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Merge remote-tracking branch 'Cadene/user/rcadene/2024_03_31_remove_torchrl' into refactor_act_remove_torchrl

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Alexander Soare
2024-04-05 11:41:11 +01:00
parent edb125b351 5af00d0c1e
commit 9d77f5773d
21 changed files with 1303 additions and 1370 deletions
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234
lerobot/common/datasets/abstract.py
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@@ -1,234 +0,0 @@
import logging
from copy import deepcopy
from math import ceil
from pathlib import Path
from typing import Callable
import einops
import torch
import torchrl
import tqdm
from huggingface_hub import snapshot_download
from tensordict import TensorDict
from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
from torchrl.data.replay_buffers.samplers import Sampler, SamplerWithoutReplacement
from torchrl.data.replay_buffers.storages import TensorStorage, _collate_id
from torchrl.data.replay_buffers.writers import ImmutableDatasetWriter, Writer
from torchrl.envs.transforms.transforms import Compose
HF_USER = "lerobot"
class AbstractDataset(TensorDictReplayBuffer):
"""
AbstractDataset represents a dataset in the context of imitation learning or reinforcement learning.
This class is designed to be subclassed by concrete implementations that specify particular types of datasets.
These implementations can vary based on the source of the data, the environment the data pertains to,
or the specific kind of data manipulation applied.
Note:
- `TensorDictReplayBuffer` is the base class from which `AbstractDataset` inherits. It provides the foundational
functionality for storing and retrieving `TensorDict`-like data.
- `available_datasets` should be overridden by concrete subclasses to list the specific dataset variants supported.
It is expected that these variants correspond to a HuggingFace dataset on the hub.
For instance, the `AlohaDataset` which inherites from `AbstractDataset` has 4 available dataset variants:
- [aloha_sim_transfer_cube_scripted](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_scripted)
- [aloha_sim_insertion_scripted](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_scripted)
- [aloha_sim_transfer_cube_human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human)
- [aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human)
- When implementing a concrete class (e.g. `AlohaDataset`, `PushtEnv`, `DiffusionPolicy`), you need to:
1. set the required class attributes:
- for classes inheriting from `AbstractDataset`: `available_datasets`
- for classes inheriting from `AbstractEnv`: `name`, `available_tasks`
- for classes inheriting from `AbstractPolicy`: `name`
2. update variables in `lerobot/__init__.py` (e.g. `available_envs`, `available_datasets_per_envs`, `available_policies`)
3. update variables in `tests/test_available.py` by importing your new class
"""
available_datasets: list[str] | None = None
def __init__(
self,
dataset_id: str,
version: str | None = None,
batch_size: int | None = None,
*,
shuffle: bool = True,
root: Path | None = None,
pin_memory: bool = False,
prefetch: int = None,
sampler: Sampler | None = None,
collate_fn: Callable | None = None,
writer: Writer | None = None,
transform: "torchrl.envs.Transform" = None,
):
assert (
self.available_datasets is not None
), "Subclasses of `AbstractDataset` should set the `available_datasets` class attribute."
assert (
dataset_id in self.available_datasets
), f"The provided dataset ({dataset_id}) is not on the list of available datasets {self.available_datasets}."
self.dataset_id = dataset_id
self.version = version
self.shuffle = shuffle
self.root = root if root is None else Path(root)
if self.root is not None and self.version is not None:
logging.warning(
f"The version of the dataset ({self.version}) is not enforced when root is provided ({self.root})."
)
storage = self._download_or_load_dataset()
super().__init__(
storage=storage,
sampler=sampler,
writer=ImmutableDatasetWriter() if writer is None else writer,
collate_fn=_collate_id if collate_fn is None else collate_fn,
pin_memory=pin_memory,
prefetch=prefetch,
batch_size=batch_size,
transform=transform,
)
@property
def stats_patterns(self) -> dict:
return {
("observation", "state"): "b c -> c",
("observation", "image"): "b c h w -> c 1 1",
("action",): "b c -> c",
}
@property
def image_keys(self) -> list:
return [("observation", "image")]
@property
def num_cameras(self) -> int:
return len(self.image_keys)
@property
def num_samples(self) -> int:
return len(self)
@property
def num_episodes(self) -> int:
return len(self._storage._storage["episode"].unique())
@property
def transform(self):
return self._transform
def set_transform(self, transform):
if not isinstance(transform, Compose):
# required since torchrl calls `len(self._transform)` downstream
if isinstance(transform, list):
self._transform = Compose(*transform)
else:
self._transform = Compose(transform)
else:
self._transform = transform
def compute_or_load_stats(self, batch_size: int = 32) -> TensorDict:
stats_path = self.data_dir / "stats.pth"
if stats_path.exists():
stats = torch.load(stats_path)
else:
logging.info(f"compute_stats and save to {stats_path}")
stats = self._compute_stats(batch_size)
torch.save(stats, stats_path)
return stats
def _download_or_load_dataset(self) -> torch.StorageBase:
if self.root is None:
self.data_dir = Path(
snapshot_download(
repo_id=f"{HF_USER}/{self.dataset_id}", repo_type="dataset", revision=self.version
)
)
else:
self.data_dir = self.root / self.dataset_id
return TensorStorage(TensorDict.load_memmap(self.data_dir / "replay_buffer"))
def _compute_stats(self, batch_size: int = 32):
"""Compute dataset statistics including minimum, maximum, mean, and standard deviation.
