There is no implementation of "player_future" sensor

[wxdublin]

I am trying to collect dataset using baselines/rulebased/autopilot/run.py. It worked but after I added "player_future" in the sensors list it still did not produce the player_future files. Looks like there is no player_future implementation?

[Morphlng]

player_future is not a sensor; it is the future trajectory based on current observation. The data collected by SaveToDiskWrapper does not contain this, it is post-processed by the process method in CARLADataset.

oatomobile/oatomobile/datasets/carla.py

Lines 238 to 325 in 1680aee

	def process(
	dataset_dir: str,
	output_dir: str,
	future_length: int = 80,
	past_length: int = 20,
	num_frame_skips: int = 5,
	) -> None:
	"""Converts a raw dataset to demonstrations for imitation learning.
	Args:
	dataset_dir: The full path to the raw dataset.
	output_dir: The full path to the output directory.
	future_length: The length of the future trajectory.
	past_length: The length of the past trajectory.
	num_frame_skips: The number of frames to skip.
	"""
	from oatomobile.utils import carla as cutil
	# Creates the necessary output directory.
	os.makedirs(output_dir, exist_ok=True)
	# Iterate over all episodes.
	for episode_token in tqdm.tqdm(os.listdir(dataset_dir)):
	logging.debug("Processes {} episode".format(episode_token))
	# Initializes episode handler.
	episode = Episode(parent_dir=dataset_dir, token=episode_token)
	# Fetches all `.npz` files from the raw dataset.
	try:
	sequence = episode.fetch()
	except FileNotFoundError:
	continue
	# Always keep `past_length+future_length+1` files open.
	assert len(sequence) >= past_length + future_length + 1
	for i in tqdm.trange(
	past_length,
	len(sequence) - future_length,
	num_frame_skips,
	):
	try:
	# Player context/observation.
	observation = episode.read_sample(sample_token=sequence[i])
	current_location = observation["location"]
	current_rotation = observation["rotation"]
	# Build past trajectory.
	player_past = list()
	for j in range(past_length, 0, -1):
	past_location = episode.read_sample(
	sample_token=sequence[i - j],
	attr="location",
	)
	player_past.append(past_location)
	player_past = np.asarray(player_past)
	assert len(player_past.shape) == 2
	player_past = cutil.world2local(
	current_location=current_location,
	current_rotation=current_rotation,
	world_locations=player_past,
	)
	# Build future trajectory.
	player_future = list()
	for j in range(1, future_length + 1):
	future_location = episode.read_sample(
	sample_token=sequence[i + j],
	attr="location",
	)
	player_future.append(future_location)
	player_future = np.asarray(player_future)
	assert len(player_future.shape) == 2
	player_future = cutil.world2local(
	current_location=current_location,
	current_rotation=current_rotation,
	world_locations=player_future,
	)
	# Store to ouput directory.
	np.savez_compressed(
	os.path.join(output_dir, "{}.npz".format(sequence[i])),
	**observation,
	player_future=player_future,
	player_past=player_past,
	)
	except Exception as e:
	if isinstance(e, KeyboardInterrupt):
	sys.exit(0)