73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
from pathlib import Path
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import datasets
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import torch
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from lerobot.common.datasets.utils import (
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load_episode_data_index,
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load_hf_dataset,
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load_info,
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load_previous_and_future_frames,
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load_stats,
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)
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class LeRobotDataset(torch.utils.data.Dataset):
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def __init__(
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self,
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repo_id: str,
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version: str | None = "v1.1",
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root: Path | None = None,
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split: str = "train",
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transform: callable = None,
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delta_timestamps: dict[list[float]] | None = None,
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):
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super().__init__()
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self.repo_id = repo_id
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self.version = version
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self.root = root
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self.split = split
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self.transform = transform
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self.delta_timestamps = delta_timestamps
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# load data from hub or locally when root is provided
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self.hf_dataset = load_hf_dataset(repo_id, version, root, split)
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self.episode_data_index = load_episode_data_index(repo_id, version, root)
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self.stats = load_stats(repo_id, version, root)
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self.info = load_info(repo_id, version, root)
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@property
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def fps(self) -> int:
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return self.info["fps"]
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@property
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def image_keys(self) -> list[str]:
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return [key for key, feats in self.hf_dataset.features.items() if isinstance(feats, datasets.Image)]
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@property
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def num_samples(self) -> int:
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return len(self.hf_dataset)
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@property
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def num_episodes(self) -> int:
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return len(self.hf_dataset.unique("episode_index"))
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def __len__(self):
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return self.num_samples
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def __getitem__(self, idx):
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item = self.hf_dataset[idx]
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if self.delta_timestamps is not None:
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item = load_previous_and_future_frames(
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item,
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self.hf_dataset,
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self.episode_data_index,
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self.delta_timestamps,
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tol=1 / self.fps - 1e-4, # 1e-4 to account for possible numerical error
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)
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if self.transform is not None:
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item = self.transform(item)
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return item
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