TODO(alexander-soare): Add a num_batches argument which essentially allows one to use a subset of the
full dataset (for handling very large datasets). The sampling would then have to be random
(preferably without replacement). Both stats computation loops would ideally sample the same
items.
"""
rb = TensorDictReplayBuffer(
storage=self._storage,
batch_size=32,
prefetch=True,
# Note: Due to be refactored soon. The point is that we should go through the whole dataset.
sampler=SamplerWithoutReplacement(drop_last=False, shuffle=False),
)
# mean and std will be computed incrementally while max and min will track the running value.
mean, std, max, min = {}, {}, {}, {}
for key in self.stats_patterns:
mean[key] = torch.tensor(0.0).float()
std[key] = torch.tensor(0.0).float()
max[key] = torch.tensor(-float("inf")).float()
min[key] = torch.tensor(float("inf")).float()
# Compute mean, min, max.
# Note: Due to be refactored soon. The point of storing `first_batch` is to make sure we don't get
# surprises when rerunning the sampler.
first_batch = None
running_item_count = 0 # for online mean computation
for _ in tqdm.tqdm(range(ceil(len(rb) / batch_size))):
batch = rb.sample()
this_batch_size = batch.batch_size[0]
running_item_count += this_batch_size
if first_batch is None:
first_batch = deepcopy(batch)
for key, pattern in self.stats_patterns.items():
batch[key] = batch[key].float()
# Numerically stable update step for mean computation.
batch_mean = einops.reduce(batch[key], pattern, "mean")
# Hint: to update the mean we need x̄ₙ = (Nₙ₋₁x̄ₙ₋₁ + Bₙxₙ) / Nₙ, where the subscript represents
# the update step, N is the running item count, B is this batch size, x̄ is the running mean,
# and x is the current batch mean. Some rearrangement is then required to avoid risking
# numerical overflow. Another hint: Nₙ₋₁ = Nₙ - Bₙ. Rearrangement yields
# x̄ₙ = x̄ₙ₋₁ + Bₙ * (xₙ - x̄ₙ₋₁) / Nₙ
mean[key] = mean[key] + this_batch_size * (batch_mean - mean[key]) / running_item_count
max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max"))
min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min"))
# Compute std.
first_batch_ = None
running_item_count = 0 # for online std computation
for _ in tqdm.tqdm(range(ceil(len(rb) / batch_size))):
batch = rb.sample()
this_batch_size = batch.batch_size[0]
running_item_count += this_batch_size
# Sanity check to make sure the batches are still in the same order as before.
if first_batch_ is None:
first_batch_ = deepcopy(batch)
for key in self.stats_patterns:
assert torch.equal(first_batch_[key], first_batch[key])
for key, pattern in self.stats_patterns.items():
batch[key] = batch[key].float()
