84 lines
2.6 KiB
Python
84 lines
2.6 KiB
Python
from pathlib import Path
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import torch
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from datasets import load_dataset, load_from_disk
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from lerobot.common.datasets.utils import load_previous_and_future_frames
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class PushtDataset(torch.utils.data.Dataset):
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"""
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https://huggingface.co/datasets/lerobot/pusht
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Arguments
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----------
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delta_timestamps : dict[list[float]] | None, optional
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Loads data from frames with a shift in timestamps with a different strategy for each data key (e.g. state, action or image)
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If `None`, no shift is applied to current timestamp and the data from the current frame is loaded.
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"""
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# Copied from lerobot/__init__.py
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available_datasets = ["pusht"]
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fps = 10
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image_keys = ["observation.image"]
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def __init__(
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self,
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dataset_id: str = "pusht",
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version: str | None = "v1.0",
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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.dataset_id = dataset_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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if self.root is not None:
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self.hf_dataset = load_from_disk(Path(self.root) / self.dataset_id / self.split)
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else:
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self.hf_dataset = load_dataset(
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f"lerobot/{self.dataset_id}", revision=self.version, split=self.split
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)
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self.hf_dataset = self.hf_dataset.with_format("torch")
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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_id"))
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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.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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# convert images from channel last (PIL) to channel first (pytorch)
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for key in self.image_keys:
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if item[key].ndim == 3:
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item[key] = item[key].permute((2, 0, 1)) # h w c -> c h w
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elif item[key].ndim == 4:
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item[key] = item[key].permute((0, 3, 1, 2)) # t h w c -> t c h w
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else:
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raise ValueError(item[key].ndim)
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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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