Method __init__

monai/transforms/post/dictionary.py:162–207

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

(
        self,
        keys: KeysCollection,
        argmax: Sequence[bool] | bool = False,
        to_onehot: Sequence[int | None] | int | None = None,
        threshold: Sequence[float | None] | float | None = None,
        rounding: Sequence[str | None] | str | None = None,
        allow_missing_keys: bool = False,
        **kwargs,
    )

    backend = AsDiscrete.backend

    def __init__(
        self,
        keys: KeysCollection,
        argmax: Sequence[bool] | bool = False,
        to_onehot: Sequence[int | None] | int | None = None,
        threshold: Sequence[float | None] | float | None = None,
        rounding: Sequence[str | None] | str | 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`
            argmax: whether to execute argmax function on input data before transform.
                it also can be a sequence of bool, each element corresponds to a key in ``keys``.
            to_onehot: if not None, convert input data into the one-hot format with specified number of classes.
                defaults to ``None``. it also can be a sequence, each element corresponds to a key in ``keys``.
            threshold: if not None, threshold the float values to int number 0 or 1 with specified threshold value.
                defaults to ``None``. it also can be a sequence, each element corresponds to a key in ``keys``.
            rounding: if not None, round the data according to the specified option,
                available options: ["torchrounding"]. it also can be a sequence of str or None,
                each element corresponds to a key in ``keys``.
            allow_missing_keys: don't raise exception if key is missing.
            kwargs: additional parameters to ``AsDiscrete``.
                ``dim``, ``keepdim``, ``dtype`` are supported, unrecognized parameters will be ignored.
                These default to ``0``, ``True``, ``torch.float`` respectively.

        """
        super().__init__(keys, allow_missing_keys)
        self.argmax = ensure_tuple_rep(argmax, len(self.keys))
        self.to_onehot = []
        for flag in ensure_tuple_rep(to_onehot, len(self.keys)):
            if isinstance(flag, bool):
                raise ValueError("`to_onehot=True/False` is deprecated, please use `to_onehot=num_classes` instead.")
            self.to_onehot.append(flag)

        self.threshold = []
        for flag in ensure_tuple_rep(threshold, len(self.keys)):
            if isinstance(flag, bool):
                raise ValueError("`threshold_values=True/False` is deprecated, please use `threshold=value` instead.")
            self.threshold.append(flag)

        self.rounding = ensure_tuple_rep(rounding, len(self.keys))
        self.converter = AsDiscrete()
        self.converter.kwargs = kwargs

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