1. 将带有Monitor的callback都抽象为HasMonitorCallback，并由这个父类进行monitor的设置和检验的; 2.支持从Trainer中设置monitor给所有的Callback使用;3.新增EarlyStopCallback.

fastNLP/core/callbacks/__init__.py

@@ -10,7 +10,8 @@ __all__ = [
     'ProgressCallback',
     'RichCallback',
     "LRSchedCallback",
-    'LoadBestModelCallback'
+    'LoadBestModelCallback',
+    "EarlyStopCallback"
 ]
 
 
@@ -21,4 +22,5 @@ from .checkpoint_callback import ModelCheckpointCallback, TrainerCheckpointCallb
 from .progress_callback import choose_progress_callback, ProgressCallback, RichCallback
 from .lr_scheduler_callback import LRSchedCallback
 from .load_best_model_callback import LoadBestModelCallback
+from .early_stop_callback import EarlyStopCallback
 

fastNLP/core/callbacks/callback.py

@@ -1,11 +1,15 @@
-from typing import Union, Callable, Dict, Optional
+from typing import Union, Callable, Dict, Optional, Any
+from abc import ABC
 
 __all__ = [
     'Callback',
 ]
 
 from .callback_events import Events, EventsList, Filter
+from .utils import _get_monitor_value
 from fastNLP.core.callbacks.callback_events import _SingleEventState
+from fastNLP.core.log import logger
+from fastNLP.core.utils import apply_to_collection
 
 
 class Callback:
@@ -150,4 +154,82 @@ class _CallbackWrapper(Callback):
         return self.fn.__name__
 
 
+class CanItemDataType(ABC):
+    """
+    检测可以进行传输的对象。
+
+    """
+
+    @classmethod
+    def __subclasshook__(cls, subclass: Any) -> Union[bool, Any]:
+        if cls is CanItemDataType:
+            item = getattr(subclass, 'item', None)
+            return callable(item)
+        return NotImplemented
+
+
+class HasMonitorCallback(Callback):
+    def __init__(self, monitor, larger_better, must_have_monitor=False):
+        self.set_monitor(monitor, larger_better)
+        self.must_have_moinitor = must_have_monitor
+
+    def set_monitor(self, monitor, larger_better):
+        self.monitor = str(monitor) if monitor is not None else None
+        self.larger_better = bool(larger_better)
+        if larger_better:
+            self.monitor_value = float('-inf')
+        else:
+            self.monitor_value = float('inf')
+        self._real_monitor = self.monitor
+
+    def on_after_trainer_initialized(self, trainer, driver):
+        """
+        如果本身的 monitor 没有设置，则根据 Trainer 中的 monitor 设置 monitor 。
+        同时对于必须要有 monitor 设置的 callback ，该函数会进行检查。
+
+        :param trainer:
+        :param driver:
+        :return:
+        """
+        if self.monitor is None and trainer.monitor is not None:
+            self.set_monitor(monitor=trainer.monitor, larger_better=trainer.larger_better)
+        if self.must_have_moinitor and self.monitor is None:
+            raise RuntimeError(f"No `monitor` is set for {self.__class__.__name__}. "
+                               f"You can set it in the initialization or through Trainer.")
+
+    def get_monitor_value(self, results:Dict)->float:
+        """
+        获取 monitor 的值，如果 monitor 没有直接找到，会尝试使用匹配的方式寻找，并把匹配到的设置到 self._real_monitor 属性上。
 
