Merge pull request #73 from huggingface/user/rcadene/2024_04_14_hf_datasets

Use Hugging Face datasets.Dataset

.github/poetry/cpu/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
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 ]
 
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+
+[package.dependencies]
+aiosignal = ">=1.1.2"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
+attrs = ">=17.3.0"
+frozenlist = ">=1.1.1"
+multidict = ">=4.5,<7.0"
+yarl = ">=1.0,<2.0"
+
+[package.extras]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
+
+[[package]]
+name = "aiosignal"
+version = "1.3.1"
+description = "aiosignal: a list of registered asynchronous callbacks"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.dependencies]
+frozenlist = ">=1.1.0"
+
 [[package]]
 name = "antlr4-python3-runtime"
 version = "4.9.3"
@@ -42,6 +152,36 @@ files = [
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 ]
 
+[[package]]
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+version = "4.0.3"
+description = "Timeout context manager for asyncio programs"
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[[package]]
+name = "attrs"
+version = "23.2.0"
+description = "Classes Without Boilerplate"
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[package.extras]
+cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
+dev = ["attrs[tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
+tests = ["attrs[tests-no-zope]", "zope-interface"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+
 [[package]]
 name = "beautifulsoup4"
 version = "4.12.3"
@@ -380,6 +520,50 @@ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.1
 [package.extras]
 toml = ["tomli"]
 
+[[package]]
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+torch = ["torch"]
+vision = ["Pillow (>=6.2.1)"]
+
 [[package]]
 name = "debugpy"
 version = "1.8.1"
@@ -441,6 +625,21 @@ test = ["GitPython (<3.1.19)", "Jinja2", "compel (==0.1.8)", "datasets", "invisi
 torch = ["accelerate (>=0.11.0)", "torch (>=1.4,<2.2.0)"]
 training = ["Jinja2", "accelerate (>=0.11.0)", "datasets", "peft (>=0.6.0)", "protobuf (>=3.20.3,<4)", "tensorboard"]
 
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 version = "0.3.8"
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+
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
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 optional = true
 python-versions = ">=3.6"
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 numpy = "*"
 pyopengl = "*"
 
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+
 [[package]]
 name = "zarr"
 version = "2.17.1"
@@ -3115,4 +3919,4 @@ xarm = ["gym-xarm"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "8d8dcf7e235db73f52527b766bb8de597a68348a62192b9c22d3a71be69ca682"
+content-hash = "bd9c506d2499d5e1e3b5e8b1a0f65df45c8feef38d89d0daeade56847fdb6a2e"

.github/poetry/cpu/pyproject.toml

@@ -53,6 +53,7 @@ pre-commit = {version = "^3.7.0", optional = true}
 debugpy = {version = "^1.8.1", optional = true}
 pytest = {version = "^8.1.0", optional = true}
 pytest-cov = {version = "^5.0.0", optional = true}
+datasets = "^2.18.0"
 
 
 [tool.poetry.extras]

