diff --git a/app/cbDimensionalityReduction.cxx b/app/cbDimensionalityReduction.cxx
index abe3875264e729e45d343fc032115e1e913fe0fa..5a019b3989e26dc5210ed11b2776895cac03f78d 100644
--- a/app/cbDimensionalityReduction.cxx
+++ b/app/cbDimensionalityReduction.cxx
@@ -28,7 +28,6 @@
#include "otbImageToVectorImageCastFilter.h"
#include "DimensionalityReductionModelFactory.h"
-#include "PCAModel.h"
namespace otb
{
diff --git a/include/AutoencoderModel.txx b/include/AutoencoderModel.txx
index 8ae6e58b9dd94c85c1f634fdd1f7a0169af166a9..b0e206532ff2672e5558c1d02b2707539b8def2d 100644
--- a/include/AutoencoderModel.txx
+++ b/include/AutoencoderModel.txx
@@ -122,18 +122,20 @@ AutoencoderModel<TInputValue,AutoencoderType>::DoPredict(const InputSampleType &
shark::RealVector samples(value.Size());
for(size_t i = 0; i < value.Size();i++)
{
- samples.push_back(value[i]);
+ samples[i]=value[i];
}
- shark::Data<shark::RealVector> data;
- data.element(0)=samples;
- data = m_net.encode(data);
+ std::vector<shark::RealVector> features;
+ features.push_back(samples);
+
+ shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
+
+ data = m_net.encode(data);
TargetSampleType target;
-
- //target.SetSize(m_NumberOfHiddenNeurons);
+ target.SetSize(m_NumberOfHiddenNeurons);
+
for(unsigned int a = 0; a < m_NumberOfHiddenNeurons; ++a){
- //target[a]=data.element(0)[a];
- target=data.element(0)[a];
+ target[a]=data.element(0)[a];
}
return target;
}
@@ -143,7 +145,7 @@ template <class TInputValue, class AutoencoderType>
void AutoencoderModel<TInputValue,AutoencoderType>
::DoPredictBatch(const InputListSampleType *input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * targets, ConfidenceListSampleType * quality) const
{
-
+ std::cout << "BATCH" << std::endl;
std::vector<shark::RealVector> features;
Shark::ListSampleRangeToSharkVector(input, features,startIndex,size);
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
diff --git a/include/ImageDimensionalityReductionFilter.txx b/include/ImageDimensionalityReductionFilter.txx
index 9d6f8d7085c5afcb2676dda4c6bdf14bd4b340c4..9f5b34cab24f0baea14dbb9618eaa9534a57c0ca 100644
--- a/include/ImageDimensionalityReductionFilter.txx
+++ b/include/ImageDimensionalityReductionFilter.txx
@@ -40,7 +40,7 @@ ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
this->SetNthOutput(0,TOutputImage::New());
this->SetNthOutput(1,ConfidenceImageType::New());
m_UseConfidenceMap = false;
- m_BatchMode = true;
+ m_BatchMode = false;
}
template <class TInputImage, class TOutputImage, class TMaskImage>
@@ -99,7 +99,7 @@ template <class TInputImage, class TOutputImage, class TMaskImage>
void
ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
::ClassicThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, itk::ThreadIdType threadId)
-{/*
+{
// Get the input pointers
InputImageConstPointerType inputPtr = this->GetInput();
MaskImageConstPointerType inputMaskPtr = this->GetInputMask();
@@ -118,61 +118,14 @@ ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
InputIteratorType inIt(inputPtr, outputRegionForThread);
OutputIteratorType outIt(outputPtr, outputRegionForThread);
- // Eventually iterate on masks
- MaskIteratorType maskIt;
- if (inputMaskPtr)