+        :param results:
+        :return:
+        """
+        if len(results)==0:
+            return 0
+        # 保证所有的 tensor 都被转换为了 python 特定的类型
+        results = apply_to_collection(results, dtype=CanItemDataType, function=lambda x: x.item())
+        use_monitor, monitor_value = _get_monitor_value(monitor=self.monitor,
+                                                        real_monitor=self._real_monitor,
+                                                        res=results)
+        if self._real_monitor != use_monitor:  # 发生了替换需要打印
+            logger.warning(
+                f"We can not find `{self.monitor}` in the evaluation result (with keys as {list(results.keys())}), "
+                f"we use the `{use_monitor}` as the monitor for {self.__class__.__name__}.")
+        self._real_monitor = use_monitor
+        return monitor_value
+
+    def is_better_monitor_value(self, monitor_value: float, keep_if_better=True):
+        """
+        检测 monitor_value 是否是更好的
+
+        :param monitor_value:
+        :param keep_if_better: 如果传入的 monitor_value 值更好，则将其保存下来。
+        :return:
+        """
+        better = False
+        if (self.larger_better and monitor_value > self.monitor_value) or \
+            (not self.larger_better and monitor_value < self.monitor_value):
+            better = True
+            if keep_if_better:
+                self.monitor_value = monitor_value
+        return better

fastNLP/core/callbacks/checkpoint_callback.py

@@ -5,12 +5,12 @@ __all__ = [
 import os
 from typing import Union, Optional, Callable, Dict, Sequence, Any, Mapping
 from pathlib import Path
-from abc import ABC
 import sys
+from copy import deepcopy
 
 
 import fastNLP
-from .callback import Callback, Filter
+from .callback import Callback, HasMonitorCallback
 from fastNLP.core.callbacks.utils import _get_monitor_value
 from fastNLP.core.log import logger
 from fastNLP.envs import FASTNLP_LAUNCH_TIME
@@ -18,22 +18,7 @@ from fastNLP.core.utils import synchronize_safe_rm, synchronize_mkdir
 from fastNLP.core.utils import apply_to_collection
 
 
-class CanItemDataType(ABC):
-    """
-    检测可以进行传输的对象。
-
-    """
-
-    @classmethod
-    def __subclasshook__(cls, subclass: Any) -> Union[bool, Any]:
-        if cls is CanItemDataType:
-            item = getattr(subclass, 'item', None)
-            return callable(item)
-        return NotImplemented
-
-
-
-class CheckpointCallback(Callback):
+class CheckpointCallback(HasMonitorCallback):
     def __init__(
             self,
             monitor,
@@ -48,13 +33,8 @@ class CheckpointCallback(Callback):
             model_save_fn: Optional[Callable] = None,
             **kwargs,
     ):
-        # 我们新加了逻辑，如果 checkpoint callback 自己没有设置 monitor 和 larger_better，那么我们会将其在 trainer 中的设置赋值给它们；
-        # if monitor is None and save_topk is not None:
-        #     raise ValueError("Parameter `monitor` must be set when you want to use 'save_topk'.")
-
-        if monitor is not None and not isinstance(monitor, str):
-            raise ValueError("Parameter `monitor` should be of 'str' type.")
-
+        super().__init__(monitor=monitor, larger_better=larger_better,
+                         must_have_monitor=save_topk is not None)
         if save_folder is None:
             logger.warning(
                 "Parameter `path` is None, and we will use the current work directory to find and load your model.")
@@ -92,13 +72,12 @@ class CheckpointCallback(Callback):
                                     "`BaseException` type.")
         else:
             save_on_exception = []
-        self.monitor = monitor
+
         self.save_folder = Path(save_folder)
         self.save_every_n_epochs = save_every_n_epochs
         self.save_every_n_batches = save_every_n_batches
         self.save_last = save_last
         self.save_topk = save_topk
-        self.larger_better = larger_better
         self.only_state_dict = only_state_dict
         self.model_save_fn = model_save_fn
         self.save_on_exception = save_on_exception
@@ -108,12 +87,6 @@ class CheckpointCallback(Callback):
         self._topk_model = {}
         self._topn = 0  # 表示目前已经保存了几个最好的模型；
 
