fixed deprecation warning evaluation ONCE (open-mmlab#1210)

pcdet/datasets/once/once_eval/eval_utils.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+
 def compute_split_parts(num_samples, num_parts):
     part_samples = num_samples // num_parts
     remain_samples = num_samples % num_parts
@@ -10,37 +11,40 @@ def compute_split_parts(num_samples, num_parts):
     else:
         return [part_samples] * num_parts + [remain_samples]
 
+
 def overall_filter(boxes):
-    ignore = np.zeros(boxes.shape[0], dtype=np.bool) # all false
+    ignore = np.zeros(boxes.shape[0], dtype=bool)  # all false
     return ignore
 
+
 def distance_filter(boxes, level):
-    ignore = np.ones(boxes.shape[0], dtype=np.bool) # all true
+    ignore = np.ones(boxes.shape[0], dtype=bool)  # all true
     dist = np.sqrt(np.sum(boxes[:, 0:3] * boxes[:, 0:3], axis=1))
 
-    if level == 0: # 0-30m
+    if level == 0:  # 0-30m
         flag = dist < 30
-    elif level == 1: # 30-50m
+    elif level == 1:  # 30-50m
         flag = (dist >= 30) & (dist < 50)
-    elif level == 2: # 50m-inf
+    elif level == 2:  # 50m-inf
         flag = dist >= 50
     else:
         assert False, 'level < 3 for distance metric, found level %s' % (str(level))
 
     ignore[flag] = False
     return ignore
 
+
 def overall_distance_filter(boxes, level):
-    ignore = np.ones(boxes.shape[0], dtype=np.bool) # all true
+    ignore = np.ones(boxes.shape[0], dtype=bool)  # all true
     dist = np.sqrt(np.sum(boxes[:, 0:3] * boxes[:, 0:3], axis=1))
 
     if level == 0:
-        flag = np.ones(boxes.shape[0], dtype=np.bool)
-    elif level == 1: # 0-30m
+        flag = np.ones(boxes.shape[0], dtype=bool)
+    elif level == 1:  # 0-30m
         flag = dist < 30
-    elif level == 2: # 30-50m
+    elif level == 2:  # 30-50m
         flag = (dist >= 30) & (dist < 50)
-    elif level == 3: # 50m-inf
+    elif level == 3:  # 50m-inf
         flag = dist >= 50
     else:
         assert False, 'level < 4 for overall & distance metric, found level %s' % (str(level))