forked from tangger/lerobot
39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
import logging
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import torch
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from omegaconf import DictConfig, OmegaConf
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from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
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def make_dataset(cfg: DictConfig, split="train") -> LeRobotDataset:
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if cfg.env.name not in cfg.dataset_repo_id:
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logging.warning(
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f"There might be a mismatch between your training dataset ({cfg.dataset_repo_id=}) and your "
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f"environment ({cfg.env.name=})."
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)
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delta_timestamps = cfg.training.get("delta_timestamps")
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if delta_timestamps is not None:
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for key in delta_timestamps:
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if isinstance(delta_timestamps[key], str):
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delta_timestamps[key] = eval(delta_timestamps[key])
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# TODO(rcadene): add data augmentations
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dataset = LeRobotDataset(
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cfg.dataset_repo_id,
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split=split,
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delta_timestamps=delta_timestamps,
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n_end_keyframes_dropped=eval(cfg.training.get("n_end_keyframes_dropped", "0")),
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)
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if cfg.get("override_dataset_stats"):
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for key, stats_dict in cfg.override_dataset_stats.items():
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for stats_type, listconfig in stats_dict.items():
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# example of stats_type: min, max, mean, std
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stats = OmegaConf.to_container(listconfig, resolve=True)
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dataset.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
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return dataset
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