Method __init__

monai/transforms/post/dictionary.py:949–999

Args: keys: keys of the corresponding items to model output, this transform only supports 1 key. See also: :py:class:`monai.transforms.compose.MapTransform` meta_keys: explicitly indicate the key of the corresponding metadata dictionary.

(
        self,
        keys: KeysCollection,
        meta_keys: KeysCollection | None = None,
        meta_key_postfix: str = DEFAULT_POST_FIX,
        saver: CSVSaver | None = None,
        output_dir: PathLike = "./",
        filename: str = "predictions.csv",
        delimiter: str = ",",
        overwrite: bool = True,
        flush: bool = True,
        allow_missing_keys: bool = False,
    )

    """

    def __init__(
        self,
        keys: KeysCollection,
        meta_keys: KeysCollection | None = None,
        meta_key_postfix: str = DEFAULT_POST_FIX,
        saver: CSVSaver | None = None,
        output_dir: PathLike = "./",
        filename: str = "predictions.csv",
        delimiter: str = ",",
        overwrite: bool = True,
        flush: bool = True,
        allow_missing_keys: bool = False,
    ) -> None:
        """
        Args:
            keys: keys of the corresponding items to model output, this transform only supports 1 key.
                See also: :py:class:`monai.transforms.compose.MapTransform`
            meta_keys: explicitly indicate the key of the corresponding metadata dictionary.
                for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
                the metadata is a dictionary object which contains: filename, original_shape, etc.
                it can be a sequence of string, map to the `keys`.
                if None, will try to construct meta_keys by `key_{meta_key_postfix}`.
                will extract the filename of input image to save classification results.
            meta_key_postfix: `key_{postfix}` was used to store the metadata in `LoadImaged`.
                so need the key to extract the metadata of input image, like filename, etc. default is `meta_dict`.
                for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
                the metadata is a dictionary object which contains: filename, original_shape, etc.
                this arg only works when `meta_keys=None`. if no corresponding metadata, set to `None`.
            saver: the saver instance to save classification results, if None, create a CSVSaver internally.
                the saver must provide `save(data, meta_data)` and `finalize()` APIs.
            output_dir: if `saver=None`, specify the directory to save the CSV file.
            filename: if `saver=None`, specify the name of the saved CSV file.
            delimiter: the delimiter character in the saved file, default to "," as the default output type is `csv`.
                to be consistent with: https://docs.python.org/3/library/csv.html#csv.Dialect.delimiter.
            overwrite: if `saver=None`, indicate whether to overwriting existing CSV file content, if True,
                will clear the file before saving. otherwise, will append new content to the CSV file.
            flush: if `saver=None`, indicate whether to write the cache data to CSV file immediately
                in this transform and clear the cache. default to True.
                If False, may need user to call `saver.finalize()` manually or use `ClassificationSaver` handler.
            allow_missing_keys: don't raise exception if key is missing.

        """
        super().__init__(keys, allow_missing_keys)
        if len(self.keys) != 1:
            raise ValueError("only 1 key is allowed when saving the classification result.")
        self.saver = saver or CSVSaver(
            output_dir=output_dir, filename=filename, overwrite=overwrite, flush=flush, delimiter=delimiter
        )
        self.flush = flush
        self.meta_keys = ensure_tuple_rep(meta_keys, len(self.keys))
        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))

    def __call__(self, data):
        d = dict(data)