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import torch.optim as optim
from pydantic import BaseModel
from typing import List, Optional
class BaseScheduler:
"""
Abstract base class for learning rate schedulers.
Wraps a PyTorch LR scheduler and provides a unified interface.
"""
def __init__(self, optimizer: optim.Optimizer, params: BaseModel):
self.scheduler: Optional[optim.lr_scheduler.LRScheduler] = None
def step(self) -> None:
"""
Performs a single scheduler step. This typically updates the learning rate
based on the current epoch or step count.
"""
if self.scheduler is not None:
self.scheduler.step()
def get_last_lr(self) -> List[float]:
"""
Returns the most recent learning rate(s).
"""
return self.scheduler.get_last_lr() if self.scheduler else []