From ea53b86767b0084fbe08a3eef9590d3de74fad0a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?C=C3=A9dric=20Traizet?= <traizetc@cesbio.cnes.fr>
Date: Wed, 21 Jun 2017 09:42:51 +0200
Subject: [PATCH] DR is now based on Machine Learning Models, it is now
possible to reduce de dimension of the pca model (it has to be changed in
PCAModel.txx for now
---
app/cbDimensionalityReductionVector.cxx | 20 +++++------
include/AutoencoderModel.h | 29 ++++++++++------
include/AutoencoderModel.txx | 4 +--
include/DimensionalityReductionModelFactory.h | 6 ++--
.../DimensionalityReductionModelFactory.txx | 24 ++++++++------
include/ImageDimensionalityReductionFilter.h | 5 +--
include/PCAModel.h | 33 ++++++++++++-------
include/PCAModel.txx | 9 +++--
include/cbLearningApplicationBaseDR.h | 16 ++++-----
include/cbLearningApplicationBaseDR.txx | 8 ++---
10 files changed, 93 insertions(+), 61 deletions(-)
diff --git a/app/cbDimensionalityReductionVector.cxx b/app/cbDimensionalityReductionVector.cxx
index 90927c56c4..25ac852bd4 100644
--- a/app/cbDimensionalityReductionVector.cxx
+++ b/app/cbDimensionalityReductionVector.cxx
@@ -27,7 +27,7 @@
#include "itkListSample.h"
#include "otbShiftScaleSampleListFilter.h"
#include "DimensionalityReductionModelFactory.h"
-#include "DimensionalityReductionModel.h"
+//#include "DimensionalityReductionModel.h"
#include <time.h>
namespace otb
@@ -57,16 +57,16 @@ class CbDimensionalityReductionVector : public Application
/** Filters typedef */
- typedef double ValueType;
- typedef DimensionalityReductionModel<ValueType,ValueType> DimensionalityReductionModelType;
- typedef DimensionalityReductionModelFactory<ValueType, ValueType> DimensionalityReductionModelFactoryType;
- typedef DimensionalityReductionModelType::Pointer ModelPointerType;
+ typedef double ValueType;
+ typedef itk::VariableLengthVector<ValueType> InputSampleType;
+ typedef itk::Statistics::ListSample<InputSampleType> ListSampleType;
+ typedef MachineLearningModel<itk::VariableLengthVector<ValueType>, itk::VariableLengthVector<ValueType>> DimensionalityReductionModelType;
+ typedef DimensionalityReductionModelFactory<ValueType,ValueType> DimensionalityReductionModelFactoryType;
+ typedef DimensionalityReductionModelType::Pointer ModelPointerType;
/** Statistics Filters typedef */
- typedef itk::VariableLengthVector<ValueType> MeasurementType;
- typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
- typedef itk::VariableLengthVector<ValueType> InputSampleType;
- typedef itk::Statistics::ListSample<InputSampleType> ListSampleType;
+ typedef itk::VariableLengthVector<ValueType> MeasurementType;
+ typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
~CbDimensionalityReductionVector() ITK_OVERRIDE
{
@@ -211,7 +211,7 @@ class CbDimensionalityReductionVector : public Application
/** Read the model */
-
+ std::cout << "create the fact ?" << std::endl;
m_Model = DimensionalityReductionModelFactoryType::CreateDimensionalityReductionModel(GetParameterString("model"),
DimensionalityReductionModelFactoryType::ReadMode);
if (m_Model.IsNull())
diff --git a/include/AutoencoderModel.h b/include/AutoencoderModel.h
index 3fd4c44748..e9dd9d2b66 100644
--- a/include/AutoencoderModel.h
+++ b/include/AutoencoderModel.h
@@ -6,23 +6,31 @@
namespace otb
{
template <class TInputValue, class AutoencoderType>
-class ITK_EXPORT AutoencoderModel: public DimensionalityReductionModel<TInputValue,TInputValue>
+class ITK_EXPORT AutoencoderModel: public MachineLearningModel<itk::VariableLengthVector< TInputValue> , itk::VariableLengthVector< TInputValue>>
{
public:
typedef AutoencoderModel Self;
- typedef DimensionalityReductionModel<TInputValue,TInputValue> Superclass;
+ typedef MachineLearningModel<itk::VariableLengthVector< TInputValue> , itk::VariableLengthVector< TInputValue>> Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
- typedef typename Superclass::InputValueType InputValueType;
