Let me explain with an example. I'm using the cosine LR scheduler and my script uses a warm up LR (1e-5), number of warm up epochs (20), base LR (1e-3), min LR (1e-5) and total epochs (300). For this let's assum
There it's mapped to get_cosine_with_hard_restarts_schedule_with_warmup(), but without a num_cycles argument, defaulting to 1, i.e. it behaves like the cosine option. Probably I could build the scheduler myself and pass it to the Trainer, but then I need to calculate the num_...
对于第二种情况,step()无需构造,直接继承_LRScheduler,需要构造的是get_lr()函数,其中warmup范围外的代码与自带的CosineAnnealingLR类中get_lr()代码一样。
warmup step 上图中的0-10epoch阶段就是一个warmup操作,学习率缓慢增加,10之后就是常规的学习率递减算法原理上很简单,接下来从代码上进行分析,warmup可以有两种构成方式:对已有的scheduler类进行包装重构 直接编写新的类 对于第一种情况,我们以CosineAnnealingLR类为例...
warmup_steps=0, hold_base_rate_steps=0): """Cosine decay schedule with warm up period. Cosine annealing learning rate as described in: Loshchilov and Hutter, SGDR: Stochastic Gradient Descent with Warm Restarts. ICLR 2017. https://arxiv.org/abs/1608.03983 ...
torch.optim.lr_scheduler.CosineAnnealingWarmRestarts 跟余弦退火类似,只是在学习率上升时使用热启动。这个 Scheduler 在各种比赛中也经常用到。文档链接 T_0: 20, T_mult: 1, eta_min: 0.001 torch.optim.lr_scheduler.CyclicLR 学习率在两个边界之间以固定频率变化,该 Scheduler 的 step 函数应该在每个 iterat...
get_cosine_with_hard_restarts_schedule_with_warmup Most of the time I find warmup useful so I basically sticked them here as well. They performed similar in the same tasks. But theget_cosine_with_hard_restarts_schedule_with_warmupconverged a little bit faster compared to other two. And wh...
"will result in PyTorch skipping the first value of the learning rate schedule. " "See more details at " "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning) self._step_count += 1 class _enable_get_lr_call: ...
offset=0, power=0.9, step_iter=None, step_epoch=None, step_factor=0.1, warmup_epochs=0):super(LRScheduler, self).__init__()assert(modein['constant','step','linear','poly','cosine'])ifmode =='step':assert(step_iterisnotNoneorstep_epochisnotNone) ...
warmup_factor, warmup_iters, warmup_method, **kwargs, ): cosine_annealing_iters = max_iters - delay_iters base_scheduler = CosineAnnealingLR(optimizer, cosine_annealing_iters, eta_min_lr) return DelayedScheduler(optimizer, delay_iters, base_scheduler, warmup_factor, warmup_iters, warmup_...