diff --git a/include/DimensionalityReductionModelFactory.txx b/include/DimensionalityReductionModelFactory.txx
index 240d68ad7136e85d004c200134ab4d823f24f7f6..5195605a894a19da3ddfebe8724e6e004ad2a2e5 100644
--- a/include/DimensionalityReductionModelFactory.txx
+++ b/include/DimensionalityReductionModelFactory.txx
@@ -48,6 +48,12 @@ using SOM2DModelFactory = SOMModelFactory<TInputValue, TTargetValue, 2> ;
template <class TInputValue, class TTargetValue>
using SOM3DModelFactory = SOMModelFactory<TInputValue, TTargetValue, 3> ;
+template <class TInputValue, class TTargetValue>
+using SOM4DModelFactory = SOMModelFactory<TInputValue, TTargetValue, 4> ;
+
+template <class TInputValue, class TTargetValue>
+using SOM5DModelFactory = SOMModelFactory<TInputValue, TTargetValue, 5> ;
+
template <class TInputValue, class TOutputValue>
typename DimensionalityReductionModel<TInputValue,TOutputValue>::Pointer
DimensionalityReductionModelFactory<TInputValue,TOutputValue>
@@ -104,9 +110,10 @@ DimensionalityReductionModelFactory<TInputValue,TOutputValue>
-
- RegisterFactory(SOM3DModelFactory<TInputValue,TOutputValue>::New());
RegisterFactory(SOM2DModelFactory<TInputValue,TOutputValue>::New());
+ RegisterFactory(SOM3DModelFactory<TInputValue,TOutputValue>::New());
+ RegisterFactory(SOM4DModelFactory<TInputValue,TOutputValue>::New());
+ RegisterFactory(SOM5DModelFactory<TInputValue,TOutputValue>::New());
#ifdef OTB_USE_SHARK
RegisterFactory(PCAModelFactory<TInputValue,TOutputValue>::New());
@@ -142,6 +149,23 @@ DimensionalityReductionModelFactory<TInputValue,TOutputValue>
{
// SOM
+
+ SOM5DModelFactory<TInputValue,TOutputValue> *som5dFactory =
+ dynamic_cast<SOM5DModelFactory<TInputValue,TOutputValue> *>(*itFac);
+ if (som5dFactory)
+ {
+ itk::ObjectFactoryBase::UnRegisterFactory(som5dFactory);
+ continue;
+ }
+
+ SOM4DModelFactory<TInputValue,TOutputValue> *som4dFactory =
+ dynamic_cast<SOM4DModelFactory<TInputValue,TOutputValue> *>(*itFac);
+ if (som4dFactory)
+ {
+ itk::ObjectFactoryBase::UnRegisterFactory(som4dFactory);
+ continue;
+ }
+
SOM3DModelFactory<TInputValue,TOutputValue> *som3dFactory =
dynamic_cast<SOM3DModelFactory<TInputValue,TOutputValue> *>(*itFac);
if (som3dFactory)
diff --git a/include/SOMModel.txx b/include/SOMModel.txx
index 983e5f62a6a92d2e4894e9eb2fad29927a2320e2..d6c9f02c45cdcdd6a3f3d22b43ea5ef62bce3062 100644
--- a/include/SOMModel.txx
+++ b/include/SOMModel.txx
@@ -54,6 +54,7 @@ void SOMModel<TInputValue, MapDimension>::Train()
}
+
template <class TInputValue, unsigned int MapDimension>
bool SOMModel<TInputValue, MapDimension>::CanReadFile(const std::string & filename)
{
@@ -75,11 +76,27 @@ bool SOMModel<TInputValue, MapDimension>::CanWriteFile(const std::string & filen
return true;
}
+template<typename T>
+std::ostream& binary_write(std::ostream& stream, const T& value){
+ return stream.write(reinterpret_cast<const char*>(&value), sizeof(T));
+}
+
+
+std::ostream& binary_write_string(std::ofstream& stream, const std::string& value){
+ return stream.write(value.c_str(), value.length());
+}
+
+template<typename T>
+std::istream & binary_read(std::istream& stream, T& value){