-        # 因为我们在 `_get_validate_metric` 函数中，当在返回的 `validate_res` 字典中找不到 `monitor` 时，是使用匹配找到的
-        #  key 对应的 value 当做结果；但是这样存在的一个问题在于如果用户传入的 metric 返回的 sub_metric 的名字可能会混淆，并且其在下一次
-        #  训练的代码中修改了这些 sub_metric 返回的顺序，那么就会导致模糊匹配拿到的 key 和 value 与之前的不是同一个，这显然不是合理的行为；
-        # 因此我们通过该变量来表示我们通过模糊匹配拿到的 key；
-        self._real_monitor = self.monitor
-
         # 注意这里应当保证只有进程 0 在执行这个操作，因为当用户使用 python -m torch.distributed.launch 来拉起进程的时候，
         #  FASTNLP_LAUNCH_TIME 在每一个进程上的值是不一样的；
         self.timestamp_path = self.save_folder.joinpath(os.environ[FASTNLP_LAUNCH_TIME])
@@ -121,20 +94,15 @@ class CheckpointCallback(Callback):
         synchronize_mkdir(self.timestamp_path)
 
     def on_after_trainer_initialized(self, trainer, driver):
-        if self.monitor is None:
-            if trainer.monitor is not None:
-                self.monitor = trainer.monitor
-                self.larger_better = trainer.larger_better
-            elif self.save_topk is not None:
-                raise RuntimeError("You are using `topk` mode, but you have not set the `monitor` value either in this"
-                                   "callback or in trainer.")
-            else:
-                self.monitor = None
+        if self.save_topk is not None:
+            super().on_after_trainer_initialized(trainer, driver)
         if self.save_topk is not None and trainer.evaluator is None:
-            raise RuntimeError("You are using `topk` mode, but there is no `evaluator` in trainer.")
+            logger.warning("You set `save_topk`, but `validate_dataloaders` is not set in Trainer.")
 
-    def on_validate_end(self, trainer, validate_res):
-        self._save_topk(trainer, validate_res)
+    def on_validate_end(self, trainer, results):
+        if len(results) == 0:
+            return
+        self._save_topk(trainer, results)
 
     def on_train_epoch_end(self, trainer: "fastNLP.Trainer"):
         if trainer.cur_epoch_idx % self.save_every_n_epochs == 0:
@@ -157,7 +125,7 @@ class CheckpointCallback(Callback):
 
     def on_sanity_check_end(self, trainer, sanity_check_res):
         # 主要核对一下 monitor 是否存在。
-        self._get_validate_metric(sanity_check_res)
+        self.get_monitor_value(results=sanity_check_res)
 
     def on_save_checkpoint(self, trainer) -> Dict:
         """
@@ -168,8 +136,7 @@ class CheckpointCallback(Callback):
 
         states = {}
         states['timestamp_path'] = str(self.timestamp_path.absolute())
-        states['_topk_model'] = apply_to_collection(self._topk_model, dtype=CanItemDataType,
-                                                    function=lambda x:x.item())
+        states['_topk_model'] = deepcopy(self._topk_model)
         states['save_topk'] = 0 if self.save_topk is None else self.save_topk
         states['_real_monitor'] = self._real_monitor
         return states
@@ -190,30 +157,30 @@ class CheckpointCallback(Callback):
             self._topk_model.update(self._topk_model)
         self._real_monitor = states["real_monitor"]
 
-    def _save_topk(self, trainer: "fastNLP.Trainer", validate_res: Dict):
+    def _save_topk(self, trainer: "fastNLP.Trainer", results: Dict):
         """
         根据validate_res决定保存哪些model的函数。会自动移除掉不满足topk的文件夹。
 
         :param trainer:
-        :param validate_res:
+        :param results:
         :return:
         """
         if self.save_topk is not None:
-            _metric_value = self._get_validate_metric(validate_res)
+            monitor_value = self.get_monitor_value(results=results)
             folder_name = f"{self.folder_prefix}-epoch_{trainer.cur_epoch_idx}-batch_{trainer.global_forward_batches}" \
-                         f"-{self._real_monitor}_{_metric_value}"
+                         f"-{self._real_monitor}_{monitor_value}"
 
