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
if args.kd: trainer = KDTrainer(cfg)
else: trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=args.resume)
return trainer.train()
def auto_scale_hyperparams(cfg, num_classes):
cfg = DefaultTrainer.auto_scale_hyperparams(cfg, num_classes)
# Save index to class dictionary
output_dir = cfg.OUTPUT_DIR
if comm.is_main_process() and output_dir:
path = os.path.join(output_dir, "idx2class.json")
with PathManager.open(path, "w") as f:
json.dump(ClasTrainer.idx2class, f)
return cfg
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
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()
def build_model(cls, cfg):
"""
Returns:
torch.nn.Module:
It now calls :func:`fastreid.modeling.build_model`.
Overwrite it if you'd like a different model.
"""
model = DefaultTrainer.build_model(cfg)
if cfg.MODEL.LOSSES.BCE.WEIGHT_ENABLED and \
AttrTrainer.sample_weights is not None:
setattr(model, "sample_weights",
AttrTrainer.sample_weights.to(model.device))
else:
setattr(model, "sample_weights", None)
return model
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 __init__(self, model_config, model_path, use_cuda=True):
cfg = get_cfg()
cfg.merge_from_file(model_config)
cfg.MODEL.BACKBONE.PRETRAIN = False
self.net = DefaultTrainer.build_model(cfg)
self.device = "cuda" if torch.cuda.is_available(
) and use_cuda else "cpu"
Checkpointer(self.net).load(model_path)
logger = logging.getLogger("root.tracker")
logger.info("Loading weights from {}... Done!".format(model_path))
self.net.to(self.device)
self.net.eval()
height, width = cfg.INPUT.SIZE_TEST
self.size = (width, height)
self.norm = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
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()