text: import os.path as osp
  import warnings
  from math import inf
  
  import mmcv
  from mmcv.runner import Hook
  from torch.utils.data import DataLoader
  
  from mmdet.utils import get_root_logger
  
  
  class EvalHook(Hook):
      """Evaluation hook.
  
      Notes:
          If new arguments are added for EvalHook, tools/test.py,
          tools/eval_metric.py may be effected.
  
      Attributes:
          dataloader (DataLoader): A PyTorch dataloader.
          start (int, optional): Evaluation starting epoch. It enables evaluation
              before the training starts if ``start`` <= the resuming epoch.
              If None, whether to evaluate is merely decided by ``interval``.
              Default: None.
          interval (int): Evaluation interval (by epochs). Default: 1.
          save_best (str, optional): If a metric is specified, it would measure
              the best checkpoint during evaluation. The information about best
              checkpoint would be save in best.json.
              Options are the evaluation metrics to the test dataset. e.g.,
              ``bbox_mAP``, ``segm_mAP`` for bbox detection and instance
              segmentation. ``AR@100`` for proposal recall. If ``save_best`` is
              ``auto``, the first key will be used. The interval of
              ``CheckpointHook`` should device EvalHook. Default: None.
          rule (str, optional): Comparison rule for best score. If set to None,
              it will infer a reasonable rule. Keys such as 'mAP' or 'AR' will
              be inferred by 'greater' rule. Keys contain 'loss' will be inferred
               by 'less' rule. Options are 'greater', 'less'. Default: None.
          **eval_kwargs: Evaluation arguments fed into the evaluate function of
              the dataset.
      """
  
      rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y}
      init_value_map = {'greater': -inf, 'less': inf}
      greater_keys = ['mAP', 'AR']
      less_keys = ['loss']
  
      def __init__(self,
                   dataloader,
                   start=None,
                   interval=1,
                   save_best=None,
                   rule=None,
                   **eval_kwargs):
          if not isinstance(dataloader, DataLoader):
              raise TypeError('dataloader must be a pytorch DataLoader, but got'
                              f' {type(dataloader)}')
          if not interval > 0:
              raise ValueError(f'interval must be positive, but got {interval}')
          if start is not None and start < 0:
              warnings.warn(
                  f'The evaluation start epoch {start} is smaller than 0, '
                  f'use 0 instead', UserWarning)
              start = 0
          self.dataloader = dataloader
          self.interval = interval
          self.start = start
          assert isinstance(save_best, str) or save_best is None
          self.save_best = save_best
          self.eval_kwargs = eval_kwargs
          self.initial_epoch_flag = True
  
          self.logger = get_root_logger()
  
          if self.save_best is not None:
              self._init_rule(rule, self.save_best)
  
      def _init_rule(self, rule, key_indicator):
          """Initialize rule, key_indicator, comparison_func, and best score.
  
          Args:
              rule (str | None): Comparison rule for best score.
              key_indicator (str | None): Key indicator to determine the
                  comparison rule.
          """
          if rule not in self.rule_map and rule is not None:
              raise KeyError(f'rule must be greater, less or None, '
                             f'but got {rule}.')
  
          if rule is None:
              if key_indicator != 'auto':
                  if any(key in key_indicator for key in self.greater_keys):
                      rule = 'greater'
                  elif any(key in key_indicator for key in self.less_keys):
                      rule = 'less'
                  else:
                      raise ValueError(f'Cannot infer the rule for key '
                                       f'{key_indicator}, thus a specific rule '
                                       f'must be specified.')
          self.rule = rule
          self.key_indicator = key_indicator
          if self.rule is not None:
              self.compare_func = self.rule_map[self.rule]
  
      def before_run(self, runner):
          if self.save_best is not None:
              if runner.meta is None:
                  warnings.warn('runner.meta is None. Creating a empty one.')
                  runner.meta = dict()
              runner.meta.setdefault('hook_msgs', dict())
  
      def before_train_epoch(self, runner):
          """Evaluate the model only at the start of training."""
          if not self.initial_epoch_flag:
              return
          if self.start is not None and runner.epoch >= self.start:
              self.after_train_epoch(runner)
          self.initial_epoch_flag = False
  
      def evaluation_flag(self, runner):
          """Judge whether to perform_evaluation after this epoch.
  
