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Add download_and_upload_dataset.py in script, update all datasets, update online training

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Cadene
2024-04-15 21:26:33 +00:00
parent c6aca7fe44
commit 67d79732f9
8 changed files with 493 additions and 519 deletions
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186
lerobot/common/datasets/aloha.py
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@@ -1,72 +1,17 @@
import logging
from pathlib import Path
import einops
import gdown
import h5py
import torch
import tqdm
from datasets import load_dataset
from lerobot.common.datasets.utils import load_data_with_delta_timestamps
FOLDER_URLS = {
"aloha_sim_insertion_human": "https://drive.google.com/drive/folders/1RgyD0JgTX30H4IM5XZn8I3zSV_mr8pyF",
"aloha_sim_insertion_scripted": "https://drive.google.com/drive/folders/1TsojQQSXtHEoGnqgJ3gmpPQR2DPLtS2N",
"aloha_sim_transfer_cube_human": "https://drive.google.com/drive/folders/1sc-E4QYW7A0o23m1u2VWNGVq5smAsfCo",
"aloha_sim_transfer_cube_scripted": "https://drive.google.com/drive/folders/1aRyoOhQwxhyt1J8XgEig4s6kzaw__LXj",
}
EP48_URLS = {
"aloha_sim_insertion_human": "https://drive.google.com/file/d/18Cudl6nikDtgRolea7je8iF_gGKzynOP/view?usp=drive_link",
"aloha_sim_insertion_scripted": "https://drive.google.com/file/d/1wfMSZ24oOh5KR_0aaP3Cnu_c4ZCveduB/view?usp=drive_link",
"aloha_sim_transfer_cube_human": "https://drive.google.com/file/d/18smMymtr8tIxaNUQ61gW6dG50pt3MvGq/view?usp=drive_link",
"aloha_sim_transfer_cube_scripted": "https://drive.google.com/file/d/1pnGIOd-E4-rhz2P3VxpknMKRZCoKt6eI/view?usp=drive_link",
}
EP49_URLS = {
"aloha_sim_insertion_human": "https://drive.google.com/file/d/1C1kZYyROzs-PrLc0SkDgUgMi4-L3lauE/view?usp=drive_link",
"aloha_sim_insertion_scripted": "https://drive.google.com/file/d/17EuCUWS6uCCr6yyNzpXdcdE-_TTNCKtf/view?usp=drive_link",
"aloha_sim_transfer_cube_human": "https://drive.google.com/file/d/1Nk7l53d9sJoGDBKAOnNrExX5nLacATc6/view?usp=drive_link",
"aloha_sim_transfer_cube_scripted": "https://drive.google.com/file/d/1GKReZHrXU73NMiC5zKCq_UtqPVtYq8eo/view?usp=drive_link",
}
NUM_EPISODES = {
"aloha_sim_insertion_human": 50,
"aloha_sim_insertion_scripted": 50,
"aloha_sim_transfer_cube_human": 50,
"aloha_sim_transfer_cube_scripted": 50,
}
EPISODE_LEN = {
"aloha_sim_insertion_human": 500,
"aloha_sim_insertion_scripted": 400,
"aloha_sim_transfer_cube_human": 400,
"aloha_sim_transfer_cube_scripted": 400,
}
CAMERAS = {
"aloha_sim_insertion_human": ["top"],
"aloha_sim_insertion_scripted": ["top"],
"aloha_sim_transfer_cube_human": ["top"],
"aloha_sim_transfer_cube_scripted": ["top"],
}
def download(data_dir, dataset_id):
assert dataset_id in FOLDER_URLS
assert dataset_id in EP48_URLS
assert dataset_id in EP49_URLS
data_dir.mkdir(parents=True, exist_ok=True)
gdown.download_folder(FOLDER_URLS[dataset_id], output=str(data_dir))
# because of the 50 files limit per directory, two files episode 48 and 49 were missing
gdown.download(EP48_URLS[dataset_id], output=str(data_dir / "episode_48.hdf5"), fuzzy=True)
gdown.download(EP49_URLS[dataset_id], output=str(data_dir / "episode_49.hdf5"), fuzzy=True)
from lerobot.common.datasets.utils import load_previous_and_future_frames
class AlohaDataset(torch.utils.data.Dataset):
"""
https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human
https://huggingface.co/datasets/lerobot/aloha_sim_insertion_scripted
https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human
https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_scripted
"""
available_datasets = [
"aloha_sim_insertion_human",
"aloha_sim_insertion_scripted",
@@ -79,129 +24,40 @@ class AlohaDataset(torch.utils.data.Dataset):
def __init__(
self,
dataset_id: str,
version: str | None = "v1.2",
root: Path | None = None,
version: str | None = "v1.0",
transform: callable = None,
