Add aloha dataset
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Remi Cadene
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import logging
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from pathlib import Path
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from typing import Callable
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import einops
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import gdown
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import h5py
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import torch
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import torchrl
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import tqdm
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from tensordict import TensorDict
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from torchrl.data.replay_buffers.samplers import SliceSampler
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from torchrl.data.replay_buffers.storages import TensorStorage
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from torchrl.data.replay_buffers.writers import Writer
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from lerobot.common.datasets.abstract import AbstractExperienceReplay
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DATASET_IDS = [
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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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FOLDER_URLS = {
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"aloha_sim_insertion_human": "https://drive.google.com/drive/folders/1RgyD0JgTX30H4IM5XZn8I3zSV_mr8pyF",
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"aloha_sim_insertion_scripted": "https://drive.google.com/drive/folders/1TsojQQSXtHEoGnqgJ3gmpPQR2DPLtS2N",
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"aloha_sim_transfer_cube_human": "https://drive.google.com/drive/folders/1sc-E4QYW7A0o23m1u2VWNGVq5smAsfCo",
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"aloha_sim_transfer_cube_scripted": "https://drive.google.com/drive/folders/1aRyoOhQwxhyt1J8XgEig4s6kzaw__LXj",
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}
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EP48_URLS = {
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"aloha_sim_insertion_human": "https://drive.google.com/file/d/18Cudl6nikDtgRolea7je8iF_gGKzynOP/view?usp=drive_link",
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"aloha_sim_insertion_scripted": "https://drive.google.com/file/d/1wfMSZ24oOh5KR_0aaP3Cnu_c4ZCveduB/view?usp=drive_link",
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"aloha_sim_transfer_cube_human": "https://drive.google.com/file/d/18smMymtr8tIxaNUQ61gW6dG50pt3MvGq/view?usp=drive_link",
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"aloha_sim_transfer_cube_scripted": "https://drive.google.com/file/d/1pnGIOd-E4-rhz2P3VxpknMKRZCoKt6eI/view?usp=drive_link",
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}
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EP49_URLS = {
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"aloha_sim_insertion_human": "https://drive.google.com/file/d/1C1kZYyROzs-PrLc0SkDgUgMi4-L3lauE/view?usp=drive_link",
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"aloha_sim_insertion_scripted": "https://drive.google.com/file/d/17EuCUWS6uCCr6yyNzpXdcdE-_TTNCKtf/view?usp=drive_link",
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"aloha_sim_transfer_cube_human": "https://drive.google.com/file/d/1Nk7l53d9sJoGDBKAOnNrExX5nLacATc6/view?usp=drive_link",
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"aloha_sim_transfer_cube_scripted": "https://drive.google.com/file/d/1GKReZHrXU73NMiC5zKCq_UtqPVtYq8eo/view?usp=drive_link",
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}
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NUM_EPISODES = {
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"aloha_sim_insertion_human": 50,
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"aloha_sim_insertion_scripted": 50,
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"aloha_sim_transfer_cube_human": 50,
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"aloha_sim_transfer_cube_scripted": 50,
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}
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EPISODE_LEN = {
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"aloha_sim_insertion_human": 500,
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"aloha_sim_insertion_scripted": 400,
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"aloha_sim_transfer_cube_human": 400,
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"aloha_sim_transfer_cube_scripted": 400,
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}
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CAMERAS = {
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"aloha_sim_insertion_human": ["top"],
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"aloha_sim_insertion_scripted": ["top"],
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"aloha_sim_transfer_cube_human": ["top"],
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"aloha_sim_transfer_cube_scripted": ["top"],
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}
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def download(data_dir, dataset_id):
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assert dataset_id in DATASET_IDS
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assert dataset_id in FOLDER_URLS
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assert dataset_id in EP48_URLS
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assert dataset_id in EP49_URLS
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data_dir.mkdir(parents=True, exist_ok=True)
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gdown.download_folder(FOLDER_URLS[dataset_id], output=data_dir)
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# because of the 50 files limit per directory, two files episode 48 and 49 were missing
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gdown.download(EP48_URLS[dataset_id], output=data_dir / "episode_48.hdf5", fuzzy=True)
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gdown.download(EP49_URLS[dataset_id], output=data_dir / "episode_49.hdf5", fuzzy=True)
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class AlohaExperienceReplay(AbstractExperienceReplay):
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def __init__(
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self,
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dataset_id: str,
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batch_size: int = None,
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*,
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shuffle: bool = True,
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root: Path = None,
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pin_memory: bool = False,
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prefetch: int = None,
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sampler: SliceSampler = None,
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collate_fn: Callable = None,
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writer: Writer = None,
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transform: "torchrl.envs.Transform" = None,
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):
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assert dataset_id in DATASET_IDS
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super().__init__(
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dataset_id,
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batch_size,
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shuffle=shuffle,
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root=root,
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pin_memory=pin_memory,
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prefetch=prefetch,
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sampler=sampler,
