elxAdaptiveStochasticVarianceReducedGradient.h

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 *  Copyright UMC Utrecht and contributors
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 *  you may not use this file except in compliance with the License.
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#ifndef elxAdaptiveStochasticVarianceReducedGradient_h
#define elxAdaptiveStochasticVarianceReducedGradient_h
 
#include "elxIncludes.h" // include first to avoid MSVS warning
#include "itkAdaptiveStochasticVarianceReducedGradientOptimizer.h"
 
#include "elxProgressCommand.h"
#include "itkAdvancedTransform.h"
#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "itkComputeJacobianTerms.h"
#include "itkComputeDisplacementDistribution.h"
#include "itkMultiThreaderBase.h"
#include "itkImageRandomSampler.h"
namespace elastix
{
 
template <typename TElastix>
class ITK_TEMPLATE_EXPORT AdaptiveStochasticVarianceReducedGradient
  : public itk::AdaptiveStochasticVarianceReducedGradientOptimizer
  , public OptimizerBase<TElastix>
{
public:
  ITK_DISALLOW_COPY_AND_MOVE(AdaptiveStochasticVarianceReducedGradient);

  using Self = AdaptiveStochasticVarianceReducedGradient;
  using Superclass1 = AdaptiveStochasticVarianceReducedGradientOptimizer;
  using Superclass2 = OptimizerBase<TElastix>;
  using Pointer = itk::SmartPointer<Self>;
  using ConstPointer = itk::SmartPointer<const Self>;

  itkNewMacro(Self);

  itkOverrideGetNameOfClassMacro(AdaptiveStochasticVarianceReducedGradient);

  elxClassNameMacro("AdaptiveStochasticVarianceReducedGradient");

  using Superclass1::CostFunctionType;
  using Superclass1::CostFunctionPointer;
  // using Superclass1::StopConditionType;

  using typename Superclass2::ElastixType;
  using typename Superclass2::RegistrationType;
  using ITKBaseType = typename Superclass2::ITKBaseType;
  using SizeValueType = itk::SizeValueType;

  using typename Superclass1::ParametersType;

  void
  BeforeRegistration() override;
  void
  BeforeEachResolution() override;
  void
  AfterEachResolution() override;
  void
  AfterEachIteration() override;
  void
  AfterRegistration() override;

  void
  StartOptimization() override;

  void
  AdvanceOneStep() override;

  void
  ResumeOptimization() override;

  void
  MetricErrorResponse(itk::ExceptionObject & err) override;

  void
  StopOptimization() override;

  itkSetMacro(AutomaticParameterEstimation, bool);
  itkGetConstMacro(AutomaticParameterEstimation, bool);

  itkSetMacro(MaximumStepLength, double);
  itkGetConstMacro(MaximumStepLength, double);

  itkSetMacro(MaximumNumberOfSamplingAttempts, SizeValueType);

  itkGetConstReferenceMacro(MaximumNumberOfSamplingAttempts, SizeValueType);

  itkGetConstReferenceMacro(MeanGradient, DerivativeType);

  using ThreadIdType = unsigned int;

  void
  SetNumberOfWorkUnits(ThreadIdType numberOfThreads)
  {
    this->m_Threader->SetNumberOfWorkUnits(numberOfThreads);
  }
 
protected:
  using FixedImageType = typename RegistrationType::FixedImageType;
  using MovingImageType = typename RegistrationType::MovingImageType;
 
  using FixedImageRegionType = typename FixedImageType::RegionType;
  using FixedImageIndexType = typename FixedImageType::IndexType;
  using FixedImagePointType = typename FixedImageType::PointType;
  using itkRegistrationType = typename RegistrationType::ITKBaseType;
  using TransformType = typename itkRegistrationType::TransformType;
  using JacobianType = typename TransformType::JacobianType;
  using JacobianValueType = typename JacobianType::ValueType;
  using typename Superclass2::SettingsType;
  using typename Superclass2::SettingsVectorType;
  using ComputeDisplacementDistributionType = itk::ComputeDisplacementDistribution<FixedImageType, TransformType>;

  using ImageSamplerBaseType = itk::ImageSamplerBase<FixedImageType>;
  using ImageSamplerBasePointer = typename ImageSamplerBaseType::Pointer;
  using ImageRandomSamplerBaseType = itk::ImageRandomSamplerBase<FixedImageType>;
  using ImageRandomSamplerBasePointer = typename ImageRandomSamplerBaseType::Pointer;
  using ImageRandomCoordinateSamplerType = itk::ImageRandomCoordinateSampler<FixedImageType>;
  using ImageRandomCoordinateSamplerPointer = typename ImageRandomCoordinateSamplerType::Pointer;
  using ImageSampleType = typename ImageSamplerBaseType::ImageSampleType;

