use p.map instead of p.imap for generating WOD gt_database

pcdet/datasets/waymo/waymo_dataset.py

@@ -488,8 +488,10 @@ class WaymoDataset(DatasetTemplate):
         stacked_gt_points = np.concatenate(stacked_gt_points, axis=0)
         np.save(db_data_save_path, stacked_gt_points)
 
-    def create_gt_database_of_single_scene(self, info_with_idx, database_save_path=None, use_sequence_data=False, used_classes=None, use_cuda=False):
+    def create_gt_database_of_single_scene(self, info_with_idx, database_save_path=None, use_sequence_data=False, used_classes=None, total_samples=0, use_cuda=False):
         info, info_idx = info_with_idx
+        print('gt_database sample: %d/%d' % (info_idx, total_samples))
+        
         all_db_infos = {}
         pc_info = info['point_cloud']
         sequence_name = pc_info['lidar_sequence']
@@ -577,14 +579,15 @@ class WaymoDataset(DatasetTemplate):
         with open(info_path, 'rb') as f:
             infos = pickle.load(f)
 
+        print(f'Number workers: {num_workers}')
         create_gt_database_of_single_scene = partial(
             self.create_gt_database_of_single_scene, 
             use_sequence_data=use_sequence_data, database_save_path=database_save_path, 
-            used_classes=used_classes, use_cuda=True
+            used_classes=used_classes, total_samples=len(infos), use_cuda=False
         )
         # create_gt_database_of_single_scene((infos[0], 0))
         with multiprocessing.Pool(num_workers) as p:
-            all_db_infos_list = list(tqdm(p.imap(create_gt_database_of_single_scene, zip(infos, np.arange(len(infos)))), total=len(infos)))
+            all_db_infos_list = list(p.map(create_gt_database_of_single_scene, zip(infos, np.arange(len(infos)))))
 
         all_db_infos = {}