text: # encoding: utf-8
  """
  @author:  liaoxingyu
  @contact: sherlockliao01@gmail.com
  """
  from bisect import bisect_right
  import torch
  
  
  # FIXME ideally this would be achieved with a CombinedLRScheduler,
  # separating MultiStepLR with WarmupLR
  # but the current LRScheduler design doesn't allow it
  
  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,
      ):
          if not list(milestones) == sorted(milestones):
              raise ValueError(
                  "Milestones should be a list of" " increasing integers. Got {}",
                  milestones,
              )
  
          if warmup_method not in ("constant", "linear"):
              raise ValueError(
                  "Only 'constant' or 'linear' warmup_method accepted"
                  "got {}".format(warmup_method)
              )
          self.milestones = milestones
          self.gamma = gamma
          self.warmup_factor = warmup_factor
          self.warmup_iters = warmup_iters
          self.warmup_method = warmup_method
          super(WarmupMultiStepLR, self).__init__(optimizer, last_epoch)
  
      def get_lr(self):
          warmup_factor = 1
          if self.last_epoch < self.warmup_iters:
              if self.warmup_method == "constant":
                  warmup_factor = self.warmup_factor
              elif self.warmup_method == "linear":
                  alpha = self.last_epoch / self.warmup_iters
                  warmup_factor = self.warmup_factor * (1 - alpha) + alpha
          return [
              base_lr
              * warmup_factor
              * self.gamma ** bisect_right(self.milestones, self.last_epoch)
              for base_lr in self.base_lrs
          ]
  