  using ImageRandomSamplerType = itk::ImageRandomSampler<FixedImageType>;
  using ImageRandomSamplerPointer = typename ImageRandomSamplerType::Pointer;
  using ImageRadomSampleContainerType = typename ImageRandomSamplerType::ImageSampleContainerType;
  using ImageRadomSampleContainerPointer = typename ImageRadomSampleContainerType::Pointer;

  using ImageGridSamplerType = itk::ImageGridSampler<FixedImageType>;
  using ImageGridSamplerPointer = typename ImageGridSamplerType::Pointer;
  using ImageSampleContainerType = typename ImageGridSamplerType::ImageSampleContainerType;
  using ImageSampleContainerPointer = typename ImageSampleContainerType::Pointer;

  using RandomGeneratorType = itk::Statistics::MersenneTwisterRandomVariateGenerator;

  using TransformJacobianType = JacobianType;
  itkStaticConstMacro(FixedImageDimension, unsigned int, FixedImageType::ImageDimension);
  itkStaticConstMacro(MovingImageDimension, unsigned int, MovingImageType::ImageDimension);
  using CoordinateRepresentationType = typename TransformType::ScalarType;
  using AdvancedTransformType =
    itk::AdvancedTransform<CoordinateRepresentationType, Self::FixedImageDimension, Self::MovingImageDimension>;
  using NonZeroJacobianIndicesType = typename AdvancedTransformType::NonZeroJacobianIndicesType;
 
  AdaptiveStochasticVarianceReducedGradient();
  ~AdaptiveStochasticVarianceReducedGradient() override = default;

  SettingsVectorType m_SettingsVector;

  SizeValueType m_NumberOfGradientMeasurements;
  SizeValueType m_NumberOfJacobianMeasurements;
  SizeValueType m_NumberOfSamplesForExactGradient;

  typename AdvancedTransformType::Pointer m_AdvancedTransform;
 
  double m_SigmoidScaleFactor;

  virtual void
  AutomaticParameterEstimation();

  virtual void
  AutomaticParameterEstimationOriginal();

  virtual void
  AutomaticParameterEstimationUsingDisplacementDistribution();

  virtual void
  SampleGradients(const ParametersType & mu0, double perturbationSigma, double & gg, double & ee);

  virtual void
  GetScaledDerivativeWithExceptionHandling(const ParametersType & parameters, DerivativeType & derivative);

  virtual void
  AddRandomPerturbation(ParametersType & parameters, double sigma);
 
  DerivativeType m_ExactGradient;
  DerivativeType m_MeanGradient;
 
  double m_NoiseFactor;
 
private:
  elxOverrideGetSelfMacro;
 
  // multi-threaded AdvanceOneStep:
  struct MultiThreaderParameterType
  {
    ParametersType * t_NewPosition;
    Self *           t_Optimizer;
  };

  static itk::ITK_THREAD_RETURN_TYPE
  AdvanceOneStepThreaderCallback(void * arg);

  inline void
  ThreadedAdvanceOneStep(ThreadIdType threadId, ParametersType & newPosition);
 
  bool   m_AutomaticParameterEstimation;
  double m_MaximumStepLength;

  SizeValueType m_MaximumNumberOfSamplingAttempts;
  SizeValueType m_CurrentNumberOfSamplingAttempts;
  SizeValueType m_PreviousErrorAtIteration;
  bool          m_AutomaticParameterEstimationDone;
 
  SizeValueType m_OutsideIterations;

  SizeValueType m_MaxBandCovSize;
  SizeValueType m_NumberOfBandStructureSamples;
  SizeValueType m_NumberOfInnerLoopSamples;
  SizeValueType m_NumberOfSpatialSamples;

  bool m_UseNoiseCompensation;
  bool m_OriginalButSigmoidToDefault;
  bool m_UseNoiseFactor;
 
}; // end class AdaptiveStochasticVarianceReducedGradient
 
 
} // end namespace elastix
 
#ifndef ITK_MANUAL_INSTANTIATION
#  include "elxAdaptiveStochasticVarianceReducedGradient.hxx"
#endif
 
#endif // end #ifndef elxAdaptiveStochasticVarianceReducedGradient_h