void SoftCascadeLearner::run(const nor_utils::Args& args)
{
// load the arguments
this->getArgs(args);
//print cascade properties
if (_verbose > 0) {
cout << "[+] Softcascade parameters :" << endl
<< "\t --> target detection rate = " << _targetDetectionRate << endl
<< "\t --> alpha (exp param) = " << _alphaExponentialParameter << endl
<< "\t --> bootstrap rate = " << _bootstrapRate << endl
<< endl;
}
// get the registered weak learner (type from name)
BaseLearner* pWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner(_baseLearnerName);
// initialize learning options; normally it's done in the strong loop
// also, here we do it for Product learners, so input data can be created
pWeakHypothesisSource->initLearningOptions(args);
// get the training input data, and load it
InputData* pTrainingData = pWeakHypothesisSource->createInputData();
pTrainingData->initOptions(args);
pTrainingData->load(_trainFileName, IT_TRAIN, 5);
InputData* pBootstrapData = NULL;
if (!_bootstrapFileName.empty()) {
pBootstrapData = pWeakHypothesisSource->createInputData();
pBootstrapData->initOptions(args);
pBootstrapData->load(_bootstrapFileName, IT_TRAIN, 5);
}
// get the testing input data, and load it
InputData* pTestData = NULL;
if ( !_testFileName.empty() )
{
pTestData = pWeakHypothesisSource->createInputData();
pTestData->initOptions(args);
pTestData->load(_testFileName, IT_TEST, 5);
}
Serialization ss(_shypFileName, false );
ss.writeHeader(_baseLearnerName);
// outputHeader();
// The output information object
OutputInfo* pOutInfo = NULL;
if ( !_outputInfoFile.empty() )
{
pOutInfo = new OutputInfo(args, true);
pOutInfo->setOutputList("sca", &args);
pOutInfo->initialize(pTrainingData);
if (pTestData)
pOutInfo->initialize(pTestData);
pOutInfo->outputHeader(pTrainingData->getClassMap(), true, true, false);
pOutInfo->outputUserHeader("thresh");
pOutInfo->headerEndLine();
}
// ofstream trainPosteriorsFile;
// ofstream testPosteriorsFile;
const NameMap& namemap = pTrainingData->getClassMap();
_positiveLabelIndex = namemap.getIdxFromName(_positiveLabelName);
// FIXME: output posteriors
// OutputInfo* pTrainPosteriorsOut = NULL;
// OutputInfo* pTestPosteriorsOut = NULL;
// if (! _trainPosteriorsFileName.empty()) {
// pTrainPosteriorsOut = new OutputInfo(_trainPosteriorsFileName, "pos", true);
// pTrainPosteriorsOut->initialize(pTrainingData);
// dynamic_cast<PosteriorsOutput*>( pTrainPosteriorsOut->getOutputInfoObject("pos") )->addClassIndex(_positiveLabelIndex );
// }
// if (! _testPosteriorsFileName.empty() && !_testFileName.empty() ) {
// pTestPosteriorsOut = new OutputInfo(_testPosteriorsFileName, "pos", true);
// pTestPosteriorsOut->initialize(pTestData);
// dynamic_cast<PosteriorsOutput*>( pTestPosteriorsOut->getOutputInfoObject("pos") )->addClassIndex(_positiveLabelIndex );
// }
const int numExamples = pTrainingData->getNumExamples();
vector<BaseLearner*> inWeakHypotheses;
if (_fullRun) {
// TODO : the full training is implementet, testing is needed
AdaBoostMHLearner* sHypothesis = new AdaBoostMHLearner();
sHypothesis->run(args, pTrainingData, _baseLearnerName, _numIterations, inWeakHypotheses );
delete sHypothesis;
}
else {
cout << "[+] Loading uncalibrated shyp file... ";
//read the shyp file of the trained classifier
UnSerialization us;
us.loadHypotheses(_unCalibratedShypFileName, inWeakHypotheses, pTrainingData);
if (_inShypLimit > 0 && _inShypLimit < inWeakHypotheses.size() ) {
inWeakHypotheses.resize(_inShypLimit);
}
if (_numIterations > inWeakHypotheses.size()) {
_numIterations = inWeakHypotheses.size();
}
cout << "weak hypotheses loaded, " << inWeakHypotheses.size() << " retained.\n";
}
// some initializations
_foundHypotheses.resize(0);
double faceRejectionFraction = 0.;
double estimatedExecutionTime = 0.;
vector<double> rejectionDistributionVector;
_rejectionThresholds.resize(0);
set<int> trainingIndices;
for (int i = 0; i < numExamples; i++) {
trainingIndices.insert(pTrainingData->getRawIndex(i) );
}
// init v_t (see the paper)