download_and_upload_dataset.py

@@ -0,0 +1,487 @@
+"""
+This file contains all obsolete download scripts. They are centralized here to not have to load
+useless dependencies when using datasets.
+"""
+
+import io
+import pickle
+import shutil
+from pathlib import Path
+
+import einops
+import h5py
+import numpy as np
+import torch
+import tqdm
+from datasets import Dataset, Features, Image, Sequence, Value
+from PIL import Image as PILImage
+
+
+def download_and_upload(root, root_tests, dataset_id):
+    if "pusht" in dataset_id:
+        download_and_upload_pusht(root, root_tests, dataset_id)
+    elif "xarm" in dataset_id:
+        download_and_upload_xarm(root, root_tests, dataset_id)
+    elif "aloha" in dataset_id:
+        download_and_upload_aloha(root, root_tests, dataset_id)
+    else:
+        raise ValueError(dataset_id)
+
+
+def download_and_extract_zip(url: str, destination_folder: Path) -> bool:
+    import zipfile
+
+    import requests
+
+    print(f"downloading from {url}")
+    response = requests.get(url, stream=True)
+    if response.status_code == 200:
+        total_size = int(response.headers.get("content-length", 0))
+        progress_bar = tqdm.tqdm(total=total_size, unit="B", unit_scale=True)
+
+        zip_file = io.BytesIO()
+        for chunk in response.iter_content(chunk_size=1024):
+            if chunk:
+                zip_file.write(chunk)
+                progress_bar.update(len(chunk))
+
+        progress_bar.close()
+
+        zip_file.seek(0)
+
+        with zipfile.ZipFile(zip_file, "r") as zip_ref:
+            zip_ref.extractall(destination_folder)
+        return True
+    else:
+        return False
+
+
+def download_and_upload_pusht(root, root_tests, dataset_id="pusht", fps=10):
+    try:
+        import pymunk
+        from gym_pusht.envs.pusht import PushTEnv, pymunk_to_shapely
+
+        from lerobot.common.datasets._diffusion_policy_replay_buffer import (
+            ReplayBuffer as DiffusionPolicyReplayBuffer,
+        )
+    except ModuleNotFoundError as e:
+        print("`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`")
+        raise e
+
+    # as define in env
+    success_threshold = 0.95  # 95% coverage,
+
+    pusht_url = "https://diffusion-policy.cs.columbia.edu/data/training/pusht.zip"
+    pusht_zarr = Path("pusht/pusht_cchi_v7_replay.zarr")
+
+    root = Path(root)
+    raw_dir = root / f"{dataset_id}_raw"
+    zarr_path = (raw_dir / pusht_zarr).resolve()
+    if not zarr_path.is_dir():
+        raw_dir.mkdir(parents=True, exist_ok=True)
+        download_and_extract_zip(pusht_url, raw_dir)
+
+    # load
+    dataset_dict = DiffusionPolicyReplayBuffer.copy_from_path(zarr_path)  # , keys=['img', 'state', 'action'])
+
+    episode_ids = torch.from_numpy(dataset_dict.get_episode_idxs())
+    num_episodes = dataset_dict.meta["episode_ends"].shape[0]
+    assert len(
+        {dataset_dict[key].shape[0] for key in dataset_dict.keys()}  # noqa: SIM118
+    ), "Some data type dont have the same number of total frames."
+
+    # TODO: verify that goal pose is expected to be fixed
+    goal_pos_angle = np.array([256, 256, np.pi / 4])  # x, y, theta (in radians)
+    goal_body = PushTEnv.get_goal_pose_body(goal_pos_angle)
+
+    imgs = torch.from_numpy(dataset_dict["img"])  # b h w c
+    states = torch.from_numpy(dataset_dict["state"])
+    actions = torch.from_numpy(dataset_dict["action"])
+
+    ep_dicts = []
+
+    id_from = 0
+    for episode_id in tqdm.tqdm(range(num_episodes)):
+        id_to = dataset_dict.meta["episode_ends"][episode_id]
+
+        num_frames = id_to - id_from
+
+        assert (episode_ids[id_from:id_to] == episode_id).all()
+
+        image = imgs[id_from:id_to]
+        assert image.min() >= 0.0
+        assert image.max() <= 255.0
+        image = image.type(torch.uint8)
+
+        state = states[id_from:id_to]
+        agent_pos = state[:, :2]
+        block_pos = state[:, 2:4]
+        block_angle = state[:, 4]
+
+        reward = torch.zeros(num_frames)
+        success = torch.zeros(num_frames, dtype=torch.bool)
+        done = torch.zeros(num_frames, dtype=torch.bool)
+        for i in range(num_frames):
+            space = pymunk.Space()
+            space.gravity = 0, 0
+            space.damping = 0
+
+            # Add walls.
+            walls = [
+                PushTEnv.add_segment(space, (5, 506), (5, 5), 2),
+                PushTEnv.add_segment(space, (5, 5), (506, 5), 2),
+                PushTEnv.add_segment(space, (506, 5), (506, 506), 2),
+                PushTEnv.add_segment(space, (5, 506), (506, 506), 2),
+            ]
+            space.add(*walls)
+
+            block_body = PushTEnv.add_tee(space, block_pos[i].tolist(), block_angle[i].item())
+            goal_geom = pymunk_to_shapely(goal_body, block_body.shapes)
+            block_geom = pymunk_to_shapely(block_body, block_body.shapes)
+            intersection_area = goal_geom.intersection(block_geom).area
+            goal_area = goal_geom.area
+            coverage = intersection_area / goal_area
+            reward[i] = np.clip(coverage / success_threshold, 0, 1)
+            success[i] = coverage > success_threshold
+
+        # last step of demonstration is considered done
+        done[-1] = True
+
+        ep_dict = {
+            "observation.image": [PILImage.fromarray(x.numpy()) for x in image],
+            "observation.state": agent_pos,
+            "action": actions[id_from:id_to],
+            "episode_id": torch.tensor([episode_id] * num_frames, dtype=torch.int),
+            "frame_id": torch.arange(0, num_frames, 1),
+            "timestamp": torch.arange(0, num_frames, 1) / 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]]]),
+            "episode_data_index_from": torch.tensor([id_from] * num_frames),
+            "episode_data_index_to": torch.tensor([id_from + num_frames] * num_frames),
+        }
+        ep_dicts.append(ep_dict)
+
+        id_from += num_frames
+
+    data_dict = {}
+
+    keys = ep_dicts[0].keys()
+    for key in keys:
+        if torch.is_tensor(ep_dicts[0][key][0]):
+            data_dict[key] = torch.cat([ep_dict[key] for ep_dict in ep_dicts])
+        else:
+            if key not in data_dict:
+                data_dict[key] = []
+            for ep_dict in ep_dicts:
+                for x in ep_dict[key]:
+                    data_dict[key].append(x)
+
+    total_frames = id_from
+    data_dict["index"] = torch.arange(0, total_frames, 1)
+
+    features = {
+        "observation.image": Image(),
+        "observation.state": Sequence(
+            length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+        ),
+        "action": Sequence(length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)),
+        "episode_id": Value(dtype="int64", id=None),
+        "frame_id": Value(dtype="int64", id=None),
+        "timestamp": Value(dtype="float32", id=None),
+        "next.reward": Value(dtype="float32", id=None),
+        "next.done": Value(dtype="bool", id=None),
+        "next.success": Value(dtype="bool", id=None),
+        "index": Value(dtype="int64", id=None),
+        "episode_data_index_from": Value(dtype="int64", id=None),
+        "episode_data_index_to": Value(dtype="int64", id=None),
+    }
+    features = Features(features)
+    dataset = Dataset.from_dict(data_dict, features=features)
+    dataset = dataset.with_format("torch")
+
+    num_items_first_ep = ep_dicts[0]["frame_id"].shape[0]
+    dataset.select(range(num_items_first_ep)).save_to_disk(f"{root_tests}/{dataset_id}/train")
+    dataset.push_to_hub(f"lerobot/{dataset_id}", token=True)
+    dataset.push_to_hub(f"lerobot/{dataset_id}", token=True, revision="v1.0")
+
+
+def download_and_upload_xarm(root, root_tests, dataset_id, fps=15):
+    root = Path(root)
+    raw_dir = root / f"{dataset_id}_raw"
+    if not raw_dir.exists():
+        import zipfile
+
+        import gdown
+
+        raw_dir.mkdir(parents=True, exist_ok=True)
+        url = "https://drive.google.com/uc?id=1nhxpykGtPDhmQKm-_B8zBSywVRdgeVya"
+        zip_path = raw_dir / "data.zip"
+        gdown.download(url, str(zip_path), quiet=False)
+        print("Extracting...")
+        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)
+        zip_path.unlink()
+
+    dataset_path = root / f"{dataset_id}" / "buffer.pkl"
+    print(f"Using offline dataset '{dataset_path}'")
+    with open(dataset_path, "rb") as f:
+        dataset_dict = pickle.load(f)
+
+    total_frames = dataset_dict["actions"].shape[0]
+
+    ep_dicts = []
+
+    id_from = 0
+    id_to = 0
+    episode_id = 0
+    for i in tqdm.tqdm(range(total_frames)):
+        id_to += 1
+
+        if not dataset_dict["dones"][i]:
+            continue
+
+        num_frames = id_to - id_from
+
+        image = torch.tensor(dataset_dict["observations"]["rgb"][id_from:id_to])
+        image = einops.rearrange(image, "b c h w -> b h w c")
+        state = torch.tensor(dataset_dict["observations"]["state"][id_from:id_to])
+        action = torch.tensor(dataset_dict["actions"][id_from:id_to])
+        # TODO(rcadene): we have a missing last frame which is the observation when the env is done
+        # it is critical to have this frame for tdmpc to predict a "done observation/state"
+        # next_image = torch.tensor(dataset_dict["next_observations"]["rgb"][id_from:id_to])
+        # next_state = torch.tensor(dataset_dict["next_observations"]["state"][id_from:id_to])
+        next_reward = torch.tensor(dataset_dict["rewards"][id_from:id_to])
+        next_done = torch.tensor(dataset_dict["dones"][id_from:id_to])
+
+        ep_dict = {
+            "observation.image": [PILImage.fromarray(x.numpy()) for x in image],
+            "observation.state": state,
+            "action": action,
+            "episode_id": torch.tensor([episode_id] * num_frames, dtype=torch.int),
+            "frame_id": torch.arange(0, num_frames, 1),
+            "timestamp": torch.arange(0, num_frames, 1) / fps,
+            # "next.observation.image": next_image,
+            # "next.observation.state": next_state,
+            "next.reward": next_reward,
+            "next.done": next_done,
+            "episode_data_index_from": torch.tensor([id_from] * num_frames),
+            "episode_data_index_to": torch.tensor([id_from + num_frames] * num_frames),
+        }
+        ep_dicts.append(ep_dict)
+
+        id_from = id_to
+        episode_id += 1
+
+    data_dict = {}
+    keys = ep_dicts[0].keys()
+    for key in keys:
+        if torch.is_tensor(ep_dicts[0][key][0]):
+            data_dict[key] = torch.cat([ep_dict[key] for ep_dict in ep_dicts])
+        else:
+            if key not in data_dict:
+                data_dict[key] = []
+            for ep_dict in ep_dicts:
+                for x in ep_dict[key]:
+                    data_dict[key].append(x)
+
+    total_frames = id_from
+    data_dict["index"] = torch.arange(0, total_frames, 1)
+
+    features = {
+        "observation.image": Image(),
+        "observation.state": Sequence(
+            length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+        ),
+        "action": Sequence(length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)),
+        "episode_id": Value(dtype="int64", id=None),
+        "frame_id": Value(dtype="int64", id=None),
+        "timestamp": Value(dtype="float32", id=None),
+        "next.reward": Value(dtype="float32", id=None),
+        "next.done": Value(dtype="bool", id=None),
+        #'next.success': Value(dtype='bool', id=None),
+        "index": Value(dtype="int64", id=None),
+        "episode_data_index_from": Value(dtype="int64", id=None),
+        "episode_data_index_to": Value(dtype="int64", id=None),
+    }
+    features = Features(features)
+    dataset = Dataset.from_dict(data_dict, features=features)
+    dataset = dataset.with_format("torch")
+
+    num_items_first_ep = ep_dicts[0]["frame_id"].shape[0]
+    dataset.select(range(num_items_first_ep)).save_to_disk(f"{root_tests}/{dataset_id}/train")
+    dataset.push_to_hub(f"lerobot/{dataset_id}", token=True)
+    dataset.push_to_hub(f"lerobot/{dataset_id}", token=True, revision="v1.0")
+
+
+def download_and_upload_aloha(root, root_tests, dataset_id, fps=50):
+    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"],
+    }
+
+    root = Path(root)
+    raw_dir = root / f"{dataset_id}_raw"
+    if not raw_dir.is_dir():
+        import gdown
+
+        assert dataset_id in folder_urls
+        assert dataset_id in ep48_urls
+        assert dataset_id in ep49_urls
+
+        raw_dir.mkdir(parents=True, exist_ok=True)
+
+        gdown.download_folder(folder_urls[dataset_id], output=str(raw_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(raw_dir / "episode_48.hdf5"), fuzzy=True)
+        gdown.download(ep49_urls[dataset_id], output=str(raw_dir / "episode_49.hdf5"), fuzzy=True)
+
+    ep_dicts = []
+
+    id_from = 0
+    for ep_id in tqdm.tqdm(range(num_episodes[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]
+            assert episode_len[dataset_id] == num_frames
+
+            # 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 = {}
+
+            for cam in cameras[dataset_id]:
+                image = torch.from_numpy(ep[f"/observations/images/{cam}"][:])  # b h w c
+                # image = einops.rearrange(image, "b h w c -> b c h w").contiguous()
+                ep_dict[f"observation.images.{cam}"] = [PILImage.fromarray(x.numpy()) for x in image]
+                # ep_dict[f"next.observation.images.{cam}"] = image
+
+            ep_dict.update(
+                {
+                    "observation.state": state,
+                    "action": action,
+                    "episode_id": torch.tensor([ep_id] * num_frames),
+                    "frame_id": torch.arange(0, num_frames, 1),
+                    "timestamp": torch.arange(0, num_frames, 1) / fps,
+                    # "next.observation.state": state,
+                    # TODO(rcadene): compute reward and success
+                    # "next.reward": reward,
+                    "next.done": done,
+                    # "next.success": success,
+                    "episode_data_index_from": torch.tensor([id_from] * num_frames),
+                    "episode_data_index_to": torch.tensor([id_from + num_frames] * num_frames),
+                }
+            )
+
+            assert isinstance(ep_id, int)
+            ep_dicts.append(ep_dict)
+
+        id_from += num_frames
+
+    data_dict = {}
+
+    data_dict = {}
+    keys = ep_dicts[0].keys()
+    for key in keys:
+        if torch.is_tensor(ep_dicts[0][key][0]):
+            data_dict[key] = torch.cat([ep_dict[key] for ep_dict in ep_dicts])
+        else:
+            if key not in data_dict:
+                data_dict[key] = []
+            for ep_dict in ep_dicts:
+                for x in ep_dict[key]:
+                    data_dict[key].append(x)
+
+    total_frames = id_from
+    data_dict["index"] = torch.arange(0, total_frames, 1)
+
+    features = {
+        "observation.images.top": Image(),
+        "observation.state": Sequence(
+            length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+        ),
+        "action": Sequence(length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)),
+        "episode_id": Value(dtype="int64", id=None),
+        "frame_id": Value(dtype="int64", id=None),
+        "timestamp": Value(dtype="float32", id=None),
+        #'next.reward': Value(dtype='float32', id=None),
+        "next.done": Value(dtype="bool", id=None),
+        #'next.success': Value(dtype='bool', id=None),
+        "index": Value(dtype="int64", id=None),
+        "episode_data_index_from": Value(dtype="int64", id=None),
+        "episode_data_index_to": Value(dtype="int64", id=None),
+    }
+    features = Features(features)
+    dataset = Dataset.from_dict(data_dict, features=features)
+    dataset = dataset.with_format("torch")
+
+    num_items_first_ep = ep_dicts[0]["frame_id"].shape[0]
+    dataset.select(range(num_items_first_ep)).save_to_disk(f"{root_tests}/{dataset_id}/train")
+    dataset.push_to_hub(f"lerobot/{dataset_id}", token=True)
+    dataset.push_to_hub(f"lerobot/{dataset_id}", token=True, revision="v1.0")
+
+
+if __name__ == "__main__":
+    root = "data"
+    root_tests = "tests/data"
+
+    dataset_ids = [
+        # "pusht",
+        # "xarm_lift_medium",
+        # "aloha_sim_insertion_human",
+        # "aloha_sim_insertion_scripted",
+        # "aloha_sim_transfer_cube_human",
+        "aloha_sim_transfer_cube_scripted",
+    ]
+    for dataset_id in dataset_ids:
+        download_and_upload(root, root_tests, dataset_id)
+        # assume stats have been precomputed
+        shutil.copy(f"{root}/{dataset_id}/stats.pth", f"{root_tests}/{dataset_id}/stats.pth")

