WIP

Co-authored-by: Remi <re.cadene@gmail.com>

examples/4_train_policy_with_script.md

@@ -70,7 +70,7 @@ python lerobot/scripts/train.py policy=act env=aloha
 
 There are two things to note here:
 - Config overrides are passed as `param_name=param_value`.
-- Here we have overridden the defaults section. `policy=act` tells Hydra to use `policy/act.yaml`, and `env=aloha` tells Hydra to use `env/pusht.yaml`.
+- Here we have overridden the defaults section. `policy=act` tells Hydra to use `policy/act.yaml`, and `env=aloha` tells Hydra to use `env/aloha.yaml`.
 
 _As an aside: we've set up all of our configurations so that they reproduce state-of-the-art results from papers in the literature._
 

examples/6_add_image_transforms.py

@@ -0,0 +1,51 @@
+"""
+This script demonstrates how to implement torchvision image augmentation on an instance of a LeRobotDataset and how to show some transformed images.
+The transformations are passed to the dataset as an argument upon creation, and transforms are applied to the observation images before they are returned.
+"""
+
+from pathlib import Path
+
+from torchvision.transforms import ToPILImage, v2
+
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+
+to_pil = ToPILImage()
+
+# Create a directory to store output images
+output_dir = Path("outputs/image_transforms")
+output_dir.mkdir(parents=True, exist_ok=True)
+
+repo_id = "lerobot/aloha_static_tape"
+
+# Create a LeRobotDataset with no transformations
+dataset = LeRobotDataset(repo_id, image_transforms=None)
+
+# Get the index of the first observation in the first episode
+first_idx = dataset.episode_data_index["from"][0].item()
+
+# Get the frame corresponding to the first camera
+frame = dataset[first_idx][dataset.camera_keys[0]]
+
+# Save the original frame
+to_pil(frame).save(output_dir / "original_frame.png", quality=100)
+print(f"Original frame saved to {output_dir / 'original_frame.png'}.")
+
+
+# Define the transformations
+transforms = v2.Compose(
+    [
+        v2.ColorJitter(brightness=(0.5, 1.5)),
+        v2.ColorJitter(contrast=(0.5, 1.5)),
+        v2.RandomAdjustSharpness(sharpness_factor=2, p=1),
+    ]
+)
+
+# Create another LeRobotDataset with the defined transformations
+transformed_dataset = LeRobotDataset(repo_id, image_transforms=transforms)
+
+# Get a frame from the transformed dataset
+transformed_frame = transformed_dataset[first_idx][transformed_dataset.camera_keys[0]]
+
+# Save the transformed frame
+to_pil(transformed_frame).save(output_dir / "transformed_frame.png", quality=100)
+print(f"Transformed frame saved to {output_dir / 'transformed_frame.png'}.")

lerobot/__init__.py

@@ -45,6 +45,9 @@ import itertools
 
 from lerobot.__version__ import __version__  # noqa: F401
 
+# TODO(rcadene): Improve policies and envs. As of now, an item in `available_policies`
+# refers to a yaml file AND a modeling name. Same for `available_envs` which refers to
+# a yaml file AND a environment name. The difference should be more obvious.
 available_tasks_per_env = {
     "aloha": [
         "AlohaInsertion-v0",
@@ -52,7 +55,7 @@ available_tasks_per_env = {
     ],
     "pusht": ["PushT-v0"],
     "xarm": ["XarmLift-v0"],
-    "dora": ["DoraAloha-v0", "DoraKoch-v0", "DoraReachy2-v0"],
+    "dora_aloha_real": ["DoraAloha-v0", "DoraKoch-v0", "DoraReachy2-v0"],
 }
 available_envs = list(available_tasks_per_env.keys())
 
@@ -78,7 +81,7 @@ available_datasets_per_env = {
         "lerobot/xarm_push_medium_image",
         "lerobot/xarm_push_medium_replay_image",
     ],
-    "dora": [
+    "dora_aloha_real": [
         "lerobot/aloha_static_battery",
         "lerobot/aloha_static_candy",
         "lerobot/aloha_static_coffee",
@@ -126,17 +129,19 @@ available_datasets = list(
     itertools.chain(*available_datasets_per_env.values(), available_real_world_datasets)
 )
 
+# lists all available policies from `lerobot/common/policies` by their class attribute: `name`.
 available_policies = [
     "act",
     "diffusion",
     "tdmpc",
 ]
 
+# keys and values refer to yaml files
 available_policies_per_env = {
     "aloha": ["act"],
-    "dora": ["act"],
     "pusht": ["diffusion"],
     "xarm": ["tdmpc"],
+    "dora_aloha_real": ["act_real"],
 }
 
 env_task_pairs = [(env, task) for env, tasks in available_tasks_per_env.items() for task in tasks]

lerobot/common/datasets/compute_stats.py

@@ -16,17 +16,15 @@
 from copy import deepcopy
 from math import ceil
 
-import datasets
 import einops
 import torch
 import tqdm
 from datasets import Image
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.common.datasets.video_utils import VideoFrame
 
 
-def get_stats_einops_patterns(dataset: LeRobotDataset | datasets.Dataset, num_workers=0):
+def get_stats_einops_patterns(dataset, num_workers=0):
     """These einops patterns will be used to aggregate batches and compute statistics.
 
     Note: We assume the images are in channel first format
@@ -66,9 +64,8 @@ def get_stats_einops_patterns(dataset: LeRobotDataset | datasets.Dataset, num_wo
     return stats_patterns
 
 
-def compute_stats(
-    dataset: LeRobotDataset | datasets.Dataset, batch_size=32, num_workers=16, max_num_samples=None
-):
+def compute_stats(dataset, batch_size=32, num_workers=16, max_num_samples=None):
+    """Compute mean/std and min/max statistics of all data keys in a LeRobotDataset."""
     if max_num_samples is None:
         max_num_samples = len(dataset)
 
