Source code for torchio.transforms.lambda_transform

from typing import Sequence, Optional
import torch
from ..typing import TypeCallable
from import Subject
from ..constants import TYPE
from .transform import Transform

[docs]class Lambda(Transform): """Applies a user-defined function as transform. Args: function: Callable that receives and returns a 4D :class:`torch.Tensor`. types_to_apply: 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. **kwargs: See :class:`~torchio.transforms.Transform` for additional keyword arguments. Example: >>> 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) """ # noqa: E501 def __init__( self, function: TypeCallable, types_to_apply: Optional[Sequence[str]] = None, **kwargs ): super().__init__(**kwargs) self.function = function self.types_to_apply = types_to_apply self.args_names = 'function', 'types_to_apply' def apply_transform(self, subject: Subject) -> Subject: images = subject.get_images( intensity_only=False, include=self.include, exclude=self.exclude, ) for image in images: image_type = image[TYPE] if self.types_to_apply is not None: if image_type not in self.types_to_apply: continue function_arg = result = self.function(function_arg) if not isinstance(result, torch.Tensor): message = ( 'The returned value from the callable argument must be' f' of type {torch.Tensor}, not {type(result)}' ) raise ValueError(message) if result.ndim != function_arg.ndim: message = ( 'The number of dimensions of the returned value must' f' be {function_arg.ndim}, not {result.ndim}' ) raise ValueError(message) image.set_data(result) return subject