lerobot/common/datasets/_diffusion_policy_replay_buffer.py

@@ -1,6 +1,6 @@
 """Helper code for loading PushT dataset from Diffusion Policy (https://diffusion-policy.cs.columbia.edu/)
 
-Copied from the original Diffusion Policy repository.
+Copied from the original Diffusion Policy repository and used in our `download_and_upload_dataset.py` script.
 """
 
 from __future__ import annotations

lerobot/common/datasets/aloha.py

@@ -1,72 +1,19 @@
-import logging
 from pathlib import Path
 
-import einops
-import gdown
-import h5py
 import torch
-import tqdm
+from datasets import load_dataset, load_from_disk
 
-from lerobot.common.datasets.utils import load_data_with_delta_timestamps
+from lerobot.common.datasets.utils import load_previous_and_future_frames
-
-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)
 
 
 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",
@@ -79,8 +26,9 @@ class AlohaDataset(torch.utils.data.Dataset):
     def __init__(
         self,
         dataset_id: str,
-        version: str | None = "v1.2",
+        version: str | None = "v1.0",
         root: Path | None = None,
+        split: str = "train",
         transform: callable = None,
         delta_timestamps: dict[list[float]] | None = None,
     ):
@@ -88,120 +36,48 @@ class AlohaDataset(torch.utils.data.Dataset):
         self.dataset_id = dataset_id
         self.version = version
         self.root = root
+        self.split = split
         self.transform = transform
         self.delta_timestamps = delta_timestamps
-
+        if self.root is not None:
-        self.data_dir = self.root / f"{self.dataset_id}"
+            self.data_dict = load_from_disk(Path(self.root) / self.dataset_id / self.split)
-        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_dict = load_dataset(
-            self.data_dir.mkdir(parents=True, exist_ok=True)
+                f"lerobot/{self.dataset_id}", revision=self.version, split=self.split
-            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 = 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
+        if self.delta_timestamps is not None:
-        current_ts = self.data_dict["timestamp"][idx].item()
+            item = load_previous_and_future_frames(
-        episode = self.data_dict["episode"][idx].item()
+                item,
+                self.data_dict,
+                self.delta_timestamps,
+            )
 
-        for key in self.data_dict:
+        # convert images from channel last (PIL) to channel first (pytorch)
-            if self.delta_timestamps is not None and key in self.delta_timestamps:
+        for key in self.image_keys:
-                data, is_pad = load_data_with_delta_timestamps(
+            if item[key].ndim == 3:
-                    self.data_dict,
+                item[key] = item[key].permute((2, 0, 1))  # h w c -> c h w
-                    self.data_ids_per_episode,
+            elif item[key].ndim == 4:
-                    self.delta_timestamps,
+                item[key] = item[key].permute((0, 3, 1, 2))  # t h w c -> t c h w
-                    key,
-                    current_ts,
-                    episode,
-                )
-                item[key] = data
-                item[f"{key}_is_pad"] = is_pad
             else:
-                item[key] = self.data_dict[key][idx]
+                raise ValueError(item[key].ndim)
 
         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)

lerobot/common/datasets/factory.py

@@ -8,9 +8,6 @@ from torchvision.transforms import v2
 from lerobot.common.datasets.utils import compute_stats
 from lerobot.common.transforms import NormalizeTransform, Prod
 
-# DATA_DIR specifies to location where datasets are loaded. By default, DATA_DIR is None and
-# we load from `$HOME/.cache/huggingface/hub/datasets`. For our unit tests, we set `DATA_DIR=tests/data`
-# to load a subset of our datasets for faster continuous integration.
 DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None
 
 
@@ -19,6 +16,7 @@ def make_dataset(
     # set normalize=False to remove all transformations and keep images unnormalized in [0,255]
     normalize=True,
     stats_path=None,
+    split="train",
 ):
     if cfg.env.name == "xarm":
         from lerobot.common.datasets.xarm import XarmDataset
@@ -54,19 +52,23 @@ def make_dataset(
             stats["action"]["min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
             stats["action"]["max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
         elif stats_path is None:
-            # 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),
-            )
-
             # load stats if the file exists already or compute stats and save it
-            precomputed_stats_path = stats_dataset.data_dir / "stats.pth"
+            if DATA_DIR is None:
+                # TODO(rcadene): clean stats
+                precomputed_stats_path = Path("data") / cfg.dataset_id / "stats.pth"
+            else:
+                precomputed_stats_path = DATA_DIR / cfg.dataset_id / "stats.pth"
             if precomputed_stats_path.exists():
                 stats = torch.load(precomputed_stats_path)
             else:
                 logging.info(f"compute_stats and save to {precomputed_stats_path}")
+                # instantiate a one frame dataset with light transform
+                stats_dataset = clsfunc(
+                    dataset_id=cfg.dataset_id,
+                    split="train",
+                    root=DATA_DIR,
+                    transform=Prod(in_keys=clsfunc.image_keys, prod=1 / 255.0),
+                )
                 stats = compute_stats(stats_dataset)
                 torch.save(stats, stats_path)
         else:
@@ -94,6 +96,7 @@ def make_dataset(
 
     dataset = clsfunc(
         dataset_id=cfg.dataset_id,
+        split=split,
         root=DATA_DIR,
         delta_timestamps=delta_timestamps,
         transform=transforms,

lerobot/common/datasets/pusht.py

@@ -1,24 +1,14 @@
 from pathlib import Path
 
-import einops
-import numpy as np
 import torch
-import tqdm
+from datasets import load_dataset, load_from_disk
 
