elif args.net == "mobilenet": G = torchvision.models.mobilenet_v2(pretrained=True) inc = 1280 G.classifier = torch.nn.Identity() else: raise ValueError('Model cannot be recognized.') if "resnet" in args.net or "densenet" in args.net or "mobilenet" in args.net: F1 = Predictor_deep(num_class=len(class_list), inc=inc) else: F1 = Predictor(num_class=len(class_list), inc=inc, temp=args.T) device = torch.device("cuda:" + args.device) G = G.to(device) F1 = F1.to(device) G.load_state_dict( torch.load( os.path.join( args.checkpath, "G_iter_model_{}_{}_{}_to_{}_num_{}.pth.tar".format( args.method, args.net, args.source, args.target, args.num)))) F1.load_state_dict( torch.load( os.path.join( args.checkpath, "F1_iter_model_{}_{}_{}_to_{}_num_{}.pth.tar".format( args.method, args.net, args.source, args.target, args.num)))) im_data_t = torch.FloatTensor(1)
params += [{ 'params': [value], 'lr': args.multi * 10, 'weight_decay': 0.0005 }] if "resnet" in args.net or "densenet" in args.net or "mobilenet" in args.net or "inception" in args.net: F1 = Predictor_deep(num_class=len(class_list), inc=inc) else: F1 = Predictor(num_class=len(class_list), inc=inc, temp=args.T) weights_init(F1) lr = args.lr device = torch.device("cuda:" + args.device) G.to(device) F1.to(device) im_data_s = torch.FloatTensor(1) im_data_t = torch.FloatTensor(1) im_data_tu = torch.FloatTensor(1) gt_labels_s = torch.LongTensor(1) gt_labels_t = torch.LongTensor(1) sample_labels_t = torch.LongTensor(1) sample_labels_s = torch.LongTensor(1) im_data_s = im_data_s.to(device) im_data_t = im_data_t.to(device) im_data_tu = im_data_tu.to(device) gt_labels_s = gt_labels_s.to(device) gt_labels_t = gt_labels_t.to(device) sample_labels_t = sample_labels_t.to(device)