args = parser.parse_args() device = torch.device( 'cuda' if args.use_gpu and torch.cuda.is_available() else 'cpu') FloatTensor = torch.cuda.FloatTensor if args.use_gpu and torch.cuda.is_available( ) else torch.FloatTensor classes = utils.load_classes( args.class_path) # Extracts class labels from file # Set up model model = Yolo().to(device) if args.weights_path is not None: # Load darknet weights model.load_darknet_weights(args.weights_path) model.eval() # Set in evaluation mode # dataloader = DataLoader( # ImageFolder(args.image_folder, img_size=args.img_size), # batch_size=args.batch_size, # shuffle=False, # num_workers=args.n_cpu, # ) if not os.path.exists(args.output_path): os.makedirs(args.output_path) if not os.path.exists(args.image_folder): print('No file or directory with the name {}'.format(
device = torch.device( 'cuda' if args.use_gpu and torch.cuda.is_available() else 'cpu') if not os.path.exists(args.output_path): os.makedirs(args.output_path) # Initiate model model = Yolo(num_classes=20).to(device) # If specified we start from checkpoint if args.pretrained_weights: if args.pretrained_weights.endswith('.pth'): model.load_state_dict(torch.load(args.pretrained_weights)) else: model.load_darknet_weights(args.pretrained_weights) # Get dataloader train_dataset = VOCDetection(args.train_path, args.img_size) train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, collate_fn=train_dataset.collate_fn) val_dataset = VOCDetection(args.val_path, args.img_size) val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, collate_fn=val_dataset.collate_fn)