-from lerobot.common.datasets._diffusion_policy_replay_buffer import (
+from lerobot.common.datasets.utils import load_previous_and_future_frames
-    ReplayBuffer as DiffusionPolicyReplayBuffer,
-)
-from lerobot.common.datasets.utils import download_and_extract_zip, load_data_with_delta_timestamps
-
-# as define in env
-SUCCESS_THRESHOLD = 0.95  # 95% coverage,
-
-PUSHT_URL = "https://diffusion-policy.cs.columbia.edu/data/training/pusht.zip"
-PUSHT_ZARR = Path("pusht/pusht_cchi_v7_replay.zarr")
 
 
 class PushtDataset(torch.utils.data.Dataset):
     """
+    https://huggingface.co/datasets/lerobot/pusht
 
     Arguments
     ----------
@@ -34,8 +24,9 @@ class PushtDataset(torch.utils.data.Dataset):
     def __init__(
         self,
         dataset_id: str,
-        version: str | None = "v1.2",
+        version: str | None = "v1.0",
         root: Path | None = None,
+        split: str = "train",
         transform: callable = None,
         delta_timestamps: dict[list[float]] | None = None,
     ):
@@ -43,174 +34,48 @@ class PushtDataset(torch.utils.data.Dataset):
         self.dataset_id = dataset_id
         self.version = version
         self.root = root
+        self.split = split
         self.transform = transform
         self.delta_timestamps = delta_timestamps
-
+        if self.root is not None:
-        self.data_dir = self.root / f"{self.dataset_id}"
+            self.data_dict = load_from_disk(Path(self.root) / self.dataset_id / self.split)
-        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_dict = load_dataset(
-            self.data_dir.mkdir(parents=True, exist_ok=True)
+                f"lerobot/{self.dataset_id}", revision=self.version, split=self.split
-            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 = 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
+        if self.delta_timestamps is not None:
-        current_ts = self.data_dict["timestamp"][idx].item()
+            item = load_previous_and_future_frames(
-        episode = self.data_dict["episode"][idx].item()
+                item,
+                self.data_dict,
+                self.delta_timestamps,
+            )
 
-        for key in self.data_dict:
+        # convert images from channel last (PIL) to channel first (pytorch)
-            if self.delta_timestamps is not None and key in self.delta_timestamps:
+        for key in self.image_keys:
-                data, is_pad = load_data_with_delta_timestamps(
+            if item[key].ndim == 3:
-                    self.data_dict,
+                item[key] = item[key].permute((2, 0, 1))  # h w c -> c h w
-                    self.data_ids_per_episode,
+            elif item[key].ndim == 4:
-                    self.delta_timestamps,
+                item[key] = item[key].permute((0, 3, 1, 2))  # t h w c -> t c h w
-                    key,
-                    current_ts,
-                    episode,
-                )
-                item[key] = data
-                item[f"{key}_is_pad"] = is_pad
             else:
-                item[key] = self.data_dict[key][idx]
+                raise ValueError(item[key].ndim)
 
         if self.transform is not None:
             item = self.transform(item)
 
         return item
-
-    def _download_and_preproc_obsolete(self):
-        try:
-            import pymunk
-            from gym_pusht.envs.pusht import PushTEnv, pymunk_to_shapely
-        except ModuleNotFoundError as e:
-            print("`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`")
-            raise e
-
-        assert self.root is not None
-        raw_dir = self.root / f"{self.dataset_id}_raw"
-        zarr_path = (raw_dir / PUSHT_ZARR).resolve()
-        if not zarr_path.is_dir():
-            raw_dir.mkdir(parents=True, exist_ok=True)
-            download_and_extract_zip(PUSHT_URL, raw_dir)
-
-        # load
-        dataset_dict = DiffusionPolicyReplayBuffer.copy_from_path(
-            zarr_path
-        )  # , keys=['img', 'state', 'action'])
-
-        episode_ids = torch.from_numpy(dataset_dict.get_episode_idxs())
-        num_episodes = dataset_dict.meta["episode_ends"].shape[0]
-        total_frames = dataset_dict["action"].shape[0]
-        # to create test artifact
-        # num_episodes = 1
-        # total_frames = 50
-        assert len(
-            {dataset_dict[key].shape[0] for key in dataset_dict.keys()}  # noqa: SIM118
-        ), "Some data type dont have the same number of total frames."
-
-        # TODO: verify that goal pose is expected to be fixed
-        goal_pos_angle = np.array([256, 256, np.pi / 4])  # x, y, theta (in radians)
-        goal_body = PushTEnv.get_goal_pose_body(goal_pos_angle)
-
-        imgs = torch.from_numpy(dataset_dict["img"])
-        imgs = einops.rearrange(imgs, "b h w c -> b c h w")
-        states = torch.from_numpy(dataset_dict["state"])
-        actions = torch.from_numpy(dataset_dict["action"])
-
-        self.data_ids_per_episode = {}
-        ep_dicts = []
-
-        idx0 = 0
-        for episode_id in tqdm.tqdm(range(num_episodes)):
-            idx1 = dataset_dict.meta["episode_ends"][episode_id]
-
-            num_frames = idx1 - idx0
-
-            assert (episode_ids[idx0:idx1] == episode_id).all()
-
-            image = imgs[idx0:idx1]
-            assert image.min() >= 0.0
-            assert image.max() <= 255.0
-            image = image.type(torch.uint8)
-
-            state = states[idx0:idx1]
-            agent_pos = state[:, :2]
-            block_pos = state[:, 2:4]
-            block_angle = state[:, 4]
-
-            reward = torch.zeros(num_frames)
-            success = torch.zeros(num_frames, dtype=torch.bool)
-            done = torch.zeros(num_frames, dtype=torch.bool)
-            for i in range(num_frames):
-                space = pymunk.Space()
-                space.gravity = 0, 0
-                space.damping = 0
-
-                # Add walls.
-                walls = [
-                    PushTEnv.add_segment(space, (5, 506), (5, 5), 2),
-                    PushTEnv.add_segment(space, (5, 5), (506, 5), 2),
-                    PushTEnv.add_segment(space, (506, 5), (506, 506), 2),
-                    PushTEnv.add_segment(space, (5, 506), (506, 506), 2),
-                ]
-                space.add(*walls)
-
-                block_body = PushTEnv.add_tee(space, block_pos[i].tolist(), block_angle[i].item())
-                goal_geom = pymunk_to_shapely(goal_body, block_body.shapes)
-                block_geom = pymunk_to_shapely(block_body, block_body.shapes)
-                intersection_area = goal_geom.intersection(block_geom).area
-                goal_area = goal_geom.area
-                coverage = intersection_area / goal_area
-                reward[i] = np.clip(coverage / SUCCESS_THRESHOLD, 0, 1)
-                success[i] = coverage > SUCCESS_THRESHOLD
-
-            # last step of demonstration is considered done
-            done[-1] = True
-
-            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)
-
-            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
-
-        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)

lerobot/common/datasets/utils.py

@@ -1,51 +1,20 @@
-import io
-import zipfile
 from copy import deepcopy
 from math import ceil
-from pathlib import Path
 
