LinearLR是线性学习率,给定起始factor和最终的factor,LinearLR会在中间阶段做线性插值。 比如学习率为0.1,起始factor为1,最终的factor为0.2,那么第0次迭代,学习率将为0.1,最终轮学习率为0.02。 lr_scheduler = lr_scheduler.LinearLR(optimizer, start_factor=1, end_factor=0.2, total_iters=80) 训练100轮,但to...
lr=1.0)scheduler1=WarmupCosineLR(optimizer1,total_num_steps=20,warmup_num_steps=10,warmup_type="linear")optimizer2=torch.optim.Adam(model.parameters(),lr=1.0)scheduler
25 ) AttributeError: module 'torch.optim.lr_scheduler' has no attribute 'LinearLR'
parameters(), 0.05) scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1) #scheduler = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.5, total_iters=4) old_lr = None for epoch in range(100): lr = scheduler.get_last_lr()[0] if lr != old_lr...
per_device_train_batch_size 8 \ # --gradient_accumulation_steps 1 \ # --include_tokens_per_second \ # --gradient_checkpointing \ # --lr_scheduler_type "linear" \ # --learning_rate 1e-6 \ # --logging_steps 1 \ # --packing True \ # --dataset_for_packing <path> \ # --...
power =1.0ifscheduler_name =='linear'elseoptimizer_conf.decay_power lr_lambda = _get_polynomial_decay(optimizer_conf.learning_rate, optimizer_conf.end_learning_rate, optimizer_conf.decay_steps, optimizer_conf.get_attr('start_decay', default=0), ...
lr_policy == 'linear': def lambda_rule(epoch): lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1) return lr_l scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) elif opt.lr_policy == 'step': scheduler = lr_scheduler.Step...
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, ): if not list(milestones) == sorted(milestones): raise ValueError( "Milestones should b...
FunctionType): state_dict['lr_lambdas'][idx] = fn.__dict__.copy() return state_dict def load_state_dict(self, state_dict): """Loads the schedulers state.When saving or loading the scheduler, please make sure to also save or load the state of the optimizer....
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...