F1 = Predictor_deep_attributes(num_class=len(class_list),inc=inc) else: F1 = Predictor_attributes(num_class=len(class_list), inc=inc, temp=args.T) """ # Loading the model weights from the checkpoint filename = "save_model_ssda/ours.ckpt.best.pth.tar" main_dict = torch.load(filename) args.step = main_dict['step'] print("Inferencing is being done with model at step: ", args.step) print("best accuracy, ", main_dict['best_acc_test']) print(filename) G.cuda() F1.cuda() G.load_state_dict(main_dict['G_state_dict']) F1.load_state_dict(main_dict['F1_state_dict']) im_data_t = torch.FloatTensor(1) gt_labels_t = torch.LongTensor(1) im_data_t = im_data_t.cuda() gt_labels_t = gt_labels_t.cuda() im_data_t = Variable(im_data_t) gt_labels_t = Variable(gt_labels_t) if os.path.exists(args.checkpath) == False: os.mkdir(args.checkpath) """ def eval(loader, output_file="output.txt"): G.eval()
if "resnet" in args.net: F1 = Predictor_deep(num_class=len(class_list), inc=inc) else: F1 = Predictor(num_class=len(class_list), inc=inc, cosine=True, temp=args.T) G.cuda() F1.cuda() G.load_state_dict(torch.load(os.path.join(args.checkpath, "G_iter_model_{}_{}_" "to_{}_step_{}.pth.tar". format(args.method, args.source, args.target, args.step)))) F1.load_state_dict(torch.load(os.path.join(args.checkpath, "F1_iter_model_{}_{}_" "to_{}_step_{}.pth.tar". format(args.method, args.source, args.target, args.step)))) im_data_t = torch.FloatTensor(1) gt_labels_t = torch.LongTensor(1) im_data_t = im_data_t.cuda() gt_labels_t = gt_labels_t.cuda() im_data_t = Variable(im_data_t) gt_labels_t = Variable(gt_labels_t) if os.path.exists(args.checkpath) == False: os.mkdir(args.checkpath)