             _should_save = False
             if self._topn < self.save_topk:
-                self._topk_model[folder_name] = _metric_value
+                self._topk_model[folder_name] = monitor_value
                 self._topn += 1
                 _should_save = True
             else:
                 _least_valuable_model = (min if self.larger_better else max)(self._topk_model,
                                                                              key=lambda x: self._topk_model[x])
-                if (self.larger_better and _metric_value > self._topk_model[_least_valuable_model]) or \
-                        (self.larger_better is False and _metric_value < self._topk_model[_least_valuable_model]):
-                    self._topk_model[folder_name] = _metric_value
+                if (self.larger_better and monitor_value > self._topk_model[_least_valuable_model]) or \
+                        (self.larger_better is False and monitor_value < self._topk_model[_least_valuable_model]):
+                    self._topk_model[folder_name] = monitor_value
                     _should_save = True
                     self._topk_model.pop(_least_valuable_model)
                     synchronize_safe_rm(self.timestamp_path.joinpath(_least_valuable_model))
@@ -249,7 +216,11 @@ class CheckpointCallback(Callback):
         :return:
         """
         use_monitor, value = _get_monitor_value(monitor=self.monitor, real_monitor=self._real_monitor, res=res)
+        if self._real_monitor != use_monitor:
+            logger.warning(f"We can not find `{self._real_monitor}` in the evaluation result (with keys as {list(res.keys())}), "
+                           f"we use the `{use_monitor}` as the monitor for {self.__class__.__name__}.")
         self._real_monitor = use_monitor
+
         return value
 
     @property
@@ -277,7 +248,7 @@ class ModelCheckpointCallback(CheckpointCallback):
     若 model_save_fn 不为 None，则 fastNLP 将 folder 绝对路径传递给该函数，fastNLP 不在该 folder 下创建任何文件。
 
     :param monitor: 监控的 metric 的名称。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最短公共字符串算法 找到最匹配
-        的那个作为 monitor 。
+        的那个作为 monitor 。如果为 None 将尝试从 Trainer 中获取该值。
     :param save_folder: 保存的文件夹，fastNLP 将在该文件下以时间戳创建子文件夹，并在里面保存。因此不同次运行可以将被保存到不同的
         时间戳文件夹中。如果为 None ，默认使用当前文件夹。
     :param save_every_n_epochs: 多少个 epoch 保存一次。
@@ -324,7 +295,7 @@ class TrainerCheckpointCallback(CheckpointCallback):
     若 model_save_fn 不为 None，则 fastNLP 只会在每个 folder 下生成 fastnlp_trainer.pkl.tar 文件。
 
     :param monitor: 监控的 metric 的名称。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最短公共字符串算法 找到最匹配
-        的那个作为 monitor 。
+        的那个作为 monitor 。如果为 None 将尝试从 Trainer 中获取该值。
     :param save_folder: 保存的文件夹，fastNLP 将在该文件下以时间戳创建子文件夹，并在里面保存。因此不同次运行可以将被保存到不同的
         时间戳文件夹中。如果为 None ，默认使用当前文件夹。
     :param save_every_n_epochs: 多少个 epoch 保存一次。

fastNLP/core/callbacks/early_stop_callback.py

@@ -0,0 +1,61 @@
+__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}'
+

fastNLP/core/callbacks/load_best_model_callback.py

@@ -4,8 +4,7 @@ __all__ = [
 
 import os
 from typing import Optional, Callable
-from .callback import Callback
-from .utils import _get_monitor_value
+from .callback import HasMonitorCallback
 from io import BytesIO
 import shutil
 
@@ -14,15 +13,15 @@ from fastNLP.core.log import logger
 from fastNLP.envs import all_rank_call
 
