Skip to content
GitLab
Commit ab7142d8 authored by Cédric Traizet's avatar Cédric Traizet
Browse files

app dim reduc working for autoencoders, using the dimensionalityReductionModel as a base class

parent 1fd2ba4d
zonalstats_histogram
No related merge requests found
Hide whitespace changes
Inline Side-by-side
Showing
with 53 additions and 58 deletions
+53 -58
include/AutoencoderModel.h
+ 1
− 1
View file @ ab7142d8
......@@ -30,7 +30,7 @@ public:
itkNewMacro(Self);
itkTypeMacro(AutoencoderModel, DimensionalityReductionModel);
unsigned int GetDimension() {return m_NumberOfHiddenNeurons;};
itkGetMacro(NumberOfHiddenNeurons,unsigned int);
itkSetMacro(NumberOfHiddenNeurons,unsigned int);
......
include/AutoencoderModel.txx
+ 10
− 3
View file @ ab7142d8
......@@ -143,22 +143,29 @@ template <class TInputValue, class AutoencoderType>
void AutoencoderModel<TInputValue,AutoencoderType>
::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);
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
TargetSampleType target;
data = m_net.encode(data);
unsigned int id = startIndex;
target.SetSize(m_NumberOfHiddenNeurons);
for(const auto& p : data.elements()){
for(unsigned int a = 0; a < m_NumberOfHiddenNeurons; ++a){
//target[a]=p[a];
target=p[a];
target[a]=p[a];
//target.SetElement(a,p[a]);
}
std::cout << p << std::endl;
//std::cout << p << std::endl;
targets->SetMeasurementVector(id,target);
++id;
}
}
......
include/DimensionalityReductionModel.h
+ 3
− 2
View file @ ab7142d8
......@@ -126,6 +126,9 @@ public:
/** THIS METHOD IS DEPRECATED AND SHOULD NOT BE USED. */
void PredictAll();
/** Get the size of the output after dimensionality reduction */
virtual unsigned int GetDimension() = 0;
/**\name Classification model file manipulation */
//@{
/** Save the model to file */
......@@ -203,8 +206,6 @@ protected:
bool m_IsDoPredictBatchMultiThreaded;
/** Size of the output after dimensionality reduction */
//unsigned int m_Size;
......
include/ImageDimensionalityReductionFilter.h
+ 4
− 1
View file @ ab7142d8
......@@ -112,7 +112,10 @@ protected:
/** Destructor */
~ImageDimensionalityReductionFilter() ITK_OVERRIDE {}
/** Threaded generate data */
/** Generate output information */
virtual void GenerateOutputInformation();
/** Threaded generate data */
void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, itk::ThreadIdType threadId) ITK_OVERRIDE;
void ClassicThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, itk::ThreadIdType threadId);
void BatchThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, itk::ThreadIdType threadId);
......
include/ImageDimensionalityReductionFilter.txx
+ 35
− 51
View file @ ab7142d8
......@@ -33,9 +33,8 @@ ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
{
this->SetNumberOfIndexedInputs(2);
this->SetNumberOfRequiredInputs(1);
LabelType empty_vect;
empty_vect.SetSize(1);
m_DefaultLabel = itk::NumericTraits<LabelType>::ZeroValue(empty_vect);
//m_DefaultLabel = itk::NumericTraits<LabelType>::ZeroValue();
this->SetNumberOfRequiredOutputs(2);
this->SetNthOutput(0,TOutputImage::New());
......@@ -100,7 +99,7 @@ template <class TInputImage, class TOutputImage, class TMaskImage>
void
ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
::ClassicThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, itk::ThreadIdType threadId)
{
{/*
// Get the input pointers
InputImageConstPointerType inputPtr = this->GetInput();
MaskImageConstPointerType inputMaskPtr = this->GetInputMask();
......@@ -113,7 +112,7 @@ ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
// Define iterators
typedef itk::ImageRegionConstIterator<InputImageType> InputIteratorType;
typedef itk::ImageRegionConstIterator<MaskImageType> MaskIteratorType;
typedef itk::ImageRegionIterator<InputImageType> OutputIteratorType;
typedef itk::ImageRegionIterator<OutputImageType> OutputIteratorType;
typedef itk::ImageRegionIterator<ConfidenceImageType> ConfidenceMapIteratorType;
