Class Decollated

monai/transforms/inverse_batch_transform.py:114–159

Decollate a batch of data. If input is a dictionary, it also supports to only decollate specified keys. Note that unlike most MapTransforms, it will delete the other keys that are not specified. if `keys=None`, it will decollate all the data in the input. It replicates the scalar va

class Decollated(MapTransform):
    """
    Decollate a batch of data. If input is a dictionary, it also supports to only decollate specified keys.
    Note that unlike most MapTransforms, it will delete the other keys that are not specified.
    if `keys=None`, it will decollate all the data in the input.
    It replicates the scalar values to every item of the decollated list.

    Args:
        keys: keys of the corresponding items to decollate, note that it will delete other keys not specified.
            if None, will decollate all the keys. see also: :py:class:`monai.transforms.compose.MapTransform`.
        detach: whether to detach the tensors. Scalars tensors will be detached into number types
            instead of torch tensors.
        pad_batch: when the items in a batch indicate different batch size,
            whether to pad all the sequences to the longest.
            If False, the batch size will be the length of the shortest sequence.
        fill_value: the value to fill the padded sequences when `pad_batch=True`.
        allow_missing_keys: don't raise exception if key is missing.

    """

    def __init__(
        self,
        keys: KeysCollection | None = None,
        detach: bool = True,
        pad_batch: bool = True,
        fill_value=None,
        allow_missing_keys: bool = False,
    ) -> None:
        super().__init__(keys, allow_missing_keys)
        self.detach = detach
        self.pad_batch = pad_batch
        self.fill_value = fill_value

    def __call__(self, data: dict | list):
        d: dict | list
        if len(self.keys) == 1 and self.keys[0] is None:
            # it doesn't support `None` as the key
            d = data
        else:
            if not isinstance(data, dict):
                raise TypeError("input data is not a dictionary, but specified keys to decollate.")
            d = {}
            for key in self.key_iterator(data):
                d[key] = data[key]

        return decollate_batch(d, detach=self.detach, pad=self.pad_batch, fill_value=self.fill_value)


DecollateD = DecollateDict = Decollated