# Logger logger.configure_logging( os.path.abspath(os.path.join(opt.experiment_dir, 'logbook.txt'))) # CUDA device = 'cuda' if torch.cuda.is_available( ) and not opt.no_cuda else 'cpu' opt.device = device if torch.cuda.is_available() and device == 'cpu': logging.info( "WARNING: You have a CUDA device, so you should probably run with --cuda" ) # Adjust scales utils.adjust_scales2image(opt.img_size, opt) # Initial parameters opt.scale_idx = 0 opt.nfc_prev = 0 opt.Noise_Amps = [] # Date dataset = SingleVideoDataset(opt) data_loader = DataLoader(dataset, shuffle=True, drop_last=True, batch_size=opt.batch_size, num_workers=2) opt.dataset = dataset
opt = parser.parse_args() if not os.path.exists(opt.out): os.makedirs(opt.out) torch.cuda.set_device(opt.gpu_id) opt.device = "cuda:%s" % opt.gpu_id opt.niter_init = opt.niter opt.noise_amp_init = opt.noise_amp_a opt.nfc_init = opt.nfc opt.min_nfc_init = opt.min_nfc opt.scale_factor_init = opt.scale_factor adjust_scales2image(opt.img_size, opt) if opt.manualSeed is None: opt.manualSeed = random.randint(1, 10000) print("Random Seed: ", opt.manualSeed) random.seed(opt.manualSeed) torch.manual_seed(opt.manualSeed) if torch.cuda.is_available() and opt.gpu_id == -1: print( "WARNING: You have a CUDA device, so you should probably run with --cuda" ) opt.print_interval = int(opt.print_interval / opt.num_images) Gs_a = [] reals_a = []