Skip to content

Lambda

Lambda

Bases: Transform

Applies a user-defined function as transform.

Parameters:

Name Type Description Default
function TypeCallable

Callable that receives and returns a 4D torch.Tensor.

 required
types_to_apply Sequence[str] | None

List of strings corresponding to the image types to which this transform should be applied. If None, the transform will be applied to all images in the subject.

 None
**kwargs

See Transform for additional keyword arguments.

 {}

Examples:

>>> import torchio as tio
>>> invert_intensity = tio.Lambda(lambda x: -x, types_to_apply=[tio.INTENSITY])
>>> invert_mask = tio.Lambda(lambda x: 1 - x, types_to_apply=[tio.LABEL])
>>> def double(x):
...     return 2 * x
>>> double_transform = tio.Lambda(double)

__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.