- {
- maskIt = MaskIteratorType(inputMaskPtr, outputRegionForThread);
- maskIt.GoToBegin();
- }
-
- // setup iterator for confidence map
- bool computeConfidenceMap(m_UseConfidenceMap && m_Model->HasConfidenceIndex() && !m_Model->GetRegressionMode());
- ConfidenceMapIteratorType confidenceIt;
- if (computeConfidenceMap)
- {
- confidenceIt = ConfidenceMapIteratorType(confidencePtr,outputRegionForThread);
- confidenceIt.GoToBegin();
- }
-
- bool validPoint = true;
- double confidenceIndex = 0.0;
-
// Walk the part of the image
for (inIt.GoToBegin(), outIt.GoToBegin(); !inIt.IsAtEnd() && !outIt.IsAtEnd(); ++inIt, ++outIt)
{
- // Check pixel validity
- if (inputMaskPtr)
- {
- validPoint = maskIt.Get() > 0;
- ++maskIt;
- }
- // If point is valid
- if (validPoint)
- {
- // Classifify
- if (computeConfidenceMap)
- {
- outIt.Set(m_Model->Predict(inIt.Get(),&confidenceIndex)[0]);
- }
- else
- {
- outIt.Set(m_Model->Predict(inIt.Get())[0]);
- }
- }
- else
- {
- // else, set default value
- outIt.Set(m_DefaultLabel);
- confidenceIndex = 0.0;
- }
- if (computeConfidenceMap)
- {
- confidenceIt.Set(confidenceIndex);
- ++confidenceIt;
- }
+ // Classifify
+
+ outIt.Set(m_Model->Predict(inIt.Get()));
progress.CompletedPixel();
- }*/
+ }
}
diff --git a/include/PCAModel.h b/include/PCAModel.h
index 529d2fa9bb8ecd2ece54d81e061c25c29e02b899..96d0e235c0cddadf90bddd390da83586799b5435 100644
--- a/include/PCAModel.h
+++ b/include/PCAModel.h
@@ -31,7 +31,7 @@ public:
itkTypeMacro(AutoencoderModel, DimensionalityReductionModel);
unsigned int GetDimension() {return m_Dimension;};
- //itkGetMacro(Dimension,unsigned int);
+ itkSetMacro(Dimension,unsigned int);
bool CanReadFile(const std::string & filename);
bool CanWriteFile(const std::string & filename);
diff --git a/include/PCAModel.txx b/include/PCAModel.txx
index 329b2558cf77626de248f505844aed3664111f38..c7625e661e8bbae0eb54cd81281b96807d5640d1 100644
--- a/include/PCAModel.txx
+++ b/include/PCAModel.txx
@@ -1,6 +1,6 @@
-#ifndef AutoencoderModel_txx
-#define AutoencoderModel_txx
+#ifndef PCAModel_txx
+#define PCAModel_txx
#include <fstream>
#include <shark/Data/Dataset.h>
@@ -24,7 +24,7 @@ PCAModel<TInputValue>::PCAModel()
template <class TInputValue>
-PCAModel<TInputValue,AutoencoderType>::~PCAModel()
+PCAModel<TInputValue>::~PCAModel()
{
}
@@ -38,10 +38,10 @@ void PCAModel<TInputValue>::Train()
Shark::ListSampleToSharkVector(this->GetInputListSample(), features);
shark::Data<shark::RealVector> inputSamples = shark::createDataFromRange( features );
-
- m_pca(inputSamples);
- pca.encoder(m_encoder, m_Dimension);
- pca.decoder(m_decoder, m_Dimension);
+ //m_pca.train(m_encoder,inputSamples);
+ m_pca.setData(inputSamples);
+ m_pca.encoder(m_encoder, m_Dimension);
+ m_pca.decoder(m_decoder, m_Dimension);
}
@@ -72,7 +72,8 @@ template <class TInputValue>
void PCAModel<TInputValue>::Save(const std::string & filename, const std::string & name)
{
std::ofstream ofs(filename);
- ofs << m_encoder.name() << std::endl; //first line
+ //ofs << m_encoder.name() << std::endl; //first line
+ ofs << "pca" << std::endl; //first line
boost::archive::polymorphic_text_oarchive oa(ofs);
m_encoder.write(oa);
ofs.close();
