这样,我们就可以根据不同的学习率计算方式设计自己的scheduler类了。warmup初始训练阶段,直接使用较大学习率会导致权重变化较大,出现振荡现象,使得模型不稳定,加大训练难度。而使用Warmup预热学习率,在开始的几个epoch,逐步增大学习率,如下图所示,使得模型逐渐趋于稳定,等模型相对稳定后再选择预先设置的基础学习率进行...
对于超过warmup范围,直接使用CosineAnnealingLR类,比较简单 对于warmup范围类,使用当前重构类的step()函数,因为也是继承于**_LRScheduler**类,所以step()同样是运用到get_lr() def get_lr(self): if self.last_epoch > self.warmup_epoch: # 超过warmup范围,使用CosineAnnealingLR类的get_lr() return self....
lr_scheduler == 'multistep': if config.steps is None: return None if isinstance(config.steps, int): config.steps = [config.steps] scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=config.steps, gamma=config.gamma) elif config.lr_scheduler == 'exp-warmup': lr_lambda = exp_warm...
schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name] or DIFFUSERS_TYPE_TO_SCHEDULER_FUNCTION[name] if name == SchedulerType.CONSTANT: return wrap_check_needless_num_warmup_steps(schedule_func(optimizer, **lr_scheduler_kwargs)) if name == SchedulerType.PIECEWISE_CONSTANT: if name == DiffusersSchedu...
(unless I have overlooked something): Thelr_scheduler_type="cosine_with_restarts"that I pass to theTrainingArgumentsis used to callget_scheduler()inoptimization.py. There it's mapped toget_cosine_with_hard_restarts_schedule_with_warmup(), but without anum_cyclesargument, defaulting to1, i....
# 需要导入模块: from torch.optim import lr_scheduler [as 别名]# 或者: from torch.optim.lr_scheduler importLambdaLR[as 别名]defget_cosine_with_hard_restarts_schedule_with_warmup( optimizer, num_warmup_steps, num_training_steps, num_cycles=1.0, last_epoch=-1):""" Create a schedule with...
if type(self.after_scheduler) != ReduceLROnPlateau: if self.finished and self.after_scheduler: return self.after_scheduler.step(epoch) else: return super(GradualWarmupScheduler, self).step(epoch) else: self.step_ReduceLROnPlateau(metrics, epoch) class WarmupMultiStepLR(torch.o...
$ pip install git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git Usage from warmup_scheduler import GradualWarmupScheduler scheduler_cosine = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, max_epoch) scheduler_warmup = GradualWarmupScheduler(optimizer, multiplier=8, total_epoch...
super(CosineAnnealingWarmRestarts, self).__init__(optimizer, last_epoch, verbose) self.T_cur = self.last_epoch def get_lr(self): if not self._get_lr_called_within_step: warnings.warn("To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.",...
class _LRScheduler(object): def __init__(self, optimizer, last_epoch=-1, verbose=False): # Attach optimizer if not isinstance(optimizer, Optimizer): raise TypeError('{} is not an Optimizer'.format( type(optimizer).__name__)) self.optimizer = optimizer # Initialize epoch and base learnin...