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from __future__ import annotations |
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from collections.abc import Callable |
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from monai.handlers.ignite_metric import IgniteMetricHandler |
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from monai.metrics import PanopticQualityMetric |
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from monai.utils import MetricReduction |
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class PanopticQuality(IgniteMetricHandler): |
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""" |
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Computes Panoptic quality from full size Tensor and collects average over batch, class-channels, iterations. |
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""" |
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def __init__( |
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self, |
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num_classes: int, |
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metric_name: str = "pq", |
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reduction: MetricReduction | str = MetricReduction.MEAN_BATCH, |
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match_iou_threshold: float = 0.5, |
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smooth_numerator: float = 1e-6, |
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output_transform: Callable = lambda x: x, |
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save_details: bool = True, |
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) -> None: |
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""" |
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Args: |
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num_classes: number of classes. The number should not count the background. |
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metric_name: output metric. The value can be "pq", "sq" or "rq". |
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reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values, |
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available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``, |
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``"mean_channel"``, ``"sum_channel"``}, default to `self.reduction`. if "none", will not do reduction. |
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match_iou_threshold: IOU threshold to determine the pairing between `y_pred` and `y`. Usually, |
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it should >= 0.5, the pairing between instances of `y_pred` and `y` are identical. |
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If set `match_iou_threshold` < 0.5, this function uses Munkres assignment to find the |
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maximal amount of unique pairing. |
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smooth_numerator: a small constant added to the numerator to avoid zero. |
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output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then |
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construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or |
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lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`. |
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`engine.state` and `output_transform` inherit from the ignite concept: |
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https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial: |
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https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb. |
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save_details: whether to save metric computation details per image, for example: panoptic quality of |
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every image. |
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default to True, will save to `engine.state.metric_details` dict with the metric name as key. |
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See also: |
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:py:meth:`monai.metrics.panoptic_quality.compute_panoptic_quality` |
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""" |
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metric_fn = PanopticQualityMetric( |
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num_classes=num_classes, |
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metric_name=metric_name, |
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reduction=reduction, |
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match_iou_threshold=match_iou_threshold, |
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smooth_numerator=smooth_numerator, |
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) |
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super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details) |
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