# Numerically stable update step for mean computation (where the mean is over squared
# residuals).See notes in the mean computation loop above.
batch_std = einops.reduce((batch[key] - mean[key]) ** 2, pattern, "mean")
std[key] = std[key] + this_batch_size * (batch_std - std[key]) / running_item_count
for key in self.stats_patterns:
std[key] = torch.sqrt(std[key])
stats = TensorDict({}, batch_size=[])
for key in self.stats_patterns:
stats[(*key, "mean")] = mean[key]
stats[(*key, "std")] = std[key]
stats[(*key, "max")] = max[key]
stats[(*key, "min")] = min[key]
if key[0] == "observation":
# use same stats for the next observations
stats[("next", *key)] = stats[key]
return stats

170
lerobot/common/datasets/aloha.py
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@@ -1,26 +1,13 @@
import logging
from pathlib import Path
from typing import Callable
import einops
import gdown
import h5py
import torch
import torchrl
import tqdm
from tensordict import TensorDict
from torchrl.data.replay_buffers.samplers import Sampler
from torchrl.data.replay_buffers.storages import TensorStorage
from torchrl.data.replay_buffers.writers import Writer
from lerobot.common.datasets.abstract import AbstractDataset
DATASET_IDS = [
"aloha_sim_insertion_human",
"aloha_sim_insertion_scripted",
"aloha_sim_transfer_cube_human",
"aloha_sim_transfer_cube_scripted",
]
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",
@@ -66,7 +53,6 @@ CAMERAS = {
def download(data_dir, dataset_id):
assert dataset_id in DATASET_IDS
assert dataset_id in FOLDER_URLS
assert dataset_id in EP48_URLS
assert dataset_id in EP49_URLS
@@ -80,51 +66,80 @@ def download(data_dir, dataset_id):
gdown.download(EP49_URLS[dataset_id], output=str(data_dir / "episode_49.hdf5"), fuzzy=True)
class AlohaDataset(AbstractDataset):
available_datasets = DATASET_IDS
class AlohaDataset(torch.utils.data.Dataset):
available_datasets = [
"aloha_sim_insertion_human",
"aloha_sim_insertion_scripted",
"aloha_sim_transfer_cube_human",
"aloha_sim_transfer_cube_scripted",
]
fps = 50
image_keys = ["observation.images.top"]
def __init__(
self,
dataset_id: str,
version: str | None = "v1.2",
batch_size: int | None = None,
*,
shuffle: bool = True,
root: Path | None = None,
pin_memory: bool = False,
prefetch: int = None,
sampler: Sampler | None = None,
collate_fn: Callable | None = None,
writer: Writer | None = None,
transform: "torchrl.envs.Transform" = None,
transform: callable = None,
delta_timestamps: dict[list[float]] | None = None,
):
super().__init__(
dataset_id,
version,
batch_size,
shuffle=shuffle,
root=root,
pin_memory=pin_memory,
prefetch=prefetch,
sampler=sampler,
collate_fn=collate_fn,
writer=writer,
transform=transform,
)
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")
@property
def stats_patterns(self) -> dict:
d = {
("observation", "state"): "b c -> c",
("action",): "b c -> c",
}
for cam in CAMERAS[self.dataset_id]:
d[("observation", "image", cam)] = "b c h w -> c 1 1"
return d
def num_samples(self) -> int:
return len(self.data_dict["index"])
@property
def image_keys(self) -> list:
return [("observation", "image", cam) for cam in CAMERAS[self.dataset_id]]
def num_episodes(self) -> int:
return len(self.data_ids_per_episode)
def __len__(self):
return self.num_samples
def __getitem__(self, idx):
item = {}
# 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.transform is not None:
item = self.transform(item)
return item
def _download_and_preproc_obsolete(self):
assert self.root is not None
@@ -132,54 +147,55 @@ class AlohaDataset(AbstractDataset):
if not raw_dir.is_dir():
download(raw_dir, self.dataset_id)
total_num_frames = 0
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_num_frames += ep["/action"].shape[0] - 1
logging.info(f"{total_num_frames=}")
total_frames += ep["/action"].shape[0] - 1
logging.info(f"{total_frames=}")
self.data_ids_per_episode = {}
ep_dicts = []
logging.info("Initialize and feed offline buffer")
idxtd = 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:
ep_num_frames = ep["/action"].shape[0]
num_frames = ep["/action"].shape[0]
# last step of demonstration is considered done
done = torch.zeros(ep_num_frames, 1, dtype=torch.bool)
done = torch.zeros(num_frames, 1, dtype=torch.bool)
done[-1] = True
state = torch.from_numpy(ep["/observations/qpos"][:])
action = torch.from_numpy(ep["/action"][:])
ep_td = TensorDict(
{
