当warmup_by_epoch=True时,warmup_t代表warmup_epochs, # 反之代表warmup_iters。 self.warmup_t = warmup_t # 表示warmup是by epoch还是by iter self.warmup_by_epoch = warmup_by_epoch # 取值为fix、auto、factor self.warmup_mode = warmup_mode # fix模式的warmup初始学习率 self.warmup_init...
warmup 余弦学习率 defcosine_scheduler(self,max_lr,min_lr,epochs,niter_per_ep,warmup_epochs=5,start_warmup_value=0,warmup_steps=-1,times=2):warmup_schedule=np.array([])warmup_iters=warmup_epochs*niter_per_epifwarmup_steps>0:warmup_iters=warmup_stepsprint("Set warmup steps =%d"%w...
iter (int): iteration at which to calculate the warmup factor. warmup_iters (int): the number of warmup iterations. 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. ""...
YOLOv3-SPP代码: def warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor):def f(x):"""根据step数返回一个学习率倍率因子"""if x >= warmup_iters: # 当迭代数大于给定的warmup_iters时,倍率因子为1return 1alpha = float(x) / warmup_iters# 迭代过程中倍率因子从warmup_factor -> 1...
warmup_iters isthe number of iterations for warmup in the initial training stage. self. warmup_factors are a constant (0.333 in this case). Only when current iteration number is below self. ... So as current iteration approaches warmup_iters , warmup_factor will gradually approach 1. ...
plt.xlabel("Iters") plt.ylabel("lr") plt.show() if __name__ == "__main__": Warmup_poly() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. ...
total_training_iters-warmup_iters=20-20=0, then ZeroDivisionError occurred. Traceback (most recent call last): File "LLMs-from-scratch/ch05/05_bonus_hparam_tuning/hparam_search.py", line 191, in <module> train_loss, val_loss = train_model( ^^^ File "/mnt/raid1/docker/ai/LLMs...
def warmup_iters(self, devices: int, max_iters: int, train_dataloader) -> int: """Number of iterations to warm up the learning rate.""" if self.lr_warmup_fraction: return min(max_iters, math.ceil(self.lr_warmup_fraction * len(train_dataloader)))...
plt.figure()# iters = 200lr_history = []forepochinrange(max_epoch):forstepinrange(steps_per_epoch): optimizer.step() current_lr = optimizer.param_groups[0]['lr']ifisinstance(scheduler, timm_BaseScheduler): scheduler.step(epoch)else: ...
iters=200 cur_lr_list = [] for epoch in range(max_epoch): for batch in range(iters): ''' 这里scheduler.step(epoch + batch / iters)的理解如下,如果是一个epoch结束后再.step 那么一个epoch内所有batch使用的都是同一个学习率,为了使得不同batch也使用不同的学习率 ...