修复metric和loss在映射时出现重复同名输入时会覆盖的bug

fastNLP/core/losses.py

@@ -128,29 +128,21 @@ class LossBase(object):
                     self.param_map[arg] = arg  # This param does not need mapping.
             self._evaluate_args = func_args
             self._reverse_param_map = {input_arg: func_arg for func_arg, input_arg in self.param_map.items()}
-        
-        # need to wrap inputs in dict.
+
         mapped_pred_dict = {}
         mapped_target_dict = {}
-        duplicated = []
-        for input_arg in set(list(pred_dict.keys()) + list(target_dict.keys())):
-            not_duplicate_flag = 0
-            if input_arg in self._reverse_param_map:
-                mapped_arg = self._reverse_param_map[input_arg]
-                not_duplicate_flag += 1
-            else:
-                mapped_arg = input_arg
+        for input_arg, mapped_arg in self._reverse_param_map.items():
             if input_arg in pred_dict:
                 mapped_pred_dict[mapped_arg] = pred_dict[input_arg]
-                not_duplicate_flag += 1
             if input_arg in target_dict:
                 mapped_target_dict[mapped_arg] = target_dict[input_arg]
-                not_duplicate_flag += 1
-            if not_duplicate_flag == 3:
-                duplicated.append(input_arg)
         
         # missing
         if not self._checked:
+            duplicated = []
+            for input_arg, mapped_arg in self._reverse_param_map.items():
+                if input_arg in pred_dict and input_arg in target_dict:
+                    duplicated.append(input_arg)
             check_res = _check_arg_dict_list(self.get_loss, [mapped_pred_dict, mapped_target_dict])
             # replace missing.
             missing = check_res.missing
@@ -204,15 +196,12 @@ class LossFunc(LossBase):
         
         super(LossFunc, self).__init__()
         _check_function_or_method(func)
+        self.get_loss = func
         if key_map is not None:
             if not isinstance(key_map, dict):
                 raise RuntimeError(f"Loss error: key_map except a {type({})} but got a {type(key_map)}")
-            self.param_map = key_map
-        if len(kwargs) > 0:
-            for key, val in kwargs.items():
-                self.param_map.update({key: val})
+        self._init_param_map(key_map, **kwargs)
         
-        self.get_loss = func
 
 
 class CrossEntropyLoss(LossBase):

fastNLP/core/metrics.py

@@ -223,25 +223,18 @@ class MetricBase(object):
         # need to wrap inputs in dict.
         mapped_pred_dict = {}
         mapped_target_dict = {}
-        duplicated = []
-        for input_arg in set(list(pred_dict.keys()) + list(target_dict.keys())):
-            not_duplicate_flag = 0
-            if input_arg in self._reverse_param_map:
-                mapped_arg = self._reverse_param_map[input_arg]
-                not_duplicate_flag += 1
-            else:
-                mapped_arg = input_arg
+        for input_arg, mapped_arg in self._reverse_param_map.items():
             if input_arg in pred_dict:
                 mapped_pred_dict[mapped_arg] = pred_dict[input_arg]
-                not_duplicate_flag += 1
             if input_arg in target_dict:
                 mapped_target_dict[mapped_arg] = target_dict[input_arg]
-                not_duplicate_flag += 1
-            if not_duplicate_flag == 3:
-                duplicated.append(input_arg)
         
         # missing
         if not self._checked:
+            duplicated = []
+            for input_arg, mapped_arg in self._reverse_param_map.items():
+                if input_arg in pred_dict and input_arg in target_dict:
+                    duplicated.append(input_arg)
             check_res = _check_arg_dict_list(self.evaluate, [mapped_pred_dict, mapped_target_dict])
             # only check missing.
             # replace missing.
@@ -411,6 +404,37 @@ def _bmeso_tag_to_spans(tags, ignore_labels=None):
             ]
 
 
+def _bioes_tag_to_spans(tags, ignore_labels=None):
+    """
+    给定一个tags的lis，比如['O', 'B-singer', 'I-singer', 'E-singer', 'O', 'O']。
+    返回[('singer', (1, 4))] (左闭右开区间)
+
+    :param tags: List[str],
+    :param ignore_labels: List[str], 在该list中的label将被忽略
+    :return: List[Tuple[str, List[int, int]]]. [(label，[start, end])]
+    """
+    ignore_labels = set(ignore_labels) if ignore_labels else set()
+
+    spans = []
+    prev_bmes_tag = None
+    for idx, tag in enumerate(tags):
+        tag = tag.lower()
+        bmes_tag, label = tag[:1], tag[2:]
+        if bmes_tag in ('b', 's'):
+            spans.append((label, [idx, idx]))
+        elif bmes_tag in ('i', 'e') and prev_bmes_tag in ('b', 'i') and label == spans[-1][0]:
+            spans[-1][1][1] = idx
+        elif bmes_tag == 'o':
+            pass
+        else:
+            spans.append((label, [idx, idx]))
+        prev_bmes_tag = bmes_tag
+    return [(span[0], (span[1][0], span[1][1] + 1))
+            for span in spans
+            if span[0] not in ignore_labels
+            ]
+
+
 def _bio_tag_to_spans(tags, ignore_labels=None):
     """
     给定一个tags的lis，比如['O', 'B-singer', 'I-singer', 'I-singer', 'O', 'O']。
@@ -471,7 +495,7 @@ class SpanFPreRecMetric(MetricBase):
     :param str pred: 用该key在evaluate()时从传入dict中取出prediction数据。 为None，则使用'pred'取数据
     :param str target: 用该key在evaluate()时从传入dict中取出target数据。 为None，则使用'target'取数据
     :param str seq_len: 用该key在evaluate()时从传入dict中取出sequence length数据。为None，则使用'seq_len'取数据。
-    :param str encoding_type: 目前支持bio, bmes
+    :param str encoding_type: 目前支持bio, bmes, bmeso, bioes
     :param list ignore_labels: str 组成的list. 这个list中的class不会被用于计算。例如在POS tagging时传入['NN']，则不会计算'NN'这
         个label
     :param bool only_gross: 是否只计算总的f1, precision, recall的值；如果为False，不仅返回总的f1, pre, rec, 还会返回每个
@@ -499,6 +523,8 @@ class SpanFPreRecMetric(MetricBase):
             self.tag_to_span_func = _bio_tag_to_spans
         elif self.encoding_type == 'bmeso':
             self.tag_to_span_func = _bmeso_tag_to_spans
+        elif self.encoding_type == 'bioes':
+            self.tag_to_span_func = _bioes_tag_to_spans
         else:
             raise ValueError("Only support 'bio', 'bmes', 'bmeso' type.")