@@ -159,3 +156,54 @@ def compute_stats(
             "min": min[key],
         }
     return stats
+
+
+def aggregate_stats(ls_datasets) -> dict[str, torch.Tensor]:
+    """Aggregate stats of multiple LeRobot datasets into one set of stats without recomputing from scratch.
+
+    The final stats will have the union of all data keys from each of the datasets.
+
+    The final stats will have the union of all data keys from each of the datasets. For instance:
+    - new_max = max(max_dataset_0, max_dataset_1, ...)
+    - new_min = min(min_dataset_0, min_dataset_1, ...)
+    - new_mean = (mean of all data)
+    - new_std = (std of all data)
+    """
+    data_keys = set()
+    for dataset in ls_datasets:
+        data_keys.update(dataset.stats.keys())
+    stats = {k: {} for k in data_keys}
+    for data_key in data_keys:
+        for stat_key in ["min", "max"]:
+            # compute `max(dataset_0["max"], dataset_1["max"], ...)`
+            stats[data_key][stat_key] = einops.reduce(
+                torch.stack([d.stats[data_key][stat_key] for d in ls_datasets if data_key in d.stats], dim=0),
+                "n ... -> ...",
+                stat_key,
+            )
+        total_samples = sum(d.num_samples for d in ls_datasets if data_key in d.stats)
+        # Compute the "sum" statistic by multiplying each mean by the number of samples in the respective
+        # dataset, then divide by total_samples to get the overall "mean".
+        # NOTE: the brackets around (d.num_samples / total_samples) are needed tor minimize the risk of
+        # numerical overflow!
+        stats[data_key]["mean"] = sum(
+            d.stats[data_key]["mean"] * (d.num_samples / total_samples)
+            for d in ls_datasets
+            if data_key in d.stats
+        )
+        # The derivation for standard deviation is a little more involved but is much in the same spirit as
+        # the computation of the mean.
+        # Given two sets of data where the statistics are known:
+        # σ_combined = sqrt[ (n1 * (σ1^2 + d1^2) + n2 * (σ2^2 + d2^2)) / (n1 + n2) ]
+        # where d1 = μ1 - μ_combined, d2 = μ2 - μ_combined
+        # NOTE: the brackets around (d.num_samples / total_samples) are needed tor minimize the risk of
+        # numerical overflow!
+        stats[data_key]["std"] = torch.sqrt(
+            sum(
+                (d.stats[data_key]["std"] ** 2 + (d.stats[data_key]["mean"] - stats[data_key]["mean"]) ** 2)
+                * (d.num_samples / total_samples)
+                for d in ls_datasets
+                if data_key in d.stats
+            )
+        )
+    return stats

lerobot/common/datasets/factory.py

@@ -16,9 +16,10 @@
 import logging
 
 import torch
-from omegaconf import OmegaConf
+from omegaconf import ListConfig, OmegaConf
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, MultiLeRobotDataset
+from lerobot.common.datasets.transforms import get_image_transforms
 
 
 def resolve_delta_timestamps(cfg):
@@ -35,25 +36,73 @@ def resolve_delta_timestamps(cfg):
                 cfg.training.delta_timestamps[key] = eval(delta_timestamps[key])
 
 
-def make_dataset(
-    cfg,
-    split="train",
-):
-    if cfg.env.name not in cfg.dataset_repo_id:
-        logging.warning(
-            f"There might be a mismatch between your training dataset ({cfg.dataset_repo_id=}) and your "
-            f"environment ({cfg.env.name=})."
+def make_dataset(cfg, split: str = "train") -> LeRobotDataset | MultiLeRobotDataset:
+    """
+    Args:
+        cfg: A Hydra config as per the LeRobot config scheme.
+        split: Select the data subset used to create an instance of LeRobotDataset.
+            All datasets hosted on [lerobot](https://huggingface.co/lerobot) contain only one subset: "train".
+            Thus, by default, `split="train"` selects all the available data. `split` aims to work like the
+            slicer in the hugging face datasets:
+            https://huggingface.co/docs/datasets/v2.19.0/loading#slice-splits
+            As of now, it only supports `split="train[:n]"` to load the first n frames of the dataset or
+            `split="train[n:]"` to load the last n frames. For instance `split="train[:1000]"`.
+    Returns:
+        The LeRobotDataset.
+    """
+    if not isinstance(cfg.dataset_repo_id, (str, ListConfig)):
+        raise ValueError(
+            "Expected cfg.dataset_repo_id to be either a single string to load one dataset or a list of "
+            "strings to load multiple datasets."
         )
 
+    # A soft check to warn if the environment matches the dataset. Don't check if we are using a real world env (dora).
+    if cfg.env.name != "dora":
+        if isinstance(cfg.dataset_repo_id, str):
+            dataset_repo_ids = [cfg.dataset_repo_id]  # single dataset
+        else:
+            dataset_repo_ids = cfg.dataset_repo_id  # multiple datasets
+
+        for dataset_repo_id in dataset_repo_ids:
+            if cfg.env.name not in dataset_repo_id:
+                logging.warning(
+                    f"There might be a mismatch between your training dataset ({dataset_repo_id=}) and your "
+                    f"environment ({cfg.env.name=})."
+                )
+
     resolve_delta_timestamps(cfg)
 
-    # TODO(rcadene): add data augmentations
+    image_transforms = None
+    if cfg.image_transforms.enable:
+        image_transforms = get_image_transforms(
+            brightness_weight=cfg.brightness.weight,
+            brightness_min_max=cfg.brightness.min_max,
+            contrast_weight=cfg.contrast.weight,
+            contrast_min_max=cfg.contrast.min_max,
+            saturation_weight=cfg.saturation.weight,
+            saturation_min_max=cfg.saturation.min_max,
+            hue_weight=cfg.hue.weight,
+            hue_min_max=cfg.hue.min_max,
+            sharpness_weight=cfg.sharpness.weight,
+            sharpness_min_max=cfg.sharpness.min_max,
+            max_num_transforms=cfg.max_num_transforms,
+            random_order=cfg.random_order,
+        )
 
-    dataset = LeRobotDataset(
-        cfg.dataset_repo_id,
-        split=split,
-        delta_timestamps=cfg.training.get("delta_timestamps"),
-    )
+    if isinstance(cfg.dataset_repo_id, str):
+        dataset = LeRobotDataset(
+            cfg.dataset_repo_id,
+            split=split,
+            delta_timestamps=cfg.training.get("delta_timestamps"),
+            image_transforms=image_transforms,
+        )
+    else:
+        dataset = MultiLeRobotDataset(
+            cfg.dataset_repo_id,
+            split=split,
+            delta_timestamps=cfg.training.get("delta_timestamps"),
+            image_transforms=image_transforms,
+        )
 
     if cfg.get("override_dataset_stats"):
         for key, stats_dict in cfg.override_dataset_stats.items():

lerobot/common/datasets/lerobot_dataset.py

@@ -13,12 +13,16 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import logging
 import os
 from pathlib import Path
+from typing import Callable
 
 import datasets
 import torch
+import torch.utils
 
+from lerobot.common.datasets.compute_stats import aggregate_stats
 from lerobot.common.datasets.utils import (
     calculate_episode_data_index,
     load_episode_data_index,
@@ -42,7 +46,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         version: str | None = CODEBASE_VERSION,
         root: Path | None = DATA_DIR,
         split: str = "train",
-        transform: callable = None,
+        image_transforms: Callable | None = None,
         delta_timestamps: dict[list[float]] | None = None,
     ):
         super().__init__()
@@ -50,7 +54,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self.version = version
         self.root = root
         self.split = split
-        self.transform = transform
+        self.image_transforms = image_transforms
         self.delta_timestamps = delta_timestamps
         # load data from hub or locally when root is provided
         # TODO(rcadene, aliberts): implement faster transfer
@@ -147,8 +151,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 self.tolerance_s,
             )
 