 
-class LoadBestModelCallback(Callback):
-    def __init__(self, monitor:str, larger_better:bool = True, only_state_dict:bool = True,
+class LoadBestModelCallback(HasMonitorCallback):
+    def __init__(self, monitor:str=None, larger_better:bool = True, only_state_dict:bool = True,
                  save_folder:Optional[str] = None, model_save_fn:Optional[Callable] = None,
                  model_load_fn:Optional[Callable] = None,
                  delete_after_train:bool = True):
         """
         保存最佳的 monitor 值最佳的模型，并在训练结束的时候重新加载模型。仅在训练正常结束的时候才能加载最好的模型。
 
-        :param str monitor: 监控的 metric 值。
+        :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: 该 metric 值是否是越大越好。
         :param save_folder: 保存的文件夹，如果为空，则保存在内存中。不为空，则保存一份权重到文件中，当为多机训练，且本值不为空时，请确保
             不同的机器均可访问当该路径。当 model_save_fn 不为 None 时该值一定不能为空。
@@ -33,6 +32,7 @@ class LoadBestModelCallback(Callback):
             请在函数内完成对模型的加载。
         :param delete_after_train: 在训练结束后是否删掉模型。
         """
+        super().__init__(monitor=monitor, larger_better=larger_better, must_have_monitor=True)
         if model_load_fn is not None:
             assert callable(model_load_fn), "`model_load_fn` must be a callable object."
             assert model_save_fn is not None, "`model_load_fn` and `model_save_fn` must be passed at the same time."
@@ -56,15 +56,11 @@ class LoadBestModelCallback(Callback):
             self.real_save_folder = None
             self.buffer = BytesIO()
 
-        self.monitor = monitor
-        self.larger_better = larger_better
         self.save_folder = save_folder
         self.only_state_dict = only_state_dict
         self.model_save_fn = model_save_fn
         self.model_load_fn = model_load_fn
         self.delete_after_after = delete_after_train
-        self._real_monitor = None
-        self.monitor_value = float('-inf') if larger_better else float('inf')
 
     def on_after_trainer_initialized(self, trainer, driver):
         if self.save_folder is not None and driver.is_distributed() and int(os.environ.get(FASTNLP_BACKEND_LAUNCH, 0))==1:
@@ -76,13 +72,16 @@ class LoadBestModelCallback(Callback):
                 raise RuntimeError(f"Currently {driver.__class__.__name__} does not support using `save_folder` to "
                                    f"save best model when launch using script.")
 
+        super().on_after_trainer_initialized(trainer, driver)
+
+    def on_sanity_check_end(self, trainer, sanity_check_res):
+        self.get_monitor_value(sanity_check_res)
+
     def on_validate_end(self, trainer, results):
-        self._real_monitor, monitor_value = _get_monitor_value(monitor=self.monitor,
-                                                               real_monitor=self._real_monitor,
-                                                               res=results)
-        if (monitor_value < self.monitor_value and self.larger_better is False) or \
-            (monitor_value > self.monitor_value and self.larger_better):
-            self.monitor_value = monitor_value
+        if len(results)==0:
+            return
+        monitor_value = self.get_monitor_value(results)
+        if self.is_better_monitor_value(monitor_value, keep_if_better=True):
             if self.real_save_folder:
                 trainer.save_model(folder=self.real_save_folder, only_state_dict=self.only_state_dict,
                                    model_save_fn=self.model_save_fn)

fastNLP/core/callbacks/progress_callback.py

@@ -8,7 +8,7 @@ __all__ = [
     'RichCallback'
 ]
 
-from .callback import Callback
+from .callback import HasMonitorCallback
 from fastNLP.core.callbacks.utils import _get_monitor_value
 from fastNLP.core.utils import f_rich_progress
 from fastNLP.core.log import logger
@@ -28,15 +28,13 @@ def choose_progress_callback(progress_bar:str):
         return None
 
 
-class ProgressCallback(Callback):
+class ProgressCallback(HasMonitorCallback):
     def on_train_end(self, trainer):
         f_rich_progress.stop()
 
     def on_sanity_check_end(self, trainer, sanity_check_res):
         if len(sanity_check_res) and getattr(self, 'monitor', None) is not None:
-            self._real_monitor, monitor_value = _get_monitor_value(monitor=self.monitor,
-                                                                   real_monitor=self._real_monitor,
-                                                                   res=sanity_check_res)
+            self.get_monitor_value(sanity_check_res)
 
 
 class RichCallback(ProgressCallback):
@@ -46,28 +44,22 @@ class RichCallback(ProgressCallback):
 
         :param print_every: 多少个 batch 更新一次显示。
         :param loss_round_ndigit: 显示的 loss 保留多少位有效数字
-        :param monitor: 当检测到这个key的结果更好时，会打印出不同的颜色进行提示。
+        :param monitor: 当检测到这个key的结果更好时，会打印出不同的颜色进行提示。如果为 None ，会尝试使用 trainer 中设置的 monitor 。
         :param larger_better: 是否是monitor的结果越大越好。
         :param format_json: 是否format json再打印
         """
-        super().__init__()
+        super().__init__(monitor=monitor, larger_better=larger_better, must_have_monitor=False)
         self.print_every = print_every
         self.progress_bar = f_rich_progress
         self.task2id = {}
         self.loss = 0
         self.loss_round_ndigit = loss_round_ndigit
-        self.monitor = monitor
-        self.larger_better = larger_better
-        if larger_better:
-            self.monitor_value = float('-inf')
-        else:
-            self.monitor_value = float('inf')
-        self._real_monitor = monitor
         self.format_json = format_json
 
     def on_after_trainer_initialized(self, trainer, driver):
         if not self.progress_bar.disable:
             self.progress_bar.set_disable(flag=trainer.driver.get_local_rank() != 0)
+        super(RichCallback, self).on_after_trainer_initialized(trainer, driver)
 
     def on_train_begin(self, trainer):
         self.task2id['epoch'] = self.progress_bar.add_task(description='Epoch:0', total=trainer.n_epochs,
@@ -109,16 +101,12 @@ class RichCallback(ProgressCallback):
         text_style = ''
         characters = '-'
         if self.monitor is not None:
-            self._real_monitor, monitor_value = _get_monitor_value(monitor=self.monitor,
-                                                                    real_monitor=self._real_monitor,
-                                                                    res=results)
-            if (self.larger_better and monitor_value > self.monitor_value) or \
-                    (not self.larger_better and monitor_value < self.monitor_value):
+            monitor_value = self.get_monitor_value(results)
+            if self.is_better_monitor_value(monitor_value, keep_if_better=True):
                 if abs(self.monitor_value) != float('inf'):
                     rule_style = 'spring_green3'
                     text_style = '[bold]'
                     characters = '+'
-                self.monitor_value = monitor_value
         self.progress_bar.print()
         self.progress_bar.console.rule(text_style+f"Eval. results on Epoch:{trainer.cur_epoch_idx}, "
                                                   f"Batch:{trainer.batch_idx_in_epoch}",
@@ -151,18 +139,12 @@ class RawTextCallback(ProgressCallback):
         :param larger_better: 是否是monitor的结果越大越好。
         :param format_json: 是否format json再打印
         """
-        super().__init__()
+        super().__init__(monitor=monitor, larger_better=larger_better, must_have_monitor=False)
         self.print_every = print_every
         self.task2id = {}
         self.loss = 0
         self.loss_round_ndigit = loss_round_ndigit
-        self.monitor = monitor
-        self.larger_better = larger_better
-        if larger_better:
-            self.monitor_value = float('-inf')
-        else:
-            self.monitor_value = float('inf')
-        self._real_monitor = monitor
+        self.set_monitor(monitor, larger_better)
         self.format_json = format_json
         self.num_signs = 10
 
