support to train single-frame model with multi-frame GT database on WOD

docs/changelog.md

@@ -9,9 +9,8 @@
         ```
         python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos --cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml --update_info_only
         ```   
-       * Generate multi-frame GT database for copy-paste augmentation of multi-frame training
+       * Generate multi-frame GT database for copy-paste augmentation of multi-frame training. There is also a faster version with parallel data generation by adding `--use_parallel`, but you need to read the codes and rename the file after getting the results.
         ```
-        # There is also a faster version with parallel data generation by adding `--use_parallel`, but you need to read the codes and rename the file after getting the results  
         python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_gt_database --cfg_file tools/cfgs/dataset_configs/waymo_dataset_multiframe.yaml 
         ```
         This will generate the new files like the following (the last three lines under `data/waymo`): 

pcdet/datasets/augmentor/database_sampler.py

@@ -30,6 +30,13 @@ class DataBaseSampler(object):
 
         for db_info_path in sampler_cfg.DB_INFO_PATH:
             db_info_path = self.root_path.resolve() / db_info_path
+            if not db_info_path.exists():
+                assert len(sampler_cfg.DB_INFO_PATH) == 1
+                sampler_cfg.DB_INFO_PATH[0] = sampler_cfg.BACKUP_DB_INFO['DB_INFO_PATH']
+                sampler_cfg.DB_DATA_PATH[0] = sampler_cfg.BACKUP_DB_INFO['DB_DATA_PATH']
+                db_info_path = self.root_path.resolve() / sampler_cfg.DB_INFO_PATH[0]
+                sampler_cfg.NUM_POINT_FEATURES = sampler_cfg.BACKUP_DB_INFO['NUM_POINT_FEATURES']
+                    
             with open(str(db_info_path), 'rb') as f:
                 infos = pickle.load(f)
                 [self.db_infos[cur_class].extend(infos[cur_class]) for cur_class in class_names]
@@ -403,9 +410,14 @@ class DataBaseSampler(object):
         obj_points = np.concatenate(obj_points_list, axis=0)
         sampled_gt_names = np.array([x['name'] for x in total_valid_sampled_dict])
 
-        if self.sampler_cfg.get('FILTER_OBJ_POINTS_BY_TIMESTAMP', False):
-            min_time = min(self.sampler_cfg.TIME_RANGE[0], self.sampler_cfg.TIME_RANGE[1])
-            max_time = max(self.sampler_cfg.TIME_RANGE[0], self.sampler_cfg.TIME_RANGE[1])
+        if self.sampler_cfg.get('FILTER_OBJ_POINTS_BY_TIMESTAMP', False) or obj_points.shape[-1] != points.shape[-1]:
+            if self.sampler_cfg.get('FILTER_OBJ_POINTS_BY_TIMESTAMP', False):
+                min_time = min(self.sampler_cfg.TIME_RANGE[0], self.sampler_cfg.TIME_RANGE[1])
+                max_time = max(self.sampler_cfg.TIME_RANGE[0], self.sampler_cfg.TIME_RANGE[1])
+            else:
+                assert obj_points.shape[-1] == points.shape[-1] + 1
+                # transform multi-frame GT points to single-frame GT points
+                min_time = max_time = 0.0 
 
             time_mask = np.logical_and(obj_points[:, -1] < max_time + 1e-6, obj_points[:, -1] > min_time - 1e-6)
             obj_points = obj_points[time_mask]
@@ -414,7 +426,7 @@ class DataBaseSampler(object):
             sampled_gt_boxes[:, 0:7], extra_width=self.sampler_cfg.REMOVE_EXTRA_WIDTH
         )
         points = box_utils.remove_points_in_boxes3d(points, large_sampled_gt_boxes)
-        points = np.concatenate([obj_points, points], axis=0)
+        points = np.concatenate([obj_points[:, :points.shape[-1]], points], axis=0)
         gt_names = np.concatenate([gt_names, sampled_gt_names], axis=0)
         gt_boxes = np.concatenate([gt_boxes, sampled_gt_boxes], axis=0)
         data_dict['gt_boxes'] = gt_boxes

pcdet/datasets/waymo/waymo_dataset.py

@@ -155,6 +155,7 @@ class WaymoDataset(DatasetTemplate):
             for sequence_file in self.sample_sequence_list
         ]
 
+        # process_single_sequence(sample_sequence_file_list[0])
         with multiprocessing.Pool(num_workers) as p:
             sequence_infos = list(tqdm(p.imap(process_single_sequence, sample_sequence_file_list),
                                        total=len(sample_sequence_file_list)))
@@ -369,7 +370,7 @@ class WaymoDataset(DatasetTemplate):
         point_offset_cnt = 0
         stacked_gt_points = []
         for k in tqdm(range(0, len(infos), sampled_interval)):
-            print('gt_database sample: %d/%d' % (k + 1, len(infos)))
+            # print('gt_database sample: %d/%d' % (k + 1, len(infos)))
             info = infos[k]
 
             pc_info = info['point_cloud']

pcdet/datasets/waymo/waymo_utils.py

@@ -210,7 +210,7 @@ def process_single_sequence(sequence_file, save_path, sampled_interval, has_labe
     sequence_infos = []
     if pkl_file.exists():
         sequence_infos = pickle.load(open(pkl_file, 'rb'))
-
+        sequence_infos_old = None
         if not update_info_only:
             print('Skip sequence since it has been processed before: %s' % pkl_file)
             return sequence_infos
@@ -248,7 +248,7 @@ def process_single_sequence(sequence_file, save_path, sampled_interval, has_labe
             annotations = generate_labels(frame, pose=pose)
             info['annos'] = annotations
 
-        if update_info_only:
+        if update_info_only and sequence_infos_old is not None:
             assert info['frame_id'] == sequence_infos_old[cnt]['frame_id']
             num_points_of_each_lidar = sequence_infos_old[cnt]['num_points_of_each_lidar']
         else:

tools/cfgs/dataset_configs/waymo_dataset.yaml

@@ -32,6 +32,12 @@ DATA_AUGMENTOR:
           DB_DATA_PATH:
               - waymo_processed_data_v0_5_0_gt_database_train_sampled_1_global.npy
 
+          BACKUP_DB_INFO:
+              # if the above DB_INFO cannot be found, will use this backup one
+              DB_INFO_PATH: waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1_multiframe_-4_to_0.pkl
+              DB_DATA_PATH: waymo_processed_data_v0_5_0_gt_database_train_sampled_1_multiframe_-4_to_0_global.npy
+              NUM_POINT_FEATURES: 6
+
           PREPARE: {
              filter_by_min_points: ['Vehicle:5', 'Pedestrian:5', 'Cyclist:5'],
              filter_by_difficulty: [-1],