Class Lambda

monai/transforms/utility/array.py:783–844

Apply a user-defined lambda as a transform. For example: .. code-block:: python :emphasize-lines: 2 image = np.ones((10, 2, 2)) lambd = Lambda(func=lambda x: x[:4, :, :]) print(lambd(image).shape) (4, 2, 2) Args: func: Lambda/funct

class Lambda(InvertibleTransform):
    """
    Apply a user-defined lambda as a transform.

    For example:

    .. code-block:: python
        :emphasize-lines: 2

        image = np.ones((10, 2, 2))
        lambd = Lambda(func=lambda x: x[:4, :, :])
        print(lambd(image).shape)
        (4, 2, 2)

    Args:
        func: Lambda/function to be applied.
        inv_func: Lambda/function of inverse operation, default to `lambda x: x`.
        track_meta:  If `False`, then standard data objects will be returned (e.g., torch.Tensor` and `np.ndarray`)
            as opposed to MONAI's enhanced objects. By default, this is `True`.

    Raises:
        TypeError: When ``func`` is not an ``Optional[Callable]``.

    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self, func: Callable | None = None, inv_func: Callable = no_collation, track_meta: bool = True
    ) -> None:
        if func is not None and not callable(func):
            raise TypeError(f"func must be None or callable but is {type(func).__name__}.")
        self.func = func
        self.inv_func = inv_func
        self.track_meta = track_meta

    def __call__(self, img: NdarrayOrTensor, func: Callable | None = None):
        """
        Apply `self.func` to `img`.

        Args:
            func: Lambda/function to be applied. Defaults to `self.func`.

        Raises:
            TypeError: When ``func`` is not an ``Optional[Callable]``.

        """
        fn = func if func is not None else self.func
        if not callable(fn):
            raise TypeError(f"func must be None or callable but is {type(fn).__name__}.")
        out = fn(img)
        # convert to MetaTensor if necessary
        if isinstance(out, (np.ndarray, torch.Tensor)) and not isinstance(out, MetaTensor) and self.track_meta:
            out = MetaTensor(out)
        if isinstance(out, MetaTensor):
            self.push_transform(out)
        return out