64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
import torch
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from datasets import load_dataset
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from lerobot.common.datasets.utils import load_previous_and_future_frames
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class AlohaDataset(torch.utils.data.Dataset):
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"""
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https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human
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https://huggingface.co/datasets/lerobot/aloha_sim_insertion_scripted
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https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human
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https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_scripted
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"""
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available_datasets = [
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"aloha_sim_insertion_human",
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"aloha_sim_insertion_scripted",
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"aloha_sim_transfer_cube_human",
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"aloha_sim_transfer_cube_scripted",
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]
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fps = 50
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image_keys = ["observation.images.top"]
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def __init__(
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self,
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dataset_id: str,
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version: str | None = "v1.0",
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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.transform = transform
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self.delta_timestamps = delta_timestamps
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self.data_dict = load_dataset(f"lerobot/{self.dataset_id}", revision=self.version, split="train")
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self.data_dict = self.data_dict.with_format("torch")
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@property
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def num_samples(self) -> int:
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return len(self.data_dict)
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@property
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def num_episodes(self) -> int:
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return len(self.data_dict.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.data_dict[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.data_dict,
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self.delta_timestamps,
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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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