("observation", "state"): state[:-1],
"action": action[:-1],
"episode": torch.tensor([ep_id] * (ep_num_frames - 1)),
"frame_id": torch.arange(0, ep_num_frames - 1, 1),
("next", "observation", "state"): state[1:],
# TODO: compute reward and success
# ("next", "reward"): reward[1:],
("next", "done"): done[1:],
# ("next", "success"): success[1:],
},
batch_size=ep_num_frames - 1,
)
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_td["observation", "image", cam] = image[:-1]
ep_td["next", "observation", "image", cam] = image[1:]
ep_dict[f"observation.images.{cam}"] = image[:-1]
# ep_dict[f"next.observation.images.{cam}"] = image[1:]
if ep_id == 0:
# hack to initialize tensordict data structure to store episodes
td_data = ep_td[0].expand(total_num_frames).memmap_like(self.root / f"{self.dataset_id}")
ep_dicts.append(ep_dict)
td_data[idxtd : idxtd + len(ep_td)] = ep_td
idxtd = idxtd + len(ep_td)
self.data_dict = {}
return TensorStorage(td_data.lock_())
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)

141
lerobot/common/datasets/factory.py
View File

@@ -1,10 +1,10 @@
import logging
import os
from pathlib import Path
import torch
from torchrl.data.replay_buffers import PrioritizedSliceSampler, SliceSampler
from torchvision.transforms import v2
from lerobot.common.datasets.utils import compute_or_load_stats
from lerobot.common.transforms import NormalizeTransform, Prod
# DATA_DIR specifies to location where datasets are loaded. By default, DATA_DIR is None and
@@ -13,57 +13,12 @@ from lerobot.common.transforms import NormalizeTransform, Prod
DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None
def make_offline_buffer(
def make_dataset(
cfg,
overwrite_sampler=None,
# set normalize=False to remove all transformations and keep images unnormalized in [0,255]
normalize=True,
overwrite_batch_size=None,
overwrite_prefetch=None,
stats_path=None,
):
if cfg.policy.balanced_sampling:
assert cfg.online_steps > 0
batch_size = None
pin_memory = False
prefetch = None
else:
assert cfg.online_steps == 0
num_slices = cfg.policy.batch_size
batch_size = cfg.policy.horizon * num_slices
pin_memory = cfg.device == "cuda"
prefetch = cfg.prefetch
if overwrite_batch_size is not None:
batch_size = overwrite_batch_size
if overwrite_prefetch is not None:
prefetch = overwrite_prefetch
if overwrite_sampler is None:
# TODO(rcadene): move batch_size outside
num_traj_per_batch = cfg.policy.batch_size # // cfg.horizon
# TODO(rcadene): Sampler outputs a batch_size <= cfg.batch_size.
# We would need to add a transform to pad the tensordict to ensure batch_size == cfg.batch_size.
if cfg.offline_prioritized_sampler:
logging.info("use prioritized sampler for offline dataset")
sampler = PrioritizedSliceSampler(
max_capacity=100_000,
alpha=cfg.policy.per_alpha,
beta=cfg.policy.per_beta,
num_slices=num_traj_per_batch,
strict_length=False,
)
else:
logging.info("use simple sampler for offline dataset")
sampler = SliceSampler(
num_slices=num_traj_per_batch,
strict_length=False,
)
else:
sampler = overwrite_sampler
if cfg.env.name == "simxarm":
from lerobot.common.datasets.simxarm import SimxarmDataset
@@ -81,47 +36,29 @@ def make_offline_buffer(
else:
raise ValueError(cfg.env.name)
offline_buffer = clsfunc(
dataset_id=cfg.dataset_id,
sampler=sampler,
batch_size=batch_size,
root=DATA_DIR,
pin_memory=pin_memory,
prefetch=prefetch if isinstance(prefetch, int) else None,
)
if cfg.policy.name == "tdmpc":
img_keys = []
for key in offline_buffer.image_keys:
img_keys.append(("next", *key))
img_keys += offline_buffer.image_keys
else:
img_keys = offline_buffer.image_keys
transforms = None
if normalize:
transforms = [Prod(in_keys=img_keys, prod=1 / 255)]
# TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max,
# min_max_from_spec
stats = offline_buffer.compute_or_load_stats() if stats_path is None else torch.load(stats_path)
# we only normalize the state and action, since the images are usually normalized inside the model for
# now (except for tdmpc: see the following)
in_keys = [("observation", "state"), ("action")]
if cfg.policy.name == "tdmpc":
# TODO(rcadene): we add img_keys to the keys to normalize for tdmpc only, since diffusion and act policies normalize the image inside the model for now
in_keys += img_keys
# TODO(racdene): since we use next observations in tdmpc, we also add them to the normalization. We are wasting a bit of compute on this for now.
