def get_criterion():
if hasattr(nn, CFG.loss_name):
return nn.__getattribute__(CFG.loss_name)(**CFG.loss_params)
elif __CRITERIONS__.get(CFG.loss_name) is not None:
return __CRITERIONS__[CFG.loss_name](**CFG.loss_params)
else:
raise NotImplementedError
def get_criterion(config: dict):
loss_config = config["loss"]
loss_name = loss_config["name"]
loss_params = {} if loss_config.get("params") is None else loss_config.get(
"params")
if hasattr(nn, loss_name):
criterion = nn.__getattribute__(loss_name)(**loss_params)
else:
criterion_cls = globals().get(loss_name)
if criterion_cls is not None:
criterion = criterion_cls(**loss_params)
else:
raise NotImplementedError
return criterion
def get_criterion(config: dict):
loss_config = config["loss"]
loss_name = loss_config["name"]
loss_params = loss_config["params"]
if (loss_params is None) or (loss_params == ""):
loss_params = {}
if hasattr(nn, loss_name):
criterion_ = nn.__getattribute__(loss_name)(**loss_params)
else:
criterion_cls = criterion.__getattribute__(loss_name)
if criterion_cls is not None:
criterion_ = criterion_cls(**loss_params)
else:
raise NotImplementedError
return criterion_
def get_criterion(config: dict):
loss_config = config["loss"]
loss_name = loss_config["name"]
return nn.__getattribute__(loss_name)()
