def load_extras(self):
# learning rate scheduler.
scheduler_lambda_fn = lambda x: warmup_lr_lambda(
x, self.config["optim"])
self.scheduler = optim.lr_scheduler.LambdaLR(
self.optimizer, lr_lambda=scheduler_lambda_fn)
# metrics.
self.meter = Meter(split="train")
def __init__(self, optimizer, config):
self.optimizer = optimizer
self.config = config.copy()
if "scheduler" in self.config:
self.scheduler_type = self.config["scheduler"]
else:
self.scheduler_type = "LambdaLR"
scheduler_lambda_fn = lambda x: warmup_lr_lambda(x, self.config)
self.config["lr_lambda"] = scheduler_lambda_fn
if self.scheduler_type != "Null":
self.scheduler = getattr(lr_scheduler, self.scheduler_type)
scheduler_args = self.filter_kwargs(config)
self.scheduler = self.scheduler(optimizer, **scheduler_args)