SSIM#

class deepinv.loss.metric.SSIM(multiscale=False, max_pixel=1.0, min_pixel=0.0, torchmetric_kwargs=None, **kwargs)[source]#

Bases: Metric

Structural Similarity Index (SSIM) metric using torchmetrics.

Calculates \(\text{SSIM}(\hat{x},x)\) where \(\hat{x}=\inverse{y}\). See https://en.wikipedia.org/wiki/Structural_similarity for more information.

To set the max pixel on the fly (as is the case in fastMRI evaluation code), set max_pixel=None.

Note

By default, no reduction is performed in the batch dimension.

Example:

>>> import torch
>>> from deepinv.loss.metric import SSIM
>>> m = SSIM()
>>> x_net = x = torch.ones(3, 2, 32, 32) # B,C,H,W
>>> m(x_net, x)
tensor([1., 1., 1.])
Parameters:

  • multiscale (bool) – if True, computes the multiscale SSIM. Default: False.

  • max_pixel (float) – maximum pixel value. If None, uses max pixel value of the ground truth image x.

  • min_pixel (float) – minimum pixel value. If None, uses min pixel value of the ground truth image x.

  • torchmetric_kwargs (dict) – kwargs for torchmetrics SSIM as dict. See https://lightning.ai/docs/torchmetrics/stable/image/structural_similarity.html

  • complex_abs (bool) – perform complex magnitude before passing data to metric function. If True, the data must either be of complex dtype or have size 2 in the channel dimension (usually the second dimension after batch).

  • train_loss (bool) – use metric as a training loss, by returning one minus the metric.

  • reduction (str) – a method to reduce metric score over individual batch scores. mean: takes the mean, sum takes the sum, none or None no reduction will be applied (default).

  • norm_inputs (str) – normalize images before passing to metric. l2 normalizes by \(\ell_2\) spatial norm, min_max normalizes by min and max of each input.

  • center_crop (int, tuple[int], None) – If not None (default), center crop the tensor(s) before computing the metrics. If an int is provided, the cropping is applied equally on all spatial dimensions (by default, all dimensions except the first two). If tuple of int, cropping is performed over the last len(center_crop) dimensions. If positive values are provided, a standard center crop is applied. If negative (or zero) values are passed, cropping will be done by removing center_crop pixels from the borders (useful when tensors vary in size across the dataset).

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