- typedef typename Superclass::InputSampleType InputSampleType;
- typedef typename Superclass::InputListSampleType InputListSampleType;
- typedef typename Superclass::TargetValueType TargetValueType;
- typedef typename Superclass::TargetSampleType TargetSampleType;
- typedef typename Superclass::TargetListSampleType TargetListSampleType;
+ typedef typename Superclass::InputValueType InputValueType;
+ typedef typename Superclass::InputSampleType InputSampleType;
+ typedef typename Superclass::InputListSampleType InputListSampleType;
+ typedef typename InputListSampleType::Pointer ListSamplePointerType;
+ typedef typename Superclass::TargetValueType TargetValueType;
+ typedef typename Superclass::TargetSampleType TargetSampleType;
+ typedef typename Superclass::TargetListSampleType TargetListSampleType;
+
+ /// Confidence map related typedefs
+ typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
+ typedef typename Superclass::ConfidenceSampleType ConfidenceSampleType;
+ typedef typename Superclass::ConfidenceListSampleType ConfidenceListSampleType;
+
+
itkNewMacro(Self);
itkTypeMacro(AutoencoderModel, DimensionalityReductionModel);
@@ -59,8 +67,9 @@ protected:
AutoencoderModel();
~AutoencoderModel() ITK_OVERRIDE;
- virtual TargetSampleType DoPredict(const InputSampleType& input) const ITK_OVERRIDE;
- virtual void DoPredictBatch(const InputListSampleType *, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType *) const ITK_OVERRIDE;
+ virtual TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType * quality = ITK_NULLPTR) const;
+
+ virtual void DoPredictBatch(const InputListSampleType *, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType *, ConfidenceListSampleType * quality = ITK_NULLPTR) const;
private:
diff --git a/include/AutoencoderModel.txx b/include/AutoencoderModel.txx
index a23adb9ba2..3060d8fb30 100644
--- a/include/AutoencoderModel.txx
+++ b/include/AutoencoderModel.txx
@@ -171,7 +171,7 @@ void AutoencoderModel<TInputValue,AutoencoderType>::Load(const std::string & fil
template <class TInputValue, class AutoencoderType>
typename AutoencoderModel<TInputValue,AutoencoderType>::TargetSampleType
-AutoencoderModel<TInputValue,AutoencoderType>::DoPredict(const InputSampleType & value) const
+AutoencoderModel<TInputValue,AutoencoderType>::DoPredict(const InputSampleType & value, ConfidenceValueType * quality) const
{
shark::RealVector samples(value.Size());
for(size_t i = 0; i < value.Size();i++)
@@ -200,7 +200,7 @@ AutoencoderModel<TInputValue,AutoencoderType>::DoPredict(const InputSampleType &
template <class TInputValue, class AutoencoderType>
void AutoencoderModel<TInputValue,AutoencoderType>
-::DoPredictBatch(const InputListSampleType *input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * targets) const
+::DoPredictBatch(const InputListSampleType *input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * targets, ConfidenceListSampleType * quality) const
{
std::vector<shark::RealVector> features;
Shark::ListSampleRangeToSharkVector(input, features,startIndex,size);
diff --git a/include/DimensionalityReductionModelFactory.h b/include/DimensionalityReductionModelFactory.h
index 791199b5e8..16fa0f416d 100644
--- a/include/DimensionalityReductionModelFactory.h
+++ b/include/DimensionalityReductionModelFactory.h
@@ -18,9 +18,11 @@
#ifndef DimensionalityReductionModelFactory_h
#define DimensionalityReductionModelFactory_h
-#include "DimensionalityReductionModel.h"
+//#include "DimensionalityReductionModel.h"
#include "otbMachineLearningModelFactoryBase.h"
+#include "otbMachineLearningModel.h"
+
namespace otb
{
/** \class MachineLearningModelFactory
@@ -44,7 +46,7 @@ public:
itkTypeMacro(DimensionalityReductionModelFactory, itk::Object);
/** Convenient typedefs. */
- typedef otb::DimensionalityReductionModel<TInputValue,TOutputValue> DimensionalityReductionModelType;
