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Structural Similarity Index Measure (SSIM)

Module Interface

class torchmetrics.StructuralSimilarityIndexMeasure(gaussian_kernel=True, sigma=1.5, kernel_size=11, reduction='elementwise_mean', data_range=None, k1=0.01, k2=0.03, return_full_image=False, return_contrast_sensitivity=False, **kwargs)[source]

Computes Structual Similarity Index Measure (SSIM).

Parameters
  • preds – estimated image

  • target – ground truth image

  • gaussian_kernel (bool) – If True (default), a gaussian kernel is used, if False a uniform kernel is used

  • sigma (Union[float, Sequence[float]]) – Standard deviation of the gaussian kernel, anisotropic kernels are possible. Ignored if a uniform kernel is used

  • kernel_size (Union[int, Sequence[int]]) – the size of the uniform kernel, anisotropic kernels are possible. Ignored if a Gaussian kernel is used

  • 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 SSIM.

  • k2 (float) – Parameter of SSIM.

  • return_full_image (bool) – If true, the full ssim image is returned as a second argument. Mutually exclusive with return_contrast_sensitivity

  • return_contrast_sensitivity (bool) – If true, the constant term is returned as a second argument. The luminance term can be obtained with luminance=ssim/contrast Mutually exclusive with return_full_image

  • kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Returns

Tensor with SSIM score

Example

>>> from torchmetrics import StructuralSimilarityIndexMeasure
>>> import torch
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> ssim = StructuralSimilarityIndexMeasure()
>>> ssim(preds, target)
tensor(0.9219)

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

compute()[source]

Computes explained variance over state.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters
  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

Return type

None

Functional Interface

torchmetrics.functional.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, return_full_image=False, return_contrast_sensitivity=False)[source]

Computes Structual Similarity Index Measure.

Parameters
  • preds (Tensor) – estimated image

  • target (Tensor) – ground truth image

  • gaussian_kernel (bool) – If true (default), a gaussian kernel is used, if false a uniform kernel is used

  • sigma (Union[float, Sequence[float]]) – Standard deviation of the gaussian kernel, anisotropic kernels are possible. Ignored if a uniform kernel is used

  • kernel_size (Union[int, Sequence[int]]) – the size of the uniform kernel, anisotropic kernels are possible. Ignored if a Gaussian kernel is used

  • 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 SSIM.

  • k2 (float) – Parameter of SSIM.

  • return_full_image (bool) – If true, the full ssim image is returned as a second argument. Mutually exclusive with return_contrast_sensitivity

  • return_contrast_sensitivity (bool) – If true, the constant term is returned as a second argument. The luminance term can be obtained with luminance=ssim/contrast Mutually exclusive with return_full_image

Return type

Union[Tensor, Tuple[Tensor, Tensor]]

Returns

Tensor with 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 structural_similarity_index_measure
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> structural_similarity_index_measure(preds, target)
tensor(0.9219)
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