Method __init__

monai/transforms/post/dictionary.py:466–491

Args: keys: keys of the corresponding items to be stack and execute ensemble. if only 1 key provided, suppose it's a PyTorch Tensor with data stacked on dimension `E`. output_key: the key to store ensemble result in the dictionary. if

(
        self,
        keys: KeysCollection,
        output_key: str | None = None,
        weights: Sequence[float] | NdarrayOrTensor | None = None,
    )

    backend = MeanEnsemble.backend

    def __init__(
        self,
        keys: KeysCollection,
        output_key: str | None = None,
        weights: Sequence[float] | NdarrayOrTensor | None = None,
    ) -> None:
        """
        Args:
            keys: keys of the corresponding items to be stack and execute ensemble.
                if only 1 key provided, suppose it's a PyTorch Tensor with data stacked on dimension `E`.
            output_key: the key to store ensemble result in the dictionary.
                if only 1 key provided in `keys`, `output_key` can be None and use `keys` as default.
            weights: can be a list or tuple of numbers for input data with shape: [E, C, H, W[, D]].
                or a Numpy ndarray or a PyTorch Tensor data.
                the `weights` will be added to input data from highest dimension, for example:
                1. if the `weights` only has 1 dimension, it will be added to the `E` dimension of input data.
                2. if the `weights` has 2 dimensions, it will be added to `E` and `C` dimensions.
                it's a typical practice to add weights for different classes:
                to ensemble 3 segmentation model outputs, every output has 4 channels(classes),
                so the input data shape can be: [3, 4, H, W, D].
                and add different `weights` for different classes, so the `weights` shape can be: [3, 4].
                for example: `weights = [[1, 2, 3, 4], [4, 3, 2, 1], [1, 1, 1, 1]]`.

        """
        ensemble = MeanEnsemble(weights=weights)
        super().__init__(keys, ensemble, output_key)


class VoteEnsembled(Ensembled):