-        if self.transform is not None:
-            item = self.transform(item)
+        if self.image_transforms is not None:
+            for cam in self.camera_keys:
+                item[cam] = self.image_transforms(item[cam])
 
         return item
 
@@ -164,14 +169,14 @@ class LeRobotDataset(torch.utils.data.Dataset):
             f"  Recorded Frames per Second: {self.fps},\n"
             f"  Camera Keys: {self.camera_keys},\n"
             f"  Video Frame Keys: {self.video_frame_keys if self.video else 'N/A'},\n"
-            f"  Transformations: {self.transform},\n"
+            f"  Transformations: {self.image_transforms},\n"
             f")"
         )
 
     @classmethod
     def from_preloaded(
         cls,
-        repo_id: str,
+        repo_id: str = "from_preloaded",
         version: str | None = CODEBASE_VERSION,
         root: Path | None = None,
         split: str = "train",
@@ -183,18 +188,214 @@ class LeRobotDataset(torch.utils.data.Dataset):
         stats=None,
         info=None,
         videos_dir=None,
-    ):
+    ) -> "LeRobotDataset":
+        """Create a LeRobot Dataset from existing data and attributes instead of loading from the filesystem.
+
+        It is especially useful when converting raw data into LeRobotDataset before saving the dataset
+        on the filesystem or uploading to the hub.
+
+        Note: Meta-data attributes like `repo_id`, `version`, `root`, etc are optional and potentially
+        meaningless depending on the downstream usage of the return dataset.
+        """
         # create an empty object of type LeRobotDataset
         obj = cls.__new__(cls)
         obj.repo_id = repo_id
         obj.version = version
         obj.root = root
         obj.split = split
-        obj.transform = transform
+        obj.image_transforms = transform
         obj.delta_timestamps = delta_timestamps
         obj.hf_dataset = hf_dataset
         obj.episode_data_index = episode_data_index
         obj.stats = stats
-        obj.info = info
+        obj.info = info if info is not None else {}
         obj.videos_dir = videos_dir
         return obj
+
+
+class MultiLeRobotDataset(torch.utils.data.Dataset):
+    """A dataset consisting of multiple underlying `LeRobotDataset`s.
+
+    The underlying `LeRobotDataset`s are effectively concatenated, and this class adopts much of the API
+    structure of `LeRobotDataset`.
+    """
+
+    def __init__(
+        self,
+        repo_ids: list[str],
+        version: str | None = CODEBASE_VERSION,
+        root: Path | None = DATA_DIR,
+        split: str = "train",
+        image_transforms: Callable | None = None,
+        delta_timestamps: dict[list[float]] | None = None,
+    ):
+        super().__init__()
+        self.repo_ids = repo_ids
+        # Construct the underlying datasets passing everything but `transform` and `delta_timestamps` which
+        # are handled by this class.
+        self._datasets = [
+            LeRobotDataset(
+                repo_id,
+                version=version,
+                root=root,
+                split=split,
+                delta_timestamps=delta_timestamps,
+                image_transforms=image_transforms,
+            )
+            for repo_id in repo_ids
+        ]
+        # Check that some properties are consistent across datasets. Note: We may relax some of these
+        # consistency requirements in future iterations of this class.
+        for repo_id, dataset in zip(self.repo_ids, self._datasets, strict=True):
+            if dataset.info != self._datasets[0].info:
+                raise ValueError(
+                    f"Detected a mismatch in dataset info between {self.repo_ids[0]} and {repo_id}. This is "
+                    "not yet supported."
+                )
+        # Disable any data keys that are not common across all of the datasets. Note: we may relax this
+        # restriction in future iterations of this class. For now, this is necessary at least for being able
+        # to use PyTorch's default DataLoader collate function.
+        self.disabled_data_keys = set()
+        intersection_data_keys = set(self._datasets[0].hf_dataset.features)
+        for dataset in self._datasets:
+            intersection_data_keys.intersection_update(dataset.hf_dataset.features)
+        if len(intersection_data_keys) == 0:
+            raise RuntimeError(
+                "Multiple datasets were provided but they had no keys common to all of them. The "
+                "multi-dataset functionality currently only keeps common keys."
+            )
+        for repo_id, dataset in zip(self.repo_ids, self._datasets, strict=True):
+            extra_keys = set(dataset.hf_dataset.features).difference(intersection_data_keys)
+            logging.warning(
+                f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
+                "other datasets."
+            )
+            self.disabled_data_keys.update(extra_keys)
+
+        self.version = version
+        self.root = root
+        self.split = split
+        self.image_transforms = image_transforms
+        self.delta_timestamps = delta_timestamps
+        self.stats = aggregate_stats(self._datasets)
+
+    @property
+    def repo_id_to_index(self):
+        """Return a mapping from dataset repo_id to a dataset index automatically created by this class.
+
+        This index is incorporated as a data key in the dictionary returned by `__getitem__`.
+        """
+        return {repo_id: i for i, repo_id in enumerate(self.repo_ids)}
+
+    @property
+    def repo_index_to_id(self):
+        """Return the inverse mapping if repo_id_to_index."""
+        return {v: k for k, v in self.repo_id_to_index}
+
+    @property
+    def fps(self) -> int:
+        """Frames per second used during data collection.
+
+        NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info.
+        """
+        return self._datasets[0].info["fps"]
+
+    @property
+    def video(self) -> bool:
+        """Returns True if this dataset loads video frames from mp4 files.
+
+        Returns False if it only loads images from png files.
+
+        NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info.
+        """
+        return self._datasets[0].info.get("video", False)
+
+    @property
+    def features(self) -> datasets.Features:
+        features = {}
+        for dataset in self._datasets:
+            features.update({k: v for k, v in dataset.features.items() if k not in self.disabled_data_keys})
+        return features
+
+    @property
+    def camera_keys(self) -> list[str]:
+        """Keys to access image and video stream from cameras."""
+        keys = []
+        for key, feats in self.features.items():
+            if isinstance(feats, (datasets.Image, VideoFrame)):
+                keys.append(key)
+        return keys
+
+    @property
+    def video_frame_keys(self) -> list[str]:
+        """Keys to access video frames that requires to be decoded into images.
+
+        Note: It is empty if the dataset contains images only,
+        or equal to `self.cameras` if the dataset contains videos only,
+        or can even be a subset of `self.cameras` in a case of a mixed image/video dataset.
+        """
+        video_frame_keys = []
+        for key, feats in self.features.items():
+            if isinstance(feats, VideoFrame):
+                video_frame_keys.append(key)
+        return video_frame_keys
+
+    @property
+    def num_samples(self) -> int:
+        """Number of samples/frames."""
+        return sum(d.num_samples for d in self._datasets)
+
+    @property
+    def num_episodes(self) -> int:
+        """Number of episodes."""
+        return sum(d.num_episodes for d in self._datasets)
+
+    @property
+    def tolerance_s(self) -> float:
+        """Tolerance in seconds used to discard loaded frames when their timestamps
+        are not close enough from the requested frames. It is only used when `delta_timestamps`
+        is provided or when loading video frames from mp4 files.
+        """
+        # 1e-4 to account for possible numerical error
+        return 1 / self.fps - 1e-4
+
+    def __len__(self):
+        return self.num_samples
+
+    def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
+        if idx >= len(self):
+            raise IndexError(f"Index {idx} out of bounds.")
+        # Determine which dataset to get an item from based on the index.
+        start_idx = 0
+        dataset_idx = 0
+        for dataset in self._datasets:
+            if idx >= start_idx + dataset.num_samples:
+                start_idx += dataset.num_samples
+                dataset_idx += 1
+                continue
+            break
+        else:
+            raise AssertionError("We expect the loop to break out as long as the index is within bounds.")
+        item = self._datasets[dataset_idx][idx - start_idx]
+        item["dataset_index"] = torch.tensor(dataset_idx)
+        for data_key in self.disabled_data_keys:
+            if data_key in item:
+                del item[data_key]
+
+        return item
+
+    def __repr__(self):
+        return (
+            f"{self.__class__.__name__}(\n"
+            f"  Repository IDs: '{self.repo_ids}',\n"
+            f"  Version: '{self.version}',\n"
+            f"  Split: '{self.split}',\n"
+            f"  Number of Samples: {self.num_samples},\n"
+            f"  Number of Episodes: {self.num_episodes},\n"
+            f"  Type: {'video (.mp4)' if self.video else 'image (.png)'},\n"
+            f"  Recorded Frames per Second: {self.fps},\n"
+            f"  Camera Keys: {self.camera_keys},\n"
+            f"  Video Frame Keys: {self.video_frame_keys if self.video else 'N/A'},\n"
+            f"  Transformations: {self.image_transforms},\n"
+            f")"
+        )

