fix comment

mmeval/metrics/char_recall_precision.py

@@ -14,14 +14,14 @@ class CharRecallPrecision(BaseMetric):
 
             - unchanged: Do not change prediction texts and labels.
             - upper: Convert prediction texts and labels into uppercase
-                     characters.
+              characters.
             - lower: Convert prediction texts and labels into lowercase
-                     characters.
+              characters.
 
             Usually, it only works for English characters. Defaults to
             'unchanged'.
         invalid_symbol (str): A regular expression to filter out invalid or
-            not cared characters. Defaults to '[^A-Z^a-z^0-9^\u4e00-\u9fa5]'.
+            not cared characters. Defaults to '[^A-Za-z0-9\u4e00-\u9fa5]'.
         **kwargs: Keyword parameters passed to :class:`BaseMetric`.
 
     Examples:
@@ -36,21 +36,21 @@ class CharRecallPrecision(BaseMetric):
 
     def __init__(self,
                  letter_case: str = 'unchanged',
-                 invalid_symbol: str = '[^A-Z^a-z^0-9^\u4e00-\u9fa5]',
+                 invalid_symbol: str = '[^A-Za-z0-9\u4e00-\u9fa5]',
                  **kwargs):
         super().__init__(**kwargs)
         assert letter_case in ['unchanged', 'upper', 'lower']
         self.letter_case = letter_case
         self.invalid_symbol = re.compile(invalid_symbol)
 
-    def add(self, predictions: Sequence[str], labels: Sequence[str]) -> None:  # type: ignore # yapf: disable # noqa: E501
+    def add(self, predictions: Sequence[str], groundtruths: Sequence[str]) -> None:  # type: ignore # yapf: disable # noqa: E501
         """Process one batch of data and predictions.
 
         Args:
             predictions (list[str]): The prediction texts.
-            labels (list[str]): The ground truth texts.
+            groundtruths (list[str]): The ground truth texts.
         """
-        for pred, label in zip(predictions, labels):
+        for pred, label in zip(predictions, groundtruths):
             if self.letter_case in ['upper', 'lower']:
                 pred = getattr(pred, self.letter_case)()
                 label = getattr(label, self.letter_case)()
@@ -79,10 +79,10 @@ def compute_metric(self, results: Sequence[Tuple[int, int, int]]) -> Dict:
             true_positive_sum += true_positive
         char_recall = true_positive_sum / max(gt_sum, 1.0)
         char_precision = true_positive_sum / max(pred_sum, 1.0)
-        eval_res = {}
-        eval_res['recall'] = char_recall
-        eval_res['precision'] = char_precision
-        return eval_res
+        metric_results = {}
+        metric_results['recall'] = char_recall
+        metric_results['precision'] = char_precision
+        return metric_results
 
     def _cal_true_positive_char(self, pred: str, gt: str) -> int:
         """Calculate correct character number in prediction.