Multiscalestructuralsimilarityindexmeasure

multiscalestructuralsimilarityindexmeasure

Metric MultiScaleStructuralSimilarityIndexMeasure from torchmetrics (torchmetrics.MultiScaleStructuralSimilarityIndexMeasure)

When to invoke this skill

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

Reference signature

from torchmetrics import MultiScaleStructuralSimilarityIndexMeasure

# _MultiScaleStructuralSimilarityIndexMeasure(gaussian_kernel: bool = True, kernel_size: Union[int, collections.abc.Sequence[int]] = 11, sigma: Union[float, collections.abc.Sequence[float]] = 1.5, reduction: Literal['elementwise_mean', 'sum', 'none', None] = 'elementwise_mean', data_range: Union[float, tuple[float, float], NoneType] = None, k1: float = 0.01, k2: float = 0.03, betas: tuple[float, ...] = (0.0448, 0.2856, 0.3001, 0.2363, 0.1333), normalize: Literal['relu', 'simple', None] = 'relu', **kwargs: Any) -> None

Library docstring

Wrapper for deprecated import.

>>> from torch import rand
>>> preds = rand([3, 3, 256, 256])
>>> target = preds * 0.75
>>> ms_ssim = _MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
>>> ms_ssim(preds, target)
tensor(0.9628)

Quick recipe

import torchmetrics as _m
score = _m.MultiScaleStructuralSimilarityIndexMeasure(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).