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