in_keys += [("next", *key) for key in img_keys]
in_keys.append(("next", "observation", "state"))
# stats = dataset.compute_or_load_stats() if stats_path is None else torch.load(stats_path)
if cfg.policy.name == "diffusion" and cfg.env.name == "pusht":
stats = {}
# TODO(rcadene): we overwrite stats to have the same as pretrained model, but we should remove this
stats["observation", "state", "min"] = torch.tensor([13.456424, 32.938293], dtype=torch.float32)
stats["observation", "state", "max"] = torch.tensor([496.14618, 510.9579], dtype=torch.float32)
stats["action", "min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
stats["action", "max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
stats["observation.state"] = {}
stats["observation.state"]["min"] = torch.tensor([13.456424, 32.938293], dtype=torch.float32)
stats["observation.state"]["max"] = torch.tensor([496.14618, 510.9579], dtype=torch.float32)
stats["action"] = {}
stats["action"]["min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
stats["action"]["max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
else:
# instantiate a one frame dataset with light transform
stats_dataset = clsfunc(
dataset_id=cfg.dataset_id,
root=DATA_DIR,
transform=Prod(in_keys=clsfunc.image_keys, prod=1 / 255.0),
)
stats = compute_or_load_stats(stats_dataset)
# TODO(rcadene): remove this and put it in config. Ideally we want to reproduce SOTA results just with mean_std
normalization_mode = "mean_std" if cfg.env.name == "aloha" else "min_max"
@@ -211,12 +148,38 @@ def make_offline_buffer(
0.38381037,
]
)
transforms.append(NormalizeTransform(stats, in_keys, mode=normalization_mode))
transforms.append(NormalizeTransform(stats, in_keys, mode=normalization_mode)) # noqa: F821
offline_buffer.set_transform(transforms)
transforms = v2.Compose(
[
# TODO(rcadene): we need to do something about image_keys
Prod(in_keys=clsfunc.image_keys, prod=1 / 255.0),
NormalizeTransform(
stats,
in_keys=[
"observation.state",
"action",
],
mode=normalization_mode,
),
]
)
if not overwrite_sampler:
index = torch.arange(0, offline_buffer.num_samples, 1)
sampler.extend(index)
if cfg.policy.name == "diffusion" and cfg.env.name == "pusht":
# TODO(rcadene): implement delta_timestamps in config
delta_timestamps = {
"observation.image": [-0.1, 0],
"observation.state": [-0.1, 0],
"action": [-0.1] + [i / clsfunc.fps for i in range(15)],
}
else:
delta_timestamps = None
return offline_buffer
dataset = clsfunc(
dataset_id=cfg.dataset_id,
root=DATA_DIR,
delta_timestamps=delta_timestamps,
transform=transforms,
)
return dataset

167
lerobot/common/datasets/pusht.py
View File

@@ -1,20 +1,13 @@
from pathlib import Path
from typing import Callable
import einops
import numpy as np
import pygame
import pymunk
import torch
import torchrl
import tqdm
from tensordict import TensorDict
from torchrl.data.replay_buffers.samplers import Sampler
from torchrl.data.replay_buffers.storages import TensorStorage
from torchrl.data.replay_buffers.writers import Writer
from lerobot.common.datasets.abstract import AbstractDataset
from lerobot.common.datasets.utils import download_and_extract_zip
from lerobot.common.datasets.utils import download_and_extract_zip, load_data_with_delta_timestamps
from lerobot.common.envs.pusht.pusht_env import pymunk_to_shapely
from lerobot.common.policies.diffusion.replay_buffer import ReplayBuffer as DiffusionPolicyReplayBuffer
@@ -83,37 +76,84 @@ def add_tee(
return body
class PushtDataset(AbstractDataset):
class PushtDataset(torch.utils.data.Dataset):
"""
Arguments
----------
delta_timestamps : dict[list[float]] | None, optional
Loads data from frames with a shift in timestamps with a different strategy for each data key (e.g. state, action or image)
If `None`, no shift is applied to current timestamp and the data from the current frame is loaded.
"""
available_datasets = ["pusht"]
fps = 10
image_keys = ["observation.image"]
def __init__(
self,
dataset_id: str,
version: str | None = "v1.2",
batch_size: int | None = None,
*,
shuffle: bool = True,
root: Path | None = None,
pin_memory: bool = False,
prefetch: int = None,
sampler: Sampler | None = None,
collate_fn: Callable | None = None,
writer: Writer | None = None,
transform: "torchrl.envs.Transform" = None,
transform: callable = None,
delta_timestamps: dict[list[float]] | None = None,
):
super().__init__(
dataset_id,
version,
batch_size,
shuffle=shuffle,
root=root,
pin_memory=pin_memory,
prefetch=prefetch,
sampler=sampler,
collate_fn=collate_fn,
writer=writer,
transform=transform,
)
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")
@property
def num_samples(self) -> int:
return len(self.data_dict["index"])
@property
def num_episodes(self) -> int:
return len(self.data_ids_per_episode)
def __len__(self):
return self.num_samples
def __getitem__(self, idx):
item = {}
# 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.transform is not None:
item = self.transform(item)
return item
def _download_and_preproc_obsolete(self):
