[Fix] fix np.arange() output not stable error

mmocr/evaluation/metrics/hmean_iou_metric.py

@@ -70,7 +70,13 @@ def __init__(self,
         super().__init__(collect_device=collect_device, prefix=prefix)
         self.match_iou_thr = match_iou_thr
         self.ignore_precision_thr = ignore_precision_thr
-        self.pred_score_thrs = np.arange(**pred_score_thrs)
+        self.pred_score_thrs = np.linspace(
+            pred_score_thrs['start'],
+            pred_score_thrs['stop'],
+            int(
+                np.round((pred_score_thrs['stop'] - pred_score_thrs['start']) /
+                         pred_score_thrs['step'])) + 1,
+            endpoint=True)
         assert strategy in ['max_matching', 'vanilla']
         self.strategy = strategy
 

projects/ABCNet/abcnet/metric/e2e_hmean_iou_metric.py

@@ -75,7 +75,13 @@ def __init__(self,
         super().__init__(collect_device=collect_device, prefix=prefix)
         self.match_iou_thr = match_iou_thr
         self.ignore_precision_thr = ignore_precision_thr
-        self.pred_score_thrs = np.arange(**pred_score_thrs)
+        self.pred_score_thrs = np.linspace(
+            pred_score_thrs['start'],
+            pred_score_thrs['stop'],
+            int(
+                np.round((pred_score_thrs['stop'] - pred_score_thrs['start']) /
+                         pred_score_thrs['step'])) + 1,
+            endpoint=True)
         self.word_spotting = word_spotting
         self.min_length_case_word = min_length_case_word
         self.special_characters = special_characters

projects/SPTS/spts/metric/e2e_point_metric.py

@@ -58,7 +58,13 @@ def __init__(self,
                  collect_device: str = 'cpu',
                  prefix: Optional[str] = None) -> None:
         super().__init__(collect_device=collect_device, prefix=prefix)
-        self.text_score_thrs = np.arange(**text_score_thrs)
+        self.text_score_thrs = np.linspace(
+            text_score_thrs['start'],
+            text_score_thrs['stop'],
+            int(
+                np.round((text_score_thrs['stop'] - text_score_thrs['start']) /
+                         text_score_thrs['step'])) + 1,
+            endpoint=True)
         self.word_spotting = word_spotting
         self.match_dist_thr = match_dist_thr
         if lexicon_path: