3jV dSSKJr SSKrSSKJr SSKJr SSjr"SS\R5rg) ) annotationsN)nn)metricsc6S[R"XU5-$)aCompute the PSNR loss. The loss is computed as follows: .. math:: \text{loss} = -\text{psnr(x, y)} See :meth:`~kornia.losses.psnr` for details abut PSNR. Args: image: the input image with shape :math:`(*)`. target : the labels image with shape :math:`(*)`. max_val: The maximum value in the image tensor. Return: the computed loss as a scalar. Examples: >>> ones = torch.ones(1) >>> psnr_loss(ones, 1.2 * ones, 2.) # 10 * log(4/((1.2-1)**2)) / log(10) tensor(-20.0000) g)rpsnr)imagetargetmax_vals L/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/losses/psnr.py psnr_lossr s2 ',,ug6 66c:^\rSrSrSrSU4SjjrSSjrSrU=r$)PSNRLoss6aDCreate a criterion that calculates the PSNR loss. The loss is computed as follows: .. math:: \text{loss} = -\text{psnr(x, y)} See :meth:`~kornia.losses.psnr` for details abut PSNR. Args: max_val: The maximum value in the image tensor. Shape: - Image: arbitrary dimensional tensor :math:`(*)`. - Target: arbitrary dimensional tensor :math:`(*)` same shape as image. - Output: a scalar. Examples: >>> ones = torch.ones(1) >>> criterion = PSNRLoss(2.) >>> criterion(ones, 1.2 * ones) # 10 * log(4/((1.2-1)**2)) / log(10) tensor(-20.0000) c.>[TU]5 XlgN)super__init__r )selfr __class__s r rPSNRLoss.__init__Qs % r c.[XUR5$r)r r )rrr s r forwardPSNRLoss.forwardUs 55r )r )r floatreturnNone)r torch.Tensorr rrr) __name__ __module__ __qualname____firstlineno____doc__rr__static_attributes__ __classcell__)rs@r rr6s4&66r r)rrr rr rrr) __future__rtorchrkorniarr Modulerr r r+s($# 78 6ryy 6r