uimnet/workers/evaluator.py [71:103]:
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class Evaluator(workers.Worker):

  def __init__(self):
    super(Evaluator, self).__init__()

    self.model_dir = None
    self.eval_cfg = None
    self.train_cfg = None
    self.algorithm = None
    self.partitions = None
    self.trace = None


  def checkpoint(self, *args, **kwargs):
    if utils.is_not_distributed_or_is_rank0():
      new_callable = Evaluator()
      utils.write_trace(f'{self.trace}.interrupted', dir_=self.train_cfg.output_dir)
      return submitit.helpers.DelayedSubmission(new_callable,
                                                model_dir=self.model_dir,
                                                eval_cfg=self.eval_cfg,
                                                train_cfg=self.train_cfg,
                                                Algorithm=self.Algorithm,
                                                partitions=self.partitions
                                                )
  def __call__(self, model_dir, eval_cfg, train_cfg, Algorithm, Measure, partitions):

    self.model_dir = model_dir
    self.eval_cfg = copy.deepcopy(eval_cfg)
    self.train_cfg = copy.deepcopy(train_cfg)
    self.Algorithm = Algorithm
    self.datasets  = datasets

    self.setup(eval_cfg)
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uimnet/workers/evaluator2.py [82:116]:
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class Evaluator(workers.Worker):

  def __init__(self):
    super(Evaluator, self).__init__()

    self.model_dir = None
    self.eval_cfg = None
    self.train_cfg = None
    self.algorithm = None
    self.partitions = None
    self.trace = None


  def checkpoint(self, *args, **kwargs):

    if utils.is_not_distributed_or_is_rank0():
      new_callable = Evaluator()
      utils.write_trace(f'{self.trace}.interrupted', dir_=self.train_cfg.output_dir)
      return submitit.helpers.DelayedSubmission(new_callable,
                                                model_dir=self.model_dir,
                                                eval_cfg=self.eval_cfg,
                                                train_cfg=self.train_cfg,
                                                Algorithm=self.Algorithm,
                                                partitions=self.partitions
                                                )
  def __call__(self, model_dir, eval_cfg, train_cfg, Algorithm, Measure, partitions):


    self.model_dir = model_dir
    self.eval_cfg = copy.deepcopy(eval_cfg)
    self.train_cfg = copy.deepcopy(train_cfg)
    self.Algorithm = Algorithm
    self.datasets  = datasets

    self.setup(eval_cfg)
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