Class Flipd

monai/transforms/spatial/dictionary.py:1503–1562

Dictionary-based wrapper of :py:class:`monai.transforms.Flip`. See `numpy.flip` for additional details. https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic ` for m

class Flipd(MapTransform, InvertibleTransform, LazyTransform):
    """
    Dictionary-based wrapper of :py:class:`monai.transforms.Flip`.

    See `numpy.flip` for additional details.
    https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html

    This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic<lazy_resampling>`
    for more information.

    Args:
        keys: Keys to pick data for transformation.
        spatial_axis: Spatial axes along which to flip over. Default is None.
        allow_missing_keys: don&#x27;t raise exception if key is missing.
        lazy: a flag to indicate whether this transform should execute lazily or not.
            Defaults to False
    """

    backend = Flip.backend

    def __init__(
        self,
        keys: KeysCollection,
        spatial_axis: Sequence[int] | int | None = None,
        allow_missing_keys: bool = False,
        lazy: bool = False,
    ) -> None:
        MapTransform.__init__(self, keys, allow_missing_keys)
        LazyTransform.__init__(self, lazy=lazy)
        self.flipper = Flip(spatial_axis=spatial_axis)

    @LazyTransform.lazy.setter  # type: ignore
    def lazy(self, val: bool):
        self.flipper.lazy = val
        self._lazy = val

    def __call__(self, data: Mapping[Hashable, torch.Tensor], lazy: bool | None = None) -> dict[Hashable, torch.Tensor]:
        """
        Args:
            data: a dictionary containing the tensor-like data to be processed. The ``keys`` specified
                in this dictionary must be tensor like arrays that are channel first and have at most
                three spatial dimensions
            lazy: a flag to indicate whether this transform should execute lazily or not
                during this call. Setting this to False or True overrides the ``lazy`` flag set
                during initialization for this call. Defaults to None.

        Returns:
            a dictionary containing the transformed data, as well as any other data present in the dictionary
        """
        d = dict(data)
        lazy_ = self.lazy if lazy is None else lazy
        for key in self.key_iterator(d):
            d[key] = self.flipper(d[key], lazy=lazy_)
        return d

    def inverse(self, data: Mapping[Hashable, torch.Tensor]) -> dict[Hashable, torch.Tensor]:
        d = dict(data)
        for key in self.key_iterator(d):