 import einops
-import requests
 import torch
 import tqdm
 
 
-def download_and_extract_zip(url: str, destination_folder: Path) -> bool:
+def load_previous_and_future_frames(
-    print(f"downloading from {url}")
+    item: dict[str, torch.Tensor],
-    response = requests.get(url, stream=True)
+    data_dict: dict[str, torch.Tensor],
-    if response.status_code == 200:
+    delta_timestamps: dict[str, list[float]],
-        total_size = int(response.headers.get("content-length", 0))
-        progress_bar = tqdm.tqdm(total=total_size, unit="B", unit_scale=True)
-
-        zip_file = io.BytesIO()
-        for chunk in response.iter_content(chunk_size=1024):
-            if chunk:
-                zip_file.write(chunk)
-                progress_bar.update(len(chunk))
-
-        progress_bar.close()
-
-        zip_file.seek(0)
-
-        with zipfile.ZipFile(zip_file, "r") as zip_ref:
-            zip_ref.extractall(destination_folder)
-        return True
-    else:
-        return False
-
-
-def load_data_with_delta_timestamps(
-    data_dict: dict[torch.Tensor],
-    data_ids_per_episode: dict[torch.Tensor],
-    delta_timestamps: list[float],
-    key: str,
-    current_ts: float,
-    episode: int,
     tol: float = 0.04,
-):
+) -> dict[torch.Tensor]:
     """
-    Given a current timestamp (e.g. current_ts=0.6) and a list of timestamps differences (e.g. delta_timestamps=[-0.8, -0.2, 0, 0.2]),
+    Given a current item in the dataset containing a timestamp (e.g. 0.6 seconds), and a list of time differences of some modalities (e.g. delta_timestamps={"observation.image": [-0.8, -0.2, 0, 0.2]}),
-    this function compute the query timestamps (e.g. [-0.2, 0.4, 0.6, 0.8]) and loads the closest frames of the specified modality (e.g. key="observation.image").
+    this function computes for each given modality a list of query timestamps (e.g. [-0.2, 0.4, 0.6, 0.8]) and loads the closest frames in the dataset.
 
     Importantly, when no frame can be found around a query timestamp within a specified tolerance window (e.g. tol=0.04), this function raises an AssertionError.
     When a timestamp is queried before the first available timestamp of the episode or after the last available timestamp,
@@ -54,56 +23,57 @@ def load_data_with_delta_timestamps(
     or to pad the observations in a specific way. Note that by default the observation frames before the start of the episode are the same as the first frame of the episode.
 
     Parameters:
-    - data_dict (dict): A dictionary containing the data, where each key corresponds to a different modality (e.g., "timestamp", "observation.image", "action").
+    - item (dict): A dictionary containing all the data related to a frame. It is the result of `dataset[idx]`. Each key corresponds to a different modality (e.g., "timestamp", "observation.image", "action").
-    - data_ids_per_episode (dict): A dictionary where keys are episode identifiers and values are lists of indices corresponding to frames associated with each episode.
+    - data_dict (dict): A dictionary containing the full dataset. Each key corresponds to a different modality (e.g., "timestamp", "observation.image", "action").
-    - delta_timestamps (dict): A dictionary containing lists of delta timestamps for each possible key to be retrieved. These deltas are added to the current_ts to form the query timestamps.
+    - delta_timestamps (dict): A dictionary containing lists of delta timestamps for each possible modality to be retrieved. These deltas are added to the item timestamp to form the query timestamps.
-    - key (str): The key specifying which data modality is to be retrieved from the data_dict.
-    - current_ts (float): The current timestamp to which the delta timestamps are added to form the query timestamps.
-    - episode (int): The identifier of the episode from which frames are to be retrieved.
     - tol (float, optional): The tolerance level used to determine if a data point is close enough to the query timestamp. Defaults to 0.04.
 
     Returns:
-    - tuple: A tuple containing two elements:
+    - The same item with the queried frames for each modality specified in delta_timestamps, with an additional key for each modality (e.g. "observation.image_is_pad").
-        - The first element is the data retrieved from the specified modality based on the closest match to the query timestamps.
-        - The second element is a boolean array indicating which frames were considered as padding (True if the distance to the closest timestamp was greater than the tolerance level).
 
     Raises:
     - AssertionError: If any of the frames unexpectedly violate the tolerance level. This could indicate synchronization issues with timestamps during data collection.
     """
     # get indices of the frames associated to the episode, and their timestamps
-    ep_data_ids = data_ids_per_episode[episode]
+    ep_data_id_from = item["episode_data_index_from"].item()
-    ep_timestamps = data_dict["timestamp"][ep_data_ids]
+    ep_data_id_to = item["episode_data_index_to"].item()
+    ep_data_ids = torch.arange(ep_data_id_from, ep_data_id_to, 1)
+
+    # load timestamps
+    ep_timestamps = data_dict.select_columns("timestamp")[ep_data_id_from:ep_data_id_to]["timestamp"]
 
     # we make the assumption that the timestamps are sorted
     ep_first_ts = ep_timestamps[0]
     ep_last_ts = ep_timestamps[-1]
+    current_ts = item["timestamp"].item()
 
-    # get timestamps used as query to retrieve data of previous/future frames
+    for key in delta_timestamps:
-    delta_ts = delta_timestamps[key]
+        # get timestamps used as query to retrieve data of previous/future frames
-    query_ts = current_ts + torch.tensor(delta_ts)
+        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
+        # compute distances between each query timestamp and all timestamps of all the frames belonging to the episode
-    dist = torch.cdist(query_ts[:, None], ep_timestamps[:, None], p=1)
+        dist = torch.cdist(query_ts[:, None], ep_timestamps[:, None], p=1)
-    min_, argmin_ = dist.min(1)
+        min_, argmin_ = dist.min(1)
 
-    # get the indices of the data that are closest to the query timestamps
+        # TODO(rcadene): synchronize timestamps + interpolation if needed
-    data_ids = ep_data_ids[argmin_]
-    # closest_ts = ep_timestamps[argmin_]
 
-    # get the data
+        is_pad = min_ > tol
-    data = data_dict[key][data_ids].clone()
 
-    # TODO(rcadene): synchronize timestamps + interpolation if needed
+        # check violated query timestamps are all outside the episode range
+        assert ((query_ts[is_pad] < ep_first_ts) | (ep_last_ts < query_ts[is_pad])).all(), (
+            f"One or several timestamps unexpectedly violate the tolerance ({min_} > {tol=}) inside episode range."
+            "This might be due to synchronization issues with timestamps during data collection."
+        )
 
-    is_pad = min_ > tol
+        # get dataset indices corresponding to frames to be loaded
+        data_ids = ep_data_ids[argmin_]
 
-    # check violated query timestamps are all outside the episode range
+        # load frames modality
-    assert ((query_ts[is_pad] < ep_first_ts) | (ep_last_ts < query_ts[is_pad])).all(), (
+        item[key] = data_dict.select_columns(key)[data_ids][key]
-        f"One or several timestamps unexpectedly violate the tolerance ({min_} > {tol=}) inside episode range."
+        item[f"{key}_is_pad"] = is_pad
-        "This might be due to synchronization issues with timestamps during data collection."
-    )
 
-    return data, is_pad
+    return item
 
 
 def get_stats_einops_patterns(dataset):

lerobot/common/datasets/xarm.py

@@ -1,30 +1,16 @@
-import pickle
-import zipfile
 from pathlib import Path
 
 import torch
-import tqdm
+from datasets import load_dataset, load_from_disk
 
-from lerobot.common.datasets.utils import load_data_with_delta_timestamps
+from lerobot.common.datasets.utils import load_previous_and_future_frames
-
-
-def download(raw_dir):
-    import gdown
-
-    raw_dir.mkdir(parents=True, exist_ok=True)
-    url = "https://drive.google.com/uc?id=1nhxpykGtPDhmQKm-_B8zBSywVRdgeVya"
-    zip_path = raw_dir / "data.zip"
-    gdown.download(url, str(zip_path), quiet=False)
-    print("Extracting...")
-    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)
-    zip_path.unlink()
 
 
 class XarmDataset(torch.utils.data.Dataset):
+    """
+    https://huggingface.co/datasets/lerobot/xarm_lift_medium
+    """
+
     available_datasets = [
         "xarm_lift_medium",
     ]
@@ -34,8 +20,9 @@ class XarmDataset(torch.utils.data.Dataset):
     def __init__(
         self,
         dataset_id: str,
-        version: str | None = "v1.1",
+        version: str | None = "v1.0",
         root: Path | None = None,
+        split: str = "train",
         transform: callable = None,
         delta_timestamps: dict[list[float]] | None = None,
     ):
@@ -43,121 +30,48 @@ class XarmDataset(torch.utils.data.Dataset):
         self.dataset_id = dataset_id
         self.version = version
         self.root = root
+        self.split = split
         self.transform = transform
         self.delta_timestamps = delta_timestamps
-
+        if self.root is not None:
-        self.data_dir = self.root / f"{self.dataset_id}"
+            self.data_dict = load_from_disk(Path(self.root) / self.dataset_id / self.split)
-        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_dict = load_dataset(
-            self.data_dir.mkdir(parents=True, exist_ok=True)
+                f"lerobot/{self.dataset_id}", revision=self.version, split=self.split
-            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 = 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
+        if self.delta_timestamps is not None:
-        current_ts = self.data_dict["timestamp"][idx].item()
+            item = load_previous_and_future_frames(
-        episode = self.data_dict["episode"][idx].item()
+                item,
+                self.data_dict,
+                self.delta_timestamps,
+            )
 
-        for key in self.data_dict:
+        # convert images from channel last (PIL) to channel first (pytorch)
-            if self.delta_timestamps is not None and key in self.delta_timestamps:
+        for key in self.image_keys:
-                data, is_pad = load_data_with_delta_timestamps(
+            if item[key].ndim == 3:
-                    self.data_dict,
+                item[key] = item[key].permute((2, 0, 1))  # h w c -> c h w
-                    self.data_ids_per_episode,
+            elif item[key].ndim == 4:
-                    self.delta_timestamps,
+                item[key] = item[key].permute((0, 3, 1, 2))  # t h w c -> t c h w
-                    key,
-                    current_ts,
-                    episode,
-                )
-                item[key] = data
-                item[f"{key}_is_pad"] = is_pad
             else:
-                item[key] = self.data_dict[key][idx]
+                raise ValueError(item[key].ndim)
 
         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.exists():
-            download(raw_dir)
-
-        dataset_path = self.root / f"{self.dataset_id}" / "buffer.pkl"
-        print(f"Using offline dataset '{dataset_path}'")
-        with open(dataset_path, "rb") as f:
-            dataset_dict = pickle.load(f)
-
-        total_frames = dataset_dict["actions"].shape[0]
-
-        self.data_ids_per_episode = {}
-        ep_dicts = []
-
-        idx0 = 0
-        idx1 = 0
-        episode_id = 0
-        for i in tqdm.tqdm(range(total_frames)):
-            idx1 += 1
-
-            if not dataset_dict["dones"][i]:
-                continue
-
-            num_frames = idx1 - idx0
-
-            image = torch.tensor(dataset_dict["observations"]["rgb"][idx0:idx1])
-            state = torch.tensor(dataset_dict["observations"]["state"][idx0:idx1])
-            action = torch.tensor(dataset_dict["actions"][idx0:idx1])
-            # TODO(rcadene): we have a missing last frame which is the observation when the env is done
-            # it is critical to have this frame for tdmpc to predict a "done observation/state"
-            # 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])
-
-            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)
-
-            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
-            episode_id += 1
-
-        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)

lerobot/common/envs/utils.py

@@ -14,7 +14,7 @@ def preprocess_observation(observation, transform=None):
         imgs = {"observation.image": observation["pixels"]}
 
     for imgkey, img in imgs.items():
-        img = torch.from_numpy(img).float()
+        img = torch.from_numpy(img)
         # convert to (b c h w) torch format
         img = einops.rearrange(img, "b h w c -> b c h w")
         obs[imgkey] = img

lerobot/scripts/eval.py

@@ -41,7 +41,9 @@ import gymnasium as gym
 import imageio
 import numpy as np
 import torch
+from datasets import Dataset
 from huggingface_hub import snapshot_download
+from PIL import Image as PILImage
 
 from lerobot.common.datasets.factory import make_dataset
 from lerobot.common.envs.factory import make_env
@@ -199,38 +201,48 @@ def eval_policy(
     ep_dicts = []
     num_episodes = dones.shape[0]
     total_frames = 0
-    idx0 = idx1 = 0
+    idx_from = 0
-    data_ids_per_episode = {}
     for ep_id in range(num_episodes):
         num_frames = done_indices[ep_id].item() + 1
+        total_frames += num_frames
+
         # TODO(rcadene): We need to add a missing last frame which is the observation
         # of a done state. it is critical to have this frame for tdmpc to predict a "done observation/state"
         ep_dict = {
             "action": actions[ep_id, :num_frames],
-            "episode": torch.tensor([ep_id] * num_frames),
+            "episode_id": torch.tensor([ep_id] * num_frames),
             "frame_id": torch.arange(0, num_frames, 1),
             "timestamp": torch.arange(0, num_frames, 1) / fps,
             "next.done": dones[ep_id, :num_frames],
             "next.reward": rewards[ep_id, :num_frames].type(torch.float32),
+            "episode_data_index_from": torch.tensor([idx_from] * num_frames),
+            "episode_data_index_to": torch.tensor([idx_from + num_frames] * num_frames),
         }
         for key in observations:
-            ep_dict[key] = observations[key][ep_id, :num_frames]
+            ep_dict[key] = observations[key][ep_id][:num_frames]
         ep_dicts.append(ep_dict)
 
-        total_frames += num_frames
+        idx_from += num_frames
-        idx1 += num_frames
-
-        data_ids_per_episode[ep_id] = torch.arange(idx0, idx1, 1)
-
-        idx0 = idx1
 
     # similar logic is implemented in dataset preprocessing
     data_dict = {}
     keys = ep_dicts[0].keys()
     for key in keys:
-        data_dict[key] = torch.cat([x[key] for x in ep_dicts])
+        if "image" not in key:
+            data_dict[key] = torch.cat([x[key] for x in ep_dicts])
+        else:
+            if key not in data_dict:
+                data_dict[key] = []
+            for ep_dict in ep_dicts:
+                for x in ep_dict[key]:
+                    # c h w -> h w c
+                    img = PILImage.fromarray(x.permute(1, 2, 0).numpy())
+                    data_dict[key].append(img)
+
     data_dict["index"] = torch.arange(0, total_frames, 1)
 
+    data_dict = Dataset.from_dict(data_dict).with_format("torch")
+
     if max_episodes_rendered > 0:
         batch_stacked_frames = np.stack(ep_frames, 1)  # (b, t, *)
 
@@ -280,10 +292,7 @@ def eval_policy(
             "eval_s": time.time() - start,
             "eval_ep_s": (time.time() - start) / num_episodes,
         },
-        "episodes": {
+        "episodes": data_dict,
-            "data_dict": data_dict,
-            "data_ids_per_episode": data_ids_per_episode,
-        },
     }
     if max_episodes_rendered > 0:
         info["videos"] = videos

lerobot/scripts/train.py

@@ -4,6 +4,8 @@ from pathlib import Path
 
 import hydra
 import torch
+from datasets import concatenate_datasets
+from datasets.utils.logging import disable_progress_bar
 
 from lerobot.common.datasets.factory import make_dataset
 from lerobot.common.datasets.utils import cycle
@@ -128,29 +130,33 @@ def calculate_online_sample_weight(n_off: int, n_on: int, pc_on: float):
     return -(n_off * pc_on) / (n_on * (pc_on - 1))
 
 
-def add_episodes_inplace(episodes, online_dataset, concat_dataset, sampler, pc_online_samples):
+def add_episodes_inplace(data_dict, online_dataset, concat_dataset, sampler, pc_online_samples):
-    data_dict = episodes["data_dict"]
+    first_episode_id = data_dict.select_columns("episode_id")[0]["episode_id"].item()
-    data_ids_per_episode = episodes["data_ids_per_episode"]
+    first_index = data_dict.select_columns("index")[0]["index"].item()
+    assert first_episode_id == 0, f"We expect the first episode_id to be 0 and not {first_episode_id}"
+    assert first_index == 0, f"We expect the first first_index to be 0 and not {first_index}"
 
     if len(online_dataset) == 0:
         # initialize online dataset
         online_dataset.data_dict = data_dict
-        online_dataset.data_ids_per_episode = data_ids_per_episode
     else:
         # find episode index and data frame indices according to previous episode in online_dataset
-        start_episode = max(online_dataset.data_ids_per_episode.keys()) + 1
+        start_episode = online_dataset.select_columns("episode_id")[-1]["episode_id"].item() + 1
-        start_index = online_dataset.data_dict["index"][-1].item() + 1
+        start_index = online_dataset.select_columns("index")[-1]["index"].item() + 1
-        data_dict["episode"] += start_episode
+
-        data_dict["index"] += start_index
+        def shift_indices(example):
+            # note: we dont shift "frame_id" since it represents the index of the frame in the episode it belongs to
+            example["episode_id"] += start_episode
+            example["index"] += start_index
+            example["episode_data_index_from"] += start_index
+            example["episode_data_index_to"] += start_index
+            return example
+
+        disable_progress_bar()  # map has a tqdm progress bar
+        data_dict = data_dict.map(shift_indices)
 