@@ -86,11 +87,12 @@ void PCAModel<TInputValue>::Load(const std::string & filename, const std::string
ifs.getline(encoder,256);
std::string encoderstr(encoder);
- if (autoencoderstr != m_encoder.name()){
+ //if (encoderstr != m_encoder.name()){
+ if (encoderstr != "pca"){
itkExceptionMacro(<< "Error opening " << filename.c_str() );
}
boost::archive::polymorphic_text_iarchive ia(ifs);
- m_net.read(ia);
+ m_encoder.read(ia);
ifs.close();
m_Dimension = m_encoder.outputSize();
//this->m_Size = m_NumberOfHiddenNeurons;
@@ -104,22 +106,20 @@ PCAModel<TInputValue>::DoPredict(const InputSampleType & value, ConfidenceValueT
shark::RealVector samples(value.Size());
for(size_t i = 0; i < value.Size();i++)
{
- samples.push_back(value[i]);
+ samples[i]=value[i];
}
- shark::Data<shark::RealVector> data;
- data.element(0)=samples;
- data = m_encoder(data);
+ std::vector<shark::RealVector> features;
+ features.push_back(samples);
+
+ shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
+
+ data = m_encoder(data);
TargetSampleType target;
-
- //target.SetSize(m_NumberOfHiddenNeurons);
+ target.SetSize(m_Dimension);
- for(unsigned int a = 0; a < m_NumberOfHiddenNeurons; ++a){
- target[a]=p[a];
-
- //target.SetElement(a,p[a]);
-
-
+ for(unsigned int a = 0; a < m_Dimension; ++a){
+ target[a]=data.element(0)[a];
}
return target;
}
@@ -136,11 +136,12 @@ void PCAModel<TInputValue>
TargetSampleType target;
data = m_encoder(data);
unsigned int id = startIndex;
- target.SetSize(m_NumberOfHiddenNeurons);
+ target.SetSize(m_Dimension);
for(const auto& p : data.elements()){
- for(unsigned int a = 0; a < m_NumberOfHiddenNeurons; ++a){
+ for(unsigned int a = 0; a < m_Dimension; ++a){
target[a]=p[a];
+ //target[a]=1;
//target.SetElement(a,p[a]);
diff --git a/include/PCAModelFactory.txx b/include/PCAModelFactory.txx
index 2a0ff98b44a2f9a352be9eb2018dba75180ffce8..bfaa4f6f624b12d8351defb1bc52ad9a4d37253e 100644
--- a/include/PCAModelFactory.txx
+++ b/include/PCAModelFactory.txx
@@ -15,15 +15,15 @@
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
-#ifndef AutoencoderModelFactory_txx
-#define AutoencoderModelFactory_txx
+#ifndef PCAFactory_txx
+#define PCAFactory_txx
-#include "AutoencoderModelFactory.h"
+#include "PCAModelFactory.h"
#include "itkCreateObjectFunction.h"
-#include "AutoencoderModel.h"
-#include <shark/Algorithms/Trainers/PCA.h>
+#include "PCAModel.h"
+//#include <shark/Algorithms/Trainers/PCA.h>
#include "itkVersion.h"
namespace otb
@@ -40,7 +40,7 @@ PCAModelFactory<TInputValue,TOutputValue>::PCAModelFactory()
"Shark PCA ML Model",
1,
// itk::CreateObjectFunction<AutoencoderModel<TInputValue,TOutputValue> >::New());
- itk::CreateObjectFunction<PCAModel<TInputValue,shark::LinearModel<> > >::New());
+ itk::CreateObjectFunction<PCAModel<TInputValue>>::New());
}
template <class TInputValue, class TOutputValue>
diff --git a/include/cbLearningApplicationBaseDR.h b/include/cbLearningApplicationBaseDR.h
index abd8cfc55f801a69747ec1e9f143e6cf36ff6ebd..7a467e242e39ce709d97df95ce30a19f83de3cf4 100644
--- a/include/cbLearningApplicationBaseDR.h
+++ b/include/cbLearningApplicationBaseDR.h
@@ -91,6 +91,8 @@ public:
typedef shark::TiedAutoencoder< shark::TanhNeuron, shark::LinearNeuron> TiedAutoencoderType;
