Multi-Scale SSIM¶

Module Interface¶

class torchmetrics.MultiScaleStructuralSimilarityIndexMeasure(gaussian_kernel=True, kernel_size=11, sigma=1.5, reduction='elementwise_mean', data_range=None, k1=0.01, k2=0.03, betas=(0.0448, 0.2856, 0.3001, 0.2363, 0.1333), normalize='relu', **kwargs)[source]

Computes MultiScaleSSIM, Multi-scale Structural Similarity Index Measure, which is a generalization of Structural Similarity Index Measure by incorporating image details at different resolution scores.

As input to forward and update the metric accepts the following input

preds (Tensor): Predictions from model
target (Tensor): Ground truth values

As output of forward and compute the metric returns the following output

msssim (: Tensor): if reduction!='none' returns float scalar tensor with average MSSSIM value over sample else returns tensor of shape (N,) with SSIM values per sample

Parameters

gaussian_kernel¶ (bool) – If True (default), a gaussian kernel is used, if false a uniform kernel is used
kernel_size¶ (Union[int, Sequence[int]]) – size of the gaussian kernel
sigma¶ (Union[float, Sequence[float]]) – Standard deviation of the gaussian kernel
reduction¶ (Literal[‘elementwise_mean’, ‘sum’, ‘none’, None]) –
a method to reduce metric score over labels.
- 'elementwise_mean': takes the mean
- 'sum': takes the sum
- 'none' or None: no reduction will be applied
data_range¶ (Optional[float]) – Range of the image. If None, it is determined from the image (max - min)
k1¶ (float) – Parameter of structural similarity index measure.
k2¶ (float) – Parameter of structural similarity index measure.
betas¶ (Tuple[float, ...]) – Exponent parameters for individual similarities and contrastive sensitivies returned by different image resolutions.
normalize¶ (Literal[‘relu’, ‘simple’, None]) – When MultiScaleStructuralSimilarityIndexMeasure loss is used for training, it is desirable to use normalizes to improve the training stability. This normalize argument is out of scope of the original implementation [1], and it is adapted from https://github.com/jorge-pessoa/pytorch-msssim instead.
kwargs¶ (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Returns

Tensor with Multi-Scale SSIM score

Raises

ValueError – If kernel_size is not an int or a Sequence of ints with size 2 or 3.
ValueError – If betas is not a tuple of floats with lengt 2.
ValueError – If normalize is neither None, ReLU nor simple.

Example

>>> from torchmetrics import MultiScaleStructuralSimilarityIndexMeasure
>>> import torch
>>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
>>> target = preds * 0.75
>>> ms_ssim = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
>>> ms_ssim(preds, target)
tensor(0.9627)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface¶

torchmetrics.functional.multiscale_structural_similarity_index_measure(preds, target, gaussian_kernel=True, sigma=1.5, kernel_size=11, reduction='elementwise_mean', data_range=None, k1=0.01, k2=0.03, betas=(0.0448, 0.2856, 0.3001, 0.2363, 0.1333), normalize='relu')[source]

Computes MultiScaleSSIM, Multi-scale Structual Similarity Index Measure, which is a generalization of Structual Similarity Index Measure by incorporating image details at different resolution scores.

Parameters

preds¶ (Tensor) – Predictions from model of shape [N, C, H, W]
target¶ (Tensor) – Ground truth values of shape [N, C, H, W]
kernel_size¶ (Union[int, Sequence[int]]) – size of the gaussian kernel
sigma¶ (Union[float, Sequence[float]]) – Standard deviation of the gaussian kernel
reduction¶ (Literal[‘elementwise_mean’, ‘sum’, ‘none’, None]) –
a method to reduce metric score over labels.
- 'elementwise_mean': takes the mean
- 'sum': takes the sum
- 'none' or None: no reduction will be applied
data_range¶ (Optional[float]) – Range of the image. If None, it is determined from the image (max - min)
k1¶ (float) – Parameter of structural similarity index measure.
k2¶ (float) – Parameter of structural similarity index measure.
betas¶ (Tuple[float, ...]) – Exponent parameters for individual similarities and contrastive sensitivies returned by different image resolutions.
normalize¶ (Optional[Literal[‘relu’, ‘simple’]]) – When MultiScaleSSIM loss is used for training, it is desirable to use normalizes to improve the training stability. This normalize argument is out of scope of the original implementation [1], and it is adapted from https://github.com/jorge-pessoa/pytorch-msssim instead.

Return type

Tensor

Returns

Tensor with Multi-Scale SSIM score

Raises

TypeError – If preds and target don’t have the same data type.
ValueError – If preds and target don’t have BxCxHxW shape.
ValueError – If the length of kernel_size or sigma is not 2.
ValueError – If one of the elements of kernel_size is not an odd positive number.
ValueError – If one of the elements of sigma is not a positive number.

Example

>>> from torchmetrics.functional import multiscale_structural_similarity_index_measure
>>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
>>> target = preds * 0.75
>>> multiscale_structural_similarity_index_measure(preds, target, data_range=1.0)
tensor(0.9627)

References

[1] Multi-Scale Structural Similarity For Image Quality Assessment by Zhou Wang, Eero P. Simoncelli and Alan C. Bovik MultiScaleSSIM