def main(args):
cfg = setup(args)
if args.eval_only:
model = DefaultTrainer.build_model(cfg)
Checkpointer(model, save_dir=cfg.OUTPUT_DIR).load(cfg.MODEL.WEIGHTS)
res = DefaultTrainer.test(cfg, model)
return res
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=args.resume)
return trainer.train()
if __name__ == "__main__":
parser = default_argument_parser()
args = parser.parse_args()
print("Command Line Args:", args)
launch(
main,
args.num_gpus,
num_machines=args.num_machines,
machine_rank=args.machine_rank,
dist_url=args.dist_url,
args=(args, ),
)
model = DefaultTrainer.build_model(cfg)
Checkpointer(model).load(cfg.MODEL.WEIGHTS) # load trained model
res = DefaultTrainer.test(cfg, model)
return res
if "CenterLoss" in cfg.MODEL.LOSSES.NAME:
trainer = CenterTrainer(cfg)
else:
trainer = DefaultTrainer(cfg)
if args.finetune:
Checkpointer(trainer.model).load(
cfg.MODEL.WEIGHTS) # load trained model to funetune
trainer.resume_or_load(resume=args.resume)
return trainer.train()
if __name__ == "__main__":
args = default_argument_parser().parse_args()
print("Command Line Args:", args)
launch(
main,
args.num_gpus,
num_machines=args.num_machines,
machine_rank=args.machine_rank,
dist_url=args.dist_url,
args=(args, ),
)