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collate_fn=collate_fn,
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writer=writer,
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transform=transform,
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)
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@property
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def stats_patterns(self) -> dict:
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d = {
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("observation", "state"): "b c -> 1 c",
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("action"): "b c -> 1 c",
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}
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for cam in CAMERAS[self.dataset_id]:
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d[("observation", "image", cam)] = "b c h w -> 1 c 1 1"
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return d
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@property
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def image_keys(self) -> list:
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return [("observation", "image", cam) for cam in CAMERAS[self.dataset_id]]
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# def _is_downloaded(self) -> bool:
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# return False
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def _download_and_preproc(self):
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raw_dir = self.data_dir.parent / f"{self.data_dir.name}_raw"
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if not raw_dir.is_dir():
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download(raw_dir, self.dataset_id)
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total_num_frames = 0
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logging.info("Compute total number of frames to initialize offline buffer")
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for ep_id in range(NUM_EPISODES[self.dataset_id]):
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ep_path = raw_dir / f"episode_{ep_id}.hdf5"
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with h5py.File(ep_path, "r") as ep:
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total_num_frames += ep["/action"].shape[0] - 1
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logging.info(f"{total_num_frames=}")
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logging.info("Initialize and feed offline buffer")
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idxtd = 0
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for ep_id in tqdm.tqdm(range(NUM_EPISODES[self.dataset_id])):
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ep_path = raw_dir / f"episode_{ep_id}.hdf5"
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with h5py.File(ep_path, "r") as ep:
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ep_num_frames = ep["/action"].shape[0]
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# last step of demonstration is considered done
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done = torch.zeros(ep_num_frames, 1, dtype=torch.bool)
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done[-1] = True
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state = torch.from_numpy(ep["/observations/qpos"][:])
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action = torch.from_numpy(ep["/action"][:])
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ep_td = TensorDict(
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{
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("observation", "state"): state[:-1],
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"action": action[:-1],
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"episode": torch.tensor([ep_id] * (ep_num_frames - 1)),
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"frame_id": torch.arange(0, ep_num_frames - 1, 1),
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("next", "observation", "state"): state[1:],
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# TODO: compute reward and success
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# ("next", "reward"): reward[1:],
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("next", "done"): done[1:],
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# ("next", "success"): success[1:],
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},
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batch_size=ep_num_frames - 1,
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)
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for cam in CAMERAS[self.dataset_id]:
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image = torch.from_numpy(ep[f"/observations/images/{cam}"][:])
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image = einops.rearrange(image, "b h w c -> b c h w").contiguous()
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ep_td["observation", "image", cam] = image[:-1]
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ep_td["next", "observation", "image", cam] = image[1:]
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if ep_id == 0:
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# hack to initialize tensordict data structure to store episodes
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td_data = ep_td[0].expand(total_num_frames).memmap_like(self.data_dir)
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td_data[idxtd : idxtd + len(ep_td)] = ep_td
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idxtd = idxtd + len(ep_td)
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return TensorStorage(td_data.lock_())
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@ -66,6 +66,12 @@ def make_offline_buffer(
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clsfunc = PushtExperienceReplay
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dataset_id = "pusht"
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elif cfg.env.name == "aloha":
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from lerobot.common.datasets.aloha import AlohaExperienceReplay
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clsfunc = AlohaExperienceReplay
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dataset_id = f"aloha_{cfg.env.task}"
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else:
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raise ValueError(cfg.env.name)
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@ -0,0 +1,25 @@
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# @package _global_
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eval_episodes: 50
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eval_freq: 7500
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save_freq: 75000
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log_freq: 250
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# TODO: same as simxarm, need to adjust
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offline_steps: 25000
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online_steps: 25000
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fps: 50
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env:
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name: aloha
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task: sim_insertion_human
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from_pixels: True
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pixels_only: False
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image_size: 96
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action_repeat: 1
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episode_length: 300
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fps: ${fps}
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policy:
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state_dim: 2
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action_dim: 2
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