initializeRejectionDistributionVector(_numIterations, rejectionDistributionVector);
if (_verbose == 1)
cout << "Learning in progress..." << endl;
///////////////////////////////////////////////////////////////////////
// Starting the SoftCascade main loop
///////////////////////////////////////////////////////////////////////
for (int t = 0; t < _numIterations; ++t)
{
if (_verbose > 0)
cout << "--------------[ iteration " << (t+1) << " ]--------------" << endl;
faceRejectionFraction += rejectionDistributionVector[t];
cout << "[+] Face rejection tolerated : " << faceRejectionFraction << " | v[t] = " << rejectionDistributionVector[t] << endl;
int numberOfNegatives = pTrainingData->getNumExamplesPerClass(1 - _positiveLabelIndex);
//vector<BaseLearner*>::const_iterator whyIt;
int selectedIndex = 0;
AlphaReal bestGap = 0;
vector<AlphaReal> posteriors;
computePosteriors(pTrainingData, _foundHypotheses, posteriors, _positiveLabelIndex);
//should use an iterator instead of i
vector<BaseLearner*>::iterator whyIt;
int i;
for (i = 0, whyIt = inWeakHypotheses.begin(); whyIt != inWeakHypotheses.end(); ++whyIt, ++i) {
vector<AlphaReal> temporaryPosteriors = posteriors;
vector<BaseLearner*> temporaryWeakHyp = _foundHypotheses;
temporaryWeakHyp.push_back(*whyIt);
updatePosteriors(pTrainingData, *whyIt, temporaryPosteriors, _positiveLabelIndex);
AlphaReal gap = computeSeparationSpan(pTrainingData, temporaryPosteriors, _positiveLabelIndex );
if (gap > bestGap) {
bestGap = gap;
selectedIndex = i;
}
}
BaseLearner* selectedWeakHypothesis = inWeakHypotheses[selectedIndex];
cout << "[+] Rank of the selected weak hypothesis : " << selectedIndex << endl
<< "\t ---> edge gap = " << bestGap << endl
<< "\t ---> alpha = " << selectedWeakHypothesis->getAlpha() << endl;
//update the stages
_foundHypotheses.push_back(selectedWeakHypothesis);
updatePosteriors(pTrainingData, selectedWeakHypothesis, posteriors, _positiveLabelIndex);
double missesFraction;
AlphaReal r = findBestRejectionThreshold(pTrainingData, posteriors, faceRejectionFraction, missesFraction);
_rejectionThresholds.push_back(r);
// update the output info object
dynamic_cast<SoftCascadeOutput*>( pOutInfo->getOutputInfoObject("sca") )->appendRejectionThreshold(r);
cout << "[+] Rejection threshold = " << r << endl;
//some updates
ss.appendHypothesisWithThreshold(t, selectedWeakHypothesis, r);
faceRejectionFraction -= missesFraction;
inWeakHypotheses.erase(inWeakHypotheses.begin() + selectedIndex);
double whypCost = 1; //just in case there are different costs for each whyp
estimatedExecutionTime += whypCost * numberOfNegatives;
// output perf in file
vector< vector< AlphaReal> > scores(0);
_output << t + 1 << setw(_sepWidth + 1) << r << setw(_sepWidth);
// update OutputInfo with the new whyp
// updateOutputInfo(pOutInfo, pTrainingData, selectedWeakHypothesis);
// if (pTestData) {
// updateOutputInfo(pOutInfo, pTestData, selectedWeakHypothesis);
// }
// output the iteration results
printOutputInfo(pOutInfo, t, pTrainingData, pTestData, selectedWeakHypothesis, r);
// if (pTrainPosteriorsOut) {
// pTrainPosteriorsOut->setTable(pTrainingData, pOutInfo->getTable(pTrainingData));
// pTrainPosteriorsOut->outputCustom(pTrainingData);
// }
//
// if (pTestPosteriorsOut) {
// pTestPosteriorsOut->setTable(pTestData, pOutInfo->getTable(pTestData));
// pTestPosteriorsOut->outputCustom(pTestData);
// }
int leftNegatives = filterDataset(pTrainingData, posteriors, r, trainingIndices);
if (leftNegatives == 0) {
cout << endl << "[+] No more negatives.\n";
break;
}
if (_bootstrapRate != 0) {
bootstrapTrainingSet(pTrainingData, pBootstrapData, trainingIndices);
}
} // loop on iterations
/////////////////////////////////////////////////////////
// write the footer of the strong hypothesis file
ss.writeFooter();
// Free the two input data objects
if (pTrainingData)
delete pTrainingData;
if (pBootstrapData) {
delete pBootstrapData;
}
if (pTestData)
delete pTestData;
if (_verbose > 0)
cout << "Learning completed." << endl;
}
