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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://www.apache.org/licenses/LICENSE-2.0.txt
*
* 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.
*
*=========================================================================*/
#include "itkGradientDescentOptimizerv4.h"
/* This test simulates the use of a metric with a transform
* with local support, testing the proper handling of scales for such a case.
*
* Cribbed originally from itkGradientDescentOptimizerTest */
/**
* \class GradientDescentOptimizerv4Test2Metric for test
*
* The version for this test returns a derivative that simulates
* the return from a metric working with a transform with local support.
* The derivative does not change, it's only meant to test the mechanics
* of applying scales in one iteration of optimization.
*
*/
class GradientDescentOptimizerv4Test2Metric : public itk::ObjectToObjectMetricBase
{
public:
using Self = GradientDescentOptimizerv4Test2Metric;
using Superclass = itk::ObjectToObjectMetricBase;
using Pointer = itk::SmartPointer<Self>;
using ConstPointer = itk::SmartPointer<const Self>;
itkNewMacro(Self);
itkOverrideGetNameOfClassMacro(GradientDescentOptimizerv4Test2Metric);
enum
{
SpaceDimension = 3
};
using ParametersType = Superclass::ParametersType;
using ParametersValueType = Superclass::ParametersValueType;
using NumberOfParametersType = Superclass::NumberOfParametersType;
using DerivativeType = Superclass::DerivativeType;
using MeasureType = Superclass::MeasureType;
GradientDescentOptimizerv4Test2Metric()
{
m_Parameters.SetSize(this->GetNumberOfParameters());
m_Parameters.Fill(0);
}
void
Initialize() override
{}
void
GetDerivative(DerivativeType & derivative) const override
{
MeasureType value;
GetValueAndDerivative(value, derivative);
}
void
GetValueAndDerivative(MeasureType & value, DerivativeType & derivative) const override
{
if (derivative.Size() != this->GetNumberOfParameters())
{
derivative.SetSize(this->GetNumberOfParameters());
}
value = 0.0;
for (NumberOfParametersType i = 0; i < this->GetNumberOfParameters(); ++i)
{
derivative[i] = i;
}
std::cout << "derivative: " << derivative << std::endl;
}
MeasureType
GetValue() const override
{
return 0.0;
}
void
UpdateTransformParameters(const DerivativeType & update, ParametersValueType) override
{
m_Parameters += update;
}
unsigned int
GetNumberOfParameters() const override
{
return SpaceDimension * 3;
}
bool
HasLocalSupport() const override
{
return false;
}
unsigned int
GetNumberOfLocalParameters() const override
{
return SpaceDimension;
}
/* These Set/Get methods are only needed for this test derivation that
* isn't using a transform */
void
SetParameters(ParametersType & parameters) override
{
m_Parameters = parameters;
}
const ParametersType &
GetParameters() const override
{
return m_Parameters;
}
private:
ParametersType m_Parameters;
};
///////////////////////////////////////////////////////////
int
itkGradientDescentOptimizerv4Test2(int, char *[])
{
std::cout << "Gradient Descent Object Optimizer Test ";
std::cout << std::endl << std::endl;
using OptimizerType = itk::GradientDescentOptimizerv4;
using ScalesType = OptimizerType::ScalesType;
// Declaration of an itkOptimizer
auto itkOptimizer = OptimizerType::New();
// Declaration of the Metric
auto metric = GradientDescentOptimizerv4Test2Metric::New();
itkOptimizer->SetMetric(metric);
using ParametersType = GradientDescentOptimizerv4Test2Metric::ParametersType;
using NumberOfParametersType = GradientDescentOptimizerv4Test2Metric::NumberOfParametersType;
ParametersType initialPosition(metric->GetNumberOfParameters());
initialPosition.Fill(0);
metric->SetParameters(initialPosition);
itkOptimizer->SetLearningRate(1.0);
itkOptimizer->SetNumberOfIterations(1);
ScalesType scales(metric->GetNumberOfLocalParameters());
for (NumberOfParametersType i = 0; i < metric->GetNumberOfLocalParameters(); ++i)
{
scales[i] = i + 2;
}
itkOptimizer->SetScales(scales);
ParametersType truth(metric->GetNumberOfParameters());
NumberOfParametersType numLocal = metric->GetNumberOfLocalParameters();
NumberOfParametersType numLoops = metric->GetNumberOfParameters() / numLocal;
for (NumberOfParametersType i = 0; i < numLoops; ++i)
{
for (NumberOfParametersType j = 0; j < numLocal; ++j)
{
NumberOfParametersType ind = i * numLocal + j;
truth[ind] = initialPosition[ind] + (ind) / scales[j];
}
}
std::cout << "truth: " << truth << std::endl;
try
{
itkOptimizer->StartOptimization();
}
catch (const itk::ExceptionObject & e)
{
std::cout << "Exception thrown ! " << std::endl;
std::cout << "An error occurred during Optimization" << std::endl;
std::cout << "Location = " << e.GetLocation() << std::endl;
std::cout << "Description = " << e.GetDescription() << std::endl;
return EXIT_FAILURE;
}
ParametersType finalPosition = metric->GetParameters();
std::cout << "finalPosition = " << finalPosition << std::endl;
//
// check results to see if it is within range
//
for (NumberOfParametersType j = 0; j < metric->GetNumberOfParameters(); ++j)
{
if (itk::Math::abs(finalPosition[j] - truth[j]) > 0.000001)
{
std::cerr << "Results do not match: " << std::endl
<< "expected: " << truth << std::endl
<< "actual: " << finalPosition << std::endl;
return EXIT_FAILURE;
}
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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