def __init__(self, cfg):
self.cfg = cfg.clone() # cfg can be modified by model
self.model = build_model(self.cfg)
self.model.eval()
# self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0])
checkpointer = Checkpointer(self.model)
checkpointer.load(cfg.MODEL.WEIGHTS)
def __init__(self, cfg, device='cpu'):
self.cfg = cfg.clone() # cfg can be modified by model
self.cfg.defrost()
self.cfg.MODEL.BACKBONE.PRETRAIN = False
self.device = device
self.model = build_model(self.cfg)
self.model.to(device)
self.model.eval()
checkpointer = Checkpointer(self.model)
checkpointer.load(cfg.MODEL.WEIGHTS)
def __init__(self, cfg):
self.cfg = cfg.clone() # cfg can be modified by model
model = build_model(self.cfg)
self.model = DataParallel(model)
self.model.cuda()
self.model.eval()
checkpointer = Checkpointer(self.model)
checkpointer.load(cfg.MODEL.WEIGHTS)
num_channels = len(cfg.MODEL.PIXEL_MEAN)
self.mean = torch.tensor(cfg.MODEL.PIXEL_MEAN).view(
1, num_channels, 1, 1)
self.std = torch.tensor(cfg.MODEL.PIXEL_STD).view(
1, num_channels, 1, 1)
def main(args):
cfg = setup(args)
if args.eval_only:
cfg.defrost()
cfg.MODEL.BACKBONE.PRETRAIN = args.imageNet
model = DefaultTrainer.build_model(cfg)
Checkpointer(model).load(cfg.MODEL.WEIGHTS) # load trained model
res = DefaultTrainer.test(cfg, model)
return res
trainer = DefaultTrainer(cfg)
if args.finetune:
C = Checkpointer(trainer.model)
C.load(cfg.MODEL.WEIGHTS) # load trained model to funetune
trainer.resume_or_load(resume=args.resume)
return trainer.train()
