a db@s2ddlZddlmZejejeejdddZdS)N)mse_loss)inputtargetmax_valreturnc Cst|tjs tdt|dt|tjs@tdt|d|j|jkrdtd|jd|jdt|dt||ddS) 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: input: the input image with arbitrary shape :math:`(*)`. labels: 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)Z reduction) isinstancetorchTensor TypeErrortypeshapelog10mse)rrrr\/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/metrics/psnr.pypsnrs#   r)r Ztorch.nn.functionalrrr floatrrrrrs