def create_lr_scheduler(optimizer, num_step: int, # every epoch has how much step epochs: int, warmup=True, warmup_epochs=1, # warmup进行多少个epoch warmup_factor=1e-3): """ :param optimizer: 优化器 :param num_step: 每个epoch迭代多少次,len(data_loader) :param epochs: 总共训练多...
MultiStepLR学习率调整:milestones=[20, 50, 100], gamma=0.1 (init_lr=0.01) ExponentialLR[2] torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma, last_epoch=-1, verbose=False) 描述:按指数衰减调整学习率,调整公式:lr = lr*gamma**epoch。 参数: gamma (float):学习率调整倍数。 last_epoch (...
min_lr) elif config.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': ...
1. BaseClass _LRScheduler class_LRScheduler(object):def__init__(self,optimizer,last_epoch=-1):#初始化defstate_dict(self):#返回self.__dict__defload_state_dict(self,state_dict):#加载self.__dict__,当模型需要resume的时候加载参数defget_lr(self):#接口,获得LRdefstep(self,epoch=None):#执行...
1.1 lr_scheduler综述 torch.optim.lr_scheduler模块提供了一些根据epoch训练次数来调整学习率(learning rate)的方法。一般情况下我们会设置随着epoch的增大而逐渐减小学习率从而达到更好的训练效果。 而torch.optim.lr_scheduler.ReduceLROnPlateau则提供了基于训练中某些测量值使学习率动态下降的方法。
21 warmup_iters = min(1000, len(data_loader) - 1) 22 ---> 23 lr_scheduler = torch.optim.lr_scheduler.LinearLR( 24 optimizer, start_factor=warmup_factor, total_iters=warmup_iters 25 ) AttributeError: module 'torch.optim.lr_scheduler' has no attribute 'LinearLR'...
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...
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...
<trackeback: AttributeError: Can't pickle local object 'get_cosine_schedule_with_warmup.<locals>.lr_lambda'>解答已经解决了,model.schedulers = None就可以保存了标签AttributeErrorTransformer 评论 已经解决了,model.schedulers = None就可以保存了...
lr_scheduler.py class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler): def __init__(self, optimizer, milestones, gamma=0.1, warmup_factor=1.0 / 3, warmup_iters=500, warmup_method="linear", last_epoch=-1): super(WarmupMultiStepLR, self).__init__(optimizer, last_epoch) if...