a d} @sddlmZddlZddlmZddlmZmZddlm Z ej ee ej dddZ dej ej e e e eej d d d ZGdddejZdS))ListN)filter2dget_gaussian_kernel2d)_compute_padding)imgcropping_shapereturncCs0tjj||d |d |d |d fS)z.Crop out the part of "valid" convolution area.r)torchnnZ functionalpad)rrr\/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/metrics/ssim.py_crop s$r?-q=same)img1img2 window_sizemax_valepspaddingrcCst|tjstdt|t|tjs>> input1 = torch.rand(1, 4, 5, 5) >>> input2 = torch.rand(1, 4, 5, 5) >>> ssim_map = ssim(input1, input2, 5) # 1x4x5x5 z+Input img1 type is not a torch.Tensor. Got z+Input img2 type is not a torch.Tensor. Got z'Input max_val type is not a float. Got z,Invalid img1 shape, we expect BxCxHxW. Got: z,Invalid img2 shape, we expect BxCxHxW. Got: z,img1 and img2 shapes must be the same. Got: z and )?rrg{Gz?r gQ?ZvalidNrg@) isinstancer Tensor TypeErrortypefloatlenshape ValueErrorrZ unsqueezerrr)rrrrrrkernelZC1ZC2Zmu1Zmu2rheightwidthZmu1_sqZmu2_sqZmu1_mu2Z mu_img1_sqZ mu_img2_sqZ mu_img1_img2Z sigma1_sqZ sigma2_sqZsigma12numZdenrrrssimsR*                  r*csHeZdZdZd eeeeddfdd Zej ej ej d d d Z Z S) SSIMa&Create a module that computes the Structural Similarity (SSIM) index between two images. Measures the (SSIM) index between each element in the input `x` and target `y`. The index can be described as: .. math:: \text{SSIM}(x, y) = \frac{(2\mu_x\mu_y+c_1)(2\sigma_{xy}+c_2)} {(\mu_x^2+\mu_y^2+c_1)(\sigma_x^2+\sigma_y^2+c_2)} where: - :math:`c_1=(k_1 L)^2` and :math:`c_2=(k_2 L)^2` are two variables to stabilize the division with weak denominator. - :math:`L` is the dynamic range of the pixel-values (typically this is :math:`2^{\#\text{bits per pixel}}-1`). Args: window_size: the size of the gaussian kernel to smooth the images. max_val: the dynamic range of the images. eps: Small value for numerically stability when dividing. padding: ``'same'`` | ``'valid'``. Whether to only use the "valid" convolution area to compute SSIM to match the MATLAB implementation of original SSIM paper. Shape: - Input: :math:`(B, C, H, W)`. - Target :math:`(B, C, H, W)`. - Output: :math:`(B, C, H, W)`. Examples: >>> input1 = torch.rand(1, 4, 5, 5) >>> input2 = torch.rand(1, 4, 5, 5) >>> ssim = SSIM(5) >>> ssim_map = ssim(input1, input2) # 1x4x5x5 rrrN)rrrrrcs&t||_||_||_||_dSN)super__init__rrrr)selfrrrr __class__rrr.s  z SSIM.__init__)rrrcCst|||j|j|j|jSr,)r*rrrr)r/rrrrrforwardsz SSIM.forward)rrr) __name__ __module__ __qualname____doc__intr"strr.r rr2 __classcell__rrr0rr+|s$r+)rrr)typingrr Ztorch.nnr Zkornia.filtersrrZkornia.filters.filterrrr7rr"r8r*Moduler+rrrrs$     k