ(svm1.get_var_count(), svm3.get_var_count()); int m = 10000, n = svm1.get_var_count(); Mat samples(m, n, CV_32F), r1, r2, r3; randu(samples, 0., 1.); svm1.predict(samples, r1); svm2.predict(samples, r2); svm3.predict(samples, r3); double eps = 1e-4; EXPECT_LE(cvtest::norm(r1, r2, NORM_INF), eps); EXPECT_LE(cvtest::norm(r1, r3, NORM_INF), eps); remove(tname.c_str()); } /* End of file. */