transforms.Resize(args.resize)) if args.train: train_set = ImageDataset(args.train_manifest, train_transforms) train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) eval_set = ImageDataset(args.val_manifest, test_transforms) eval_loader = DataLoader(eval_set, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers) logger.add_general_data(model, train_loader) train(model, criterion, optimizer, train_loader, eval_loader, args.epochs, lr_scheduler, args.gpu) print_training_summary(logger, model.name) else: training_ = torch.load(args.model_path) model.load_state_dict(training_["model"]) print( f"Loaded model {model.name} trained for {training_['epoch']} epochs. Results: {training_['result']}" ) test_set = ImageDataset(args.test_manifest, test_transforms) test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=4)