Method __init__

monai/transforms/post/dictionary.py:116–146

Args: keys: keys of the corresponding items to model output and label. See also: :py:class:`monai.transforms.compose.MapTransform` sigmoid: whether to execute sigmoid function on model output before transform. it also can be a sequence

(
        self,
        keys: KeysCollection,
        sigmoid: Sequence[bool] | bool = False,
        softmax: Sequence[bool] | bool = False,
        other: Sequence[Callable] | Callable | None = None,
        allow_missing_keys: bool = False,
        **kwargs,
    )

    backend = Activations.backend

    def __init__(
        self,
        keys: KeysCollection,
        sigmoid: Sequence[bool] | bool = False,
        softmax: Sequence[bool] | bool = False,
        other: Sequence[Callable] | Callable | None = None,
        allow_missing_keys: bool = False,
        **kwargs,
    ) -> None:
        """
        Args:
            keys: keys of the corresponding items to model output and label.
                See also: :py:class:`monai.transforms.compose.MapTransform`
            sigmoid: whether to execute sigmoid function on model output before transform.
                it also can be a sequence of bool, each element corresponds to a key in ``keys``.
            softmax: whether to execute softmax function on model output before transform.
                it also can be a sequence of bool, each element corresponds to a key in ``keys``.
            other: callable function to execute other activation layers,
                for example: `other = torch.tanh`. it also can be a sequence of Callable, each
                element corresponds to a key in ``keys``.
            allow_missing_keys: don't raise exception if key is missing.
            kwargs: additional parameters to `torch.softmax` (used when ``softmax=True``).
                Defaults to ``dim=0``, unrecognized parameters will be ignored.

        """
        super().__init__(keys, allow_missing_keys)
        self.sigmoid = ensure_tuple_rep(sigmoid, len(self.keys))
        self.softmax = ensure_tuple_rep(softmax, len(self.keys))
        self.other = ensure_tuple_rep(other, len(self.keys))
        self.converter = Activations()
        self.converter.kwargs = kwargs

    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]:
        d = dict(data)