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vboussot merged 1 commit into from
Oct 3, 2026
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STYLE: Mark the sources for the ITK style checks and format them #3

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6 changes: 6 additions & 0 deletions .gitattributes
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@@ -0,0 +1,6 @@
# The sources the ITK style checks read (KWStyle, clang-format), as ITK marks its own.
[attr]our-c-style whitespace=tab-in-indent,no-lf-at-eof hooks.style=KWStyle,clangformat

*.h our-c-style
*.cxx our-c-style
*.hxx our-c-style
18 changes: 9 additions & 9 deletions examples/ImpactMetricExample.cxx
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Expand Up @@ -49,11 +49,11 @@ main(int argc, char * argv[])
std::cerr << " device: \"cpu\" (default), \"cuda\", \"cuda:0\", ...\n";
return EXIT_FAILURE;
}
const char * const modelPath = argv[1];
const char * const fixedPath = argv[2];
const char * const movingPath = argv[3];
const char * const outputPath = argv[4];
const std::string device = (argc > 5) ? argv[5] : "cpu";
const char * const modelPath = argv[1];
const char * const fixedPath = argv[2];
const char * const movingPath = argv[3];
const char * const outputPath = argv[4];
const std::string device = (argc > 5) ? argv[5] : "cpu";

constexpr unsigned int Dimension = 3;
using PixelType = float;
Expand Down Expand Up @@ -84,14 +84,14 @@ main(int argc, char * argv[])

// --- 2. metric: register moving onto fixed by comparing anatomical features -------
using MetricType = itk::ImpactImageToImageMetricv4<ImageType, ImageType>;
auto metric = MetricType::New();
auto metric = MetricType::New();
std::vector<itk::ImpactModelConfiguration> models{ config };
metric->SetModelsConfiguration(models);
metric->SetDistance({ "L2" }); // per-layer loss: L1, L2, NCC, Cosine, Dice, ...
metric->SetDistance({ "L2" }); // per-layer loss: L1, L2, NCC, Cosine, Dice, ...
metric->SetLayersWeight({ 1.0f });
metric->SetSubsetFeatures({ 4 }); // random channel subset for speed (0 = all)
metric->SetSubsetFeatures({ 4 }); // random channel subset for speed (0 = all)
metric->SetPCA({ 0 });
metric->SetMode("Static"); // "Static" (precomputed features) or "Jacobian"
metric->SetMode("Static"); // "Static" (precomputed features) or "Jacobian"
metric->SetDevice(device);

using TransformType = itk::TranslationTransform<double, Dimension>;
Expand Down
6 changes: 2 additions & 4 deletions include/ImpactLoss.h
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Expand Up @@ -537,8 +537,7 @@ class L1Cosine : public Loss
}
};

inline RegisterLoss<L1Cosine> L1Cosine_reg(
"L1Cosine");
inline RegisterLoss<L1Cosine> L1Cosine_reg("L1Cosine");

/**
* \class Cosine
Expand Down Expand Up @@ -763,8 +762,7 @@ class NCC : public Loss
if (N <= 0)
return 0.0;
torch::Tensor u = this->m_sfm - (this->m_sf * this->m_sm / N);
torch::Tensor v =
Denominator(Variance(this->m_sff, this->m_sf, N), Variance(this->m_smm, this->m_sm, N));
torch::Tensor v = Denominator(Variance(this->m_sff, this->m_sf, N), Variance(this->m_smm, this->m_sm, N));
return 1.0 - (u / v).mean().item<double>();
}

Expand Down
5 changes: 2 additions & 3 deletions include/itkImageToFeaturesMap.hxx
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Expand Up @@ -53,14 +53,13 @@ ImageToFeaturesMap<TInputImage, TInterpolator>::ImageToFeaturesMap()

template <typename TInputImage, typename TInterpolator>
void
ImageToFeaturesMap<TInputImage, TInterpolator>
::PrintSelf(std::ostream & os, Indent indent) const
ImageToFeaturesMap<TInputImage, TInterpolator>::PrintSelf(std::ostream & os, Indent indent) const
{
Superclass::PrintSelf(os, indent);
}

template <typename TInputImage, typename TInterpolator>
void
void
ImageToFeaturesMap<TInputImage, TInterpolator>::AddInput(const TInputImage * input)
{
if (!m_Interpolator)
Expand Down
24 changes: 15 additions & 9 deletions include/itkImageToTensorFilter.h
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Expand Up @@ -54,10 +54,10 @@ class ITK_TEMPLATE_EXPORT ImageToTensorFilter : public ProcessObject
using InputImagePointer = typename InputImageType::Pointer;
using InputImageConstPointer = typename InputImageType::ConstPointer;

