Refactoring

logreplay/sensors/semantic_lidar.py

@@ -130,11 +130,10 @@ def tick(self):
  vehicle_idx = self.obj_idx[self.obj_tag == 10]
  # each individual instance id
  vehicle_unique_id = list(np.unique(vehicle_idx))
- vehicle_id_filter = []
-
- for veh_id in vehicle_unique_id:
- if vehicle_idx[vehicle_idx == veh_id].shape[0] > self.thresh:
- vehicle_id_filter.append(veh_id)
-
+ vehicle_id_filter = [
+ veh_id for veh_id in vehicle_unique_id 
+ if vehicle_idx[vehicle_idx == veh_id].shape[0] > self.thresh
+ ]
+
  # these are the ids that are visible
  return vehicle_id_filter

opencood/utils/eval_utils.py

@@ -27,14 +27,9 @@ def voc_ap(rec, prec):
  for i in range(len(mpre) - 2, -1, -1):
  mpre[i] = max(mpre[i], mpre[i + 1])
 
- i_list = []
- for i in range(1, len(mrec)):
- if mrec[i] != mrec[i - 1]:
- i_list.append(i)
-
- ap = 0.0
- for i in i_list:
- ap += ((mrec[i] - mrec[i - 1]) * mpre[i])
+ i_list = [i for i in range(1, len(mrec)) if mrec[i] != mrec[i - 1]]
+
+ ap = np.dot([mrec[i] - mrec[i - 1] for i in i_list], [mrec[i] for i in i_list])
  return ap, mrec, mpre
 
 
@@ -110,16 +105,9 @@ def calculate_ap(result_stat, iou):
 
  gt_total = iou_5['gt']
 
- cumsum = 0
- for idx, val in enumerate(fp):
- fp[idx] += cumsum
- cumsum += val
-
- cumsum = 0
- for idx, val in enumerate(tp):
- tp[idx] += cumsum
- cumsum += val
-
+ fp = np.cumsum(fp)
+ tp = np.cumsum(tp)
+
  rec = tp[:]
  for idx, val in enumerate(tp):
  rec[idx] = float(tp[idx]) / gt_total