+ typedef otb::MachineLearningModel<itk::VariableLengthVector< TInputValue> , itk::VariableLengthVector< TOutputValue>> DimensionalityReductionModelType;
typedef typename DimensionalityReductionModelType::Pointer DimensionalityReductionModelTypePointer;
/** Mode in which the files is intended to be used */
diff --git a/include/DimensionalityReductionModelFactory.txx b/include/DimensionalityReductionModelFactory.txx
index a546978fb7..ac8f40c680 100644
--- a/include/DimensionalityReductionModelFactory.txx
+++ b/include/DimensionalityReductionModelFactory.txx
@@ -22,25 +22,25 @@
#include "otbConfigure.h"
#include "SOMModelFactory.h"
-/*
+
#ifdef OTB_USE_SHARK
#include "AutoencoderModelFactory.h"
#include "PCAModelFactory.h"
#endif
-*/
+
#include "itkMutexLockHolder.h"
namespace otb
{
-/*
+
template <class TInputValue, class TTargetValue>
using AutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::Autoencoder<shark::TanhNeuron, shark::LinearNeuron>> ;
template <class TInputValue, class TTargetValue>
using TiedAutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::TiedAutoencoder< shark::TanhNeuron, shark::LinearNeuron>> ;
-*/
+
template <class TInputValue, class TTargetValue>
using SOM2DModelFactory = SOMModelFactory<TInputValue, TTargetValue, 2> ;
@@ -56,7 +56,7 @@ using SOM5DModelFactory = SOMModelFactory<TInputValue, TTargetValue, 5> ;
template <class TInputValue, class TOutputValue>
-typename DimensionalityReductionModel<TInputValue,TOutputValue>::Pointer
+typename MachineLearningModel<itk::VariableLengthVector< TInputValue> , itk::VariableLengthVector< TOutputValue>>::Pointer
DimensionalityReductionModelFactory<TInputValue,TOutputValue>
::CreateDimensionalityReductionModel(const std::string& path, FileModeType mode)
{
@@ -65,26 +65,30 @@ DimensionalityReductionModelFactory<TInputValue,TOutputValue>
std::list<DimensionalityReductionModelTypePointer> possibleDimensionalityReductionModel;
std::list<LightObject::Pointer> allobjects =
itk::ObjectFactoryBase::CreateAllInstance("DimensionalityReductionModel");
+
for(std::list<LightObject::Pointer>::iterator i = allobjects.begin();
i != allobjects.end(); ++i)
{
- DimensionalityReductionModel<TInputValue,TOutputValue> * io = dynamic_cast<DimensionalityReductionModel<TInputValue,TOutputValue>*>(i->GetPointer());
+ MachineLearningModel<itk::VariableLengthVector< TInputValue> , itk::VariableLengthVector< TOutputValue>> * io = dynamic_cast<MachineLearningModel<itk::VariableLengthVector< TInputValue> , itk::VariableLengthVector< TOutputValue>>*>(i->GetPointer());
if(io)
{
possibleDimensionalityReductionModel.push_back(io);
}
else
{
+
std::cerr << "Error DimensionalityReductionModel Factory did not return an DimensionalityReductionModel: "
<< (*i)->GetNameOfClass()
<< std::endl;
}
}
+
for(typename std::list<DimensionalityReductionModelTypePointer>::iterator k = possibleDimensionalityReductionModel.begin();
k != possibleDimensionalityReductionModel.end(); ++k)
{
if( mode == ReadMode )
{
+
if((*k)->CanReadFile(path))
{
return *k;
@@ -115,13 +119,13 @@ DimensionalityReductionModelFactory<TInputValue,TOutputValue>
RegisterFactory(SOM3DModelFactory<TInputValue,TOutputValue>::New());
RegisterFactory(SOM4DModelFactory<TInputValue,TOutputValue>::New());
RegisterFactory(SOM5DModelFactory<TInputValue,TOutputValue>::New());
- /*
+
#ifdef OTB_USE_SHARK
RegisterFactory(PCAModelFactory<TInputValue,TOutputValue>::New());
RegisterFactory(AutoencoderModelFactory<TInputValue,TOutputValue>::New());
RegisterFactory(TiedAutoencoderModelFactory<TInputValue,TOutputValue>::New());
#endif
- */
+
}
template <class TInputValue, class TOutputValue>
@@ -182,7 +186,7 @@ DimensionalityReductionModelFactory<TInputValue,TOutputValue>
itk::ObjectFactoryBase::UnRegisterFactory(som2dFactory);
continue;
}
- /*
+
#ifdef OTB_USE_SHARK
// Autoencoder
@@ -211,7 +215,7 @@ DimensionalityReductionModelFactory<TInputValue,TOutputValue>
continue;
}
#endif
