batch convert nuscenes

scenarionet/converter/__init__.py

@@ -1,3 +0,0 @@
-from scenarionet.converter.nuscenes.utils import convert_nuscenes_scenario
-from scenarionet.converter.nuplan.utils import convert_nuplan_scenario
-from scenarionet.converter.utils import write_to_directory

scenarionet/converter/nuscenes/utils.py

@@ -379,7 +379,7 @@ def convert_nuscenes_scenario(scene, nuscenes: NuScenes):
     result[SD.TRACKS] = get_tracks_from_frames(nuscenes, scene_info, frames, num_to_interpolate=5)
     result[SD.METADATA][SD.SDC_ID] = "ego"
 
-    # TODO Traffic Light
+    # No traffic light in nuscenes at this stage
     result[SD.DYNAMIC_MAP_STATES] = {}
 
     # map

scenarionet/converter/scripts/convert_nuscenes.py

@@ -4,12 +4,14 @@ MetaDrive.
 """
 import os.path
 
-from nuscenes import NuScenes
+try:
+    from nuscenes import NuScenes
+except ImportError:
+    raise ImportError("nuscenes-devkit has to be set up before running data conversion")
 from scenarionet import SCENARIONET_DATASET_PATH
 from scenarionet.converter.nuscenes.utils import convert_nuscenes_scenario
 from scenarionet.converter.utils import write_to_directory
 
-#
 if __name__ == "__main__":
     output_path = os.path.join(SCENARIONET_DATASET_PATH, "nuscenes")
     version = 'v1.0-mini'

scenarionet/converter/utils.py

@@ -60,7 +60,7 @@ def mph_to_kmh(speed_in_mph: float):
 
 def get_agent_summary(state_dict, id, type):
     track = state_dict["position"]
-    valid_track = track[state_dict["valid"], :2]
+    valid_track = track[state_dict["valid"].astype(int), :2]
     distance = float(sum(np.linalg.norm(valid_track[i] - valid_track[i + 1]) for i in range(valid_track.shape[0] - 1)))
     valid_length = int(sum(state_dict["valid"]))
 
@@ -173,10 +173,11 @@ def write_to_directory(convert_func,
 
     # rename and save
     if delay_remove is not None:
+        assert delay_remove == save_path
         shutil.rmtree(delay_remove)
     os.rename(output_path, save_path)
     summary_file = os.path.join(save_path, summary_file)
     with open(summary_file, "wb") as file:
         pickle.dump(dict_recursive_remove_array(metadata_recorder), file)
     print("Summary is saved at: {}".format(summary_file))
-    assert delay_remove == save_path
+