186 lines
6.4 KiB
C++
186 lines
6.4 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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#include <iostream>
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#include <fstream>
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using namespace cv;
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using namespace std;
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CV_SLMLTest::CV_SLMLTest( const char* _modelName ) : CV_MLBaseTest( _modelName )
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{
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validationFN = "slvalidation.xml";
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}
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int CV_SLMLTest::run_test_case( int testCaseIdx )
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{
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int code = cvtest::TS::OK;
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code = prepare_test_case( testCaseIdx );
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if( code == cvtest::TS::OK )
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{
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data.mix_train_and_test_idx();
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code = train( testCaseIdx );
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if( code == cvtest::TS::OK )
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{
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get_error( testCaseIdx, CV_TEST_ERROR, &test_resps1 );
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fname1 = tempfile(".yml.gz");
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save( fname1.c_str() );
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load( fname1.c_str() );
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get_error( testCaseIdx, CV_TEST_ERROR, &test_resps2 );
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fname2 = tempfile(".yml.gz");
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save( fname2.c_str() );
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}
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else
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ts->printf( cvtest::TS::LOG, "model can not be trained" );
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}
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return code;
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}
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int CV_SLMLTest::validate_test_results( int testCaseIdx )
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{
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int code = cvtest::TS::OK;
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// 1. compare files
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FILE *fs1 = fopen(fname1.c_str(), "rb"), *fs2 = fopen(fname2.c_str(), "rb");
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size_t sz1 = 0, sz2 = 0;
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if( !fs1 || !fs2 )
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code = cvtest::TS::FAIL_MISSING_TEST_DATA;
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if( code >= 0 )
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{
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fseek(fs1, 0, SEEK_END); fseek(fs2, 0, SEEK_END);
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sz1 = ftell(fs1);
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sz2 = ftell(fs2);
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fseek(fs1, 0, SEEK_SET); fseek(fs2, 0, SEEK_SET);
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}
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if( sz1 != sz2 )
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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if( code >= 0 )
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{
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const int BUFSZ = 1024;
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uchar buf1[BUFSZ], buf2[BUFSZ];
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for( size_t pos = 0; pos < sz1; )
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{
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size_t r1 = fread(buf1, 1, BUFSZ, fs1);
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size_t r2 = fread(buf2, 1, BUFSZ, fs2);
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if( r1 != r2 || memcmp(buf1, buf2, r1) != 0 )
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{
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ts->printf( cvtest::TS::LOG,
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"in test case %d first (%s) and second (%s) saved files differ in %d-th kb\n",
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testCaseIdx, fname1.c_str(), fname2.c_str(),
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(int)pos );
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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break;
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}
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pos += r1;
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}
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}
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if(fs1)
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fclose(fs1);
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if(fs2)
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fclose(fs2);
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// delete temporary files
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if( code >= 0 )
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{
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remove( fname1.c_str() );
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remove( fname2.c_str() );
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}
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// 2. compare responses
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CV_Assert( test_resps1.size() == test_resps2.size() );
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vector<float>::const_iterator it1 = test_resps1.begin(), it2 = test_resps2.begin();
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for( ; it1 != test_resps1.end(); ++it1, ++it2 )
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{
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if( fabs(*it1 - *it2) > FLT_EPSILON )
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{
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ts->printf( cvtest::TS::LOG, "in test case %d responses predicted before saving and after loading is different", testCaseIdx );
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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}
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}
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return code;
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}
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TEST(ML_NaiveBayes, save_load) { CV_SLMLTest test( CV_NBAYES ); test.safe_run(); }
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//CV_SLMLTest lsmlknearest( CV_KNEAREST, "slknearest" ); // does not support save!
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TEST(ML_SVM, save_load) { CV_SLMLTest test( CV_SVM ); test.safe_run(); }
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//CV_SLMLTest lsmlem( CV_EM, "slem" ); // does not support save!
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TEST(ML_ANN, save_load) { CV_SLMLTest test( CV_ANN ); test.safe_run(); }
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TEST(ML_DTree, save_load) { CV_SLMLTest test( CV_DTREE ); test.safe_run(); }
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TEST(ML_Boost, save_load) { CV_SLMLTest test( CV_BOOST ); test.safe_run(); }
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TEST(ML_RTrees, save_load) { CV_SLMLTest test( CV_RTREES ); test.safe_run(); }
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TEST(ML_ERTrees, save_load) { CV_SLMLTest test( CV_ERTREES ); test.safe_run(); }
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TEST(DISABLED_ML_SVM, linear_save_load)
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{
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CvSVM svm1, svm2, svm3;
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svm1.load("SVM45_X_38-1.xml");
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svm2.load("SVM45_X_38-2.xml");
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string tname = tempfile("a.xml");
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svm2.save(tname.c_str());
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svm3.load(tname.c_str());
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ASSERT_EQ(svm1.get_var_count(), svm2.get_var_count());
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ASSERT_EQ(svm1.get_var_count(), svm3.get_var_count());
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int m = 10000, n = svm1.get_var_count();
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Mat samples(m, n, CV_32F), r1, r2, r3;
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randu(samples, 0., 1.);
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svm1.predict(samples, r1);
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svm2.predict(samples, r2);
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svm3.predict(samples, r3);
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double eps = 1e-4;
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EXPECT_LE(norm(r1, r2, NORM_INF), eps);
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EXPECT_LE(norm(r1, r3, NORM_INF), eps);
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remove(tname.c_str());
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}
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/* End of file. */
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