assert self.root is not None
@@ -147,8 +187,10 @@ class PushtDataset(AbstractDataset):
states = torch.from_numpy(dataset_dict["state"])
actions = torch.from_numpy(dataset_dict["action"])
self.data_ids_per_episode = {}
ep_dicts = []
idx0 = 0
idxtd = 0
for episode_id in tqdm.tqdm(range(num_episodes)):
idx1 = dataset_dict.meta["episode_ends"][episode_id]
# to create test artifact
@@ -194,30 +236,45 @@ class PushtDataset(AbstractDataset):
# last step of demonstration is considered done
done[-1] = True
ep_td = TensorDict(
{
("observation", "image"): image[:-1],
("observation", "state"): agent_pos[:-1],
"action": actions[idx0:idx1][:-1],
"episode": episode_ids[idx0:idx1][:-1],
"frame_id": torch.arange(0, num_frames - 1, 1),
("next", "observation", "image"): image[1:],
("next", "observation", "state"): agent_pos[1:],
# TODO: verify that reward and done are aligned with image and agent_pos
("next", "reward"): reward[1:],
("next", "done"): done[1:],
("next", "success"): success[1:],
},
batch_size=num_frames - 1,
)
ep_dict = {
"observation.image": image,
"observation.state": agent_pos,
"action": actions[idx0:idx1],
"episode": torch.tensor([episode_id] * num_frames, dtype=torch.int),
"frame_id": torch.arange(0, num_frames, 1),
"timestamp": torch.arange(0, num_frames, 1) / self.fps,
# "next.observation.image": image[1:],
# "next.observation.state": agent_pos[1:],
# TODO(rcadene): verify that reward and done are aligned with image and agent_pos
"next.reward": torch.cat([reward[1:], reward[[-1]]]),
"next.done": torch.cat([done[1:], done[[-1]]]),
"next.success": torch.cat([success[1:], success[[-1]]]),
}
ep_dicts.append(ep_dict)
if episode_id == 0:
# hack to initialize tensordict data structure to store episodes
td_data = ep_td[0].expand(total_frames).memmap_like(self.root / f"{self.dataset_id}")
td_data[idxtd : idxtd + len(ep_td)] = ep_td
assert isinstance(episode_id, int)
self.data_ids_per_episode[episode_id] = torch.arange(idx0, idx1, 1)
assert len(self.data_ids_per_episode[episode_id]) == num_frames
idx0 = idx1
idxtd = idxtd + len(ep_td)
return TensorStorage(td_data.lock_())
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)
if __name__ == "__main__":
dataset = PushtDataset(
"pusht",
root=Path("data"),
delta_timestamps={
"observation.image": [0, -1, -0.2, -0.1],
"observation.state": [0, -1, -0.2, -0.1],
"action": [-0.1, 0, 1, 2, 3],
},
)
dataset[10]

171
lerobot/common/datasets/simxarm.py
View File

@@ -1,75 +1,106 @@
import pickle
import zipfile
from pathlib import Path
from typing import Callable
import torch
import torchrl
import tqdm
from tensordict import TensorDict
from torchrl.data.replay_buffers.samplers import (
Sampler,
)
from torchrl.data.replay_buffers.storages import TensorStorage
from torchrl.data.replay_buffers.writers import Writer
from lerobot.common.datasets.abstract import AbstractDataset
from lerobot.common.datasets.utils import load_data_with_delta_timestamps
def download():
raise NotImplementedError()
def download(raw_dir):
import gdown
raw_dir.mkdir(parents=True, exist_ok=True)
url = "https://drive.google.com/uc?id=1nhxpykGtPDhmQKm-_B8zBSywVRdgeVya"
download_path = "data.zip"
gdown.download(url, download_path, quiet=False)
zip_path = raw_dir / "data.zip"
gdown.download(url, str(zip_path), quiet=False)
print("Extracting...")
with zipfile.ZipFile(download_path, "r") as zip_f:
with zipfile.ZipFile(str(zip_path), "r") as zip_f:
for member in zip_f.namelist():
if member.startswith("data/xarm") and member.endswith(".pkl"):
print(member)
zip_f.extract(member=member)
Path(download_path).unlink()
zip_path.unlink()
class SimxarmDataset(AbstractDataset):
class SimxarmDataset(torch.utils.data.Dataset):
available_datasets = [
"xarm_lift_medium",
]
fps = 15
image_keys = ["observation.image"]
def __init__(
self,
dataset_id: str,
version: str | None = "v1.1",
batch_size: int | None = None,
*,
shuffle: bool = True,
root: Path | None = None,
pin_memory: bool = False,
prefetch: int = None,
sampler: Sampler | None = None,
collate_fn: Callable | None = None,
writer: Writer | None = None,
transform: "torchrl.envs.Transform" = None,
transform: callable = None,
delta_timestamps: dict[list[float]] | None = None,
):
super().__init__(
dataset_id,
version,
batch_size,
shuffle=shuffle,
root=root,
pin_memory=pin_memory,
prefetch=prefetch,
sampler=sampler,
collate_fn=collate_fn,
writer=writer,
transform=transform,
)
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")
@property
def num_samples(self) -> int:
return len(self.data_dict["index"])
@property
def num_episodes(self) -> int:
return len(self.data_ids_per_episode)
def __len__(self):
return self.num_samples
def __getitem__(self, idx):
item = {}
# 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.transform is not None:
item = self.transform(item)
return item
def _download_and_preproc_obsolete(self):
# assert self.root is not None
# TODO(rcadene): finish download
# download()
assert self.root is not None
