Remove 2D annotations on Lyft (#867)

tools/create_data.py

@@ -81,45 +81,23 @@ def nuscenes_data_prep(root_path,
                                 f'{out_dir}/{info_prefix}_infos_train.pkl')
 
 
-def lyft_data_prep(root_path,
-                   info_prefix,
-                   version,
-                   dataset_name,
-                   out_dir,
-                   max_sweeps=10):
+def lyft_data_prep(root_path, info_prefix, version, max_sweeps=10):
     """Prepare data related to Lyft dataset.
 
-    Related data consists of '.pkl' files recording basic infos,
-    and 2D annotations.
-    Although the ground truth database is not used in Lyft, it can also be
-    generated like nuScenes.
+    Related data consists of '.pkl' files recording basic infos.
+    Although the ground truth database and 2D annotations are not used in
+    Lyft, it can also be generated like nuScenes.
 
     Args:
         root_path (str): Path of dataset root.
         info_prefix (str): The prefix of info filenames.
         version (str): Dataset version.
-        dataset_name (str): The dataset class name.
-        out_dir (str): Output directory of the groundtruth database info.
-            Not used here if the groundtruth database is not generated.
-        max_sweeps (int): Number of input consecutive frames. Default: 10
+        max_sweeps (int, optional): Number of input consecutive frames.
+            Defaults to 10.
     """
     lyft_converter.create_lyft_infos(
         root_path, info_prefix, version=version, max_sweeps=max_sweeps)
 
-    if version == 'v1.01-test':
-        return
-
-    train_info_name = f'{info_prefix}_infos_train'
-    val_info_name = f'{info_prefix}_infos_val'
-
-    info_train_path = osp.join(root_path, f'{train_info_name}.pkl')
-    info_val_path = osp.join(root_path, f'{val_info_name}.pkl')
-
-    lyft_converter.export_2d_annotation(
-        root_path, info_train_path, version=version)
-    lyft_converter.export_2d_annotation(
-        root_path, info_val_path, version=version)
-
 
 def scannet_data_prep(root_path, info_prefix, out_dir, workers):
     """Prepare the info file for scannet dataset.
@@ -273,16 +251,12 @@ if __name__ == '__main__':
             root_path=args.root_path,
             info_prefix=args.extra_tag,
             version=train_version,
-            dataset_name='LyftDataset',
-            out_dir=args.out_dir,
             max_sweeps=args.max_sweeps)
         test_version = f'{args.version}-test'
         lyft_data_prep(
             root_path=args.root_path,
             info_prefix=args.extra_tag,
             version=test_version,
-            dataset_name='LyftDataset',
-            out_dir=args.out_dir,
             max_sweeps=args.max_sweeps)
     elif args.dataset == 'waymo':
         waymo_data_prep(

tools/data_converter/lyft_converter.py

 import mmcv
 import numpy as np
 import os
+from logging import warning
 from lyft_dataset_sdk.lyftdataset import LyftDataset as Lyft
 from os import path as osp
 from pyquaternion import Quaternion
@@ -217,6 +218,9 @@ def export_2d_annotation(root_path, info_path, version):
         info_path (str): Path of the info file.
         version (str): Dataset version.
     """
+    warning.warn('DeprecationWarning: 2D annotations are not used on the '
+                 'Lyft dataset. The function export_2d_annotation will be '
+                 'deprecated.')
     # get bbox annotations for camera
     camera_types = [
         'CAM_FRONT',