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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)

Transform data and return a result of the same type.

Parameters:

Name Type Description Default
data InputType

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

get_base_args()

Provides easy access to the arguments used to instantiate the base class (Transform) of any transform.

This method is particularly useful when a new transform can be represented as a variant of an existing transform (e.g. all random transforms), allowing for seamless instantiation of the existing transform with the same arguments as the new transform during apply_transform.

Note

The p argument (probability of applying the transform) is excluded to avoid multiplying the probability of both existing and new transform.

add_base_args(arguments, overwrite_on_existing=False)

Add the init args to existing arguments

validate_keys_sequence(keys, name) staticmethod

Ensure that the input is not a string but a sequence of strings.

to_hydra_config()

Return a dictionary representation of the transform for Hydra instantiation.