lerobot/common/datasets/push_dataset_to_hub/aloha_dora_format.py

@@ -78,29 +78,15 @@ def load_from_raw(raw_dir: Path, out_dir: Path, fps: int):
 
     image_keys = [key for key in df if "observation.images." in key]
 
-    num_unaligned_images = 0
-    max_episode = 0
-
     def get_episode_index(row):
-        nonlocal num_unaligned_images
-        nonlocal max_episode
         episode_index_per_cam = {}
         for key in image_keys:
-            if isinstance(row[key], float):
-                num_unaligned_images += 1
-                return float("nan")
             path = row[key][0]["path"]
             match = re.search(r"_(\d{6}).mp4", path)
             if not match:
                 raise ValueError(path)
             episode_index = int(match.group(1))
             episode_index_per_cam[key] = episode_index
-
-            if episode_index > max_episode:
-                assert episode_index - max_episode == 1
-                max_episode = episode_index
-            else:
-                assert episode_index == max_episode
         if len(set(episode_index_per_cam.values())) != 1:
             raise ValueError(
                 f"All cameras are expected to belong to the same episode, but getting {episode_index_per_cam}"
@@ -125,24 +111,11 @@ def load_from_raw(raw_dir: Path, out_dir: Path, fps: int):
     del df["timestamp_utc"]
 
     # sanity check
-    num_rows_with_nan = df.isna().any(axis=1).sum()
-    assert (
-        num_rows_with_nan == num_unaligned_images
-    ), f"Found {num_rows_with_nan} rows with NaN values but {num_unaligned_images} unaligned images."
-    if num_unaligned_images > max_episode * 2:
-        # We allow a few unaligned images, typically at the beginning and end of the episodes for instance
-        # but if there are too many, we raise an error to avoid large chunks of missing data
-        raise ValueError(
-            f"Found {num_unaligned_images} unaligned images out of {max_episode} episodes. "
-            f"Check the timestamps of the cameras."
-        )
-
-    # Drop rows with NaN values now that we double checked and convert episode_index to int
-    df = df.dropna()
-    df["episode_index"] = df["episode_index"].astype(int)
+    has_nan = df.isna().any().any()
+    if has_nan:
+        raise ValueError("Dataset contains Nan values.")
 
     # sanity check episode indices go from 0 to n-1
-    assert df["episode_index"].max() == max_episode
     ep_ids = [ep_idx for ep_idx, _ in df.groupby("episode_index")]
     expected_ep_ids = list(range(df["episode_index"].max() + 1))
     if ep_ids != expected_ep_ids:
@@ -241,6 +214,8 @@ def from_raw_to_lerobot_format(raw_dir: Path, out_dir: Path, fps=None, video=Tru
 
     if fps is None:
         fps = 30
+    else:
+        raise NotImplementedError()
 
     if not video:
         raise NotImplementedError()

lerobot/common/datasets/sampler.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Iterator, Union
+
+import torch
+
+
+class EpisodeAwareSampler:
+    def __init__(
+        self,
+        episode_data_index: dict,
+        episode_indices_to_use: Union[list, None] = None,
+        drop_n_first_frames: int = 0,
+        drop_n_last_frames: int = 0,
+        shuffle: bool = False,
+    ):
+        """Sampler that optionally incorporates episode boundary information.
+
+        Args:
+            episode_data_index: Dictionary with keys 'from' and 'to' containing the start and end indices of each episode.
+            episode_indices_to_use: List of episode indices to use. If None, all episodes are used.
+                                    Assumes that episodes are indexed from 0 to N-1.
+            drop_n_first_frames: Number of frames to drop from the start of each episode.
+            drop_n_last_frames: Number of frames to drop from the end of each episode.
+            shuffle: Whether to shuffle the indices.
+        """
+        indices = []
+        for episode_idx, (start_index, end_index) in enumerate(
+            zip(episode_data_index["from"], episode_data_index["to"], strict=True)
+        ):
+            if episode_indices_to_use is None or episode_idx in episode_indices_to_use:
+                indices.extend(
+                    range(start_index.item() + drop_n_first_frames, end_index.item() - drop_n_last_frames)
+                )
+
+        self.indices = indices
+        self.shuffle = shuffle
+
+    def __iter__(self) -> Iterator[int]:
+        if self.shuffle:
+            for i in torch.randperm(len(self.indices)):
+                yield self.indices[i]
+        else:
+            for i in self.indices:
+                yield i
+
+    def __len__(self) -> int:
+        return len(self.indices)