InputIteratorType inIt(inputPtr, outputRegionForThread);
......@@ -173,15 +172,25 @@ ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
++confidenceIt;
}
progress.CompletedPixel();
}
}*/
}
template <class TInputImage, class TOutputImage, class TMaskImage>
void ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>::GenerateOutputInformation()
{
Superclass::GenerateOutputInformation();
this->GetOutput()->SetNumberOfComponentsPerPixel( m_Model->GetDimension() );
}
template <class TInputImage, class TOutputImage, class TMaskImage>
void
ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
::BatchThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, itk::ThreadIdType threadId)
{
bool computeConfidenceMap(m_UseConfidenceMap && m_Model->HasConfidenceIndex()
&& !m_Model->GetRegressionMode());
// Get the input pointers
......@@ -189,7 +198,7 @@ ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
MaskImageConstPointerType inputMaskPtr = this->GetInputMask();
OutputImagePointerType outputPtr = this->GetOutput();
ConfidenceImagePointerType confidencePtr = this->GetOutputConfidence();
// Progress reporting
itk::ProgressReporter progress(this, threadId, outputRegionForThread.GetNumberOfPixels());
......@@ -202,13 +211,6 @@ ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
InputIteratorType inIt(inputPtr, outputRegionForThread);
OutputIteratorType outIt(outputPtr, outputRegionForThread);
MaskIteratorType maskIt;
if (inputMaskPtr)
{
maskIt = MaskIteratorType(inputMaskPtr, outputRegionForThread);
maskIt.GoToBegin();
}
// typedef typename ModelType::InputValueType InputValueType;
typedef typename ModelType::InputSampleType InputSampleType;
typedef typename ModelType::InputListSampleType InputListSampleType;
......@@ -224,24 +226,17 @@ ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
samples->SetMeasurementVectorSize(num_features);
InputSampleType sample(num_features);
// Fill the samples
bool validPoint = true;
for (inIt.GoToBegin(); !inIt.IsAtEnd(); ++inIt)
{
// Check pixel validity
if (inputMaskPtr)
{
validPoint = maskIt.Get() > 0;
++maskIt;
}
if(validPoint)
{
typename InputImageType::PixelType pix = inIt.Get();
for(size_t feat=0; feat<num_features; ++feat)
typename InputImageType::PixelType pix = inIt.Get();
for(size_t feat=0; feat<num_features; ++feat)
{
sample[feat]=pix[feat];
}
samples->PushBack(sample);
}
}
//Make the batch prediction
typename TargetListSampleType::Pointer labels;
......@@ -250,8 +245,8 @@ ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
confidences = ConfidenceListSampleType::New();
// This call is threadsafe
labels = m_Model->PredictBatch(samples,confidences);
//labels = m_Model->PredictBatch(samples,confidences);
labels = m_Model->PredictBatch(samples);
// Set the output values
ConfidenceMapIteratorType confidenceIt;
if (computeConfidenceMap)
......@@ -259,36 +254,25 @@ ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
confidenceIt = ConfidenceMapIteratorType(confidencePtr,outputRegionForThread);
confidenceIt.GoToBegin();
}
typename TargetListSampleType::ConstIterator labIt = labels->Begin();
maskIt.GoToBegin();
for (outIt.GoToBegin(); !outIt.IsAtEnd(); ++outIt)
{
double confidenceIndex = 0.0;
TargetValueType labelValue(m_DefaultLabel);
if (inputMaskPtr)
{
validPoint = maskIt.Get() > 0;
++maskIt;
}
if (validPoint && labIt!=labels->End())
{
labelValue = labIt.GetMeasurementVector()[0];
itk::VariableLengthVector<TargetValueType> labelValue;
labelValue = labIt.GetMeasurementVector();
if(computeConfidenceMap)
{
confidenceIndex = confidences->GetMeasurementVector(labIt.GetInstanceIdentifier())[0];
}
if(computeConfidenceMap)
{
confidenceIndex = confidences->GetMeasurementVector(labIt.GetInstanceIdentifier())[0];
}
++labIt;
}
else
{
labelValue = m_DefaultLabel;
}
++labIt;
outIt.Set(labelValue);
if(computeConfidenceMap)
{
confidenceIt.Set(confidenceIndex);
......
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment