优化了monitor相关callback的信息打印

fastNLP/core/callbacks/checkpoint_callback.py

@@ -89,6 +89,7 @@ class CheckpointCallback(Callback):
         self.topk_saver = TopkSaver(topk=topk, monitor=monitor, larger_better=larger_better, folder=folder,
                                     save_object=save_object, only_state_dict=only_state_dict, model_save_fn=model_save_fn,
                                     save_evaluate_results=save_evaluate_results, **kwargs)
+        self.topk_saver.log_name = self.__class__.__name__
 
         self.topk = topk
         self.save_object = save_object

fastNLP/core/callbacks/fitlog_callback.py

@@ -49,7 +49,7 @@ class FitlogCallback(HasMonitorCallback):
     def on_sanity_check_end(self, trainer, sanity_check_res):
         super(FitlogCallback, self).on_sanity_check_end(trainer, sanity_check_res)
         if self.monitor is None:
-            logger.rank_zero_warning(f"No monitor set for {self.__class__.__name__}. Therefore, no best metric will "
+            logger.rank_zero_warning(f"No monitor set for {self.log_name}. Therefore, no best metric will "
                                      f"be logged.")
 
     def on_evaluate_end(self, trainer, results):

fastNLP/core/callbacks/has_monitor_callback.py

@@ -42,6 +42,7 @@ class ResultsMonitor:
     """
     def __init__(self, monitor:Union[Callback, str], larger_better:bool=True):
         self.set_monitor(monitor, larger_better)
+        self._log_name = self.__class__.__name__
 
     def set_monitor(self, monitor, larger_better):
         if callable(monitor):  # 检查是否能够接受一个参数
@@ -84,11 +85,12 @@ class ResultsMonitor:
             return monitor_value
         # 第一次运行
         if isinstance(self.monitor, str) and self._real_monitor == self.monitor and use_monitor != self.monitor:
-            logger.rank_zero_warning(f"We can not find `{self.monitor}` in the evaluation result (with keys as "
+            logger.rank_zero_warning(f"We can not find monitor:`{self.monitor}` for `{self.log_name}` in the "
+                                     f"evaluation result (with keys as "
                                      f"{list(results.keys())}), we use the `{use_monitor}` as the monitor.", once=True)
         # 检测到此次和上次不同。
         elif isinstance(self.monitor, str) and self._real_monitor != self.monitor and use_monitor != self._real_monitor:
-            logger.rank_zero_warning(f"Change of monitor detected for `{self.__class__.__name__}`. "
+            logger.rank_zero_warning(f"Change of monitor detected for `{self.log_name}`. "
                            f"The expected monitor is:`{self.monitor}`, last used monitor is:"
                            f"`{self._real_monitor}` and current monitor is:`{use_monitor}`. Please consider using a "
                            f"customized monitor function when the evaluation results are varying between validation.")
@@ -166,6 +168,19 @@ class ResultsMonitor:
             monitor_name = str(self.monitor)
         return monitor_name
 
+    @property
+    def log_name(self) -> str:
+        """
+        内部用于打印信息使用
+
+        :return:
+        """
+        return self._log_name
+
+    @log_name.setter
+    def log_name(self, value):
+        self._log_name = value
+
 
 class HasMonitorCallback(ResultsMonitor, Callback):
     """
@@ -201,10 +216,10 @@ class HasMonitorCallback(ResultsMonitor, Callback):
         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_monitor and self.monitor is None:
-            raise RuntimeError(f"No `monitor` is set for {self.__class__.__name__}. "
+            raise RuntimeError(f"No `monitor` is set for {self.log_name}. "
                                f"You can set it in the initialization or through Trainer.")
         if self.must_have_monitor and self.monitor is not None and trainer.evaluator is None:
-            raise RuntimeError(f"No `evaluate_dataloaders` is set for Trainer. But Callback: {self.__class__.__name__}"
+            raise RuntimeError(f"No `evaluate_dataloaders` is set for Trainer. But Callback: {self.log_name}"
                                f" need to watch the monitor:`{self.monitor_name}`.")
 
     def on_sanity_check_end(self, trainer, sanity_check_res):

fastNLP/core/collators/collator.py

@@ -11,6 +11,7 @@ import re
 from fastNLP.core.log import logger
 from .padders.get_padder import get_padder
 from ...envs import SUPPORT_BACKENDS
+from .padders import Padder
 
 
 from .packer_unpacker import SequencePackerUnpacker, SinglePackerUnpacker, MappingPackerUnpacker, \
@@ -89,6 +90,11 @@ class Collator:
     数据转为 batch 类型后为 [1, [1,2]], 会被判定为不可 pad ，因为第一个 sample 与 第二个 sample 深度不同）（3）当前这个 field 的类
     型是否是可以 pad （例如 str 类型的数据）。可以通过设置 logger.setLevel('debug') 来打印是判定不可 pad 的原因。
 
+    .. note::
+
+        ``Collator`` 的原理是使用第一个 ``batch`` 的数据尝试推断每个``field``应该使用哪种类型的 ``Padder``，如果第一个 ``batch``
+        的数据刚好比较特殊，可能导致在之后的 pad 中遭遇失败，这种情况请通过 ``set_pad()`` 函数手动设置一下。
+
     todo 补充 code example 。
 
     如果需要将某个本可以 pad 的 field 设置为不可 pad ，则可以通过 :meth:`~fastNLP.Collator.set_pad` 的 pad_val 设置为 None 实现。
@@ -168,10 +174,16 @@ class Collator:
 
             if self.batch_data_type == 'l':
                 self.padders = dict(sorted(self.padders.items(), key=lambda x:int(x[0][1:])))  # sort, 这样 _0, _1 能够保持顺序
-
-        for key, padder in self.padders.items():
-            batch = unpack_batch.get(key)
-            pad_batch[key] = padder(batch)
+        try:
+            for key, padder in self.padders.items():
+                    batch = unpack_batch.get(key)
+                    pad_batch[key] = padder(batch)
+        except BaseException as e:
+            try:
+                logger.error(f"The following exception happens when try to pad the `{key}` field with padder:{padder}:")
+            except:
+                pass
+            raise e
 
         return self.packer_unpacker.pack_batch(pad_batch)  # 根据情况恢复成与输入一致的类型