3jnSSKrSSKJr S\RS\RS\S\R4Sjrg)N)mse_lossimagetargetmax_valreturnc [U[R5(d[S[ U5S35e[U[R5(d[S[ U5S35eUR UR :wa%[SUR SUR 35eS[R "US-[XSS9- 5-$) aCreate a function that calculates the PSNR between 2 images. PSNR is Peek Signal to Noise Ratio, which is similar to mean squared error. Given an m x n image, the PSNR is: .. math:: \text{PSNR} = 10 \log_{10} \bigg(\frac{\text{MAX}_I^2}{MSE(I,T)}\bigg) where .. math:: \text{MSE}(I,T) = \frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1} [I(i,j) - T(i,j)]^2 and :math:`\text{MAX}_I` is the maximum possible input value (e.g for floating point images :math:`\text{MAX}_I=1`). Args: image: the input image with arbitrary shape :math:`(*)`. target: the labels image with arbitrary shape :math:`(*)`. max_val: The maximum value in the input tensor. Return: the computed loss as a scalar. Examples: >>> ones = torch.ones(1) >>> psnr(ones, 1.2 * ones, 2.) # 10 * log(4/((1.2-1)**2)) / log(10) tensor(20.0000) Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Definition zExpected torch.Tensor but got .z*Expected tensors of equal shapes, but got z and g$@mean) reduction) isinstancetorchTensor TypeErrortypeshapelog10mse)rrrs M/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/metrics/psnr.pypsnrrsH eU\\ * *8e QGHH fell + +8faHII {{fll"DU[[MQVW]WcWcVdeff %++gqj3u+OOP PP)rtorch.nn.functionalrrrfloatrrrrs?$ /-Q -Qell-QU-Qu||-Qr