using TensorType = torch::Tensor;
using TensorType = torch::Tensor;
using TensorHandleType = std::shared_ptr<TensorType>;
using TensorDataObject = itk::SimpleDataObjectDecorator<TensorHandleType>;

using InterpolatorPointer = typename TInterpolator::Pointer;

using InputImagePixelType = typename InputImageType::PixelType;
Expand Down Expand Up @@ -89,9 +89,14 @@ class ITK_TEMPLATE_EXPORT ImageToTensorFilter : public ProcessObject
itkSetMacro(OutputSpacing, InputSpacingType);
itkGetConstReferenceMacro(OutputSpacing, InputSpacingType);
itkSetVectorMacro(OutputSpacing, const float, ImageDimension);

void SetInterpolator(typename TInterpolator::Pointer interp) { m_Interpolator = interp; }
void SetTransform(std::function<InputImagePointType(const InputImagePointType &)> fct)

void
SetInterpolator(typename TInterpolator::Pointer interp)
{
m_Interpolator = interp;
}
void
SetTransform(std::function<InputImagePointType(const InputImagePointType &)> fct)
{
m_Transform = fct;
}
Expand Down Expand Up @@ -141,15 +146,16 @@ class ITK_TEMPLATE_EXPORT ImageToTensorFilter : public ProcessObject

void
PrintSelf(std::ostream & os, Indent indent) const override;

void
VerifyPreconditions() const override;

void GenerateData() override;
void
GenerateData() override;

private:
InterpolatorPointer m_Interpolator;
InputSpacingType m_OutputSpacing{ MakeFilled<InputSpacingType>(1.0) };
InterpolatorPointer m_Interpolator;
InputSpacingType m_OutputSpacing{ MakeFilled<InputSpacingType>(1.0) };
std::function<InputImagePointType(const InputImagePointType &)> m_Transform;
};

Expand Down
14 changes: 7 additions & 7 deletions include/itkImpactCoarseRegistration.hxx
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Expand Up @@ -276,8 +276,7 @@ ImpactCoarseRegistration<TFixedImage, TMovingImage>::GetDisplacementField() -> D

template <typename TFixedImage, typename TMovingImage>
auto
ImpactCoarseRegistration<TFixedImage, TMovingImage>::GetDisplacementFieldTransform()
-> DisplacementFieldTransformType *
ImpactCoarseRegistration<TFixedImage, TMovingImage>::GetDisplacementFieldTransform() -> DisplacementFieldTransformType *
{
return m_DisplacementFieldTransform.GetPointer();
}
Expand Down Expand Up @@ -421,7 +420,9 @@ ImpactCoarseRegistration<TFixedImage, TMovingImage>::GenerateData()
if (kept > 0 && kept < fixedLayers[l].size(1))
{
const torch::Tensor channels =
torch::randperm(fixedLayers[l].size(1), torch::TensorOptions().dtype(torch::kLong)).narrow(0, 0, kept).to(device);
torch::randperm(fixedLayers[l].size(1), torch::TensorOptions().dtype(torch::kLong))
.narrow(0, 0, kept)
.to(device);
fixedLayers[l] = fixedLayers[l].index_select(1, channels).contiguous();
movingLayers[l] = movingLayers[l].index_select(1, channels).contiguous();
}
Expand Down Expand Up @@ -741,8 +742,7 @@ ImpactCoarseRegistration<TFixedImage, TMovingImage>::GenerateData()
torch::Tensor f2 = (dispBack / scaleT).flip(1);