+ return stream.read(reinterpret_cast<char*>(&value), sizeof(T));
+}
+
+
+
template <class TInputValue, unsigned int MapDimension>
void SOMModel<TInputValue, MapDimension>::Save(const std::string & filename, const std::string & name)
{
- std::cout << m_SOMMap->GetNumberOfComponentsPerPixel() << std::endl;
-
+
//Ecriture
auto kwl = m_SOMMap->GetImageKeywordlist();
kwl.AddKey("MachineLearningModelType", "SOM"+std::to_string(MapDimension));
@@ -92,22 +109,21 @@ void SOMModel<TInputValue, MapDimension>::Save(const std::string & filename, con
// test text
itk::ImageRegionConstIterator<MapType> inputIterator(m_SOMMap,m_SOMMap->GetLargestPossibleRegion());
- std::ofstream ofs(filename+"2");
- ofs << "SOM" << std::endl;
- ofs << MapDimension << std::endl;
+ inputIterator.GoToBegin();
+ std::ofstream ofs(filename+"2", std::ios::binary);
+ binary_write_string(ofs,"som");
+ binary_write(ofs,static_cast<int>(MapDimension));
SizeType size = m_SOMMap->GetLargestPossibleRegion().GetSize() ;
- //ofs << m_SOMMap->GetLargestPossibleRegion().GetSize() << std::endl;
for (size_t i=0;i<MapDimension;i++){
- ofs << size[i] << " " ;
+ binary_write(ofs,size[i]);
}
- ofs << std::endl;
- ofs << inputIterator.Get().GetNumberOfElements() << std::endl;;
+
+ binary_write(ofs,inputIterator.Get().GetNumberOfElements());
while(!inputIterator.IsAtEnd()){
InputSampleType vect = inputIterator.Get();
for (size_t i=0;i<vect.GetNumberOfElements();i++){
- ofs << vect[i] << " " ;
+ binary_write(ofs,vect[i]);
}
-
++inputIterator;
}
ofs.close();
@@ -117,113 +133,58 @@ void SOMModel<TInputValue, MapDimension>::Save(const std::string & filename, con
template <class TInputValue, unsigned int MapDimension>
void SOMModel<TInputValue, MapDimension>::Load(const std::string & filename, const std::string & name)
{
- /*
- auto reader = otb::ImageFileReader<MapType>::New();
- reader->SetFileName(filename);
- reader->Update();
- if (reader->GetOutput()->GetImageKeywordlist().GetMetadataByKey("MachineLearningModelType") != "SOM"+std::to_string(MapDimension)){
- itkExceptionMacro(<< "Error opening " << filename.c_str() );
- }
- m_SOMMap = reader->GetOutput();
- */
- // test text
-
-
+ std::ifstream ifs(filename+"2", std::ios::binary);
+ /** Read the model key (should be som) */
+ char s[]=" ";
+ for (int i=0; i<3; i++){
+ binary_read(ifs,s[i]);
+ }
+ std::string modelType(s);
+ /** Read the dimension of the map (should be equal to MapDimension) */
- std::ifstream ifs(filename+"2");
- std::string model_type_str;
- std::string dimension_str;
- std::string size_str;
- std::string number_of_elements_str;
+ int dimension;
+ binary_read(ifs,dimension);
- std::getline(ifs,model_type_str);
- std::getline(ifs,dimension_str);
- if (model_type_str+dimension_str != "SOM"+std::to_string(MapDimension)){
+ if (modelType != "som" || dimension != MapDimension){
itkExceptionMacro(<< "Error opening " << filename.c_str() );
}
- std::cout << "bug-1?" << std::endl;
SizeType size;
- itk::Point<double, MapDimension> origin;
- SpacingType spacing;
itk::Index< MapDimension > index;
for (int i=0 ; i<MapDimension; i++)
{
- std::getline(ifs,size_str , ' ');
- size[i] = stof(size_str);