         # extend online dataset
-        for key in data_dict:
+        online_dataset.data_dict = concatenate_datasets([online_dataset.data_dict, data_dict])
-            # TODO(rcadene): avoid reallocating memory at every step by preallocating memory or changing our data structure
-            online_dataset.data_dict[key] = torch.cat([online_dataset.data_dict[key], data_dict[key]])
-        for ep_id in data_ids_per_episode:
-            online_dataset.data_ids_per_episode[ep_id + start_episode] = (
-                data_ids_per_episode[ep_id] + start_index
-            )
 
     # update the concatenated dataset length used during sampling
     concat_dataset.cumulative_sizes = concat_dataset.cumsum(concat_dataset.datasets)
@@ -269,7 +275,6 @@ def train(cfg: dict, out_dir=None, job_name=None):
     # create an empty online dataset similar to offline dataset
     online_dataset = deepcopy(offline_dataset)
     online_dataset.data_dict = {}
-    online_dataset.data_ids_per_episode = {}
 
     # create dataloader for online training
     concat_dataset = torch.utils.data.ConcatDataset([offline_dataset, online_dataset])

lerobot/scripts/visualize_dataset.py

@@ -62,12 +62,12 @@ def render_dataset(dataset, out_dir, max_num_episodes):
     )
     dl_iter = iter(dataloader)
 
-    num_episodes = len(dataset.data_ids_per_episode)
+    for ep_id in range(min(max_num_episodes, dataset.num_episodes)):
-    for ep_id in range(min(max_num_episodes, num_episodes)):
         logging.info(f"Rendering episode {ep_id}")
 
         frames = {}
-        for _ in dataset.data_ids_per_episode[ep_id]:
+        end_of_episode = False
+        while not end_of_episode:
             item = next(dl_iter)
 
             for im_key in dataset.image_keys:
@@ -77,6 +77,8 @@ def render_dataset(dataset, out_dir, max_num_episodes):
                 # add current frame to list of frames to render
                 frames[im_key].append(item[im_key])
 
+            end_of_episode = item["index"].item() == item["episode_data_index_to"].item() - 1
+
         out_dir.mkdir(parents=True, exist_ok=True)
         for im_key in dataset.image_keys:
             if len(dataset.image_keys) > 1:

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -11,6 +11,116 @@ files = [
     {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
 ]
 
+[[package]]
+name = "aiohttp"
+version = "3.9.4"
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+attrs = ">=17.3.0"
+frozenlist = ">=1.1.1"
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+yarl = ">=1.0,<2.0"
+
+[package.extras]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
+
+[[package]]
+name = "aiosignal"
+version = "1.3.1"
+description = "aiosignal: a list of registered asynchronous callbacks"
+optional = false
+python-versions = ">=3.7"
+files = [
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+
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+
 [[package]]
 name = "antlr4-python3-runtime"
 version = "4.9.3"
@@ -42,6 +152,36 @@ files = [
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 ]
 
+[[package]]
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+description = "Timeout context manager for asyncio programs"
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+
+[[package]]
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+description = "Classes Without Boilerplate"
+optional = false
+python-versions = ">=3.7"
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+dev = ["attrs[tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
+tests = ["attrs[tests-no-zope]", "zope-interface"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+
 [[package]]
 name = "beautifulsoup4"
 version = "4.12.3"
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 [package.extras]
 toml = ["tomli"]
 
+[[package]]
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+
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 version = "1.8.1"
@@ -441,6 +625,21 @@ test = ["GitPython (<3.1.19)", "Jinja2", "compel (==0.1.8)", "datasets", "invisi
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+
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
@@ -1542,6 +1830,129 @@ glfw = "*"
 numpy = "*"
 pyopengl = "*"
 
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+    {file = "yarl-1.9.4-cp39-cp39-win_amd64.whl", hash = "sha256:824d6c50492add5da9374875ce72db7a0733b29c2394890aef23d533106e2b15"},
+    {file = "yarl-1.9.4-py3-none-any.whl", hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"},
+    {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
 [[package]]
 name = "zarr"
 version = "2.17.2"
@@ -3395,4 +4195,4 @@ xarm = ["gym-xarm"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "89320756fbcb5d03d61ed710f3d3ca58d30d366d5f89ecb9972262e3b3174b8a"
+content-hash = "01ad4eb04061ec9f785d4574bf66d3e5cb4549e2ea11ab175895f94cb62c1f1c"

pyproject.toml

@@ -53,7 +53,7 @@ pre-commit = {version = "^3.7.0", optional = true}
 debugpy = {version = "^1.8.1", optional = true}
 pytest = {version = "^8.1.0", optional = true}
 pytest-cov = {version = "^5.0.0", optional = true}
-
+datasets = "^2.18.0"
 
 [tool.poetry.extras]
 pusht = ["gym-pusht"]

tests/data/aloha_sim_insertion_human/train/dataset_info.json

@@ -0,0 +1,55 @@
+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "observation.images.top": {
+      "_type": "Image"
+    },
+    "observation.state": {
+      "feature": {
+        "dtype": "float32",
+        "_type": "Value"
+      },
+      "length": 14,
+      "_type": "Sequence"
+    },
+    "action": {
+      "feature": {
+        "dtype": "float32",
+        "_type": "Value"
+      },
+      "length": 14,
+      "_type": "Sequence"
+    },
+    "episode_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "frame_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "timestamp": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "next.done": {
+      "dtype": "bool",
+      "_type": "Value"
+    },
+    "episode_data_index_from": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "episode_data_index_to": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "index": {
+      "dtype": "int64",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

tests/data/aloha_sim_insertion_human/train/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "d79cf82ffc86f110",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": "torch",
+  "_output_all_columns": false,
+  "_split": null
+}

tests/data/aloha_sim_insertion_scripted/train/dataset_info.json

@@ -0,0 +1,55 @@
+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "observation.images.top": {
+      "_type": "Image"
+    },
+    "observation.state": {
+      "feature": {
+        "dtype": "float32",
+        "_type": "Value"
+      },
+      "length": 14,
+      "_type": "Sequence"
+    },
+    "action": {
+      "feature": {
+        "dtype": "float32",
+        "_type": "Value"
+      },
+      "length": 14,
+      "_type": "Sequence"
+    },
+    "episode_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "frame_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "timestamp": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "next.done": {
+      "dtype": "bool",
+      "_type": "Value"
+    },
+    "episode_data_index_from": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "episode_data_index_to": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "index": {
+      "dtype": "int64",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

tests/data/aloha_sim_insertion_scripted/train/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "d8e4a817b5449498",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": "torch",
+  "_output_all_columns": false,
+  "_split": null
+}

tests/data/aloha_sim_transfer_cube_human/train/dataset_info.json

@@ -0,0 +1,55 @@
+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "observation.images.top": {
+      "_type": "Image"
+    },
+    "observation.state": {
+      "feature": {
+        "dtype": "float32",
+        "_type": "Value"
+      },
+      "length": 14,
+      "_type": "Sequence"
+    },
+    "action": {
+      "feature": {
+        "dtype": "float32",
+        "_type": "Value"
+      },
+      "length": 14,
+      "_type": "Sequence"
+    },
+    "episode_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "frame_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "timestamp": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "next.done": {
+      "dtype": "bool",
+      "_type": "Value"
+    },
+    "episode_data_index_from": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "episode_data_index_to": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "index": {
+      "dtype": "int64",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

tests/data/aloha_sim_transfer_cube_human/train/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "f03482befa767127",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": "torch",
+  "_output_all_columns": false,
+  "_split": null
+}

tests/data/aloha_sim_transfer_cube_scripted/train/dataset_info.json

@@ -0,0 +1,55 @@
+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "observation.images.top": {
+      "_type": "Image"
+    },
+    "observation.state": {
+      "feature": {
+        "dtype": "float32",
+        "_type": "Value"
+      },
+      "length": 14,
+      "_type": "Sequence"
+    },
+    "action": {
+      "feature": {
+        "dtype": "float32",
+        "_type": "Value"
+      },
+      "length": 14,
+      "_type": "Sequence"
+    },
+    "episode_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "frame_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "timestamp": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "next.done": {
+      "dtype": "bool",
+      "_type": "Value"
+    },
+    "episode_data_index_from": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "episode_data_index_to": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "index": {
+      "dtype": "int64",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

