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trace: initial support for code trace

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This commit is contained in:
Alexander Alekhin
2017-05-25 18:59:01 +03:00
parent 07aff8e85f
commit 006966e629
58 changed files with 2963 additions and 185 deletions
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modules/ml/src/data.cpp
+2
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@@ -1028,6 +1028,7 @@ Ptr<TrainData> TrainData::loadFromCSV(const String& filename,
const String& varTypeSpec,
char delimiter, char missch)
{
CV_TRACE_FUNCTION_SKIP_NESTED();
Ptr<TrainDataImpl> td = makePtr<TrainDataImpl>();
if(!td->loadCSV(filename, headerLines, responseStartIdx, responseEndIdx, varTypeSpec, delimiter, missch))
td.release();
@@ -1038,6 +1039,7 @@ Ptr<TrainData> TrainData::create(InputArray samples, int layout, InputArray resp
InputArray varIdx, InputArray sampleIdx, InputArray sampleWeights,
InputArray varType)
{
CV_TRACE_FUNCTION_SKIP_NESTED();
Ptr<TrainDataImpl> td = makePtr<TrainDataImpl>();
td->setData(samples, layout, responses, varIdx, sampleIdx, sampleWeights, varType, noArray());
return td;
modules/ml/src/inner_functions.cpp
+6
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@@ -45,6 +45,7 @@ namespace cv { namespace ml {
ParamGrid::ParamGrid() { minVal = maxVal = 0.; logStep = 1; }
ParamGrid::ParamGrid(double _minVal, double _maxVal, double _logStep)
{
CV_TRACE_FUNCTION();
minVal = std::min(_minVal, _maxVal);
maxVal = std::max(_minVal, _maxVal);
logStep = std::max(_logStep, 1.);
@@ -60,17 +61,20 @@ int StatModel::getVarCount() const { return 0; }
bool StatModel::train( const Ptr<TrainData>&, int )
{
CV_TRACE_FUNCTION();
CV_Error(CV_StsNotImplemented, "");
return false;
}
bool StatModel::train( InputArray samples, int layout, InputArray responses )
{
CV_TRACE_FUNCTION();
return train(TrainData::create(samples, layout, responses));
}
float StatModel::calcError( const Ptr<TrainData>& data, bool testerr, OutputArray _resp ) const
{
CV_TRACE_FUNCTION_SKIP_NESTED();
Mat samples = data->getSamples();
int layout = data->getLayout();
Mat sidx = testerr ? data->getTestSampleIdx() : data->getTrainSampleIdx();
@@ -119,6 +123,7 @@ float StatModel::calcError( const Ptr<TrainData>& data, bool testerr, OutputArra
/* Calculates upper triangular matrix S, where A is a symmetrical matrix A=S'*S */
static void Cholesky( const Mat& A, Mat& S )
{
CV_TRACE_FUNCTION();
CV_Assert(A.type() == CV_32F);
S = A.clone();
@@ -133,6 +138,7 @@ static void Cholesky( const Mat& A, Mat& S )
average row vector, <cov> - symmetric covariation matrix */
void randMVNormal( InputArray _mean, InputArray _cov, int nsamples, OutputArray _samples )
{
CV_TRACE_FUNCTION();
// check mean vector and covariance matrix
Mat mean = _mean.getMat(), cov = _cov.getMat();
int dim = (int)mean.total(); // dimensionality
modules/ml/src/lr.cpp
+5
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@@ -135,6 +135,7 @@ Ptr<LogisticRegression> LogisticRegression::load(const String& filepath, const S
bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
{
CV_TRACE_FUNCTION_SKIP_NESTED();
// return value
bool ok = false;
@@ -313,6 +314,7 @@ float LogisticRegressionImpl::predict(InputArray samples, OutputArray results, i
Mat LogisticRegressionImpl::calc_sigmoid(const Mat& data) const
{
CV_TRACE_FUNCTION();
Mat dest;
exp(-data, dest);
return 1.0/(1.0+dest);
@@ -320,6 +322,7 @@ Mat LogisticRegressionImpl::calc_sigmoid(const Mat& data) const
double LogisticRegressionImpl::compute_cost(const Mat& _data, const Mat& _labels, const Mat& _init_theta)
{
CV_TRACE_FUNCTION();
float llambda = 0; /*changed llambda from int to float to solve issue #7924*/
int m;
int n;
@@ -410,6 +413,7 @@ struct LogisticRegressionImpl_ComputeDradient_Impl : ParallelLoopBody
void LogisticRegressionImpl::compute_gradient(const Mat& _data, const Mat& _labels, const Mat &_theta, const double _lambda, Mat & _gradient )
{
CV_TRACE_FUNCTION();
const int m = _data.rows;
Mat pcal_a, pcal_b, pcal_ab;
@@ -431,6 +435,7 @@ void LogisticRegressionImpl::compute_gradient(const Mat& _data, const Mat& _labe
Mat LogisticRegressionImpl::batch_gradient_descent(const Mat& _data, const Mat& _labels, const Mat& _init_theta)
{
CV_TRACE_FUNCTION();
// implements batch gradient descent
if(this->params.alpha<=0)
{
modules/ml/src/rtrees.cpp
+22 -1
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@@ -49,6 +49,7 @@ namespace ml {
//////////////////////////////////////////////////////////////////////////////////////////
RTreeParams::RTreeParams()
{
CV_TRACE_FUNCTION();
calcVarImportance = false;
nactiveVars = 0;
termCrit = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 50, 0.1);
@@ -58,6 +59,7 @@ RTreeParams::RTreeParams(bool _calcVarImportance,
int _nactiveVars,
TermCriteria _termCrit )
{
CV_TRACE_FUNCTION();
calcVarImportance = _calcVarImportance;
nactiveVars = _nactiveVars;
termCrit = _termCrit;
@@ -69,6 +71,7 @@ class DTreesImplForRTrees : public DTreesImpl
public:
DTreesImplForRTrees()
{
CV_TRACE_FUNCTION();
params.setMaxDepth(5);
params.setMinSampleCount(10);
params.setRegressionAccuracy(0.f);
@@ -83,6 +86,7 @@ public:
void clear()
{
CV_TRACE_FUNCTION();
DTreesImpl::clear();
oobError = 0.;
rng = RNG((uint64)-1);
@@ -90,6 +94,7 @@ public:
const vector<int>& getActiveVars()
{
CV_TRACE_FUNCTION();
int i, nvars = (int)allVars.size(), m = (int)activeVars.size();
for( i = 0; i < nvars; i++ )
{
@@ -104,6 +109,7 @@ public:
void startTraining( const Ptr<TrainData>& trainData, int flags )
{
CV_TRACE_FUNCTION();
DTreesImpl::startTraining(trainData, flags);
int nvars = w->data->getNVars();
int i, m = rparams.nactiveVars > 0 ? rparams.nactiveVars : cvRound(std::sqrt((double)nvars));
@@ -116,6 +122,7 @@ public:
void endTraining()
{
CV_TRACE_FUNCTION();
DTreesImpl::endTraining();
vector<int> a, b;
std::swap(allVars, a);
@@ -124,6 +131,7 @@ public:
bool train( const Ptr<TrainData>& trainData, int flags )
{
CV_TRACE_FUNCTION();
startTraining(trainData, flags);
int treeidx, ntrees = (rparams.termCrit.type & TermCriteria::COUNT) != 0 ?
