o  iC@sPddlmZddlmZddlmZddlmZddlm Z GdddeZ dS) ) annotations)Callable)IgniteMetricHandler)PanopticQualityMetric)MetricReductioncs6eZdZdZdejdddddfdfdd ZZS)PanopticQualityzv Computes Panoptic quality from full size Tensor and collects average over batch, class-channels, iterations. pqg?gư>cCs|S)N)xr r a/home/dell461/cl/sdc2/last_ska_mid/HISourceFinder-master-l/src/monai/handlers/panoptic_quality.py!szPanopticQuality.T num_classesint metric_namestr reductionMetricReduction | strmatch_iou_thresholdfloatsmooth_numeratoroutput_transformr save_detailsboolreturnNonec s(t|||||d}tj|||ddS)a Args: num_classes: number of classes. The number should not count the background. metric_name: output metric. The value can be "pq", "sq" or "rq". reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values, available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``, ``"mean_channel"``, ``"sum_channel"``}, default to `self.reduction`. if "none", will not do reduction. match_iou_threshold: IOU threshold to determine the pairing between `y_pred` and `y`. Usually, it should >= 0.5, the pairing between instances of `y_pred` and `y` are identical. If set `match_iou_threshold` < 0.5, this function uses Munkres assignment to find the maximal amount of unique pairing. smooth_numerator: a small constant added to the numerator to avoid zero. 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: panoptic quality 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.panoptic_quality.compute_panoptic_quality` )r rrrr) metric_fnrrN)rsuper__init__) selfr rrrrrrr __class__r r rs$zPanopticQuality.__init__)r rrrrrrrrrrrrrrr)__name__ __module__ __qualname____doc__r MEAN_BATCHr __classcell__r r rr rsrN) __future__rcollections.abcrmonai.handlers.ignite_metricr monai.metricsr monai.utilsrrr r r r s