bradyz / 2020_CARLA_challenge

"Learning by Cheating" (CoRL 2019) submission for the 2020 CARLA Challenge
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Trying to update to work with CARLA 0.9.13 #49

Closed ajdroid closed 2 years ago

ajdroid commented 2 years ago

I see that the scenario runner and leaderboard submodules in your repo are forks of the carla ones and seem to have been updated to 0.9.10 (from 0.9.7, it looks like using one large merge commit). Do you have any custom changes in here, or are they just the 0.9.10 compatible versions of those 2 repositories?

I ask because I'm trying to update this code to work with the latest Carla. However, the PythonAPI has several changes which will necessitate updates to these submodules and I don't want to miss any custom changes you may have had during that update if I update from the vanilla Carla versions

bradyz commented 2 years ago

Hello! Thanks for the interest - I don't think there's any edits from our side that are version specific. There have been a lot of changes with the latest CARLA though so it might get be a little bit of work.

I won't update this repo since I am lazy :(, but if you have specific errors you run into you can post them here and I'd be happy to try and help!

ajdroid commented 2 years ago

Thanks for your response! I wanted to clarify that the 2 submodules which come from carla (scenario_runner and leaderboard) are unmodified by you (except for the team_code folder within leaderboard)? Or are you saying that you have edits but they should translate across carla versions? My plan is to just get the 0.9.13 compatible versions of those 2 from the carla-simulator github org. Then, the only code I would need to update is only the code in the carla_project and the team_code folder. Am I understanding correctly?

ajdroid commented 2 years ago

Hi Brady, one other question -- what does the mean and scale correspond to here? https://github.com/bradyz/leaderboard/blob/7104247204e0e591d43640d2c98490533bc5bbb8/team_code/planner.py#L45

class RoutePlanner(object):
    def __init__(self, min_distance, max_distance, debug_size=256):
        self.route = deque()
        self.min_distance = min_distance
        self.max_distance = max_distance

        self.mean = np.array([49.0, 8.0])
        self.scale = np.array([111324.60662786, 73032.1570362])

EDIT: For posterity, the answer can be found here: https://github.com/autonomousvision/transfuser/issues/48#issuecomment-1054376659