Peaksignalnoiseratio

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

# peaksignalnoiseratio

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import PeakSignalNoiseRatio

# _PeakSignalNoiseRatio(data_range: Union[float, tuple[float, float]] = 3.0, base: float = 10.0, reduction: Literal['elementwise_mean', 'sum', 'none', None] = 'elementwise_mean', dim: Union[int, tuple[int, ...], NoneType] = None, **kwargs: Any) -> None
```

## Library docstring

```
Wrapper for deprecated import.

>>> from torch import tensor
>>> psnr = _PeakSignalNoiseRatio()
>>> preds = tensor([[0.0, 1.0], [2.0, 3.0]])
>>> target = tensor([[3.0, 2.0], [1.0, 0.0]])
>>> psnr(preds, target)
tensor(2.5527)
```

## Quick recipe

```python
import torchmetrics as _m
score = _m.PeakSignalNoiseRatio(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)`.