Add a tool to create interated database from existing database; Add minor modifications to docs

docs/GETTING_STARTED.md

@@ -94,6 +94,7 @@ and you could refer to `data/waymo/waymo_processed_data_v0_5_0` to see how many
 ```python 
 python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos \
     --cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml
+# Ignore 'CUDA_ERROR_NO_DEVICE' error as this process does not require GPU.
 ```
 
 Note that you do not need to install `waymo-open-dataset` if you have already processed the data before and do not need to evaluate with official Waymo Metrics. 

tools/process_tools/create_integrated_databse.py

+import numpy as np
+import pickle as pkl
+from pathlib import Path
+import tqdm
+import copy
+
+
+def create_integrated_db_with_infos(args, root_path):
+    """
+    Args:
+        args:
+    Returns:
+
+    """
+    # prepare
+    db_infos_path = root_path / args.src_db_info
+    db_info_global_path = str(db_infos_path)[:-4] + '_global' + '.pkl'
+    global_db_path = root_path / (args.new_db_name + '.npy')
+
+    db_infos = pkl.load(open(db_infos_path, 'rb'))
+    db_info_global = copy.deepcopy(db_infos)
+    start_idx = 0
+    global_db_list = []
+
+    for category, class_info in db_infos.items():
+        print('>>> Start processing %s' % category)
+        for idx, info in tqdm.tqdm(enumerate(class_info), total=len(class_info)):
+            obj_path = root_path / info['path']
+            obj_points = np.fromfile(str(obj_path), dtype=np.float32).reshape(
+                [-1, args.num_point_features])
+            num_points = obj_points.shape[0]
+            db_info_global[category][idx]['global_data_offset'] = (start_idx, start_idx + num_points)
+            start_idx += num_points
+            global_db_list.append(obj_points)
+
+    global_db = np.concatenate(global_db_list)
+
+    with open(global_db_path, 'wb') as f:
+        np.save(f, global_db)
+
+    with open(db_info_global_path, 'wb') as f:
+        pkl.dump(db_info_global, f)
+
+    print(f"Successfully create integrated database at {global_db_path}")
+    print(f"Successfully create integrated database info at {db_info_global_path}")
+
+    return db_info_global, global_db
+
+
+def verify(info, whole_db, root_path, num_point_features):
+    obj_path = root_path / info['path']
+    obj_points = np.fromfile(str(obj_path), dtype=np.float32).reshape([-1, num_point_features])
+    mean_origin = obj_points.mean()
+
+    start_idx, end_idx = info['global_data_offset']
+    obj_points_new = whole_db[start_idx:end_idx]
+    mean_new = obj_points_new.mean()
+
+    assert mean_origin == mean_new
+
+    print("Verification pass!")
+
+
+if __name__ == '__main__':
+    import argparse
+
+    parser = argparse.ArgumentParser(description='arg parser')
+    parser.add_argument('--root_path', type=str, default=None, help='specify the root path')
+    parser.add_argument('--src_db_info', type=str, default='waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1.pkl', help='')
+    parser.add_argument('--new_db_name', type=str, default='waymo_processed_data_v0_5_0_gt_database_train_sampled_1_global', help='')
+    parser.add_argument('--num_point_features', type=int, default=5,
+                        help='number of feature channels for points')
+    parser.add_argument('--class_name', type=str, default='Vehicle',
+                        help='category name for verification')
+
+    args = parser.parse_args()
+
+    root_path = Path(args.root_path)
+
+    db_infos_global, whole_db = create_integrated_db_with_infos(args, root_path)
+    # simple verify
+    verify(db_infos_global[args.class_name][0], whole_db, root_path, args.num_point_features)