UOMOP

import torchimport torch.nn as nnimport torchvision.transforms as transformsimport torchvision.datasets as datasetsfrom torch.utils.data import DataLoaderimport mathfrom params import *import torchvisionimport timedevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad)def psnr(img1..

import torchimport torch.nn as nnimport torchvision.transforms as transformsimport torchvision.datasets as datasetsfrom torch.utils.data import DataLoaderimport mathfrom params import *import torchvisionimport timedevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad)def psnr(img1..