Source code for monai.handlers.panoptic_quality

# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

from import Callable

from monai.handlers.ignite_metric import IgniteMetricHandler
from monai.metrics import PanopticQualityMetric
from monai.utils import MetricReduction

[docs] class PanopticQuality(IgniteMetricHandler): """ Computes Panoptic quality from full size Tensor and collects average over batch, class-channels, iterations. """
[docs] def __init__( self, num_classes: int, metric_name: str = "pq", reduction: MetricReduction | str = MetricReduction.MEAN_BATCH, match_iou_threshold: float = 0.5, smooth_numerator: float = 1e-6, output_transform: Callable = lambda x: x, save_details: bool = True, ) -> None: """ 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:, explanation and usage example are in the tutorial: 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` """ metric_fn = PanopticQualityMetric( num_classes=num_classes, metric_name=metric_name, reduction=reduction, match_iou_threshold=match_iou_threshold, smooth_numerator=smooth_numerator, ) super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)