def load_scheduler(
cfg: Config, optimizer: Optimizer
) -> Union[torch.optim.lr_scheduler.LambdaLR, Type[DummyScheduler]]:
cls_str = get_by_dotkey(cfg, "scheduler.class")
if not cls_str:
return DummyScheduler
cls = cast(Type[torch.optim.lr_scheduler.LambdaLR], import_attr(cls_str))
params = OmegaConf.to_container(cfg.scheduler.params) or {}
return cls(optimizer, **params)
def create_optimizer(self, params: OptimizerParameters,
**kwargs) -> Optimizer:
cls = import_attr(self.optimizer_config["class"])
return cls(params, **self.optimizer_config.get("params", {}))
def create_optimizer(self, params: Iterable[Union[torch.Tensor,
Dict[str, Any]]],
**kwargs) -> Optimizer:
cls = import_attr(self.optimizer_config["class"])
return cls(params, **self.optimizer_config.get("params", {}))