@@ -189,14 +171,10 @@ class RawTextCallback(ProgressCallback):
         base_text = f'Eval. results on Epoch:{trainer.cur_epoch_idx}, Batch:{trainer.batch_idx_in_epoch}'
         text = ''
         if self.monitor is not None:
-            self._real_monitor, monitor_value = _get_monitor_value(monitor=self.monitor,
-                                                                   real_monitor=self._real_monitor,
-                                                                   res=results)
-            if (self.larger_better and monitor_value > self.monitor_value) or \
-                    (not self.larger_better and monitor_value < self.monitor_value):
+            monitor_value = self.get_monitor_value(results)
+            if self.is_better_monitor_value(monitor_value, keep_if_better=True):
                 if abs(self.monitor_value) != float('inf'):
                     text = '+'*self.num_signs + base_text + '+'*self.num_signs
-                self.monitor_value = monitor_value
         if len(text) == 0:
             text = '-'*self.num_signs + base_text + '-'*self.num_signs
 

fastNLP/core/callbacks/utils.py

@@ -19,23 +19,31 @@ def _get_monitor_value(monitor: str, real_monitor: Optional[str], res: dict) ->(
     if monitor in res:
         return monitor, res[monitor]
 
+    if real_monitor in res:
+        return real_monitor, res[real_monitor]
+
     pairs = []
     for idx, (key, value) in enumerate(res.items()):
-        match = SequenceMatcher(None, key, monitor).find_longest_match(0, len(key), 0, len(monitor))
-        pairs.append((key, value, match.size, idx))
+        match_size = _match_length(monitor, key)
+        pairs.append((key, value, match_size, idx))
 
     pairs.sort(key=lambda pair: (pair[2], -pair[3]), reverse=True)
     key, value, match_size = pairs[0][:3]
 
-    if real_monitor is not None and real_monitor in res and real_monitor != key:
-        # 如果 real_monitor 比新找的更长就继续用之前的。
-        match = SequenceMatcher(None, real_monitor, monitor).find_longest_match(0, len(real_monitor), 0, len(monitor))
-        if match.size > match_size:
-            return real_monitor, res[real_monitor]
+    return key, value
+
 
-        logger.warning(f"We can not find `{monitor}` in the evaluation result (with keys as {list(res.keys())}), "
-                       f"we use the `{key}` as the monitor.")
-    real_monitor = key
-    return real_monitor, value
+def _match_length(a:str, b:str)->int:
+    """
+    需要把长度短的放在前面
+
+    :param a:
+    :param b:
+    :return:
+    """
+    short = a if len(a) < len(b) else b
+    long = a if len(a)>=len(b) else b
+    match = SequenceMatcher(None, short, long).find_longest_match(0, len(short), 0, len(long))
+    return match.size
 
 

fastNLP/core/controllers/trainer.py

@@ -25,6 +25,7 @@ from fastNLP.core.utils import check_fn_not_empty_params, get_fn_arg_names, matc
 from fastNLP.envs import rank_zero_call
 from fastNLP.core.log import logger
 from fastNLP.envs import FASTNLP_MODEL_FILENAME
+from fastNLP.core.utils.exceptions import EarlyStopException
 