- origin[i] = 0;
- spacing[i]=0;
+ binary_read(ifs,size[i]);
index[i]=0;
}
+ unsigned int numberOfElements;
+ binary_read(ifs,numberOfElements);
- std::getline(ifs,number_of_elements_str);
- std::getline(ifs,number_of_elements_str);
- std::cout << "bug0?" << number_of_elements_str << std::endl;
- auto number_of_elements = stof(number_of_elements_str);
-
- //typedef itk::Image< unsigned char, 3 > ImageType;
- //typename MapType::Pointer image = MapType::New();
m_SOMMap = MapType::New();
typename MapType::RegionType region;
region.SetSize( size );
- m_SOMMap->SetNumberOfComponentsPerPixel(number_of_elements);
+ m_SOMMap->SetNumberOfComponentsPerPixel(numberOfElements);
region.SetIndex( index );
m_SOMMap->SetRegions( region );
m_SOMMap->Allocate();
- std::cout << m_SOMMap << std::endl;
-/*
-
- std::cout << "bug1?" << number_of_elements << std::endl;
- itk::ImageRegion<MapDimension> outputRegion;
-
-
- std::cout << "bugoriggin?" << origin << std::endl;
- m_SOMMap->SetNumberOfComponentsPerPixel(number_of_elements);
- outputRegion.SetIndex(index);
- std::cout << "setindex?" << index << std::endl;
- outputRegion.SetSize(size);
- std::cout << origin << size << std::endl;
- m_SOMMap->SetLargestPossibleRegion(outputRegion);
- std::cout << "setRegion" << origin << std::endl;
-
- m_SOMMap->Allocate();
- std::cout << "bug2?" << std::endl;
-
- */
itk::ImageRegionIterator<MapType> outputIterator(m_SOMMap,region);
-
+ outputIterator.GoToBegin();
std::string value;
- size_t j=0;
while(!outputIterator.IsAtEnd()){
- std::cout << j << std::endl;
- std::getline(ifs,value, ' ');
- itk::VariableLengthVector<float> vect(number_of_elements);
- for (int i=0 ; i<number_of_elements; i++)
+ InputSampleType vect(numberOfElements);
+ for (int i=0 ; i<numberOfElements; i++)
{
- std::getline(ifs,value , ' ');
- //std::cout << value << " ";
- std::cout << stof(value) << " ";
- vect[i]=std::stof(value);
+ binary_read(ifs,vect[i]);
}
- std::cout << vect << std::endl;
outputIterator.Set(vect);
++outputIterator;
- j++;
- std::cout << j << "end" << std::endl;
- //std::cout << value << std::endl;
- //std::string line;
- //std::getline(ifs, line);
}
- std::cout << j << std::endl;
+
ifs.close();
- std::cout << "model type " << model_type_str << std::endl;
- std::cout << "dimension " << dimension_str << std::endl;
- std::cout << "size " << size_str << std::endl;
}
@@ -236,8 +197,6 @@ SOMModel<TInputValue, MapDimension>::DoPredict(const InputSampleType & value) co
target.SetSize(dimension);
auto winner =m_SOMMap->GetWinner(value);
- // std::cout << winner << std::endl;
-
for (int i=0; i< dimension ;i++) {
target[i] = winner.GetElement(i);
}
diff --git a/include/cbLearningApplicationBaseDR.h b/include/cbLearningApplicationBaseDR.h
index f78dc01e272b4a136e964e42767f39a6e3a48f7c..328373b24f9b1ae99bb002d787298dc779160786 100644
--- a/include/cbLearningApplicationBaseDR.h
+++ b/include/cbLearningApplicationBaseDR.h
@@ -86,12 +86,19 @@ public:
typedef typename ModelType::InputListSampleType ListSampleType;
// Dimensionality reduction models
+
+ typedef SOMMap<itk::VariableLengthVector<TInputValue>,itk::Statistics::EuclideanDistanceMetric<itk::VariableLengthVector<TInputValue>>, 2> Map2DType;
