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- __all__ = [
- 'EarlyStopCallback'
- ]
-
- from typing import Dict
-
- from .callback import HasMonitorCallback
- from fastNLP.core.utils.exceptions import EarlyStopException
-
-
- class EarlyStopCallback(HasMonitorCallback):
- def __init__(self, monitor:str=None, larger_better:bool=True, patience:int=10):
- """
-
- :param str monitor: 监控的 metric 值。如果为 None，将尝试使用 Trainer 设置的 monitor 。
- :param larger_better: monitor 的值是否是越大越好。
- :param patience: 多少次 validate 不没有提升就停止。
- """
- super(EarlyStopCallback, self).__init__(monitor=monitor, larger_better=larger_better, must_have_monitor=True)
- self.wait = 0
- self.patience = patience
-
- def on_validate_end(self, trainer, results):
- if len(results)==0:
- return
- monitor_value = self.get_monitor_value(results)
- if self.is_better_monitor_value(monitor_value, keep_if_better=True):
- self.wait = 0
- else:
- self.wait += 1
-
- def on_fetch_data_begin(self, trainer):
- # 当是 step validate 的时候，下一步执行的就是这个， 所以在这里检查。
- if self.wait >= self.patience:
- raise EarlyStopException(f"After {self.wait} validations, no improvement for "
- f"metric `{self._real_monitor}`")
-
- def on_train_epoch_begin(self, trainer):
- # 当是 epoch validate 的时候，下一步执行的就是这个， 所以在这里检查。
- if self.wait >= self.patience:
- raise EarlyStopException(f"After {self.wait} validations, no improvement for "
- f"metric `{self._real_monitor}`(best value: {self.monitor_value})")
-
- def on_save_checkpoint(self, trainer) -> Dict:
- states = {
- 'patience': self.patience,
- 'wait': self.wait,
- 'monitor': self.monitor,
- 'monitor_value': self.monitor_value
- }
- return states
-
- def on_load_checkpoint(self, trainer, states):
- self.patience = states['patience']
- self.wait = states['wait']
- self.monitor = states['monitor']
- self.monitor_value = float(states['monitor_value'])
-
- def callback_name(self):
- return f'EarlyStopCallback#monitor-{self.monitor}#patience-{self.patience}'
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