 
 class Trainer(TrainerEventTrigger):
@@ -49,6 +50,8 @@ class Trainer(TrainerEventTrigger):
             output_mapping: Optional[Union[Callable, Dict]] = None,
             accumulation_steps: int = 1,
             fp16: bool = False,
+            monitor: str = None,
+            larger_better: bool = True,
             marker: Optional[str] = None,
             **kwargs
     ):
@@ -102,6 +105,10 @@ class Trainer(TrainerEventTrigger):
          如果 batch 是一个 `dataclass`，那么我们会先将该 dataclass 转换为一个 Dict，然后再进行上述转换
         :param accumulation_steps: 梯度累积的步数，表示每隔几个 batch 优化器迭代一次；默认为 1；
         :param fp16: 是否开启混合精度训练；默认为 False；
+        :param monitor: 当存在 validate_dataloaders 时，默认的 monitor metric 的名字。传入的 callback 如果有 monitor 参数且没有
+            在 callback 初始化设定的，将采取这个值。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最短公共字符串算法 找到最匹配
+            的那个作为 monitor 。
+        :param larger_better: monitor 的值是否是越大越好。
         :param marker: 用于标记一个 Trainer 实例，从而在用户调用 `Trainer.on` 函数时，标记该 callback 函数属于哪一个具体的 'trainer' 实例；默认为 None；
         :param kwargs: 一些其它的可能需要的参数；
             torch_non_blocking: 表示用于 pytorch 的 tensor 的 to 方法的参数 non_blocking；
@@ -210,6 +217,8 @@ class Trainer(TrainerEventTrigger):
         self.evaluator = None
         self.epoch_validate = lambda *args, **kwargs: ...
         self.step_validate = lambda *args, **kwargs: ...
+        self.monitor = monitor
+        self.larger_better = larger_better
         if metrics is not None and validate_dataloaders is not None:
             if not callable(validate_every) and (not isinstance(validate_every, int) or validate_every == 0):
                 raise ValueError("Parameter 'validate_every' should be set to 'int' type and either < 0 or > 0.")
@@ -239,6 +248,7 @@ class Trainer(TrainerEventTrigger):
             else:
                 # validate_every > 0
                 self._step_validate_filter = Filter(every=validate_every)
+
         self.metrics = metrics
         self.validate_every = validate_every
 
@@ -320,6 +330,10 @@ class Trainer(TrainerEventTrigger):
                 self.driver.barrier()
             self.on_train_end()
             self.driver.barrier()
+
+        except EarlyStopException as e:
+            logger.info(f"Catch early stop exception: {e.msg}.")
+            self.on_exception(e)
         except KeyboardInterrupt as e:
             self.driver.on_exception()
             self.on_exception(e)

fastNLP/core/utils/exceptions.py

@@ -0,0 +1,10 @@
+
+class EarlyStopException(BaseException):
+    r"""
+    用于EarlyStop时从Trainer训练循环中跳出。
+
+    """
+
+    def __init__(self, msg):
+        super(EarlyStopException, self).__init__(msg)
+        self.msg = msg

tests/core/callbacks/test_utils.py

@@ -12,32 +12,27 @@ def test_get_monitor_value():
     with Capturing() as output:
         monitor, value = _get_monitor_value(monitor='f1', real_monitor=None, res=res)
     assert monitor == 'f1' and value==0.2
-    assert 'We can not find' not in output[0]
 
     # 测试可以匹配，且选择更靠前的
     res = {'acc#f1': 0.2, 'acc#rec': 0.3, 'add#f':0.4}
     with Capturing() as output:
         monitor, value = _get_monitor_value(monitor='f1', real_monitor=None, res=res)
     assert monitor=='acc#f1' and value==0.2
-    assert 'We can not find' in output[0]
 
     # 测试monitor匹配不上，使用real_monitor
     res = {'acc#f1': 0.2, 'acc#rec': 0.3, 'add#f':0.4}
     with Capturing() as output:
-        monitor, value = _get_monitor_value(monitor='acc#f', real_monitor='acc#rec', res=res)
+        monitor, value = _get_monitor_value(monitor='acc', real_monitor='acc#rec', res=res)
     assert monitor=='acc#rec' and value==0.3
-    assert 'We can not find' not in output[0]
 
     # 测试monitor/real_monitor匹配不上, 重新选择
     res = {'acc#f1': 0.2, 'acc#rec': 0.3, 'add#f':0.4}
     with Capturing() as output:
         monitor, value = _get_monitor_value(monitor='acc#f', real_monitor='acc#r', res=res)
     assert monitor=='acc#f1' and value==0.2
-    assert 'We can not find' in output[0]
 
     # 测试partial的位置
     res = {"acc#acc": 0.52, "loss#loss": 2}
     with Capturing() as output:
         monitor, value = _get_monitor_value(monitor='-loss', real_monitor=None, res=res)
     assert monitor=='loss#loss' and value==2
-    assert 'We can not find' in output[0]