class_docstr: Compute the `Panoptic Quality`_ for panoptic segmentations.
  
  .. math::
      PQ = \frac{IOU}{TP + 0.5 FP + 0.5 FN}
  
  where IOU, TP, FP and FN are respectively the sum of the intersection over union for true positives,
  the number of true positives, false positives and false negatives. This metric is inspired by the PQ
  implementation of panopticapi, a standard implementation for the PQ metric for panoptic segmentation.
  
  .. note:
      Points in the target tensor that do not map to a known category ID are automatically ignored in the metric
      computation.
  
  Args:
      things:
          Set of ``category_id`` for countable things.
      stuffs:
          Set of ``category_id`` for uncountable stuffs.
      allow_unknown_preds_category:
          Boolean flag to specify if unknown categories in the predictions are to be ignored in the metric
          computation or raise an exception when found.
  
  
  Raises:
      ValueError:
          If ``things``, ``stuffs`` have at least one common ``category_id``.
      TypeError:
          If ``things``, ``stuffs`` contain non-integer ``category_id``.
  
  Example:
      >>> from torch import tensor
      >>> from torchmetrics.detection import PanopticQuality
      >>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
      ...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
      ...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
      ...                  [[0, 0], [7, 0], [6, 0], [1, 0]],
      ...                  [[0, 0], [7, 0], [7, 0], [7, 0]]]])
      >>> target = tensor([[[[6, 0], [0, 1], [6, 0], [0, 1]],
      ...                   [[0, 1], [0, 1], [6, 0], [0, 1]],
      ...                   [[0, 1], [0, 1], [6, 0], [1, 0]],
      ...                   [[0, 1], [7, 0], [1, 0], [1, 0]],
      ...                   [[0, 1], [7, 0], [7, 0], [7, 0]]]])
      >>> panoptic_quality = PanopticQuality(things = {0, 1}, stuffs = {6, 7})
      >>> panoptic_quality(preds, target)
      tensor(0.5463, dtype=torch.float64)
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code: def __init__(
      self,
      things: Collection[int],
      stuffs: Collection[int],
      allow_unknown_preds_category: bool = False,
      **kwargs: Any,
  ) -> None:
      super().__init__(**kwargs)
  
      things, stuffs = _parse_categories(things, stuffs)
      self.things = things
      self.stuffs = stuffs
      self.void_color = _get_void_color(things, stuffs)
      self.cat_id_to_continuous_id = _get_category_id_to_continuous_id(
          things, stuffs
      )
      self.allow_unknown_preds_category = allow_unknown_preds_category
  
      # per category intermediate metrics
      num_categories = len(things) + len(stuffs)
      self.add_state(
          "iou_sum",
          default=torch.zeros(num_categories, dtype=torch.double),
          dist_reduce_fx="sum",
      )
      self.add_state(
          "true_positives",
          default=torch.zeros(num_categories, dtype=torch.int),
          dist_reduce_fx="sum",
      )
      self.add_state(
          "false_positives",
          default=torch.zeros(num_categories, dtype=torch.int),
          dist_reduce_fx="sum",
      )
      self.add_state(
          "false_negatives",
          default=torch.zeros(num_categories, dtype=torch.int),
          dist_reduce_fx="sum",
      )
  
file_path: torchmetrics/src/torchmetrics/detection/panoptic_qualities.py
imports: from typing import Any, Collection, Optional, Sequence, Union
  import torch
  from torch import Tensor
  from torchmetrics.functional.detection._panoptic_quality_common import _get_category_id_to_continuous_id, _get_void_color, _panoptic_quality_compute, _panoptic_quality_update, _parse_categories, _prepocess_inputs, _validate_inputs
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 1 to create a new PanopticQuality object from inherited base classes: Metric with example: obj = PanopticQuality(things, stuffs, allow_unknown_preds_category)