Method __call__

monai/transforms/spatial/dictionary.py:1000–1017

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)

        self.affine.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
        """
        lazy_ = self.lazy if lazy is None else lazy
        d = dict(data)
        for key, mode, padding_mode in self.key_iterator(d, self.mode, self.padding_mode):
            d[key], _ = self.affine(d[key], mode=mode, padding_mode=padding_mode, lazy=lazy_)
        return d

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