lerobot/common/datasets/transforms.py

@@ -0,0 +1,157 @@
+from typing import Any, Callable, Dict, Sequence
+
+import torch
+from torchvision.transforms import v2
+from torchvision.transforms.v2 import Transform
+from torchvision.transforms.v2 import functional as F  # noqa: N812
+
+
+class RandomSubsetApply(Transform):
+    """
+    Apply a random subset of N transformations from a list of transformations.
+
+    Args:
+        transforms (sequence or torch.nn.Module): list of transformations
+        p (list of floats or None, optional): probability of each transform being picked.
+            If ``p`` doesn't sum to 1, it is automatically normalized. If ``None``
+            (default), all transforms have the same probability.
+        n_subset (int or None): number of transformations to apply. If ``None``,
+            all transforms are applied.
+        random_order (bool): apply transformations in a random order
+    """
+
+    def __init__(
+        self,
+        transforms: Sequence[Callable],
+        p: list[float] | None = None,
+        n_subset: int | None = None,
+        random_order: bool = False,
+    ) -> None:
+        super().__init__()
+        if not isinstance(transforms, Sequence):
+            raise TypeError("Argument transforms should be a sequence of callables")
+        if p is None:
+            p = [1] * len(transforms)
+        elif len(p) != len(transforms):
+            raise ValueError(
+                f"Length of p doesn't match the number of transforms: {len(p)} != {len(transforms)}"
+            )
+
+        if n_subset is None:
+            n_subset = len(transforms)
+        elif not isinstance(n_subset, int):
+            raise TypeError("n_subset should be an int or None")
+        elif not (0 <= n_subset <= len(transforms)):
+            raise ValueError(f"n_subset should be in the interval [0, {len(transforms)}]")
+
+        self.transforms = transforms
+        total = sum(p)
+        self.p = [prob / total for prob in p]
+        self.n_subset = n_subset
+        self.random_order = random_order
+
+    def forward(self, *inputs: Any) -> Any:
+        needs_unpacking = len(inputs) > 1
+
+        selected_indices = torch.multinomial(torch.tensor(self.p), self.n_subset)
+        if not self.random_order:
+            selected_indices = selected_indices.sort().values
+
+        selected_transforms = [self.transforms[i] for i in selected_indices]
+
+        for transform in selected_transforms:
+            outputs = transform(*inputs)
+            inputs = outputs if needs_unpacking else (outputs,)
+
+        return outputs
+
+    def extra_repr(self) -> str:
+        return (
+            f"transforms={self.transforms}, "
+            f"p={self.p}, "
+            f"n_subset={self.n_subset}, "
+            f"random_order={self.random_order}"
+        )
+
+
+class RangeRandomSharpness(Transform):
+    """Similar to v2.RandomAdjustSharpness but with p=1 and a sharpness_factor sampled randomly
+    each time in [range_min, range_max].
+
+    If the input is a :class:`torch.Tensor`,
+    it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
+    """
+
+    def __init__(self, range_min: float, range_max) -> None:
+        super().__init__()
+        self.range_min, self.range_max = self._check_input(range_min, range_max)
+
+    def _check_input(self, range_min, range_max):
+        if range_min < 0:
+            raise ValueError("range_min must be non negative.")
+        if range_min > range_max:
+            raise ValueError("range_max must greater or equal to range_min")
+        return range_min, range_max
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        sharpness_factor = self.range_min + (self.range_max - self.range_min) * torch.rand(1).item()
+        return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor)
+
+
+def get_image_transforms(
+        brightness_weight: float = 1.0,
+        brightness_min_max: tuple[float, float] | None = None,
+        contrast_weight: float = 1.0,
+        contrast_min_max: tuple[float, float] | None = None,
+        saturation_weight: float = 1.0,
+        saturation_min_max: tuple[float, float] | None = None,
+        hue_weight: float = 1.0,
+        hue_min_max: tuple[float, float] | None = None,
+        sharpness_weight: float = 1.0,
+        sharpness_min_max: tuple[float, float] | None = None,
+        max_num_transforms: int | None = None,
+        random_order: bool = False,
+    ):
+    
+    def check_value_error(name, weight, min_max):
+        if min_max is not None:
+            if len(min_max) != 2:
+                raise ValueError(f"`{name}_min_max` is expected to be a tuple of 2 dimensions, but {min_max} provided.")
+            if weight < 0.:
+                raise ValueError(f"`{name}_weight` is expected to be 0 or positive, but is negative ({weight}).")
+
+    check_value_error("brightness", brightness_weight, brightness_min_max)
+    check_value_error("contrast", contrast_weight, contrast_min_max)
+    check_value_error("saturation", saturation_weight, saturation_min_max)
+    check_value_error("hue", hue_weight, hue_min_max)
+    check_value_error("sharpness", sharpness_weight, sharpness_min_max)
+
+    weights = []
+    transforms = []
+    if brightness_min_max is not None:
+        weights.append(brightness_weight)
+        transforms.append(v2.ColorJitter(brightness=brightness_min_max))
+    if contrast_min_max is not None:
+        weights.append(contrast_weight)
+        transforms.append(v2.ColorJitter(contrast=contrast_min_max))
+    if saturation_min_max is not None:
+        weights.append(saturation_weight)
+        transforms.append(v2.ColorJitter(saturation=saturation_min_max))
+    if hue_min_max is not None:
+        weights.append(hue_weight)
+        transforms.append(v2.ColorJitter(hue=hue_min_max))
+    if sharpness_min_max is not None:
+        weights.append(sharpness_weight)
+        transforms.append(RangeRandomSharpness(**sharpness_min_max))
+    
+    if max_num_transforms is None:
+        n_subset = len(transforms)
+    else:
+        n_subset = min(len(transforms), max_num_transforms)
+
+    final_transforms = RandomSubsetApply(
+        transforms, p=weights, n_subset=n_subset, random_order=random_order
+    )
+
+    # TODO(rcadene, aliberts): add v2.ToDtype float16?
+    return final_transforms

lerobot/common/datasets/utils.py

@@ -59,7 +59,7 @@ def unflatten_dict(d, sep="/"):
     return outdict
 
 
-def hf_transform_to_torch(items_dict):
+def hf_transform_to_torch(items_dict: dict[torch.Tensor | None]):
     """Get a transform function that convert items from Hugging Face dataset (pyarrow)
     to torch tensors. Importantly, images are converted from PIL, which corresponds to
     a channel last representation (h w c) of uint8 type, to a torch image representation
@@ -73,6 +73,8 @@ def hf_transform_to_torch(items_dict):
         elif isinstance(first_item, dict) and "path" in first_item and "timestamp" in first_item:
             # video frame will be processed downstream
             pass
+        elif first_item is None:
+            pass
         else:
             items_dict[key] = [torch.tensor(x) for x in items_dict[key]]
     return items_dict
@@ -318,8 +320,7 @@ def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> Dict[str, torc
 
 
 def reset_episode_index(hf_dataset: datasets.Dataset) -> datasets.Dataset:
-    """
-    Reset the `episode_index` of the provided HuggingFace Dataset.
+    """Reset the `episode_index` of the provided HuggingFace Dataset.
 
