metric bug fix

fastNLP/core/metrics.py

@@ -115,10 +115,10 @@ class MetricBase(object):
 
 
 class AccuracyMetric(MetricBase):
-    def __init__(self, predictions=None, targets=None, masks=None, seq_lens=None):
+    def __init__(self, input=None, targets=None, masks=None, seq_lens=None):
         super().__init__()
 
-        self._init_param_map(predictions=predictions, targets=targets,
+        self._init_param_map(input=input, targets=targets,
                              masks=masks, seq_lens=seq_lens)
 
         self.total = 0
@@ -138,7 +138,7 @@ class AccuracyMetric(MetricBase):
         :return: dict({'acc': float})
         """
         if not isinstance(input, torch.Tensor):
-            raise NameError(f"`predictions` in {get_func_signature(self.evaluate())} expects torch.Tensor,"
+            raise NameError(f"`input` in {get_func_signature(self.evaluate())} expects torch.Tensor,"
                             f"got {type(input)}.")
         if not isinstance(targets, torch.Tensor):
             raise NameError(f"`targets` in {get_func_signature(self.evaluate())} expects torch.Tensor,"
@@ -157,9 +157,9 @@ class AccuracyMetric(MetricBase):
         if input.size()==targets.size():
             pass
         elif len(input.size())==len(targets.size())+1:
-            predictions = input.argmax(dim=-1)
+            input = input.argmax(dim=-1)
         else:
-            raise RuntimeError(f"In {get_func_signature(self.evaluate())}, when predictions with "
+            raise RuntimeError(f"In {get_func_signature(self.evaluate())}, when input with "
                                f"size:{input.size()}, targets should with size: {input.size()} or "
                                f"{input.size()[:-1]}, got {targets.size()}.")