raw_dir = self.root / f"{self.dataset_id}_raw"
if not raw_dir.exists():
download(raw_dir)
dataset_path = self.root / f"{self.dataset_id}" / "buffer.pkl"
print(f"Using offline dataset '{dataset_path}'")
@@ -78,6 +109,9 @@ class SimxarmDataset(AbstractDataset):
total_frames = dataset_dict["actions"].shape[0]
self.data_ids_per_episode = {}
ep_dicts = []
idx0 = 0
idx1 = 0
episode_id = 0
@@ -91,37 +125,38 @@ class SimxarmDataset(AbstractDataset):
image = torch.tensor(dataset_dict["observations"]["rgb"][idx0:idx1])
state = torch.tensor(dataset_dict["observations"]["state"][idx0:idx1])
next_image = torch.tensor(dataset_dict["next_observations"]["rgb"][idx0:idx1])
next_state = torch.tensor(dataset_dict["next_observations"]["state"][idx0:idx1])
action = torch.tensor(dataset_dict["actions"][idx0:idx1])
# TODO(rcadene): concat the last "next_observations" to "observations"
# next_image = torch.tensor(dataset_dict["next_observations"]["rgb"][idx0:idx1])
# next_state = torch.tensor(dataset_dict["next_observations"]["state"][idx0:idx1])
next_reward = torch.tensor(dataset_dict["rewards"][idx0:idx1])
next_done = torch.tensor(dataset_dict["dones"][idx0:idx1])
episode = TensorDict(
{
("observation", "image"): image,
("observation", "state"): state,
"action": torch.tensor(dataset_dict["actions"][idx0:idx1]),
"episode": torch.tensor([episode_id] * num_frames, dtype=torch.int),
"frame_id": torch.arange(0, num_frames, 1),
("next", "observation", "image"): next_image,
("next", "observation", "state"): next_state,
("next", "reward"): next_reward,
("next", "done"): next_done,
},
batch_size=num_frames,
)
ep_dict = {
"observation.image": image,
"observation.state": state,
"action": action,
"episode": torch.tensor([episode_id] * num_frames, dtype=torch.int),
"frame_id": torch.arange(0, num_frames, 1),
"timestamp": torch.arange(0, num_frames, 1) / self.fps,
# "next.observation.image": next_image,
# "next.observation.state": next_state,
"next.reward": next_reward,
"next.done": next_done,
}
ep_dicts.append(ep_dict)
if episode_id == 0:
# hack to initialize tensordict data structure to store episodes
td_data = (
episode[0]
.expand(total_frames)
.memmap_like(self.root / f"{self.dataset_id}" / "replay_buffer")
)
assert isinstance(episode_id, int)
self.data_ids_per_episode[episode_id] = torch.arange(idx0, idx1, 1)
assert len(self.data_ids_per_episode[episode_id]) == num_frames
td_data[idx0:idx1] = episode
episode_id += 1
idx0 = idx1
episode_id += 1
return TensorStorage(td_data.lock_())
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)

175
lerobot/common/datasets/utils.py
View File

@@ -1,8 +1,13 @@
import io
import logging
import zipfile
from copy import deepcopy
from math import ceil
from pathlib import Path
import einops
import requests
import torch
import tqdm
@@ -28,3 +33,173 @@ def download_and_extract_zip(url: str, destination_folder: Path) -> bool:
return True
else:
return False
def euclidean_distance_matrix(mat0, mat1):
# Compute the square of the distance matrix
sq0 = torch.sum(mat0**2, dim=1, keepdim=True)
sq1 = torch.sum(mat1**2, dim=1, keepdim=True)
distance_sq = sq0 + sq1.transpose(0, 1) - 2 * mat0 @ mat1.transpose(0, 1)
# Taking the square root to get the euclidean distance
distance = torch.sqrt(torch.clamp(distance_sq, min=0))
return distance
def is_contiguously_true_or_false(bool_vector):
assert bool_vector.ndim == 1
assert bool_vector.dtype == torch.bool
# Compare each element with its neighbor to find changes
changes = bool_vector[1:] != bool_vector[:-1]
# Count the number of changes
num_changes = changes.sum().item()
# If there's more than one change, the list is not contiguous
return num_changes <= 1
# examples = [
# ([True, False, True, False, False, False], False),
# ([True, True, True, False, False, False], True),
# ([False, False, False, False, False, False], True)
# ]
# for bool_list, expected in examples:
# result = is_contiguously_true_or_false(bool_list)
def load_data_with_delta_timestamps(
data_dict, data_ids_per_episode, delta_timestamps, key, current_ts, episode
):
# get indices of the frames associated to the episode, and their timestamps
ep_data_ids = data_ids_per_episode[episode]
ep_timestamps = data_dict["timestamp"][ep_data_ids]
# get timestamps used as query to retrieve data of previous/future frames
delta_ts = delta_timestamps[key]
query_ts = current_ts + torch.tensor(delta_ts)
# compute distances between each query timestamp and all timestamps of all the frames belonging to the episode
dist = euclidean_distance_matrix(query_ts[:, None], ep_timestamps[:, None])
min_, argmin_ = dist.min(1)
# get the indices of the data that are closest to the query timestamps
data_ids = ep_data_ids[argmin_]
# closest_ts = ep_timestamps[argmin_]
# get the data
data = data_dict[key][data_ids].clone()
# TODO(rcadene): synchronize timestamps + interpolation if needed
tol = 0.02
is_pad = min_ > tol
assert is_contiguously_true_or_false(is_pad), (
"One or several timestamps unexpectedly violate the tolerance."