-*/
+
}
}
diff --git a/include/ImageDimensionalityReductionFilter.h b/include/ImageDimensionalityReductionFilter.h
index e550b1944d..008c57d2a4 100644
--- a/include/ImageDimensionalityReductionFilter.h
+++ b/include/ImageDimensionalityReductionFilter.h
@@ -19,7 +19,8 @@
#define ImageDimensionalityReduction_h
#include "itkImageToImageFilter.h"
-#include "DimensionalityReductionModel.h"
+//#include "DimensionalityReductionModel.h"
+#include "otbMachineLearningModel.h"
#include "otbImage.h"
namespace otb
@@ -66,7 +67,7 @@ public:
typedef typename OutputImageType::RegionType OutputImageRegionType;
typedef typename OutputImageType::InternalPixelType LabelType;
- typedef DimensionalityReductionModel<ValueType, LabelType> ModelType;
+ typedef MachineLearningModel<itk::VariableLengthVector<ValueType>, itk::VariableLengthVector<LabelType>> ModelType;
typedef typename ModelType::Pointer ModelPointerType;
typedef otb::Image<double> ConfidenceImageType;
diff --git a/include/PCAModel.h b/include/PCAModel.h
index 45693dfc8a..d5557eb169 100644
--- a/include/PCAModel.h
+++ b/include/PCAModel.h
@@ -7,29 +7,39 @@
namespace otb
{
template <class TInputValue>
-class ITK_EXPORT PCAModel: public DimensionalityReductionModel<TInputValue,TInputValue>
+class ITK_EXPORT PCAModel: public MachineLearningModel<itk::VariableLengthVector< TInputValue> , itk::VariableLengthVector< TInputValue>>
{
public:
typedef PCAModel Self;
- typedef DimensionalityReductionModel<TInputValue,TInputValue> Superclass;
+ typedef MachineLearningModel<itk::VariableLengthVector< TInputValue> , itk::VariableLengthVector< TInputValue>> Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
- typedef typename Superclass::InputValueType InputValueType;
- typedef typename Superclass::InputSampleType InputSampleType;
- typedef typename Superclass::InputListSampleType InputListSampleType;
- typedef typename Superclass::TargetValueType TargetValueType;
- typedef typename Superclass::TargetSampleType TargetSampleType;
- typedef typename Superclass::TargetListSampleType TargetListSampleType;
+ typedef typename Superclass::InputValueType InputValueType;
+ typedef typename Superclass::InputSampleType InputSampleType;
+ typedef typename Superclass::InputListSampleType InputListSampleType;
+ typedef typename InputListSampleType::Pointer ListSamplePointerType;
+ typedef typename Superclass::TargetValueType TargetValueType;
+ typedef typename Superclass::TargetSampleType TargetSampleType;
+ typedef typename Superclass::TargetListSampleType TargetListSampleType;
+
+ /// Confidence map related typedefs
+ typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
+ typedef typename Superclass::ConfidenceSampleType ConfidenceSampleType;
+ typedef typename Superclass::ConfidenceListSampleType ConfidenceListSampleType;
+
+
itkNewMacro(Self);
itkTypeMacro(PCAModel, DimensionalityReductionModel);
unsigned int GetDimension() {return m_Dimension;};
itkSetMacro(Dimension,unsigned int);
+ itkSetMacro(Do_resize_flag,bool);
+
bool CanReadFile(const std::string & filename);
bool CanWriteFile(const std::string & filename);
@@ -44,15 +54,16 @@ protected:
PCAModel();
~PCAModel() ITK_OVERRIDE;
- virtual TargetSampleType DoPredict(const InputSampleType& input) const ITK_OVERRIDE;
- virtual void DoPredictBatch(const InputListSampleType *, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType *) const ITK_OVERRIDE;
+ virtual TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType * quality = ITK_NULLPTR) const;
+
+ virtual void DoPredictBatch(const InputListSampleType *, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType *, ConfidenceListSampleType * quality = ITK_NULLPTR) const ITK_OVERRIDE;
private:
shark::LinearModel<> m_encoder;
shark::LinearModel<> m_decoder;
shark::PCA m_pca;
unsigned int m_Dimension;
-
+ bool m_Do_resize_flag;
};
} // end namespace otb
diff --git a/include/PCAModel.txx b/include/PCAModel.txx
