Panopticquality

---
name: panopticquality
description: Compute the PanopticQuality metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute PanopticQuality, or asks how to score with PanopticQuality.
metadata:
  skill_kind: metric
  source_lib: torchmetrics
  import_path: torchmetrics.PanopticQuality
  source: library_introspection
---

# panopticquality

> Metric `PanopticQuality` from `torchmetrics` (torchmetrics.PanopticQuality)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with PanopticQuality, or
mentions `torchmetrics.PanopticQuality` directly, or wants the standard torchmetrics implementation.

## Reference signature

```python
from torchmetrics import PanopticQuality

# _PanopticQuality(things: collections.abc.Collection[int], stuffs: collections.abc.Collection[int], allow_unknown_preds_category: bool = False, **kwargs: Any) -> None
```

## Library docstring

```
Wrapper for deprecated import.

>>> from torch import tensor
>>> 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)
```

## Quick recipe

```python
import torchmetrics as _m
score = _m.PanopticQuality(y_true, y_pred)
```

## Don'ts

- Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
- Always check the library version's argument order — sklearn is `(y_true, y_pred)` while torchmetrics is `(preds, target)`.