content: from mmcv.runner import HOOKS, LrUpdaterHook
  from mmcv.runner.hooks.lr_updater import annealing_cos
  
  
  @HOOKS.register_module()
  class TINLrUpdaterHook(LrUpdaterHook):
  
      def __init__(self, min_lr, **kwargs):
          self.min_lr = min_lr
          super(TINLrUpdaterHook, self).__init__(**kwargs)
  
      def get_warmup_lr(self, cur_iters):
          if self.warmup == 'linear':
              # 'linear' warmup is rewritten according to TIN repo:
              # https://github.com/deepcs233/TIN/blob/master/main.py#L409-L412
              k = (cur_iters / self.warmup_iters) * (
                  1 - self.warmup_ratio) + self.warmup_ratio
              warmup_lr = [_lr * k for _lr in self.regular_lr]
          elif self.warmup == 'constant':
              warmup_lr = [_lr * self.warmup_ratio for _lr in self.regular_lr]
          elif self.warmup == 'exp':
              k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters)
              warmup_lr = [_lr * k for _lr in self.regular_lr]
          return warmup_lr
  
      def get_lr(self, runner, base_lr):
          if self.by_epoch:
              progress = runner.epoch
              max_progress = runner.max_epochs
          else:
              progress = runner.iter
              max_progress = runner.max_iters
  
          target_lr = self.min_lr
          if self.warmup is not None:
              progress = progress - self.warmup_iters
              max_progress = max_progress - self.warmup_iters
          factor = progress / max_progress
          return annealing_cos(base_lr, target_lr, factor)
  