delta_timestamps: dict[list[float]] | None = None,
):
super().__init__()
self.dataset_id = dataset_id
self.version = version
self.root = root
self.transform = transform
self.delta_timestamps = delta_timestamps
self.data_dir = self.root / f"{self.dataset_id}"
if (self.data_dir / "data_dict.pth").exists() and (
self.data_dir / "data_ids_per_episode.pth"
).exists():
self.data_dict = torch.load(self.data_dir / "data_dict.pth")
self.data_ids_per_episode = torch.load(self.data_dir / "data_ids_per_episode.pth")
else:
self._download_and_preproc_obsolete()
self.data_dir.mkdir(parents=True, exist_ok=True)
torch.save(self.data_dict, self.data_dir / "data_dict.pth")
torch.save(self.data_ids_per_episode, self.data_dir / "data_ids_per_episode.pth")
self.data_dict = load_dataset(f"lerobot/{self.dataset_id}", revision=self.version, split="train")
self.data_dict = self.data_dict.with_format("torch")
@property
def num_samples(self) -> int:
return len(self.data_dict["index"]) if "index" in self.data_dict else 0
return len(self.data_dict)
@property
def num_episodes(self) -> int:
return len(self.data_ids_per_episode)
return len(self.data_dict.unique("episode_id"))
def __len__(self):
return self.num_samples
def __getitem__(self, idx):
item = {}
item = self.data_dict[idx]
# get episode id and timestamp of the sampled frame
current_ts = self.data_dict["timestamp"][idx].item()
episode = self.data_dict["episode"][idx].item()
for key in self.data_dict:
if self.delta_timestamps is not None and key in self.delta_timestamps:
data, is_pad = load_data_with_delta_timestamps(
self.data_dict,
self.data_ids_per_episode,
self.delta_timestamps,
key,
current_ts,
episode,
)
item[key] = data
item[f"{key}_is_pad"] = is_pad
else:
item[key] = self.data_dict[key][idx]
if self.delta_timestamps is not None:
item = load_previous_and_future_frames(
item,
self.data_dict,
self.delta_timestamps,
)
if self.transform is not None:
item = self.transform(item)
return item
def _download_and_preproc_obsolete(self):
assert self.root is not None
raw_dir = self.root / f"{self.dataset_id}_raw"
if not raw_dir.is_dir():
download(raw_dir, self.dataset_id)
total_frames = 0
logging.info("Compute total number of frames to initialize offline buffer")
for ep_id in range(NUM_EPISODES[self.dataset_id]):
ep_path = raw_dir / f"episode_{ep_id}.hdf5"
with h5py.File(ep_path, "r") as ep:
total_frames += ep["/action"].shape[0] - 1
logging.info(f"{total_frames=}")
self.data_ids_per_episode = {}
ep_dicts = []
frame_idx = 0
for ep_id in tqdm.tqdm(range(NUM_EPISODES[self.dataset_id])):
ep_path = raw_dir / f"episode_{ep_id}.hdf5"
with h5py.File(ep_path, "r") as ep:
num_frames = ep["/action"].shape[0]
# last step of demonstration is considered done
done = torch.zeros(num_frames, dtype=torch.bool)
done[-1] = True
state = torch.from_numpy(ep["/observations/qpos"][:])
action = torch.from_numpy(ep["/action"][:])
ep_dict = {
"observation.state": state,
"action": action,
"episode": torch.tensor([ep_id] * num_frames),
"frame_id": torch.arange(0, num_frames, 1),
"timestamp": torch.arange(0, num_frames, 1) / self.fps,
# "next.observation.state": state,
# TODO(rcadene): compute reward and success
# "next.reward": reward[1:],
"next.done": done[1:],
# "next.success": success[1:],
}
for cam in CAMERAS[self.dataset_id]:
image = torch.from_numpy(ep[f"/observations/images/{cam}"][:])
image = einops.rearrange(image, "b h w c -> b c h w").contiguous()
ep_dict[f"observation.images.{cam}"] = image[:-1]
# ep_dict[f"next.observation.images.{cam}"] = image[1:]
assert isinstance(ep_id, int)
self.data_ids_per_episode[ep_id] = torch.arange(frame_idx, frame_idx + num_frames, 1)
assert len(self.data_ids_per_episode[ep_id]) == num_frames
ep_dicts.append(ep_dict)
frame_idx += num_frames
self.data_dict = {}
keys = ep_dicts[0].keys()
for key in keys:
self.data_dict[key] = torch.cat([x[key] for x in ep_dicts])
self.data_dict["index"] = torch.arange(0, total_frames, 1)