o  i @sPddlmZddlmZddlmZddlmZddlm Z GdddeZ dS) ) annotations)Callable)IgniteMetricHandler)MeanIoU)MetricReductioncs2eZdZdZdejdddfdfdd ZZS)MeanIoUHandlerzv Computes IoU score metric from full size Tensor and collects average over batch, class-channels, iterations. TcCs|S)N)xrrY/home/dell461/cl/sdc2/last_ska_mid/HISourceFinder-master-l/src/monai/handlers/mean_iou.pyszMeanIoUHandler.include_backgroundbool reductionMetricReduction | stroutput_transformr save_detailsreturnNonecs"t||d}tj|||ddS)a Args: include_background: whether to include iou 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. 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 iou of every image. default to True, will save to `engine.state.metric_details` dict with the metric name as key. See also: :py:meth:`monai.metrics.meaniou.compute_iou` )r r) metric_fnrrN)rsuper__init__)selfr rrrr __class__rr rs zMeanIoUHandler.__init__) r r rrrrrr rr)__name__ __module__ __qualname____doc__rMEANr __classcell__rrrr rsrN) __future__rcollections.abcrmonai.handlers.ignite_metricr monai.metricsr monai.utilsrrrrrr s