          Returns:
              bool: The flag indicating whether to perform evaluation.
          """
          if self.start is None:
              if not self.every_n_epochs(runner, self.interval):
                  # No evaluation during the interval epochs.
                  return False
          elif (runner.epoch + 1) < self.start:
              # No evaluation if start is larger than the current epoch.
              return False
          else:
              # Evaluation only at epochs 3, 5, 7... if start==3 and interval==2
              if (runner.epoch + 1 - self.start) % self.interval:
                  return False
          return True
  
      def after_train_epoch(self, runner):
          if not self.evaluation_flag(runner):
              return
          from mmdet.apis import single_gpu_test
          results = single_gpu_test(runner.model, self.dataloader, show=False)
          key_score = self.evaluate(runner, results)
          if self.save_best:
              best_score = runner.meta['hook_msgs'].get(
                  'best_score', self.init_value_map[self.rule])
              if self.compare_func(key_score, best_score):
                  best_score = key_score
                  runner.meta['hook_msgs']['best_score'] = best_score
                  last_ckpt = runner.meta['hook_msgs']['last_ckpt']
                  runner.meta['hook_msgs']['best_ckpt'] = last_ckpt
                  mmcv.symlink(
                      last_ckpt,
                      osp.join(runner.work_dir,
                               f'best_{self.key_indicator}.pth'))
                  self.logger.info(
                      f'Now best checkpoint is {last_ckpt}. '
                      f'Best {self.key_indicator} is {best_score:0.4f}')
  
      def evaluate(self, runner, results):
          eval_res = self.dataloader.dataset.evaluate(
              results, logger=runner.logger, **self.eval_kwargs)
          for name, val in eval_res.items():
              runner.log_buffer.output[name] = val
          runner.log_buffer.ready = True
          if self.save_best is not None:
              if self.key_indicator == 'auto':
                  # infer from eval_results
                  self._init_rule(self.rule, list(eval_res.keys())[0])
              return eval_res[self.key_indicator]
          else:
              return None
  
  
  class DistEvalHook(EvalHook):
      """Distributed evaluation hook.
  
      Notes:
          If new arguments are added, tools/test.py may be effected.
  
      Attributes:
          dataloader (DataLoader): A PyTorch dataloader.
          start (int, optional): Evaluation starting epoch. It enables evaluation
              before the training starts if ``start`` <= the resuming epoch.
              If None, whether to evaluate is merely decided by ``interval``.
              Default: None.
          interval (int): Evaluation interval (by epochs). Default: 1.
          tmpdir (str | None): Temporary directory to save the results of all
              processes. Default: None.
          gpu_collect (bool): Whether to use gpu or cpu to collect results.
              Default: False.
          save_best (str, optional): If a metric is specified, it would measure
              the best checkpoint during evaluation. The information about best
              checkpoint would be save in best.json.
              Options are the evaluation metrics to the test dataset. e.g.,
              ``bbox_mAP``, ``segm_mAP`` for bbox detection and instance
              segmentation. ``AR@100`` for proposal recall. If ``save_best`` is
              ``auto``, the first key will be used. The interval of
              ``CheckpointHook`` should device EvalHook. Default: None.
          rule (str | None): Comparison rule for best score. If set to None,
              it will infer a reasonable rule. Default: 'None'.
          **eval_kwargs: Evaluation arguments fed into the evaluate function of
              the dataset.
      """
  
      def __init__(self,
                   dataloader,
                   start=None,
                   interval=1,
                   tmpdir=None,
                   gpu_collect=False,
                   save_best=None,
                   rule=None,
                   **eval_kwargs):
          super().__init__(
              dataloader,
              start=start,
              interval=interval,
              save_best=save_best,
              rule=rule,
              **eval_kwargs)
          self.tmpdir = tmpdir
          self.gpu_collect = gpu_collect
  
      def after_train_epoch(self, runner):
          if not self.evaluation_flag(runner):
              return
  
          from mmdet.apis import multi_gpu_test
          tmpdir = self.tmpdir
          if tmpdir is None:
              tmpdir = osp.join(runner.work_dir, '.eval_hook')
          results = multi_gpu_test(
              runner.model,
              self.dataloader,
              tmpdir=tmpdir,
              gpu_collect=self.gpu_collect)
          if runner.rank == 0:
              print('\n')
              key_score = self.evaluate(runner, results)
              if self.save_best:
                  best_score = runner.meta['hook_msgs'].get(
                      'best_score', self.init_value_map[self.rule])
                  if self.compare_func(key_score, best_score):
                      best_score = key_score
                      runner.meta['hook_msgs']['best_score'] = best_score
                      last_ckpt = runner.meta['hook_msgs']['last_ckpt']
                      runner.meta['hook_msgs']['best_ckpt'] = last_ckpt
                      mmcv.symlink(
                          last_ckpt,
                          osp.join(runner.work_dir,
                                   f'best_{self.key_indicator}.pth'))
                      self.logger.info(
                          f'Now best checkpoint is {last_ckpt}. '
                          f'Best {self.key_indicator} is {best_score:0.4f}')
  