// Identity sampling grid at coarse resolution, channel-first {1, Dim, coarse...}, x,y,z.
torch::Tensor idAffine =
torch::eye(ImageDimension, torch::TensorOptions().dtype(torch::kFloat32).device(device));
torch::Tensor idAffine = torch::eye(ImageDimension, torch::TensorOptions().dtype(torch::kFloat32).device(device));
idAffine =
torch::cat({ idAffine, torch::zeros({ static_cast<int64_t>(ImageDimension), 1 }, idAffine.options()) }, 1)
.unsqueeze(0);
Expand All @@ -753,7 +753,7 @@ ImpactCoarseRegistration<TFixedImage, TMovingImage>::GenerateData()
{
gridSize.push_back(c);
}
torch::Tensor idGrid = torch::affine_grid_generator(idAffine, gridSize, /*align_corners=*/true);
torch::Tensor idGrid = torch::affine_grid_generator(idAffine, gridSize, /*align_corners=*/true);
std::vector<int64_t> toChannelFirst;
toChannelFirst.push_back(0);
toChannelFirst.push_back(idGrid.dim() - 1);
Expand Down Expand Up @@ -803,7 +803,7 @@ ImpactCoarseRegistration<TFixedImage, TMovingImage>::GenerateData()
std::vector<int64_t> componentShape(ImageDimension + 2, 1); // {1, Dim, 1, ...}: one factor per component
componentShape[1] = static_cast<int64_t>(ImageDimension);
const torch::Tensor cellsToVoxels = torch::tensor(cellVoxels, torch::kLong).to(disp.options()).view(componentShape);
torch::Tensor dispFull;
torch::Tensor dispFull;
if constexpr (ImageDimension == 3)
dispFull = F::interpolate(
disp * cellsToVoxels, F::InterpolateFuncOptions().size(exactSize).mode(torch::kTrilinear).align_corners(false));
Expand Down
18 changes: 9 additions & 9 deletions include/itkImpactFineRegistration.h
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Expand Up @@ -287,18 +287,18 @@ class ITK_TEMPLATE_EXPORT ImpactFineRegistration
GenerateData() override;

private:
typename FixedImageType::ConstPointer m_FixedImage{ nullptr };
typename MovingImageType::ConstPointer m_MovingImage{ nullptr };
typename MaskImageType::ConstPointer m_FixedMask{ nullptr };
typename MaskImageType::ConstPointer m_MovingMask{ nullptr };
typename DisplacementFieldType::Pointer m_InitialDisplacementField{ nullptr };
typename FixedImageType::ConstPointer m_FixedImage{ nullptr };
typename MovingImageType::ConstPointer m_MovingImage{ nullptr };
typename MaskImageType::ConstPointer m_FixedMask{ nullptr };
typename MaskImageType::ConstPointer m_MovingMask{ nullptr };
typename DisplacementFieldType::Pointer m_InitialDisplacementField{ nullptr };

std::vector<ImpactModelConfiguration> m_FixedModelsConfiguration;
std::vector<ImpactModelConfiguration> m_MovingModelsConfiguration;
std::vector<std::string> m_Distance;
std::vector<float> m_LayersWeight;
std::vector<unsigned int> m_SubsetFeatures;
std::vector<unsigned int> m_PCA;
std::vector<std::string> m_Distance;
std::vector<float> m_LayersWeight;
std::vector<unsigned int> m_SubsetFeatures;
std::vector<unsigned int> m_PCA;

std::string m_Device{ "cpu" };
unsigned int m_Seed{ 0 };
Expand Down
67 changes: 34 additions & 33 deletions include/itkImpactFineRegistration.hxx
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Expand Up @@ -181,8 +181,7 @@ ImpactFineRegistration<TFixedImage, TMovingImage>::GetDisplacementField() -> Dis

template <typename TFixedImage, typename TMovingImage>
auto
ImpactFineRegistration<TFixedImage, TMovingImage>::GetDisplacementFieldTransform()
-> DisplacementFieldTransformType *
ImpactFineRegistration<TFixedImage, TMovingImage>::GetDisplacementFieldTransform() -> DisplacementFieldTransformType *
{
return m_DisplacementFieldTransform.GetPointer();
}
Expand Down Expand Up @@ -311,25 +310,27 @@ ImpactFineRegistration<TFixedImage, TMovingImage>::GenerateData()
if (coarseSpatial != spatial)
{
if constexpr (ImageDimension == 3)
initField = torch::nn::functional::interpolate(
initField,
torch::nn::functional::InterpolateFuncOptions().size(coarseSpatial).mode(torch::kTrilinear).align_corners(true));
initField = torch::nn::functional::interpolate(initField,
torch::nn::functional::InterpolateFuncOptions()
.size(coarseSpatial)
.mode(torch::kTrilinear)
.align_corners(true));
else
initField = torch::nn::functional::interpolate(
initField,
torch::nn::functional::InterpolateFuncOptions().size(coarseSpatial).mode(torch::kBilinear).align_corners(true));
initField = torch::nn::functional::interpolate(initField,
torch::nn::functional::InterpolateFuncOptions()
.size(coarseSpatial)
.mode(torch::kBilinear)
.align_corners(true));
}
theta = initField.set_requires_grad(true);
}
else
{
theta =
torch::zeros(fieldShape, torch::TensorOptions().dtype(torch::kFloat32).device(device).requires_grad(true));
theta = torch::zeros(fieldShape, torch::TensorOptions().dtype(torch::kFloat32).device(device).requires_grad(true));
}

