sopt::ONNXDifferentiableFunc< SCALAR > Class Template Reference

#include <onnx_differentiable_func.h>

Inheritance diagram for sopt::ONNXDifferentiableFunc< SCALAR >:

Collaboration diagram for sopt::ONNXDifferentiableFunc< SCALAR >:

Public Member Functions
	ONNXDifferentiableFunc (const std::string &function_model_path, const std::string &gradient_model_path, const Real sigma, const Real mu, const Real lambda, const LinearTransform &Phi, const std::vector< int64_t > dimensions={})
void	log_message () const override
void	gradient (Vector &output, const Vector &image, const Vector &residual, const LinearTransform &Phi) override
void	infer_dimensions (const size_t image_size)
void	set_dimensions (const std::vector< int64_t > &dims)
Real	function (Vector const &image, Vector const &y, LinearTransform const &Phi) override
Public Member Functions inherited from DifferentiableFunc< SCALAR >
virtual t_Gradient	gradient ()
Real	get_step_size () const

Additional Inherited Members
Public Types inherited from DifferentiableFunc< SCALAR >
using	FB = sopt::algorithm::ForwardBackward< SCALAR >
using	Real = typename FB::Real
using	t_Vector = typename FB::t_Vector
using	t_Gradient = typename FB::t_Gradient
using	t_LinearTransform = typename FB::t_LinearTransform

Detailed Description

template<typename SCALAR>
class sopt::ONNXDifferentiableFunc< SCALAR >

Definition at line 12 of file onnx_differentiable_func.h.

Constructor & Destructor Documentation

ONNXDifferentiableFunc()

template<typename SCALAR >

sopt::ONNXDifferentiableFunc< SCALAR >::ONNXDifferentiableFunc ( const std::string &  function_model_path, const std::string &  gradient_model_path, const Real  sigma, const Real  mu, const Real  lambda, const LinearTransform &  Phi, const std::vector< int64_t >  dimensions = {}  )

inline

Definition at line 20 of file onnx_differentiable_func.h.

                                                                 {}): LT(Phi), sigma(sigma), mu(mu), lambda(lambda),
                                                              function_model(function_model_path),
                                                              gradient_model(gradient_model_path)
    {
        Real L_CRR;  // Lipschitz constant
        if(dimensions.empty()) infer_square_dimensions = true;
        try
        {
            L_CRR = gradient_model.retrieve<double>("L_CRR");
            this->step_size = 0.98 / (1/(sigma*sigma) + mu * lambda * L_CRR);
            SOPT_MEDIUM_LOG("Lipschitz Constant for CRR = {}", L_CRR);
            SOPT_MEDIUM_LOG("Step size for CRR = {}", this->step_size);
        }
        catch(const std::exception &e)
        {
          SOPT_HIGH_LOG(
              "Failed to find a Lipschitz constant for the current model. Please ensure that the "
              "Lipschitz constant is included in the gradient model meta-data with the key "
              "\"L_CRR\". Setting step size to 1 by default.");
          SOPT_HIGH_LOG("Exception message retrieving L_CRR: {}", e.what());
          this->step_size = 1;
        }
    }

Member Function Documentation

function()

template<typename SCALAR >

Real sopt::ONNXDifferentiableFunc< SCALAR >::function ( Vector const &  image, Vector const &  y, LinearTransform const &  Phi  )

inlineoverridevirtual

Implements DifferentiableFunc< SCALAR >.

Definition at line 82 of file onnx_differentiable_func.h.

    {
        if(infer_square_dimensions) infer_dimensions(image.size());
        Real Likelihood = 0.5 * ((Phi*image) - y).squaredNorm() / (sigma * sigma);
        Vector scaled_image = image * mu;
        std::vector<float> float_image = utilities::imageToFloat(scaled_image);
        Real Prior = (lambda / mu) * (function_model.compute(float_image, dimensions)[0]);
        return Likelihood + Prior;
    }

References sopt::ORTsession::compute(), sopt::utilities::imageToFloat(), sopt::ONNXDifferentiableFunc< SCALAR >::infer_dimensions(), and sopt::sigma().

gradient()

template<typename SCALAR >

void sopt::ONNXDifferentiableFunc< SCALAR >::gradient ( Vector &  output, const Vector &  image, const Vector &  residual, const LinearTransform &  Phi  )

inlineoverridevirtual

Implements DifferentiableFunc< SCALAR >.

Definition at line 55 of file onnx_differentiable_func.h.

    {
      if(infer_square_dimensions) infer_dimensions(image.size());
 
      output = Phi.adjoint() * (residual / (sigma * sigma));  // L2 norm
      Vector scaled_image = image * mu;
      std::vector<float> float_image = utilities::imageToFloat(scaled_image);      
      Vector ANN_gradient = utilities::floatToImage<SCALAR>(gradient_model.compute(float_image, dimensions));  // regulariser
      output += (ANN_gradient * lambda);
    }

References sopt::ORTsession::compute(), sopt::utilities::imageToFloat(), sopt::ONNXDifferentiableFunc< SCALAR >::infer_dimensions(), and sopt::sigma().

infer_dimensions()

template<typename SCALAR >

void sopt::ONNXDifferentiableFunc< SCALAR >::infer_dimensions ( const size_t  image_size )

inline

Definition at line 67 of file onnx_differentiable_func.h.

    {
        set_dimensions({1, static_cast<int64_t>(sqrt(image_size)), static_cast<int64_t>(sqrt(image_size))});
        if(dimensions[1] * dimensions[2] != image_size)
        {
          throw std::runtime_error("Image dimensions are not provided and image size is not compatible with a square image.");
        }
        infer_square_dimensions = false;
    }

References sopt::ONNXDifferentiableFunc< SCALAR >::set_dimensions().

Referenced by sopt::ONNXDifferentiableFunc< SCALAR >::function(), and sopt::ONNXDifferentiableFunc< SCALAR >::gradient().

log_message()

template<typename SCALAR >

void sopt::ONNXDifferentiableFunc< SCALAR >::log_message ( ) const

inlineoverridevirtual

Implements DifferentiableFunc< SCALAR >.

Definition at line 50 of file onnx_differentiable_func.h.

    {
        SOPT_HIGH_LOG("Using ONNX model differentiable function f(x)");
    }

References SOPT_HIGH_LOG.

set_dimensions()

template<typename SCALAR >

void sopt::ONNXDifferentiableFunc< SCALAR >::set_dimensions ( const std::vector< int64_t > &  dims )

inline

Definition at line 77 of file onnx_differentiable_func.h.

    {
        dimensions = dims;
    }

Referenced by sopt::ONNXDifferentiableFunc< SCALAR >::infer_dimensions().

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The documentation for this class was generated from the following file:

• /home/runner/work/sopt/sopt/cpp/sopt/onnx_differentiable_func.h