Remove obsolete code
This commit is contained in:
Simon Alibert
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443a9eec88
commit
5ea7c78237
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@ -368,100 +368,6 @@ def get_delta_indices(delta_timestamps: dict[str, list[float]], fps: int) -> dic
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return delta_indices
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# TODO(aliberts): remove
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def load_previous_and_future_frames(
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item: dict[str, torch.Tensor],
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hf_dataset: datasets.Dataset,
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episode_data_index: dict[str, torch.Tensor],
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delta_timestamps: dict[str, list[float]],
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tolerance_s: float,
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) -> dict[torch.Tensor]:
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"""
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Given a current item in the dataset containing a timestamp (e.g. 0.6 seconds), and a list of time differences of
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some modalities (e.g. delta_timestamps={"observation.image": [-0.8, -0.2, 0, 0.2]}), this function computes for each
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given modality (e.g. "observation.image") a list of query timestamps (e.g. [-0.2, 0.4, 0.6, 0.8]) and loads the closest
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frames in the dataset.
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Importantly, when no frame can be found around a query timestamp within a specified tolerance window, this function
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raises an AssertionError. When a timestamp is queried before the first available timestamp of the episode or after
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the last available timestamp, the violation of the tolerance doesnt raise an AssertionError, and the function
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populates a boolean array indicating which frames are outside of the episode range. For instance, this boolean array
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is useful during batched training to not supervise actions associated to timestamps coming after the end of the
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episode, or to pad the observations in a specific way. Note that by default the observation frames before the start
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of the episode are the same as the first frame of the episode.
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Parameters:
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- item (dict): A dictionary containing all the data related to a frame. It is the result of `dataset[idx]`. Each key
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corresponds to a different modality (e.g., "timestamp", "observation.image", "action").
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- hf_dataset (datasets.Dataset): A dictionary containing the full dataset. Each key corresponds to a different
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modality (e.g., "timestamp", "observation.image", "action").
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- episode_data_index (dict): A dictionary containing two keys ("from" and "to") associated to dataset indices.
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They indicate the start index and end index of each episode in the dataset.
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- delta_timestamps (dict): A dictionary containing lists of delta timestamps for each possible modality to be
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retrieved. These deltas are added to the item timestamp to form the query timestamps.
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- tolerance_s (float, optional): The tolerance level (in seconds) used to determine if a data point is close enough to the query
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timestamp by asserting `tol > difference`. It is suggested to set `tol` to a smaller value than the
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smallest expected inter-frame period, but large enough to account for jitter.
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Returns:
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- The same item with the queried frames for each modality specified in delta_timestamps, with an additional key for
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each modality (e.g. "observation.image_is_pad").
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Raises:
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- AssertionError: If any of the frames unexpectedly violate the tolerance level. This could indicate synchronization
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issues with timestamps during data collection.
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"""
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# get indices of the frames associated to the episode, and their timestamps
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ep_id = item["episode_index"].item()
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ep_data_id_from = episode_data_index["from"][ep_id].item()
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ep_data_id_to = episode_data_index["to"][ep_id].item()
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ep_data_ids = torch.arange(ep_data_id_from, ep_data_id_to, 1)
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# load timestamps
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ep_timestamps = hf_dataset.select_columns("timestamp")[ep_data_id_from:ep_data_id_to]["timestamp"]
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ep_timestamps = torch.stack(ep_timestamps)
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# we make the assumption that the timestamps are sorted
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ep_first_ts = ep_timestamps[0]
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ep_last_ts = ep_timestamps[-1]
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current_ts = item["timestamp"].item()
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for key in delta_timestamps:
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# get timestamps used as query to retrieve data of previous/future frames
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delta_ts = delta_timestamps[key]
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query_ts = current_ts + torch.tensor(delta_ts)
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# compute distances between each query timestamp and all timestamps of all the frames belonging to the episode
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dist = torch.cdist(query_ts[:, None], ep_timestamps[:, None], p=1)
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min_, argmin_ = dist.min(1)
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# TODO(rcadene): synchronize timestamps + interpolation if needed
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is_pad = min_ > tolerance_s
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# check violated query timestamps are all outside the episode range
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assert ((query_ts[is_pad] < ep_first_ts) | (ep_last_ts < query_ts[is_pad])).all(), (
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f"One or several timestamps unexpectedly violate the tolerance ({min_} > {tolerance_s=}) inside episode range."
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"This might be due to synchronization issues with timestamps during data collection."
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)
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# get dataset indices corresponding to frames to be loaded
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data_ids = ep_data_ids[argmin_]
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# load frames modality
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item[key] = hf_dataset.select_columns(key)[data_ids][key]
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if isinstance(item[key][0], dict) and "path" in item[key][0]:
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# video mode where frame are expressed as dict of path and timestamp
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item[key] = item[key]
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else:
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item[key] = torch.stack(item[key])
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item[f"{key}_is_pad"] = is_pad
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return item
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# TODO(aliberts): remove
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def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> Dict[str, torch.Tensor]:
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"""
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