"This might be due to synchronization issues with timestamps during data collection."
)
return data, is_pad
def compute_or_load_stats(dataset, batch_size=32, max_num_samples=None):
stats_path = dataset.data_dir / "stats.pth"
if stats_path.exists():
return torch.load(stats_path)
logging.info(f"compute_stats and save to {stats_path}")
if max_num_samples is None:
max_num_samples = len(dataset)
else:
raise NotImplementedError("We need to set shuffle=True, but this violate an assert for now.")
dataloader = torch.utils.data.DataLoader(
dataset,
num_workers=4,
batch_size=batch_size,
shuffle=False,
# pin_memory=cfg.device != "cpu",
drop_last=False,
)
# these einops patterns will be used to aggregate batches and compute statistics
stats_patterns = {
"action": "b c -> c",
"observation.state": "b c -> c",
}
for key in dataset.image_keys:
stats_patterns[key] = "b c h w -> c 1 1"
# mean and std will be computed incrementally while max and min will track the running value.
mean, std, max, min = {}, {}, {}, {}
for key in stats_patterns:
mean[key] = torch.tensor(0.0).float()
std[key] = torch.tensor(0.0).float()
max[key] = torch.tensor(-float("inf")).float()
min[key] = torch.tensor(float("inf")).float()
# Note: Due to be refactored soon. The point of storing `first_batch` is to make sure we don't get
# surprises when rerunning the sampler.
first_batch = None
running_item_count = 0 # for online mean computation
for i, batch in enumerate(
tqdm.tqdm(dataloader, total=ceil(max_num_samples / batch_size), desc="Compute mean, min, max")
):
this_batch_size = len(batch["index"])
running_item_count += this_batch_size
if first_batch is None:
first_batch = deepcopy(batch)
for key, pattern in stats_patterns.items():
batch[key] = batch[key].float()
# Numerically stable update step for mean computation.
batch_mean = einops.reduce(batch[key], pattern, "mean")
# Hint: to update the mean we need x̄ₙ = (Nₙ₋₁x̄ₙ₋₁ + Bₙxₙ) / Nₙ, where the subscript represents
# the update step, N is the running item count, B is this batch size, x̄ is the running mean,
# and x is the current batch mean. Some rearrangement is then required to avoid risking
# numerical overflow. Another hint: Nₙ₋₁ = Nₙ - Bₙ. Rearrangement yields
# x̄ₙ = x̄ₙ₋₁ + Bₙ * (xₙ - x̄ₙ₋₁) / Nₙ
mean[key] = mean[key] + this_batch_size * (batch_mean - mean[key]) / running_item_count
max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max"))
min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min"))
if i == ceil(max_num_samples / batch_size) - 1:
break
first_batch_ = None
running_item_count = 0 # for online std computation
for i, batch in enumerate(
tqdm.tqdm(dataloader, total=ceil(max_num_samples / batch_size), desc="Compute std")
):
this_batch_size = len(batch["index"])
running_item_count += this_batch_size
# Sanity check to make sure the batches are still in the same order as before.
if first_batch_ is None:
first_batch_ = deepcopy(batch)
for key in stats_patterns:
assert torch.equal(first_batch_[key], first_batch[key])
for key, pattern in stats_patterns.items():
batch[key] = batch[key].float()
# Numerically stable update step for mean computation (where the mean is over squared
# residuals).See notes in the mean computation loop above.
batch_std = einops.reduce((batch[key] - mean[key]) ** 2, pattern, "mean")
std[key] = std[key] + this_batch_size * (batch_std - std[key]) / running_item_count
if i == ceil(max_num_samples / batch_size) - 1:
break
for key in stats_patterns:
std[key] = torch.sqrt(std[key])
stats = {}
for key in stats_patterns:
stats[key] = {
"mean": mean[key],
"std": std[key],
"max": max[key],
"min": min[key],
}
torch.save(stats, stats_path)
return stats