typedef otb::AutoencoderModel<InputValueType, TiedAutoencoderType> TiedAutoencoderModelType;
+
+ typedef otb::PCAModel<InputValueType> PCAModelType;
#endif
protected:
@@ -117,8 +119,10 @@ private:
#ifdef OTB_USE_SHARK
void InitAutoencoderParams();
+ void InitPCAParams();
template <class autoencoderchoice>
void TrainAutoencoder(typename ListSampleType::Pointer trainingListSample, std::string modelPath);
+ void TrainPCA(typename ListSampleType::Pointer trainingListSample, std::string modelPath);
#endif
//@}
};
@@ -130,6 +134,7 @@ private:
#include "cbLearningApplicationBaseDR.txx"
#ifdef OTB_USE_SHARK
#include "cbTrainAutoencoder.txx"
+#include "cbTrainPCA.txx"
#endif
#endif
diff --git a/include/cbLearningApplicationBaseDR.txx b/include/cbLearningApplicationBaseDR.txx
index 344b6eccfc85ee309d8e1a51214b94133fb7a7b4..562b5e395b5599d11e0885cf2d4c73f9096b908a 100644
--- a/include/cbLearningApplicationBaseDR.txx
+++ b/include/cbLearningApplicationBaseDR.txx
@@ -51,6 +51,7 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
#ifdef OTB_USE_SHARK
InitAutoencoderParams();
+ InitPCAParams();
#endif
}
@@ -114,6 +115,15 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration.");
#endif
}
+
+ if(modelName == "pca")
+ {
+ #ifdef OTB_USE_SHARK
+ TrainPCA(trainingListSample,modelPath);
+ #else
+ otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration.");
+ #endif
+ }
}
}
diff --git a/include/cbTrainPCA.txx b/include/cbTrainPCA.txx
new file mode 100644
index 0000000000000000000000000000000000000000..9c02c566cd83f49c4d011050140acaca3e10cee2
--- /dev/null
+++ b/include/cbTrainPCA.txx
@@ -0,0 +1,50 @@
+
+#ifndef cbTrainPCA_txx
+#define cbTrainPCA_txx
+
+#include "cbLearningApplicationBaseDR.h"
+
+namespace otb
+{
+namespace Wrapper
+{
+
+template <class TInputValue, class TOutputValue>
+void
+cbLearningApplicationBaseDR<TInputValue,TOutputValue>
+::InitPCAParams()
+{
+
+
+ AddChoice("model.pca", "Shark PCA");
+ SetParameterDescription("model.pca",
+ "This group of parameters allows setting Shark PCA parameters. "
+ );
+
+
+ //Output Dimension
+ AddParameter(ParameterType_Int, "model.pca.dim",
+ "Dimension of the output of the pca transformation");
+ SetParameterInt("model.pca.dim",10, false);
+ SetParameterDescription(
+ "model.pca.dim",
+ "Dimension of the output of the pca transformation.");
+
+
+}
+
+template <class TInputValue, class TOutputValue>
+void cbLearningApplicationBaseDR<TInputValue,TOutputValue>
+::TrainPCA(typename ListSampleType::Pointer trainingListSample,std::string modelPath)
+{
+ typename PCAModelType::Pointer dimredTrainer = PCAModelType::New();
+ dimredTrainer->SetDimension(GetParameterInt("model.pca.dim"));
+ dimredTrainer->SetInputListSample(trainingListSample);
+ dimredTrainer->Train();
+ dimredTrainer->Save(modelPath);
+}
+
+} //end namespace wrapper
+} //end namespace otb
+
+#endif
diff --git a/otb-module.cmake b/otb-module.cmake
index 8b1eb2c05c22e88c2a0a938f46b0ec2f2ea87af0..f4a7c8f8cab47d1b8ab0ec5a3a9af4f91e07bdd7 100644
--- a/otb-module.cmake
+++ b/otb-module.cmake
@@ -8,6 +8,7 @@ otb_module(CbDimensionalityReduction
OTBBoost
OTBSupervised
OTBAppClassification
+ OTBSOM
DESCRIPTION
"${DOCUMENTATION}"
)