Class EnsureChannelFirstd

monai/transforms/utility/dictionary.py:259–288

Dictionary-based wrapper of :py:class:`monai.transforms.EnsureChannelFirst`.

class EnsureChannelFirstd(MapTransform):
    """
    Dictionary-based wrapper of :py:class:`monai.transforms.EnsureChannelFirst`.
    """

    backend = EnsureChannelFirst.backend

    def __init__(
        self, keys: KeysCollection, strict_check: bool = True, allow_missing_keys: bool = False, channel_dim=None
    ) -> None:
        """
        Args:
            keys: keys of the corresponding items to be transformed.
                See also: :py:class:`monai.transforms.compose.MapTransform`
            strict_check: whether to raise an error when the meta information is insufficient.
            allow_missing_keys: don't raise exception if key is missing.
            channel_dim: This argument can be used to specify the original channel dimension (integer) of the input array.
                It overrides the `original_channel_dim` from provided MetaTensor input.
                If the input array doesn't have a channel dim, this value should be ``'no_channel'``.
                If this is set to `None`, this class relies on `img` or `meta_dict` to provide the channel dimension.
        """
        super().__init__(keys, allow_missing_keys)
        self.adjuster = EnsureChannelFirst(strict_check=strict_check, channel_dim=channel_dim)

    def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> dict[Hashable, torch.Tensor]:
        d = dict(data)
        for key in self.key_iterator(d):
            meta_dict = d[key].meta if isinstance(d[key], MetaTensor) else None  # type: ignore[attr-defined]
            d[key] = self.adjuster(d[key], meta_dict)
        return d


class RepeatChanneld(MapTransform):