// ---- 3. Base identity sampling grid (normalized [-1,1], last-dim order x,y,z), align_corners=true. ----
torch::Tensor idAffine =
torch::eye(ImageDimension, torch::TensorOptions().dtype(torch::kFloat32).device(device));
torch::Tensor idAffine = torch::eye(ImageDimension, torch::TensorOptions().dtype(torch::kFloat32).device(device));
idAffine = torch::cat({ idAffine, torch::zeros({ static_cast<int64_t>(ImageDimension), 1 }, idAffine.options()) }, 1)
.unsqueeze(0); // {1, N, N+1}
std::vector<int64_t> gridSize;
Expand All @@ -339,8 +340,7 @@ ImpactFineRegistration<TFixedImage, TMovingImage>::GenerateData()
{
gridSize.push_back(s);
}
torch::Tensor grid0 =
torch::affine_grid_generator(idAffine, gridSize, /*align_corners=*/true); // {1, z,y,x, N}
torch::Tensor grid0 = torch::affine_grid_generator(idAffine, gridSize, /*align_corners=*/true); // {1, z,y,x, N}

// Per-component normalization in (z, y, x) order: the field units (s_min) per grid_sample unit, (size-1)/2 voxels
// (exact for align_corners=true) of s_a / s_min units each.
Expand Down Expand Up @@ -394,7 +394,8 @@ ImpactFineRegistration<TFixedImage, TMovingImage>::GenerateData()
return field;
}
if constexpr (ImageDimension == 3)
return F::interpolate(field, F::InterpolateFuncOptions().size(target).mode(torch::kTrilinear).align_corners(true));
return F::interpolate(field,
F::InterpolateFuncOptions().size(target).mode(torch::kTrilinear).align_corners(true));
else
return F::interpolate(field, F::InterpolateFuncOptions().size(target).mode(torch::kBilinear).align_corners(true));
};
Expand Down Expand Up @@ -510,15 +511,15 @@ ImpactFineRegistration<TFixedImage, TMovingImage>::GenerateData()
// Feature-mode setup: extract the fixed/moving feature layers (constants, not differentiated
// through), optionally PCA-reduce them (fit on fixed), and build one loss per kept layer.
// Intensity mode skips this and compares raw voxels.
std::vector<torch::Tensor> fixedLayers;
std::vector<torch::Tensor> movingLayers;
std::vector<torch::Tensor> fixedLayersOnline; // "Jacobian" mode: fixed features, pooled
std::vector<torch::Tensor> pcaBasis; // per kept layer; undefined entry = no PCA
std::vector<std::unique_ptr<Impact::Loss>> losses;
std::vector<float> layerWeights;
std::vector<torch::Tensor> fixedLayers;
std::vector<torch::Tensor> movingLayers;
std::vector<torch::Tensor> fixedLayersOnline; // "Jacobian" mode: fixed features, pooled
std::vector<torch::Tensor> pcaBasis; // per kept layer; undefined entry = no PCA
std::vector<std::unique_ptr<Impact::Loss>> losses;
std::vector<float> layerWeights;
// SubsetFeatures: per kept layer, that many of its channels drawn at random at every iteration (0 = all).
std::vector<torch::Tensor> subsets; // per layer; an undefined entry keeps every channel
auto drawSubsets = [&](const std::vector<torch::Tensor> & layers) {
auto drawSubsets = [&](const std::vector<torch::Tensor> & layers) {
subsets.assign(layers.size(), torch::Tensor());
for (size_t l = 0; l < layers.size(); ++l)
{
Expand Down Expand Up @@ -731,8 +732,7 @@ ImpactFineRegistration<TFixedImage, TMovingImage>::GenerateData()
}
for (size_t l = 0; l < layerCount; ++l)
{
const std::string name =
m_Distance.empty() ? std::string("L2") : m_Distance[std::min(l, m_Distance.size() - 1)];
const std::string name = m_Distance.empty() ? std::string("L2") : m_Distance[std::min(l, m_Distance.size() - 1)];
losses.push_back(Impact::LossFactory::Instance().Create(name));
layerWeights.push_back(l < m_LayersWeight.size() ? m_LayersWeight[l] : 1.0f);
if (sampled && losses.back()->IsSpatial())
Expand Down Expand Up @@ -951,18 +951,16 @@ ImpactFineRegistration<TFixedImage, TMovingImage>::GenerateData()
}

