Class Invertd

monai/transforms/post/dictionary.py:772–940

Utility transform to invert the previously applied transforms. Taking the ``transform`` previously applied on ``orig_keys``, this ``Invertd`` will apply the inverse of it to the data stored at ``keys``. ``Invertd``'s output will also include a copy of the metadata dictionary (

class Invertd(MapTransform):
    """
    Utility transform to invert the previously applied transforms.

    Taking the ``transform`` previously applied on ``orig_keys``, this ``Invertd`` will apply the inverse of it
    to the data stored at ``keys``.

    ``Invertd``'s output will also include a copy of the metadata
    dictionary (originally from  ``orig_meta_keys`` or the metadata of ``orig_keys``),
    with the relevant fields inverted and stored at ``meta_keys``.

    A typical usage is to apply the inverse of the preprocessing (``transform=preprocessings``) on
    input ``orig_keys=image`` to the model predictions ``keys=pred``.

    A detailed usage example is available in the tutorial:
    https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/torch/unet_inference_dict.py

    Note:

        - The output of the inverted data and metadata will be stored at ``keys`` and ``meta_keys`` respectively.
        - To correctly invert the transforms, the information of the previously applied transforms should be
          available at ``{orig_keys}_transforms``, and the original metadata at ``orig_meta_keys``.
          (``meta_key_postfix`` is an optional string to conveniently construct "meta_keys" and/or "orig_meta_keys".)
          see also: :py:class:`monai.transforms.TraceableTransform`.
        - The transform will not change the content in ``orig_keys`` and ``orig_meta_key``.
          These keys are only used to represent the data status of ``key`` before inverting.

    """

    def __init__(
        self,
        keys: KeysCollection,
        transform: InvertibleTransform,
        orig_keys: KeysCollection | None = None,
        meta_keys: KeysCollection | None = None,
        orig_meta_keys: KeysCollection | None = None,
        meta_key_postfix: str = DEFAULT_POST_FIX,
        nearest_interp: bool | Sequence[bool] = True,
        to_tensor: bool | Sequence[bool] = True,
        device: str | torch.device | Sequence[str | torch.device] | None = None,
        post_func: Callable | Sequence[Callable] | None = None,
        allow_missing_keys: bool = False,
    ) -> None:
        """
        Args:
            keys: the key of expected data in the dict, the inverse of ``transforms`` will be applied on it in-place.
                It also can be a list of keys, will apply the inverse transform respectively.
            transform: the transform applied to ``orig_key``, its inverse will be applied on ``key``.
            orig_keys: the key of the original input data in the dict. These keys default to `self.keys` if not set.
                the transform trace information of ``transforms`` should be stored at ``{orig_keys}_transforms``.
                It can also be a list of keys, each matches the ``keys``.
            meta_keys: The key to output the inverted metadata dictionary.
                The metadata is a dictionary optionally containing: filename, original_shape.
                It can be a sequence of strings, maps to ``keys``.
                If None, will try to create a metadata dict with the default key: `{key}_{meta_key_postfix}`.
            orig_meta_keys: the key of the metadata of original input data.
                The metadata is a dictionary optionally containing: filename, original_shape.
                It can be a sequence of strings, maps to the `keys`.