Class SomeOf

monai/transforms/compose.py:664–827

``SomeOf`` samples a different sequence of transforms to apply each time it is called. It can be configured to sample a fixed or varying number of transforms each time its called. Samples are drawn uniformly, or from user supplied transform weights. When varying the number of transform

class SomeOf(Compose):
    """
    ``SomeOf`` samples a different sequence of transforms to apply each time it is called.

    It can be configured to sample a fixed or varying number of transforms each time its called. Samples are drawn
    uniformly, or from user supplied transform weights. When varying the number of transforms sampled per call,
    the number of transforms to sample that call is sampled uniformly from a range supplied by the user.

    Args:
        transforms: list of callables.
        map_items: whether to apply transform to each item in the input `data` if `data` is a list or tuple.
            Defaults to `True`.
        unpack_items: whether to unpack input `data` with `*` as parameters for the callable function of transform.
            Defaults to `False`.
        log_stats: this optional parameter allows you to specify a logger by name for logging of pipeline execution.
            Setting this to False disables logging. Setting it to True enables logging to the default loggers.
            Setting a string overrides the logger name to which logging is performed.
        num_transforms: a 2-tuple, int, or None. The 2-tuple specifies the minimum and maximum (inclusive) number of
            transforms to sample at each iteration. If an int is given, the lower and upper bounds are set equal.
            None sets it to `len(transforms)`. Default to `None`.
        replace: whether to sample with replacement. Defaults to `False`.
        weights: weights to use in for sampling transforms. Will be normalized to 1. Default: None (uniform).
        lazy: whether to enable :ref:`Lazy Resampling<lazy_resampling>` for lazy transforms. If False, transforms will
            be carried out on a transform by transform basis. If True, all lazy transforms will be executed by
            accumulating changes and resampling as few times as possible. If lazy is None, `Compose` will
            perform lazy execution on lazy transforms that have their `lazy` property set to True.
        overrides: this optional parameter allows you to specify a dictionary of parameters that should be overridden
            when executing a pipeline. These each parameter that is compatible with a given transform is then applied
            to that transform before it is executed. Note that overrides are currently only applied when
            :ref:`Lazy Resampling<lazy_resampling>` is enabled for the pipeline or a given transform. If lazy is False
            they are ignored. Currently supported args are:
            {``"mode"``, ``"padding_mode"``, ``"dtype"``, ``"align_corners"``, ``"resample_mode"``, ``device``}.
    """

    def __init__(
        self,
        transforms: Sequence[Callable] | Callable | None = None,
        map_items: bool = True,
        unpack_items: bool = False,
        log_stats: bool | str = False,
        num_transforms: int | tuple[int, int] | None = None,
        replace: bool = False,
        weights: list[int] | None = None,
        lazy: bool | None = False,
        overrides: dict | None = None,
    ) -> None:
        super().__init__(transforms, map_items, unpack_items, log_stats=log_stats, lazy=lazy, overrides=overrides)
        self.min_num_transforms, self.max_num_transforms = self._ensure_valid_num_transforms(num_transforms)
        self.replace = replace
        self.weights = self._normalize_probabilities(weights)
        self.log_stats = log_stats

    def _ensure_valid_num_transforms(self, num_transforms: int | tuple[int, int] | None) -> tuple:
        if (
            not isinstance(num_transforms, tuple)
            and not isinstance(num_transforms, list)
            and not isinstance(num_transforms, int)
            and num_transforms is not None