// ---- 4. Adam loop (entirely on device; no host copies). ----
torch::optim::Adam optimizer({ theta },
torch::optim::AdamOptions(m_LearningRate)
.betas(std::make_tuple(m_Beta1, m_Beta2))
.eps(m_Epsilon));
torch::optim::Adam optimizer(
{ theta }, torch::optim::AdamOptions(m_LearningRate).betas(std::make_tuple(m_Beta1, m_Beta2)).eps(m_Epsilon));

m_MetricValuesPerIteration.clear();
m_MetricValuesPerIteration.reserve(m_NumberOfIterations);

// Every layer's loss divided by its value at the stage's first iteration, so each starts at 1 and
// LayersWeight weighs comparable quantities (see Impact::LossNormalization).
Impact::LossNormalization normalization;
auto normalized = [&](size_t l, const torch::Tensor & value) -> torch::Tensor {
auto normalized = [&](size_t l, const torch::Tensor & value) -> torch::Tensor {
if (!m_NormalizeLosses)
{
return value;
Expand Down Expand Up @@ -1032,7 +1030,7 @@ ImpactFineRegistration<TFixedImage, TMovingImage>::GenerateData()
const torch::Tensor smoothedControl = smoothControl(theta); // graph to theta
torch::Tensor smoothed = smoothedControl.detach().requires_grad_(true);
smoothed.mutable_grad() = torch::zeros_like(smoothed);
int64_t voxels = 1;
int64_t voxels = 1;
for (const int64_t extent : spatial)
{
voxels *= extent;
Expand Down Expand Up @@ -1439,9 +1437,11 @@ ImpactFineRegistration<TFixedImage, TMovingImage>::GenerateData()
if (cur != tgt)
{
if constexpr (ImageDimension == 3)
mll = F::interpolate(mll, F::InterpolateFuncOptions().size(tgt).mode(torch::kTrilinear).align_corners(true));
mll =
F::interpolate(mll, F::InterpolateFuncOptions().size(tgt).mode(torch::kTrilinear).align_corners(true));
else
mll = F::interpolate(mll, F::InterpolateFuncOptions().size(tgt).mode(torch::kBilinear).align_corners(true));
mll =
F::interpolate(mll, F::InterpolateFuncOptions().size(tgt).mode(torch::kBilinear).align_corners(true));
}
torch::Tensor counted;
if (masked)
Expand Down Expand Up @@ -1575,7 +1575,8 @@ ImpactFineRegistration<TFixedImage, TMovingImage>::GenerateData()
m_WarpedMovingImage->SetDirection(m_FixedImage->GetDirection());
m_WarpedMovingImage->Allocate();

ImageRegionIteratorWithIndex<WarpedImageType> wit(m_WarpedMovingImage, m_WarpedMovingImage->GetLargestPossibleRegion());
ImageRegionIteratorWithIndex<WarpedImageType> wit(m_WarpedMovingImage,
m_WarpedMovingImage->GetLargestPossibleRegion());
for (wit.GoToBegin(); !wit.IsAtEnd(); ++wit)
{
const auto idx = wit.GetIndex();
Expand Down
14 changes: 7 additions & 7 deletions include/itkImpactImageToImageMetricv4.h
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Expand Up @@ -303,18 +303,18 @@ class ITK_TEMPLATE_EXPORT ImpactImageToImageMetricv4
int m_FeaturesMapUpdateInterval{ 0 };
/** Derivative evaluations since Initialize(), the metric's stand-in for an iteration count.
* Mutable because GetValueAndDerivative() is const, as the base metric declares it. */
mutable unsigned long m_CurrentIteration{ 0 };
std::string m_Mode;
std::string m_FeatureMapsPath;
bool m_NormalizeLosses{ true };
mutable unsigned long m_CurrentIteration{ 0 };
std::string m_Mode;
std::string m_FeatureMapsPath;
bool m_NormalizeLosses{ true };
/** Latched at the first evaluation after Initialize(); mutable because the evaluation is const. */
mutable Impact::LossNormalization m_LossNormalization;
std::string m_Device = "cpu";
std::string m_Device = "cpu";
// Zero means "seed from the clock", which is what the per-work-unit generator does with it.
// It must have a value even when the user never calls SetSeed(): it is read on every
// evaluation, and an indeterminate one would make the metric unreproducible at random.
unsigned int m_Seed{ 0 };
unsigned int m_BatchSize{ 64 };
unsigned int m_Seed{ 0 };
unsigned int m_BatchSize{ 64 };

std::vector<std::vector<unsigned int>> m_features_indexes;
};
Expand Down
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