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OneOf

OneOf

Bases: RandomTransform

Apply only one of the given transforms.

Parameters:

Name Type Description Default
transforms TypeTransformsDict

Dictionary with instances of Transform as keys and probabilities as values. Probabilities are normalized so they sum to one. If a sequence is given, the same probability will be assigned to each transform.

 required
**kwargs

See Transform for additional keyword arguments.

 {}

Examples:

>>> import torchio as tio
>>> colin = tio.datasets.Colin27()
>>> transforms_dict = {
...     tio.RandomAffine(): 0.75,
...     tio.RandomElasticDeformation(): 0.25,
... }  # Using 3 and 1 as probabilities would have the same effect
>>> transform = tio.OneOf(transforms_dict)
>>> transformed = transform(colin)

__call__(data) 

__call__(data: Subject) -> Subject

__call__(data: ImageT) -> ImageT

__call__(data: torch.Tensor) -> torch.Tensor

__call__(data: np.ndarray) -> np.ndarray

__call__(data: sitk.Image) -> sitk.Image

__call__(data: dict[str, object]) -> dict[str, object]

__call__(data: nib.Nifti1Image) -> nib.Nifti1Image

Transform data and return a result of the same type.

Parameters:

Name Type Description Default
data TypeTransformInput

Instance of torchio.Subject, 4D torch.Tensor or numpy.ndarray with dimensions \((C, W, H, D)\), where \(C\) is the number of channels and \(W, H, D\) are the spatial dimensions. If the input is a tensor, the affine matrix will be set to identity. Other valid input types are a SimpleITK image, a torchio.Image, a NiBabel Nifti1 image or a dict. The output type is the same as the input type.

 required

to_hydra_config()

Return a dictionary representation of the transform for Hydra instantiation.