tests/data/aloha_sim_transfer_cube_scripted/train/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "93e03c6320c7d56e",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": "torch",
+  "_output_all_columns": false,
+  "_split": null
+}

tests/data/pusht/train/dataset_info.json

@@ -0,0 +1,63 @@
+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "observation.image": {
+      "_type": "Image"
+    },
+    "observation.state": {
+      "feature": {
+        "dtype": "float32",
+        "_type": "Value"
+      },
+      "length": 2,
+      "_type": "Sequence"
+    },
+    "action": {
+      "feature": {
+        "dtype": "float32",
+        "_type": "Value"
+      },
+      "length": 2,
+      "_type": "Sequence"
+    },
+    "episode_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "frame_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "timestamp": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "next.reward": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "next.done": {
+      "dtype": "bool",
+      "_type": "Value"
+    },
+    "next.success": {
+      "dtype": "bool",
+      "_type": "Value"
+    },
+    "episode_data_index_from": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "episode_data_index_to": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "index": {
+      "dtype": "int64",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

tests/data/pusht/train/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "21bb9a76ed78a475",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": "torch",
+  "_output_all_columns": false,
+  "_split": null
+}

tests/data/xarm_lift_medium/train/dataset_info.json

@@ -0,0 +1,59 @@
+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "observation.image": {
+      "_type": "Image"
+    },
+    "observation.state": {
+      "feature": {
+        "dtype": "float32",
+        "_type": "Value"
+      },
+      "length": 4,
+      "_type": "Sequence"
+    },
+    "action": {
+      "feature": {
+        "dtype": "float32",
+        "_type": "Value"
+      },
+      "length": 4,
+      "_type": "Sequence"
+    },
+    "episode_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "frame_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "timestamp": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "next.reward": {
+      "dtype": "float32",
+      "_type": "Value"
+    },
+    "next.done": {
+      "dtype": "bool",
+      "_type": "Value"
+    },
+    "episode_data_index_from": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "episode_data_index_to": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "index": {
+      "dtype": "int64",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

tests/data/xarm_lift_medium/train/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "a95cbec45e3bb9d6",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": "torch",
+  "_output_all_columns": false,
+  "_split": null
+}

tests/test_datasets.py

@@ -4,13 +4,12 @@ import einops
 import pytest
 import torch
 
-from lerobot.common.datasets.utils import compute_stats, get_stats_einops_patterns, load_data_with_delta_timestamps
+from lerobot.common.datasets.utils import compute_stats, get_stats_einops_patterns, load_previous_and_future_frames
-from lerobot.common.datasets.xarm import XarmDataset
 from lerobot.common.transforms import Prod
 from lerobot.common.utils import init_hydra_config
 import logging
 from lerobot.common.datasets.factory import make_dataset
-
+from datasets import Dataset
 from .utils import DEVICE, DEFAULT_CONFIG_PATH
 
 
@@ -38,7 +37,7 @@ def test_factory(env_name, dataset_id, policy_name):
 
     keys_ndim_required = [
         ("action", 1, True),
-        ("episode", 0, True),
+        ("episode_id", 0, True),
         ("frame_id", 0, True),
         ("timestamp", 0, True),
         # TODO(rcadene): should we rename it agent_pos?
@@ -94,6 +93,8 @@ def test_compute_stats():
     We compare with taking a straight min, mean, max, std of all the data in one pass (which we can do
     because we are working with a small dataset).
     """
+    from lerobot.common.datasets.xarm import XarmDataset
+
     DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None
 
     # get transform to convert images from uint8 [0,255] to float32 [0,1]
@@ -114,7 +115,13 @@ def test_compute_stats():
     stats_patterns = get_stats_einops_patterns(dataset)
 
     # get all frames from the dataset in the same dtype and range as during compute_stats
-    data_dict = transform(dataset.data_dict)
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        num_workers=8,
+        batch_size=len(dataset),
+        shuffle=False,
+    )
+    data_dict = next(iter(dataloader))
 
     # compute stats based on all frames from the dataset without any batching
     expected_stats = {}
@@ -145,47 +152,50 @@ def test_compute_stats():
     #     assert torch.allclose(loaded_stats[k]["max"], expected_stats[k]["max"])
 
 
-def test_load_data_with_delta_timestamps_within_tolerance():
+def test_load_previous_and_future_frames_within_tolerance():
-    data_dict = {
+    data_dict = Dataset.from_dict({
-        "timestamp": torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5]),
+        "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5],
-        "index": torch.tensor([0, 1, 2, 3, 4]),
+        "index": [0, 1, 2, 3, 4],
-    }
+        "episode_data_index_from": [0, 0, 0, 0, 0],
-    data_ids_per_episode = {0: torch.tensor([0, 1, 2, 3, 4])}
+        "episode_data_index_to": [5, 5, 5, 5, 5],
+    })
+    data_dict = data_dict.with_format("torch")
+    item = data_dict[2]
     delta_timestamps = {"index": [-0.2, 0, 0.139]}
-    key = "index"
-    current_ts = 0.3
-    episode = 0
     tol = 0.04
-    data, is_pad = load_data_with_delta_timestamps(data_dict, data_ids_per_episode, delta_timestamps, key, current_ts, episode, tol)
+    item = load_previous_and_future_frames(item, data_dict, delta_timestamps, tol)
-    assert not is_pad.any(), "Unexpected padding detected"
+    data, is_pad = item["index"], item["index_is_pad"]
     assert torch.equal(data, torch.tensor([0, 2, 3])), "Data does not match expected values"
+    assert not is_pad.any(), "Unexpected padding detected"
 
-def test_load_data_with_delta_timestamps_outside_tolerance_inside_episode_range():
+def test_load_previous_and_future_frames_outside_tolerance_inside_episode_range():
-    data_dict = {
+    data_dict = Dataset.from_dict({
-        "timestamp": torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5]),
+        "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5],
-        "index": torch.tensor([0, 1, 2, 3, 4]),
+        "index": [0, 1, 2, 3, 4],
-    }
+        "episode_data_index_from": [0, 0, 0, 0, 0],
-    data_ids_per_episode = {0: torch.tensor([0, 1, 2, 3, 4])}
+        "episode_data_index_to": [5, 5, 5, 5, 5],
+    })
+    data_dict = data_dict.with_format("torch")
+    item = data_dict[2]
     delta_timestamps = {"index": [-0.2, 0, 0.141]}
-    key = "index"
-    current_ts = 0.3
-    episode = 0
     tol = 0.04
     with pytest.raises(AssertionError):
-        load_data_with_delta_timestamps(data_dict, data_ids_per_episode, delta_timestamps, key, current_ts, episode, tol)
+        load_previous_and_future_frames(item, data_dict, delta_timestamps, tol)
 
-def test_load_data_with_delta_timestamps_outside_tolerance_outside_episode_range():
+def test_load_previous_and_future_frames_outside_tolerance_outside_episode_range():
-    data_dict = {
+    data_dict = Dataset.from_dict({
-        "timestamp": torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5]),
+        "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5],
-        "index": torch.tensor([0, 1, 2, 3, 4]),
+        "index": [0, 1, 2, 3, 4],
-    }
+        "episode_data_index_from": [0, 0, 0, 0, 0],
-    data_ids_per_episode = {0: torch.tensor([0, 1, 2, 3, 4])}
+        "episode_data_index_to": [5, 5, 5, 5, 5],
+    })
+    data_dict = data_dict.with_format("torch")
+    item = data_dict[2]
     delta_timestamps = {"index": [-0.3, -0.24, 0, 0.26, 0.3]}
-    key = "index"
-    current_ts = 0.3
-    episode = 0
     tol = 0.04
-    data, is_pad = load_data_with_delta_timestamps(data_dict, data_ids_per_episode, delta_timestamps, key, current_ts, episode, tol)
+    item = load_previous_and_future_frames(item, data_dict, delta_timestamps, tol)
-    assert torch.equal(is_pad, torch.tensor([True, False, False, True, True])), "Padding does not match expected values"
+    data, is_pad = item["index"], item["index_is_pad"]
     assert torch.equal(data, torch.tensor([0, 0, 2, 4, 4])), "Data does not match expected values"
+    assert torch.equal(is_pad, torch.tensor([True, False, False, True, True])), "Padding does not match expected values"
+