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)