rparams.termCrit.maxCount : 10000;
@@ -286,12 +294,14 @@ public:
void writeTrainingParams( FileStorage& fs ) const
{
CV_TRACE_FUNCTION();
DTreesImpl::writeTrainingParams(fs);
fs << "nactive_vars" << rparams.nactiveVars;
}
void write( FileStorage& fs ) const
{
CV_TRACE_FUNCTION();
if( roots.empty() )
CV_Error( CV_StsBadArg, "RTrees have not been trained" );
@@ -319,6 +329,7 @@ public:
void readParams( const FileNode& fn )
{
CV_TRACE_FUNCTION();
DTreesImpl::readParams(fn);
FileNode tparams_node = fn["training_params"];
@@ -327,6 +338,7 @@ public:
void read( const FileNode& fn )
{
CV_TRACE_FUNCTION();
clear();
//int nclasses = (int)fn["nclasses"];
@@ -351,6 +363,7 @@ public:
void getVotes( InputArray input, OutputArray output, int flags ) const
{
CV_TRACE_FUNCTION();
CV_Assert( !roots.empty() );
int nclasses = (int)classLabels.size(), ntrees = (int)roots.size();
Mat samples = input.getMat(), results;
@@ -435,6 +448,7 @@ public:
bool train( const Ptr<TrainData>& trainData, int flags )
{
CV_TRACE_FUNCTION();
if (impl.getCVFolds() != 0)
CV_Error(Error::StsBadArg, "Cross validation for RTrees is not implemented");
return impl.train(trainData, flags);
@@ -442,22 +456,26 @@ public:
float predict( InputArray samples, OutputArray results, int flags ) const
{
CV_TRACE_FUNCTION();
return impl.predict(samples, results, flags);
}
void write( FileStorage& fs ) const
{
CV_TRACE_FUNCTION();
impl.write(fs);
}
void read( const FileNode& fn )
{
CV_TRACE_FUNCTION();
impl.read(fn);
}
void getVotes_( InputArray samples, OutputArray results, int flags ) const
{
impl.getVotes(samples, results, flags);
CV_TRACE_FUNCTION();
impl.getVotes(samples, results, flags);
}
Mat getVarImportance() const { return Mat_<float>(impl.varImportance, true); }
@@ -477,17 +495,20 @@ public:
Ptr<RTrees> RTrees::create()
{
CV_TRACE_FUNCTION();
return makePtr<RTreesImpl>();
}
//Function needed for Python and Java wrappers
Ptr<RTrees> RTrees::load(const String& filepath, const String& nodeName)
{
CV_TRACE_FUNCTION();
return Algorithm::load<RTrees>(filepath, nodeName);
}
void RTrees::getVotes(InputArray input, OutputArray output, int flags) const
{
CV_TRACE_FUNCTION();
const RTreesImpl* this_ = dynamic_cast<const RTreesImpl*>(this);
if(!this_)
CV_Error(Error::StsNotImplemented, "the class is not RTreesImpl");
modules/ml/test/test_emknearestkmeans.cpp
+3
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@@ -50,6 +50,7 @@ using cv::ml::KNearest;
static
void defaultDistribs( Mat& means, vector<Mat>& covs, int type=CV_32FC1 )
{
CV_TRACE_FUNCTION();
float mp0[] = {0.0f, 0.0f}, cp0[] = {0.67f, 0.0f, 0.0f, 0.67f};
float mp1[] = {5.0f, 0.0f}, cp1[] = {1.0f, 0.0f, 0.0f, 1.0f};
float mp2[] = {1.0f, 5.0f}, cp2[] = {1.0f, 0.0f, 0.0f, 1.0f};
@@ -76,6 +77,7 @@ void defaultDistribs( Mat& means, vector<Mat>& covs, int type=CV_32FC1 )
static
void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int dataType, int labelType )
{
CV_TRACE_FUNCTION();
vector<int>::const_iterator sit = sizes.begin();
int total = 0;
for( ; sit != sizes.end(); ++sit )
@@ -226,6 +228,7 @@ protected:
void CV_KMeansTest::run( int /*start_from*/ )
{
CV_TRACE_FUNCTION();
const int iters = 100;
int sizesArr[] = { 5000, 7000, 8000 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
modules/ml/test/test_lr.cpp
+3
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@@ -64,6 +64,7 @@ using namespace cv::ml;
static bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& error)
{
CV_TRACE_FUNCTION();
error = 0.0f;
float accuracy = 0.0f;
Mat _p_labels_temp;
@@ -91,6 +92,7 @@ protected:
void CV_LRTest::run( int /*start_from*/ )
{
CV_TRACE_FUNCTION();
// initialize varibles from the popular Iris Dataset
