def main():
args = parse_args()
exp = Experiment(args.exp_name, args, mode=args.mode)
if args.cfg is None:
cfg_path = exp.cfg_path
else:
cfg_path = args.cfg
cfg = Config(cfg_path)
exp.set_cfg(cfg, override=False)
device = torch.device('cpu') if not torch.cuda.is_available() or args.cpu else torch.device('cuda')
runner = Runner(cfg, exp, device, view=args.view, resume=args.resume, deterministic=args.deterministic)
if args.mode == 'train':
try:
runner.train()
except KeyboardInterrupt:
logging.info('Training interrupted.')
runner.eval(epoch=args.epoch or exp.get_last_checkpoint_epoch(), save_predictions=args.save_predictions)
def main() -> None:
args = parse_args()
exp = Experiment(args.exp_name, args, mode=args.mode)
if args.cfg is None:
cfg_path = exp.cfg_path
else:
cfg_path = args.cfg
cfg = Config(cfg_path)
exp.set_cfg(cfg, override=False)
device = (
torch.device("cpu") if not torch.cuda.is_available() else torch.device("cuda")
)
runner = Runner(cfg, exp, device, resume=args.resume)
if args.mode == "train":
try:
runner.train()
except KeyboardInterrupt:
logging.info("Training interrupted.")