# Totalvariation

> Compute the TotalVariation metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute TotalVariation, or asks how to score with TotalVariation.

- Skill: `qhjqhj00/totalvariation` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/totalvariation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/totalvariation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/totalvariation

---


# totalvariation

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import TotalVariation

# _TotalVariation(reduction: Literal['mean', 'sum', 'none', None] = 'sum', **kwargs: Any) -> None
```

## Library docstring

```
Wrapper for deprecated import.

>>> from torch import rand
>>> tv = _TotalVariation()
>>> img = rand(5, 3, 28, 28)
>>> tv(img)
tensor(7546.8018)
```

## Quick recipe

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