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()
def main(args):
cfg = setup(args)
if args.eval_only:
cfg.defrost()
cfg.MODEL.BACKBONE.PRETRAIN = False
model = DefaultTrainer.build_model(cfg)
Checkpointer(model).load(cfg.MODEL.WEIGHTS) # load trained model
res = DefaultTrainer.test(cfg, model)
return res
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=args.resume)
return trainer.train()
def main(args):
cfg = setup(args)
if args.eval_only:
model = DefaultTrainer.build_model(cfg)
Checkpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume)
res = DefaultTrainer.test(cfg, model)
return res
trainer = DefaultTrainer(cfg)
# moco pretrain
# import torch
# state_dict = torch.load('logs/model_0109999.pth')['model_ema']
# ret = trainer.model.module.load_state_dict(state_dict, strict=False)
#
trainer.resume_or_load(resume=args.resume)
return trainer.train()