+ typedef otb::SOMModel<InputValueType, 2> SOM2DModelType;
typedef SOMMap<itk::VariableLengthVector<TInputValue>,itk::Statistics::EuclideanDistanceMetric<itk::VariableLengthVector<TInputValue>>, 3> Map3DType;
typedef otb::SOMModel<InputValueType, 3> SOM3DModelType;
- typedef SOMMap<itk::VariableLengthVector<TInputValue>,itk::Statistics::EuclideanDistanceMetric<itk::VariableLengthVector<TInputValue>>, 2> Map2DType;
- typedef otb::SOMModel<InputValueType, 2> SOM2DModelType;
+ typedef SOMMap<itk::VariableLengthVector<TInputValue>,itk::Statistics::EuclideanDistanceMetric<itk::VariableLengthVector<TInputValue>>, 4> Map4DType;
+ typedef otb::SOMModel<InputValueType, 4> SOM4DModelType;
+
+ typedef SOMMap<itk::VariableLengthVector<TInputValue>,itk::Statistics::EuclideanDistanceMetric<itk::VariableLengthVector<TInputValue>>, 5> Map5DType;
+ typedef otb::SOMModel<InputValueType, 5> SOM5DModelType;
+
#ifdef OTB_USE_SHARK
typedef shark::Autoencoder< shark::TanhNeuron, shark::LinearNeuron> AutoencoderType;
@@ -134,6 +141,7 @@ private:
void TrainPCA(typename ListSampleType::Pointer trainingListSample, std::string modelPath);
template <class somchoice>
void TrainSOM(typename ListSampleType::Pointer trainingListSample, std::string modelPath);
+ void BeforeTrainSOM(typename ListSampleType::Pointer trainingListSample, std::string modelPath);
#endif
//@}
};
diff --git a/include/cbLearningApplicationBaseDR.txx b/include/cbLearningApplicationBaseDR.txx
index 12f8d69d9cf5acdb0e93c1348c51bc1f73cf96a9..916d7d6f8247b85300593999687ce9a219173d90 100644
--- a/include/cbLearningApplicationBaseDR.txx
+++ b/include/cbLearningApplicationBaseDR.txx
@@ -44,9 +44,10 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
{
AddDocTag(Tags::Learning);
- // main choice parameter that will contain all machine learning options
+ // main choice parameter that will contain all dimensionality reduction options
AddParameter(ParameterType_Choice, "model", "moddel to use for the training");
SetParameterDescription("model", "Choice of the dimensionality reduction model to use for the training.");
+
InitSOMParams();
#ifdef OTB_USE_SHARK
@@ -60,32 +61,7 @@ template <class TInputValue, class TOutputValue>
void
cbLearningApplicationBaseDR<TInputValue,TOutputValue>
::Reduce(typename ListSampleType::Pointer validationListSample,std::string modelPath)
-{/*
- // Setup fake reporter
- RGBAPixelConverter<int,int>::Pointer dummyFilter =
- RGBAPixelConverter<int,int>::New();
- dummyFilter->SetProgress(0.0f);
- this->AddProcess(dummyFilter,"Classify...");
- dummyFilter->InvokeEvent(itk::StartEvent());
-
- // load a machine learning model from file and predict the input sample list
- ModelPointerType model = ModelFactoryType::CreateMachineLearningModel(modelPath,
- ModelFactoryType::ReadMode);
-
- if (model.IsNull())
- {
- otbAppLogFATAL(<< "Error when loading model " << modelPath);
- }
-
- model->Load(modelPath);
- model->SetRegressionMode(this->m_RegressionFlag);
- model->SetInputListSample(validationListSample);
- model->SetTargetListSample(predictedList);
- model->PredictAll();
