Method __call__

monai/transforms/spatial/dictionary.py:748–773

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 wh

(
        self, data: Mapping[Hashable, torch.Tensor], lazy: bool | None = None
    )

        super().randomize(None)

    def __call__(
        self, data: Mapping[Hashable, torch.Tensor], lazy: bool | None = None
    ) -> Mapping[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
        """
        self.randomize()
        d = dict(data)

        # FIXME: here we didn't use array version `RandRotate90` transform as others, because we need
        # to be compatible with the random status of some previous integration tests
        lazy_ = self.lazy if lazy is None else lazy
        rotator = Rotate90(self._rand_k, self.spatial_axes, lazy=lazy_)
        for key in self.key_iterator(d):
            d[key] = rotator(d[key]) if self._do_transform else convert_to_tensor(d[key], track_meta=get_track_meta())
            self.push_transform(d[key], replace=True, lazy=lazy_)
        return d

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