Multi-Scale SSIM¶

Module Interface¶

class torchmetrics.image.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]¶

Compute MultiScaleSSIM, Multi-scale Structural Similarity Index Measure.

This metric is 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¶ (Union[float, tuple[float, float], None]) – the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then the range is calculated as the difference and input is clamped between the values. The data_range must be given when dim is not None.
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 sensitivities 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 length 2.
ValueError – If normalize is neither None, ReLU nor simple.

Example

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

plot(val=None, ax=None)[source]¶

Plot a single or multiple values from the metric.

Parameters:

val¶ (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.
ax¶ (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> from torch import rand
>>> from torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
>>> preds = rand([3, 3, 256, 256])
>>> target = preds * 0.75
>>> metric = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()

../_images/multi_scale_structural_similarity-1.png

>>> # Example plotting multiple values
>>> from torch import rand
>>> from torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
>>> preds = rand([3, 3, 256, 256])
>>> target = preds * 0.75
>>> metric = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)

../_images/multi_scale_structural_similarity-2.png

Functional Interface¶

torchmetrics.functional.image.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]¶

Compute MultiScaleSSIM, Multi-scale Structural Similarity Index Measure.

This metric is a generalization of Structural 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]
gaussian_kernel¶ (bool) – If true, a gaussian kernel is used, if false a uniform kernel is used
sigma¶ (Union[float, Sequence[float]]) – Standard deviation of the gaussian kernel
kernel_size¶ (Union[int, Sequence[int]]) – size 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¶ (Union[float, tuple[float, float], None]) – the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then the range is calculated as the difference and input is clamped between the values.
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 sensitivities 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 torch import rand
>>> from torchmetrics.functional.image import multiscale_structural_similarity_index_measure
>>> preds = rand([3, 3, 256, 256])
>>> target = preds * 0.75
>>> multiscale_structural_similarity_index_measure(preds, target, data_range=1.0)
tensor(0.9628)

References

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