index 8f48040388..9cda11ec90 100644
--- a/include/PCAModel.txx
+++ b/include/PCAModel.txx
@@ -40,6 +40,7 @@ void PCAModel<TInputValue>::Train()
shark::Data<shark::RealVector> inputSamples = shark::createDataFromRange( features );
m_pca.setData(inputSamples);
m_pca.encoder(m_encoder, m_Dimension);
+ std::cout << m_encoder.matrix() << std::endl;
m_pca.decoder(m_decoder, m_Dimension);
}
@@ -94,13 +95,17 @@ void PCAModel<TInputValue>::Load(const std::string & filename, const std::string
m_encoder.read(ia);
ifs.close();
m_Dimension = m_encoder.outputSize();
+ auto eigenvectors = m_encoder.matrix();
+ eigenvectors.resize(2,m_encoder.inputSize());
+ m_encoder.setStructure(eigenvectors, m_encoder.offset() );
+ std::cout << m_encoder.matrix() << std::endl;
//this->m_Size = m_NumberOfHiddenNeurons;
}
template <class TInputValue>
typename PCAModel<TInputValue>::TargetSampleType
-PCAModel<TInputValue>::DoPredict(const InputSampleType & value) const
+PCAModel<TInputValue>::DoPredict(const InputSampleType & value, ConfidenceValueType * quality) const
{
shark::RealVector samples(value.Size());
for(size_t i = 0; i < value.Size();i++)
@@ -126,7 +131,7 @@ PCAModel<TInputValue>::DoPredict(const InputSampleType & value) const
template <class TInputValue>
void PCAModel<TInputValue>
-::DoPredictBatch(const InputListSampleType *input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * targets) const
+::DoPredictBatch(const InputListSampleType *input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * targets, ConfidenceListSampleType * quality) const
{
std::vector<shark::RealVector> features;
diff --git a/include/cbLearningApplicationBaseDR.h b/include/cbLearningApplicationBaseDR.h
index 71c31f40fe..b3bf030786 100644
--- a/include/cbLearningApplicationBaseDR.h
+++ b/include/cbLearningApplicationBaseDR.h
@@ -15,12 +15,12 @@
#include "DimensionalityReductionModelFactory.h"
#include "SOMModel.h"
-/*
+
#ifdef OTB_USE_SHARK
#include "AutoencoderModel.h"
#include "PCAModel.h"
#endif
-*/
+
namespace otb
{
namespace Wrapper
@@ -99,7 +99,7 @@ public:
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;
typedef otb::AutoencoderModel<InputValueType, AutoencoderType> AutoencoderModelType;
@@ -109,7 +109,7 @@ public:
typedef otb::PCAModel<InputValueType> PCAModelType;
#endif
- */
+
protected:
cbLearningApplicationBaseDR();
@@ -136,7 +136,7 @@ private:
template <class somchoice>
void TrainSOM(typename ListSampleType::Pointer trainingListSample, std::string modelPath);
void BeforeTrainSOM(typename ListSampleType::Pointer trainingListSample, std::string modelPath);
-/*
+
#ifdef OTB_USE_SHARK
void InitAutoencoderParams();
void InitPCAParams();
@@ -148,7 +148,7 @@ private:
void TrainPCA(typename ListSampleType::Pointer trainingListSample, std::string modelPath);
-#endif */
+#endif
//@}
};
@@ -158,11 +158,11 @@ private:
#ifndef OTB_MANUAL_INSTANTIATION
#include "cbLearningApplicationBaseDR.txx"
#include "cbTrainSOM.txx"
-/*
+
#ifdef OTB_USE_SHARK
#include "cbTrainAutoencoder.txx"
#include "cbTrainPCA.txx"
-#endif*/
+#endif
#endif
#endif
diff --git a/include/cbLearningApplicationBaseDR.txx b/include/cbLearningApplicationBaseDR.txx
index 2befe3e396..a42ad73857 100644
--- a/include/cbLearningApplicationBaseDR.txx
+++ b/include/cbLearningApplicationBaseDR.txx
@@ -50,12 +50,12 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
InitSOMParams();
- /*
+
#ifdef OTB_USE_SHARK
InitAutoencoderParams();
InitPCAParams();
#endif
- */
+
}
template <class TInputValue, class TOutputValue>
@@ -80,7 +80,7 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
{
BeforeTrainSOM(trainingListSample,modelPath);
}
- /*
+
if(modelName == "autoencoder")
{
#ifdef OTB_USE_SHARK
@@ -105,7 +105,7 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
#else
otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration.");
#endif
- }*/
+ }
}
}
--
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