     `episode_data_index` (and related functionality such as `load_previous_and_future_frames`) requires the
     `episode_index` to be sorted, continuous (1,1,1 and not 1,2,1) and start at 0.
@@ -338,6 +339,7 @@ def reset_episode_index(hf_dataset: datasets.Dataset) -> datasets.Dataset:
         return example
 
     hf_dataset = hf_dataset.map(modify_ep_idx_func)
+
     return hf_dataset
 
 

lerobot/common/policies/act/configuration_act.py

@@ -26,11 +26,10 @@ class ACTConfig:
     Those are: `input_shapes` and 'output_shapes`.
 
     Notes on the inputs and outputs:
-        - "observation.state" is required as an input key.
         - At least one key starting with "observation.image is required as an input.
-        - If there are multiple keys beginning with "observation.image" they are treated as multiple camera
-          views.
-          Right now we only support all images having the same shape.
+        - If there are multiple keys beginning with "observation.images." they are treated as multiple camera
+          views. Right now we only support all images having the same shape.
+        - May optionally work without an "observation.state" key for the proprioceptive robot state.
         - "action" is required as an output key.
 
     Args:

lerobot/common/policies/act/modeling_act.py

@@ -198,15 +198,14 @@ class ACT(nn.Module):
     def __init__(self, config: ACTConfig):
         super().__init__()
         self.config = config
-        # BERT style VAE encoder with input [cls, *joint_space_configuration, *action_sequence].
+        # BERT style VAE encoder with input tokens [cls, robot_state, *action_sequence].
         # The cls token forms parameters of the latent's distribution (like this [*means, *log_variances]).
-        self.has_state = "observation.state" in config.input_shapes
-        self.latent_dim = config.latent_dim
+        self.use_input_state = "observation.state" in config.input_shapes
         if self.config.use_vae:
             self.vae_encoder = ACTEncoder(config)
             self.vae_encoder_cls_embed = nn.Embedding(1, config.dim_model)
             # Projection layer for joint-space configuration to hidden dimension.
-            if self.has_state:
+            if self.use_input_state:
                 self.vae_encoder_robot_state_input_proj = nn.Linear(
                     config.input_shapes["observation.state"][0], config.dim_model
                 )
@@ -215,10 +214,12 @@ class ACT(nn.Module):
                 config.output_shapes["action"][0], config.dim_model
             )
             # Projection layer from the VAE encoder's output to the latent distribution's parameter space.
-            self.vae_encoder_latent_output_proj = nn.Linear(config.dim_model, self.latent_dim * 2)
-            # Fixed sinusoidal positional embedding the whole input to the VAE encoder. Unsqueeze for batch
+            self.vae_encoder_latent_output_proj = nn.Linear(config.dim_model, config.latent_dim * 2)
+            # Fixed sinusoidal positional embedding for the input to the VAE encoder. Unsqueeze for batch
             # dimension.
-            num_input_token_encoder = 1 + 1 + config.chunk_size if self.has_state else 1 + config.chunk_size
+            num_input_token_encoder = 1 + config.chunk_size
+            if self.use_input_state:
+                num_input_token_encoder += 1
             self.register_buffer(
                 "vae_encoder_pos_enc",
                 create_sinusoidal_pos_embedding(num_input_token_encoder, config.dim_model).unsqueeze(0),
@@ -241,16 +242,16 @@ class ACT(nn.Module):
 
         # Transformer encoder input projections. The tokens will be structured like
         # [latent, robot_state, image_feature_map_pixels].
-        if self.has_state:
+        if self.use_input_state:
             self.encoder_robot_state_input_proj = nn.Linear(
                 config.input_shapes["observation.state"][0], config.dim_model
             )
-        self.encoder_latent_input_proj = nn.Linear(self.latent_dim, config.dim_model)
+        self.encoder_latent_input_proj = nn.Linear(config.latent_dim, config.dim_model)
         self.encoder_img_feat_input_proj = nn.Conv2d(
             backbone_model.fc.in_features, config.dim_model, kernel_size=1
         )
         # Transformer encoder positional embeddings.
-        num_input_token_decoder = 2 if self.has_state else 1
+        num_input_token_decoder = 2 if self.use_input_state else 1
         self.encoder_robot_and_latent_pos_embed = nn.Embedding(num_input_token_decoder, config.dim_model)
         self.encoder_cam_feat_pos_embed = ACTSinusoidalPositionEmbedding2d(config.dim_model // 2)
 
@@ -298,12 +299,12 @@ class ACT(nn.Module):
             cls_embed = einops.repeat(
                 self.vae_encoder_cls_embed.weight, "1 d -> b 1 d", b=batch_size
             )  # (B, 1, D)
-            if self.has_state:
+            if self.use_input_state:
                 robot_state_embed = self.vae_encoder_robot_state_input_proj(batch["observation.state"])
                 robot_state_embed = robot_state_embed.unsqueeze(1)  # (B, 1, D)
             action_embed = self.vae_encoder_action_input_proj(batch["action"])  # (B, S, D)
 
-            if self.has_state:
+            if self.use_input_state:
                 vae_encoder_input = [cls_embed, robot_state_embed, action_embed]  # (B, S+2, D)
             else:
                 vae_encoder_input = [cls_embed, action_embed]
@@ -318,9 +319,9 @@ class ACT(nn.Module):
                 vae_encoder_input.permute(1, 0, 2), pos_embed=pos_embed.permute(1, 0, 2)
             )[0]  # select the class token, with shape (B, D)
             latent_pdf_params = self.vae_encoder_latent_output_proj(cls_token_out)
-            mu = latent_pdf_params[:, : self.latent_dim]
+            mu = latent_pdf_params[:, : self.config.latent_dim]
             # This is 2log(sigma). Done this way to match the original implementation.
-            log_sigma_x2 = latent_pdf_params[:, self.latent_dim :]
+            log_sigma_x2 = latent_pdf_params[:, self.config.latent_dim :]
 
             # Sample the latent with the reparameterization trick.
             latent_sample = mu + log_sigma_x2.div(2).exp() * torch.randn_like(mu)
@@ -328,7 +329,7 @@ class ACT(nn.Module):
             # When not using the VAE encoder, we set the latent to be all zeros.
             mu = log_sigma_x2 = None
             # TODO(rcadene, alexander-soare): remove call to `.to` to speedup forward ; precompute and use buffer
-            latent_sample = torch.zeros([batch_size, self.latent_dim], dtype=torch.float32).to(
+            latent_sample = torch.zeros([batch_size, self.config.latent_dim], dtype=torch.float32).to(
                 batch["observation.state"].device
             )
 
@@ -350,12 +351,12 @@ class ACT(nn.Module):
         cam_pos_embed = torch.cat(all_cam_pos_embeds, axis=-1)
 