-
- // update reporter
- dummyFilter->UpdateProgress(1.0f);
- dummyFilter->InvokeEvent(itk::EndEvent());*/
+{
}
template <class TInputValue, class TOutputValue>
@@ -95,20 +71,15 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
std::string modelPath)
{
- // get the name of the chosen machine learning model
- const std::string modelName = GetParameterString("model");
- // call specific train function
+ // get the name of the chosen machine learning model
+ const std::string modelName = GetParameterString("model");
+ // call specific train function
if(modelName == "som")
- {
- TrainSOM<SOM2DModelType >(trainingListSample,modelPath);
- }
-
- if(modelName == "som3d")
- {
- TrainSOM<SOM3DModelType >(trainingListSample,modelPath);
- }
-
+ {
+ BeforeTrainSOM(trainingListSample,modelPath);
+ }
+
if(modelName == "autoencoder")
{
#ifdef OTB_USE_SHARK
diff --git a/include/cbTrainSOM.txx b/include/cbTrainSOM.txx
index 10d62fb80b30e009ec9aab478de162da485c3e5f..539504b92b71490cc25f10732e2f7d6a1eeba488 100644
--- a/include/cbTrainSOM.txx
+++ b/include/cbTrainSOM.txx
@@ -24,6 +24,20 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
"This group of parameters allows setting SOM parameters. "
);
+ AddChoice("model.som4d", "OTB SOM");
+ SetParameterDescription("model.som4d",
+ "This group of parameters allows setting SOM parameters. "
+ );
+
+ AddChoice("model.som5d", "OTB SOM");
+ SetParameterDescription("model.som5d",
+ "This group of parameters allows setting SOM parameters. "
+ );
+
+
+ AddParameter(ParameterType_Int, "model.som.dim","Dimension of the map");
+ SetParameterDescription("model.som.dim","Dimension of the SOM map.");
+
AddParameter(ParameterType_StringList , "model.som.s", "Size");
SetParameterDescription("model.som.s", "Size of the SOM map");
MandatoryOff("model.som.s");
@@ -63,7 +77,7 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
AddParameter(ParameterType_Float, "model.som.iv", "InitialValue");
SetParameterDescription("model.som.iv", "Maximum initial neuron weight");
MandatoryOff("model.som.iv");
- ;
+
SetDefaultParameterInt("model.som.sx", 32);
SetDefaultParameterInt("model.som.sy", 32);
SetDefaultParameterInt("model.som.nx", 10);
@@ -76,6 +90,41 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
}
+template <class TInputValue, class TOutputValue>
+void
+cbLearningApplicationBaseDR<TInputValue,TOutputValue>
+::BeforeTrainSOM(typename ListSampleType::Pointer trainingListSample,
+ std::string modelPath)
+{
+ int SomDim = GetParameterInt("model.som.dim");
+ std::cout << SomDim << std::endl;
+
+ if(SomDim == 2)
+ {
+ TrainSOM<SOM2DModelType >(trainingListSample,modelPath);
+ }
+
+ if(SomDim == 3)
+ {
+ TrainSOM<SOM3DModelType >(trainingListSample,modelPath);
+ }
+
+ if(SomDim == 4)
+ {
+ TrainSOM<SOM4DModelType >(trainingListSample,modelPath);
+ }
+
+ if(SomDim == 5)
+ {
+ TrainSOM<SOM5DModelType >(trainingListSample,modelPath);
+ }
+ if(SomDim > 5 || SomDim < 2)
+ {
+ std::cerr << "invalid dimension" << std::endl;
+ }
+}
+
+
template <class TInputValue, class TOutputValue>
template <typename somchoice>
void cbLearningApplicationBaseDR<TInputValue,TOutputValue>