# encoding: utf-8 """ @author: liaoxingyu @contact: sherlockliao01@gmail.com """ from typing import List import torch from torch.optim.lr_scheduler import * class WarmupLR(torch.optim.lr_scheduler._LRScheduler): def __init__( self, optimizer: torch.optim.Optimizer, warmup_factor: float = 0.1, warmup_iters: int = 1000, warmup_method: str = "linear", last_epoch: int = -1, ): self.warmup_factor = warmup_factor self.warmup_iters = warmup_iters self.warmup_method = warmup_method super().__init__(optimizer, last_epoch) def get_lr(self) -> List[float]: warmup_factor = _get_warmup_factor_at_epoch( self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor ) return [ base_lr * warmup_factor for base_lr in self.base_lrs ] def _compute_values(self) -> List[float]: # The new interface return self.get_lr() def _get_warmup_factor_at_epoch( method: str, iter: int, warmup_iters: int, warmup_factor: float ) -> float: """ Return the learning rate warmup factor at a specific iteration. See https://arxiv.org/abs/1706.02677 for more details. Args: method (str): warmup method; either "constant" or "linear". iter (int): iter at which to calculate the warmup factor. warmup_iters (int): the number of warmup epochs. warmup_factor (float): the base warmup factor (the meaning changes according to the method used). Returns: float: the effective warmup factor at the given iteration. """ if iter >= warmup_iters: return 1.0 if method == "constant": return warmup_factor elif method == "linear": alpha = iter / warmup_iters return warmup_factor * (1 - alpha) + alpha elif method == "exp": return warmup_factor ** (1 - iter / warmup_iters) else: raise ValueError("Unknown warmup method: {}".format(method))