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/*
* Copyright (C) 2005-2020 Centre National d'Etudes Spatiales (CNES)
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef otbAutoencoderModel_h
#define otbAutoencoderModel_h
#include "otbMachineLearningModelTraits.h"
#include "otbMachineLearningModel.h"
#include <string>
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wshadow"
#pragma GCC diagnostic ignored "-Wunused-parameter"
#pragma GCC diagnostic ignored "-Woverloaded-virtual"
#pragma GCC diagnostic ignored "-Wsign-compare"
#pragma GCC diagnostic ignored "-Wunused-local-typedefs"
#if defined(__clang__)
#pragma clang diagnostic ignored "-Wheader-guard"
#pragma clang diagnostic ignored "-Wdivision-by-zero"
#if defined(__apple_build_version__)
/* Need AppleClang >= 9.0.0 to support -Wexpansion-to-defined */
#if __apple_build_version__ >= 9000000
#pragma clang diagnostic ignored "-Wexpansion-to-defined"
#endif
#elif __clang_major__ > 3
#pragma clang diagnostic ignored "-Wexpansion-to-defined"
#endif
#else
#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
#endif
#endif
#include "otb_shark.h"
#include <shark/Algorithms/StoppingCriteria/AbstractStoppingCriterion.h>
#include <shark/Models/LinearModel.h>
#include <shark/Models/ConcatenatedModel.h>
#include <shark/Models/NeuronLayers.h>
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic pop
#endif
namespace otb
{
/**
* \class AutoencoderModel
*
* Autoencoder model wrapper class
*
* \ingroup OTBDimensionalityReductionLearning
*/
template <class TInputValue, class NeuronType>
class ITK_EXPORT AutoencoderModel : public MachineLearningModel<itk::VariableLengthVector<TInputValue>, itk::VariableLengthVector<TInputValue>>
{
public:
typedef AutoencoderModel Self;
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 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;
typedef typename Superclass::ProbaSampleType ProbaSampleType;
typedef typename Superclass::ProbaListSampleType ProbaListSampleType;
/// Neural network related typedefs
typedef shark::ConcatenatedModel<shark::RealVector> ModelType;
typedef shark::LinearModel<shark::RealVector, NeuronType> LayerType;
typedef shark::LinearModel<shark::RealVector, shark::LinearNeuron> OutLayerType;
itkNewMacro(Self);
itkTypeMacro(AutoencoderModel, DimensionalityReductionModel);
itkGetMacro(NumberOfHiddenNeurons, itk::Array<unsigned int>);
itkSetMacro(NumberOfHiddenNeurons, itk::Array<unsigned int>);
itkGetMacro(NumberOfIterations, unsigned int);
itkSetMacro(NumberOfIterations, unsigned int);
itkGetMacro(NumberOfIterationsFineTuning, unsigned int);
itkSetMacro(NumberOfIterationsFineTuning, unsigned int);
itkGetMacro(Epsilon, double);
itkSetMacro(Epsilon, double);
itkGetMacro(InitFactor, double);
itkSetMacro(InitFactor, double);
itkGetMacro(Regularization, itk::Array<double>);
itkSetMacro(Regularization, itk::Array<double>);
itkGetMacro(Noise, itk::Array<double>);
itkSetMacro(Noise, itk::Array<double>);
itkGetMacro(Rho, itk::Array<double>);
itkSetMacro(Rho, itk::Array<double>);
itkGetMacro(Beta, itk::Array<double>);
itkSetMacro(Beta, itk::Array<double>);
itkGetMacro(WriteLearningCurve, bool);
itkSetMacro(WriteLearningCurve, bool);
itkSetMacro(WriteWeights, bool);
itkGetMacro(WriteWeights, bool);
itkGetMacro(LearningCurveFileName, std::string);
itkSetMacro(LearningCurveFileName, std::string);
bool CanReadFile(const std::string& filename) override;
bool CanWriteFile(const std::string& filename) override;
void Save(const std::string& filename, const std::string& name = "") override;
void Load(const std::string& filename, const std::string& name = "") override;
void Train() override;
template <class T>
void TrainOneLayer(shark::AbstractStoppingCriterion<T>& criterion, unsigned int, shark::Data<shark::RealVector>&, std::ostream&);
template <class T>
void TrainOneSparseLayer(shark::AbstractStoppingCriterion<T>& criterion, unsigned int, shark::Data<shark::RealVector>&, std::ostream&);
template <class T>
void TrainNetwork(shark::AbstractStoppingCriterion<T>& criterion, shark::Data<shark::RealVector>&, std::ostream&);
protected:
AutoencoderModel();
~AutoencoderModel() override;
virtual TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;
virtual void DoPredictBatch(const InputListSampleType*, const unsigned int& startIndex, const unsigned int& size, TargetListSampleType*,
ConfidenceListSampleType* quality = nullptr, ProbaListSampleType* proba = nullptr) const override;
private:
/** Internal Network */
ModelType m_Encoder;
std::vector<LayerType> m_InLayers;
OutLayerType m_OutLayer;
itk::Array<unsigned int> m_NumberOfHiddenNeurons;
/** Training parameters */
unsigned int m_NumberOfIterations; // stop the training after a fixed number of iterations
unsigned int m_NumberOfIterationsFineTuning; // stop the fine tuning after a fixed number of iterations
double m_Epsilon; // Stops the training when the training error seems to converge
itk::Array<double> m_Regularization; // L2 Regularization parameter
itk::Array<double> m_Noise; // probability for an input to be set to 0 (denosing autoencoder)
itk::Array<double> m_Rho; // Sparsity parameter
itk::Array<double> m_Beta; // Sparsity regularization parameter
double m_InitFactor; // Weight initialization factor (the weights are intialized at m_initfactor/sqrt(inputDimension) )
bool m_WriteLearningCurve; // Flag for writing the learning curve into a txt file
std::string m_LearningCurveFileName; // Name of the output learning curve printed after training
bool m_WriteWeights;
};
} // end namespace otb
#ifndef OTB_MANUAL_INSTANTIATION
#include "otbAutoencoderModel.hxx"
#endif
#endif
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