string dataFileName = ts->get_data_path() + "iris.data";
Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0);
@@ -150,6 +152,7 @@ protected:
void CV_LRTest_SaveLoad::run( int /*start_from*/ )
{
CV_TRACE_FUNCTION();
int code = cvtest::TS::OK;
// initialize varibles from the popular Iris Dataset
modules/ml/test/test_mltests.cpp
+2
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@@ -51,6 +51,7 @@ CV_AMLTest::CV_AMLTest( const char* _modelName ) : CV_MLBaseTest( _modelName )
int CV_AMLTest::run_test_case( int testCaseIdx )
{
CV_TRACE_FUNCTION();
int code = cvtest::TS::OK;
code = prepare_test_case( testCaseIdx );
@@ -91,6 +92,7 @@ int CV_AMLTest::run_test_case( int testCaseIdx )
int CV_AMLTest::validate_test_results( int testCaseIdx )
{
CV_TRACE_FUNCTION();
int iters;
float mean, sigma;
// read validation params
modules/ml/test/test_mltests2.cpp
+11
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@@ -87,6 +87,7 @@ int str_to_ann_train_method( String& str )
void ann_check_data( Ptr<TrainData> _data )
{
CV_TRACE_FUNCTION();
Mat values = _data->getSamples();
Mat var_idx = _data->getVarIdx();
int nvars = (int)var_idx.total();
@@ -99,6 +100,7 @@ void ann_check_data( Ptr<TrainData> _data )
// unroll the categorical responses to binary vectors
Mat ann_get_new_responses( Ptr<TrainData> _data, map<int, int>& cls_map )
{
CV_TRACE_FUNCTION();
Mat train_sidx = _data->getTrainSampleIdx();
int* train_sidx_ptr = train_sidx.ptr<int>();
Mat responses = _data->getResponses();
@@ -130,6 +132,7 @@ Mat ann_get_new_responses( Ptr<TrainData> _data, map<int, int>& cls_map )
float ann_calc_error( Ptr<StatModel> ann, Ptr<TrainData> _data, map<int, int>& cls_map, int type, vector<float> *resp_labels )
{
CV_TRACE_FUNCTION();
float err = 0;
Mat samples = _data->getSamples();
Mat responses = _data->getResponses();
@@ -241,6 +244,7 @@ CV_MLBaseTest::~CV_MLBaseTest()
int CV_MLBaseTest::read_params( CvFileStorage* __fs )
{
CV_TRACE_FUNCTION();
FileStorage _fs(__fs, false);
if( !_fs.isOpened() )
test_case_count = -1;
@@ -265,6 +269,7 @@ int CV_MLBaseTest::read_params( CvFileStorage* __fs )
void CV_MLBaseTest::run( int )
{
CV_TRACE_FUNCTION();
string filename = ts->get_data_path();
filename += get_validation_filename();
validationFS.open( filename, FileStorage::READ );
@@ -273,6 +278,7 @@ void CV_MLBaseTest::run( int )
int code = cvtest::TS::OK;
for (int i = 0; i < test_case_count; i++)
{
CV_TRACE_REGION("iteration");
int temp_code = run_test_case( i );
if (temp_code == cvtest::TS::OK)
temp_code = validate_test_results( i );
@@ -289,6 +295,7 @@ void CV_MLBaseTest::run( int )
int CV_MLBaseTest::prepare_test_case( int test_case_idx )
{
CV_TRACE_FUNCTION();
clear();
string dataPath = ts->get_data_path();
@@ -331,6 +338,7 @@ string& CV_MLBaseTest::get_validation_filename()
int CV_MLBaseTest::train( int testCaseIdx )
{
CV_TRACE_FUNCTION();
bool is_trained = false;
FileNode modelParamsNode =
validationFS.getFirstTopLevelNode()["validation"][modelName][dataSetNames[testCaseIdx]]["model_params"];
@@ -489,6 +497,7 @@ int CV_MLBaseTest::train( int testCaseIdx )
float CV_MLBaseTest::get_test_error( int /*testCaseIdx*/, vector<float> *resp )
{
CV_TRACE_FUNCTION();
int type = CV_TEST_ERROR;
float err = 0;
Mat _resp;
@@ -506,11 +515,13 @@ float CV_MLBaseTest::get_test_error( int /*testCaseIdx*/, vector<float> *resp )
void CV_MLBaseTest::save( const char* filename )
{
CV_TRACE_FUNCTION();
model->save( filename );
}
void CV_MLBaseTest::load( const char* filename )
{
CV_TRACE_FUNCTION();
if( modelName == CV_NBAYES )
model = Algorithm::load<NormalBayesClassifier>( filename );
else if( modelName == CV_KNEAREST )