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, ), )