         # Get positional embeddings for robot state and latent.
-        if self.has_state:
+        if self.use_input_state:
             robot_state_embed = self.encoder_robot_state_input_proj(batch["observation.state"])  # (B, C)
         latent_embed = self.encoder_latent_input_proj(latent_sample)  # (B, C)
 
         # Stack encoder input and positional embeddings moving to (S, B, C).
-        encoder_in_feats = [latent_embed, robot_state_embed] if self.has_state else [latent_embed]
+        encoder_in_feats = [latent_embed, robot_state_embed] if self.use_input_state else [latent_embed]
         encoder_in = torch.cat(
             [
                 torch.stack(encoder_in_feats, axis=0),

lerobot/common/policies/diffusion/configuration_diffusion.py

@@ -28,10 +28,7 @@ class DiffusionConfig:
 
     Notes on the inputs and outputs:
         - "observation.state" is required as an input key.
-        - At least one key starting with "observation.image is required as an input.
-        - If there are multiple keys beginning with "observation.image" they are treated as multiple camera
-          views.
-          Right now we only support all images having the same shape.
+        - A key starting with "observation.image is required as an input.
         - "action" is required as an output key.
 
     Args:

lerobot/common/policies/diffusion/modeling_diffusion.py

@@ -239,10 +239,8 @@ class DiffusionModel(nn.Module):
         global_cond = torch.cat([batch["observation.state"], img_features], dim=-1).flatten(start_dim=1)
 
         # run sampling
-        sample = self.conditional_sample(batch_size, global_cond=global_cond)
+        actions = self.conditional_sample(batch_size, global_cond=global_cond)
 
-        # `horizon` steps worth of actions (from the first observation).
-        actions = sample[..., : self.config.output_shapes["action"][0]]
         # Extract `n_action_steps` steps worth of actions (from the current observation).
         start = n_obs_steps - 1
         end = start + self.config.n_action_steps

lerobot/common/policies/normalize.py

@@ -147,7 +147,7 @@ class Normalize(nn.Module):
                 assert not torch.isinf(min).any(), _no_stats_error_str("min")
                 assert not torch.isinf(max).any(), _no_stats_error_str("max")
                 # normalize to [0,1]
-                batch[key] = (batch[key] - min) / (max - min)
+                batch[key] = (batch[key] - min) / (max - min + 1e-8)
                 # normalize to [-1, 1]
                 batch[key] = batch[key] * 2 - 1
             else:

lerobot/common/utils/utils.py

@@ -120,13 +120,13 @@ def init_logging():
     logging.getLogger().addHandler(console_handler)
 
 
-def format_big_number(num):
+def format_big_number(num, precision=0):
     suffixes = ["", "K", "M", "B", "T", "Q"]
     divisor = 1000.0
 
     for suffix in suffixes:
         if abs(num) < divisor:
-            return f"{num:.0f}{suffix}"
+            return f"{num:.{precision}f}{suffix}"
         num /= divisor
 
     return num

lerobot/configs/default.yaml

@@ -23,6 +23,10 @@ use_amp: false
 # `seed` is used for training (eg: model initialization, dataset shuffling)
 # AND for the evaluation environments.
 seed: ???
+# You may provide a list of datasets here. `train.py` creates them all and concatenates them. Note: only data
+# keys common between the datasets are kept. Each dataset gets and additional transform that inserts the
+# "dataset_index" into the returned item. The index mapping is made according to the order in which the
+# datsets are provided.
 dataset_repo_id: lerobot/pusht
 
 training:
@@ -53,3 +57,37 @@ wandb:
   disable_artifact: false
   project: lerobot
   notes: ""
+
+image_transforms:
+  # These transforms are all using standard torchvision.transforms.v2
+  # You can find out how these transformations affect images here:
+  # https://pytorch.org/vision/0.18/auto_examples/transforms/plot_transforms_illustrations.html
+  # We use a custom RandomSubsetApply container to sample them.
+  # For each transform, the following parameters are available:
+  #   weight: This represents the multinomial probability (with no replacement)
+  #           used for sampling the transform. If the sum of the weights is not 1,
+  #           they will be normalized.
+  #   min_max: Lower & upper bound respectively used for sampling the transform's parameter
+  #           (following uniform distribution) when it's applied.
+  enable: false
+  # This is the number of transforms (sampled from these below) that will be applied to each frame.
+  # It's an integer in the interval [0, number of available transforms].
+  max_num_transforms: 3
+  # By default, transforms are applied in Torchvision's suggested order (shown below).
+  # Set this to True to apply them in a random order.
+  random_order: false
+  brightness:
+    weight: 1
+    min_max: [0.8, 1.2]
+  contrast:
+    weight: 1
+    min_max: [0.8, 1.2]
+  saturation:
+    weight: 1
+    min_max: [0.5, 1.5]
+  hue:
+    weight: 1
+    min_max: [-0.05, 0.05]
+  sharpness:
+    weight: 1
+    min_max: [0.8, 1.2]

lerobot/configs/policy/act_real.yaml

@@ -11,7 +11,7 @@
 # ```bash
 # python lerobot/scripts/train.py \
 #   policy=act_real \
-#   env=aloha_real
+#   env=dora_aloha_real
 # ```
 
 seed: 1000

lerobot/configs/policy/act_real_no_state.yaml

@@ -9,7 +9,7 @@
 # ```bash
 # python lerobot/scripts/train.py \
 #   policy=act_real_no_state \
-#   env=aloha_real
+#   env=dora_aloha_real
 # ```
 
 seed: 1000

lerobot/configs/policy/diffusion.yaml

@@ -44,6 +44,10 @@ training:
     observation.state: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
     action: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1 - ${policy.n_obs_steps} + ${policy.horizon})]"
 
+  # The original implementation doesn't sample frames for the last 7 steps,
+  # which avoids excessive padding and leads to improved training results.
+  drop_n_last_frames: 7  # ${policy.horizon} - ${policy.n_action_steps} - ${policy.n_obs_steps} + 1
+
 eval:
   n_episodes: 50
   batch_size: 50

lerobot/scripts/eval.py

@@ -209,7 +209,7 @@ def eval_policy(
     policy: torch.nn.Module,
     n_episodes: int,
     max_episodes_rendered: int = 0,
-    video_dir: Path | None = None,
+    videos_dir: Path | None = None,
     return_episode_data: bool = False,
     start_seed: int | None = None,
     enable_progbar: bool = False,
@@ -221,7 +221,7 @@ def eval_policy(
         policy: The policy.
         n_episodes: The number of episodes to evaluate.
         max_episodes_rendered: Maximum number of episodes to render into videos.
-        video_dir: Where to save rendered videos.
+        videos_dir: Where to save rendered videos.
         return_episode_data: Whether to return episode data for online training. Incorporates the data into
             the "episodes" key of the returned dictionary.
         start_seed: The first seed to use for the first individual rollout. For all subsequent rollouts the
@@ -347,8 +347,8 @@ def eval_policy(
             ):
                 if n_episodes_rendered >= max_episodes_rendered:
                     break
-                video_dir.mkdir(parents=True, exist_ok=True)
-                video_path = video_dir / f"eval_episode_{n_episodes_rendered}.mp4"
+                videos_dir.mkdir(parents=True, exist_ok=True)
+                video_path = videos_dir / f"eval_episode_{n_episodes_rendered}.mp4"
                 video_paths.append(str(video_path))
                 thread = threading.Thread(
                     target=write_video,
@@ -503,9 +503,10 @@ def _compile_episode_data(
     }
 
