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#include "test_precomp.hpp"
#include <iostream>
#include <fstream>
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->setTrainTestSplit(data->getNTrainSamples(), true);
code = train( testCaseIdx );
if( code == cvtest::TS::OK )
{
get_test_error( testCaseIdx, &test_resps1 );
fname1 = tempfile(".yml.gz");
save( fname1.c_str() );
load( fname1.c_str() );
get_test_error( testCaseIdx, &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() );
}
if( code >= 0 )
{
// 2. compare responses
CV_Assert( test_resps1.size() == test_resps2.size() );
vector<float>::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;
break;
}
}
}
return code;
}
TEST(ML_NaiveBayes, save_load) { CV_SLMLTest test( CV_NBAYES ); test.safe_run(); }
TEST(ML_KNearest, save_load) { CV_SLMLTest test( CV_KNEAREST ); test.safe_run(); }
TEST(ML_SVM, save_load) { CV_SLMLTest test( CV_SVM ); test.safe_run(); }
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(DISABLED_ML_ERTrees, save_load) { CV_SLMLTest test( CV_ERTREES ); test.safe_run(); }
class CV_LegacyTest : public cvtest::BaseTest
{
public:
CV_LegacyTest(const std::string &_modelName, const std::string &_suffixes = std::string())
: cvtest::BaseTest(), modelName(_modelName), suffixes(_suffixes)
{
}
virtual ~CV_LegacyTest() {}
protected:
void run(int)
{
unsigned int idx = 0;
for (;;)
{
if (idx >= suffixes.size())
break;
int found = (int)suffixes.find(';', idx);
string piece = suffixes.substr(idx, found - idx);
if (piece.empty())
break;
oneTest(piece);
idx += (unsigned int)piece.size() + 1;
}
}
void oneTest(const string & suffix)
{
using namespace cv::ml;
int code = cvtest::TS::OK;
string filename = ts->get_data_path() + "legacy/" + modelName + suffix;
bool isTree = modelName == CV_BOOST || modelName == CV_DTREE || modelName == CV_RTREES;
Ptr<StatModel> model;
if (modelName == CV_BOOST)
model = Algorithm::load<Boost>(filename);
else if (modelName == CV_ANN)
model = Algorithm::load<ANN_MLP>(filename);
else if (modelName == CV_DTREE)
model = Algorithm::load<DTrees>(filename);
else if (modelName == CV_NBAYES)
model = Algorithm::load<NormalBayesClassifier>(filename);
else if (modelName == CV_SVM)
model = Algorithm::load<SVM>(filename);
else if (modelName == CV_RTREES)
model = Algorithm::load<RTrees>(filename);
if (!model)
{
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
}
else
{
Mat input = Mat(isTree ? 10 : 1, model->getVarCount(), CV_32F);
ts->get_rng().fill(input, RNG::UNIFORM, 0, 40);
if (isTree)
randomFillCategories(filename, input);
Mat output;
model->predict(input, output, StatModel::RAW_OUTPUT | (isTree ? DTrees::PREDICT_SUM : 0));
// just check if no internal assertions or errors thrown
}
ts->set_failed_test_info(code);
}
void randomFillCategories(const string & filename, Mat & input)
{
Mat catMap;
Mat catCount;
std::vector<uchar> varTypes;
FileStorage fs(filename, FileStorage::READ);
FileNode root = fs.getFirstTopLevelNode();
root["cat_map"] >> catMap;
root["cat_count"] >> catCount;
root["var_type"] >> varTypes;
int offset = 0;
int countOffset = 0;
uint var = 0, varCount = (uint)varTypes.size();
for (; var < varCount; ++var)
{
if (varTypes[var] == ml::VAR_CATEGORICAL)
{
int size = catCount.at<int>(0, countOffset);
for (int row = 0; row < input.rows; ++row)
{
int randomChosenIndex = offset + ((uint)ts->get_rng()) % size;
int value = catMap.at<int>(0, randomChosenIndex);
input.at<float>(row, var) = (float)value;
}
offset += size;
++countOffset;
}
}
}
string modelName;
string suffixes;
};
TEST(ML_ANN, legacy_load) { CV_LegacyTest test(CV_ANN, "_waveform.xml"); test.safe_run(); }
TEST(ML_Boost, legacy_load) { CV_LegacyTest test(CV_BOOST, "_adult.xml;_1.xml;_2.xml;_3.xml"); test.safe_run(); }
TEST(ML_DTree, legacy_load) { CV_LegacyTest test(CV_DTREE, "_abalone.xml;_mushroom.xml"); test.safe_run(); }
TEST(ML_NBayes, legacy_load) { CV_LegacyTest test(CV_NBAYES, "_waveform.xml"); test.safe_run(); }
TEST(ML_SVM, legacy_load) { CV_LegacyTest test(CV_SVM, "_poletelecomm.xml;_waveform.xml"); test.safe_run(); }
TEST(ML_RTrees, legacy_load) { CV_LegacyTest test(CV_RTREES, "_waveform.xml"); test.safe_run(); }
/*TEST(ML_SVM, throw_exception_when_save_untrained_model)
{
Ptr<cv::ml::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)
{
Ptr<cv::ml::SVM> svm1, svm2, svm3;
svm1 = Algorithm::load<SVM>("SVM45_X_38-1.xml");
svm2 = Algorithm::load<SVM>("SVM45_X_38-2.xml");
string tname = tempfile("a.xml");
svm2->save(tname);
svm3 = Algorithm::load<SVM>(tname);
ASSERT_EQ(svm1->getVarCount(), svm2->getVarCount());
ASSERT_EQ(svm1->getVarCount(), svm3->getVarCount());
int m = 10000, n = svm1->getVarCount();
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(norm(r1, r2, NORM_INF), eps);
EXPECT_LE(norm(r1, r3, NORM_INF), eps);
remove(tname.c_str());
}
/* End of file. */