修改element无法与element运算的问题

fastNLP/core/controllers/trainer.py

@@ -919,16 +919,19 @@ class Trainer(TrainerEventTrigger):
                     _not_called_callback_fns.append(each_callback_fn)
 
         if check_mode:
-            logger.rank_zero_warning("You have customized your 'batch_step_fn' in the 'train_batch_loop' and also use these "
+            if len(_not_called_callback_fns) != 0:
+                logger.rank_zero_warning("You have customized your 'batch_step_fn' in the 'train_batch_loop' and also use these "
                            f"callback_fns: {_not_called_callback_fns}, but it seems that"
-                           "you don't call the corresponding callback hook explicitly in your 'batch_step_fn'.")
+                           "you don't call the corresponding callback hook explicitly in your 'batch_step_fn'.",
+                                         once=True)
             # 对于 'batch_step_fn' 来讲，其只需要在第一次的 step 后进行检测即可，因此在第一次检测后将 check_batch_step_fn 置为 pass
             #  函数；
             self.check_batch_step_fn = lambda *args, **kwargs: ...
-        else:
-            logger.warning("You have customized your 'TrainBatchLoop' and also use these callback_fns: "
+        elif len(_not_called_callback_fns)!=0:
+            logger.rank_zero_warning("You have customized your 'TrainBatchLoop' and also use these callback_fns: "
                            f"{_not_called_callback_fns}, but it seems that"
-                           "you don't call the corresponding callback hook explicitly in your 'batch_step_fn'.")
+                           "you don't call the corresponding callback hook explicitly in your 'batch_step_fn'.",
+                                     once=True)
 
     def _check_train_batch_loop_legality(self):
         r"""

fastNLP/core/dataset/dataset.py

@@ -405,7 +405,7 @@ class DataSet:
         if isinstance(item, str) and item in self.field_arrays:
             return self.field_arrays[item]
         else:
-            raise AttributeError
+            raise AttributeError(f"Dataset has no attribute named:{item}.")
 
     def __setstate__(self, state):
         self.__dict__ = state

fastNLP/core/metrics/element.py

@@ -136,15 +136,13 @@ class Element:
         if self.value is None:
             prefix = f'Element:`{self.name}`'
             raise RuntimeError(prefix + " is not initialized. Please either specify backend when creating this "
-                                        "element, or use it after it being used by the `Metric.compute()` method.")
+                                        "element, or use it after it being used by the `Metric.update()` method.")
 
     def __add__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            self.value += other.value
-        else:
-            self.value += other
-        return self
+            other = other.value
+        return self.value + other
 
     def __radd__(self, other):
         self._check_value_when_call()
@@ -314,7 +312,7 @@ class Element:
             if self._value is None:
                 prefix = f'Element:`{self.name}`'
                 raise RuntimeError(prefix + " is not initialized. Please either specify backend when creating this "
-                                            "element, or use it after it being used by the `Metric.compute()` method.")
+                                            "element, or use it after it being used by the `Metric.update()` method.")
             return getattr(self._value, item)
         except AttributeError as e:
             logger.error(f"Element:{self.name} has no `{item}` attribute.")

fastNLP/core/metrics/metric.py

@@ -99,8 +99,14 @@ class Metric:
 
     def __setattr__(self, key, value):
         if hasattr(self, '_cannot_change_element') and self._cannot_change_element is True:
-            if key in self.elements and value is not self.elements[key]:
-                raise RuntimeError(f"self.`{key}` is an element, cannot assign to a new value:{value}")
+            if key in self.elements and isinstance(value, (float, int, bool)):
+                self.elements[key].fill_value(value)
+                return
+            elif key in self.elements:
+                raise TypeError(f"self.{key} is an Element, only float/int/bool type value can be assigned to it, "
+                                f"instead of {type(value)}.")
+        if isinstance(value, Element) and key not in self.elements:
+            raise RuntimeError("Please use register_element() function to add Element.")
         object.__setattr__(self, key, value)
 
     def _wrap_update(self, update):
@@ -163,13 +169,6 @@ class Metric:
         """
         self.aggregate_when_get_metric = flag
 
-    def __getattr__(self, name: str) -> Element:
-        if 'elements' in self.__dict__:
-            elements = self.__dict__['elements']
-            if name in elements:
-                return elements[name]
-        raise AttributeError("`{}` object has no attribute `{}`".format(type(self).__name__, name))
-
     def tensor2numpy(self, tensor) -> np.array:
         """
         将tensor向量转为numpy类型变量