]), evaluation_data: transforms.Compose([ transforms.Scale(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), } use_gpu = torch.cuda.is_available() torch.manual_seed(args.seed) if args.cuda: torch.cuda.manual_seed(args.seed) train_loader, test_loader = get_loader(source_data, target_data, evaluation_data, data_transforms, batch_size=args.batch_size) dataset_train = train_loader.load_data() dataset_test = test_loader if args.dataset == 'VISDA': num_class = 7 class_list = [ "bicycle", "bus", "car", "motorcycle", "train", "truck", "unk" ] elif args.dataset in ['UCM', 'AID']: num_class = 6 class_list = ["baseballdiamond", "beach", "mediumresidential", "parkinglot", \ "sparseresidential", "unkn"] else:
transforms.RandomCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), evaluation_data: transforms.Compose([ transforms.Scale((256, 256)), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), } use_gpu = torch.cuda.is_available() source_loader, target_loader, \ test_loader, target_folder = get_loader(source_data, target_data, evaluation_data, data_transforms, batch_size=batch_size, return_id=True, balanced=conf.data.dataloader.class_balance) dataset_test = test_loader n_share = conf.data.dataset.n_share n_source_private = conf.data.dataset.n_source_private num_class = n_share + n_source_private G, C1 = get_model_mme(conf.model.base_model, num_class=num_class, temp=conf.model.temp) device = torch.device("cuda") if args.cuda: G.cuda() C1.cuda() G.to(device) C1.to(device) ndata = target_folder.__len__()