o  i@sPddlmZddlmZddlmZddlmZddlm Z GdddeZ dS) ) annotations)Callable)IgniteMetricHandler) DiceMetric)MetricReductioncs6eZdZdZdejdddddfdfdd ZZS)MeanDicezw Computes Dice score metric from full size Tensor and collects average over batch, class-channels, iterations. TNcCs|S)N)xrrZ/home/dell461/cl/sdc2/last_ska_mid/HISourceFinder-master-l/src/monai/handlers/mean_dice.pyszMeanDice.Finclude_backgroundbool reductionMetricReduction | str num_classes int | Noneoutput_transformr save_detailsreturn_with_labelbool | list[str]returnNonecs&t||||d}tj|||ddS)as Args: include_background: whether to include dice computation on the first channel of the predicted output. Defaults to True. reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values, available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``, ``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction. num_classes: number of input channels (always including the background). When this is None, ``y_pred.shape[1]`` will be used. This option is useful when both ``y_pred`` and ``y`` are single-channel class indices and the number of classes is not automatically inferred from data. output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`. `engine.state` and `output_transform` inherit from the ignite concept: https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial: https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb. save_details: whether to save metric computation details per image, for example: mean dice of every image. default to True, will save to `engine.state.metric_details` dict with the metric name as key. return_with_label: whether to return the metrics with label, only works when reduction is "mean_batch". If `True`, use "label_{index}" as the key corresponding to C channels; if 'include_background' is True, the index begins at "0", otherwise at "1". It can also take a list of label names. The outcome will then be returned as a dictionary. See also: :py:meth:`monai.metrics.meandice.compute_dice` )r rrr) metric_fnrrN)rsuper__init__)selfr rrrrrr __class__rr rs$zMeanDice.__init__)r r rrrrrrrr rrrr)__name__ __module__ __qualname____doc__rMEANr __classcell__rrrr rsrN) __future__rcollections.abcrmonai.handlers.ignite_metricr monai.metricsr monai.utilsrrrrrr s