 
-def eval(
+def main(
     pretrained_policy_path: str | None = None,
     hydra_cfg_path: str | None = None,
+    out_dir: str | None = None,
     config_overrides: list[str] | None = None,
 ):
     assert (pretrained_policy_path is None) ^ (hydra_cfg_path is None)
@@ -513,12 +514,8 @@ def eval(
         hydra_cfg = init_hydra_config(pretrained_policy_path / "config.yaml", config_overrides)
     else:
         hydra_cfg = init_hydra_config(hydra_cfg_path, config_overrides)
-    out_dir = (
-        f"outputs/eval/{dt.now().strftime('%Y-%m-%d/%H-%M-%S')}_{hydra_cfg.env.name}_{hydra_cfg.policy.name}"
-    )
-
     if out_dir is None:
-        raise NotImplementedError()
+        out_dir = f"outputs/eval/{dt.now().strftime('%Y-%m-%d/%H-%M-%S')}_{hydra_cfg.env.name}_{hydra_cfg.policy.name}"
 
     # Check device is available
     device = get_safe_torch_device(hydra_cfg.device, log=True)
@@ -546,7 +543,7 @@ def eval(
             policy,
             hydra_cfg.eval.n_episodes,
             max_episodes_rendered=10,
-            video_dir=Path(out_dir) / "eval",
+            videos_dir=Path(out_dir) / "videos",
             start_seed=hydra_cfg.seed,
             enable_progbar=True,
             enable_inner_progbar=True,
@@ -586,6 +583,13 @@ if __name__ == "__main__":
         ),
     )
     parser.add_argument("--revision", help="Optionally provide the Hugging Face Hub revision ID.")
+    parser.add_argument(
+        "--out-dir",
+        help=(
+            "Where to save the evaluation outputs. If not provided, outputs are saved in "
+            "outputs/eval/{timestamp}_{env_name}_{policy_name}"
+        ),
+    )
     parser.add_argument(
         "overrides",
         nargs="*",
@@ -594,7 +598,7 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     if args.pretrained_policy_name_or_path is None:
-        eval(hydra_cfg_path=args.config, config_overrides=args.overrides)
+        main(hydra_cfg_path=args.config, out_dir=args.out_dir, config_overrides=args.overrides)
     else:
         try:
             pretrained_policy_path = Path(
@@ -618,4 +622,8 @@ if __name__ == "__main__":
                 "repo ID, nor is it an existing local directory."
             )
 
-        eval(pretrained_policy_path=pretrained_policy_path, config_overrides=args.overrides)
+        main(
+            pretrained_policy_path=pretrained_policy_path,
+            out_dir=args.out_dir,
+            config_overrides=args.overrides,
+        )

lerobot/scripts/push_dataset_to_hub.py

@@ -71,9 +71,9 @@ import torch
 from huggingface_hub import HfApi
 from safetensors.torch import save_file
 
+from lerobot.common.datasets.compute_stats import compute_stats
 from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
 from lerobot.common.datasets.push_dataset_to_hub._download_raw import download_raw
-from lerobot.common.datasets.push_dataset_to_hub.compute_stats import compute_stats
 from lerobot.common.datasets.utils import flatten_dict
 
 

lerobot/scripts/train.py

@@ -16,7 +16,6 @@
 import logging
 import time
 from contextlib import nullcontext
-from copy import deepcopy
 from pathlib import Path
 from pprint import pformat
 
@@ -28,6 +27,8 @@ from termcolor import colored
 from torch.cuda.amp import GradScaler
 
 from lerobot.common.datasets.factory import make_dataset, resolve_delta_timestamps
+from lerobot.common.datasets.lerobot_dataset import MultiLeRobotDataset
+from lerobot.common.datasets.sampler import EpisodeAwareSampler
 from lerobot.common.datasets.utils import cycle
 from lerobot.common.envs.factory import make_env
 from lerobot.common.logger import Logger, log_output_dir
@@ -149,6 +150,7 @@ def log_train_info(logger: Logger, info, step, cfg, dataset, is_offline):
     grad_norm = info["grad_norm"]
     lr = info["lr"]
     update_s = info["update_s"]
+    dataloading_s = info["dataloading_s"]
 
     # A sample is an (observation,action) pair, where observation and action
     # can be on multiple timestamps. In a batch, we have `batch_size`` number of samples.
@@ -169,6 +171,7 @@ def log_train_info(logger: Logger, info, step, cfg, dataset, is_offline):
         f"lr:{lr:0.1e}",
         # in seconds
         f"updt_s:{update_s:.3f}",
+        f"data_s:{dataloading_s:.3f}",  # if not ~0, you are bottlenecked by cpu or io
     ]
     logging.info(" ".join(log_items))
 
@@ -280,6 +283,11 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
 
     logging.info("make_dataset")
     offline_dataset = make_dataset(cfg)
+    if isinstance(offline_dataset, MultiLeRobotDataset):
+        logging.info(
+            "Multiple datasets were provided. Applied the following index mapping to the provided datasets: "
+            f"{pformat(offline_dataset.repo_id_to_index , indent=2)}"
+        )
 
     # Create environment used for evaluating checkpoints during training on simulation data.
     # On real-world data, no need to create an environment as evaluations are done outside train.py,
@@ -319,6 +327,9 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
 
     # Note: this helper will be used in offline and online training loops.
     def evaluate_and_checkpoint_if_needed(step):
+        _num_digits = max(6, len(str(cfg.training.offline_steps + cfg.training.online_steps)))
+        step_identifier = f"{step:0{_num_digits}d}"
+
         if cfg.training.eval_freq > 0 and step % cfg.training.eval_freq == 0:
             logging.info(f"Eval policy at step {step}")
             with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.use_amp else nullcontext():
@@ -326,11 +337,11 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
                     eval_env,
                     policy,
                     cfg.eval.n_episodes,
-                    video_dir=Path(out_dir) / "eval",
+                    videos_dir=Path(out_dir) / "eval" / f"videos_step_{step_identifier}",
                     max_episodes_rendered=4,
                     start_seed=cfg.seed,
                 )
-            log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_offline)
+            log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_offline=True)
             if cfg.wandb.enable:
                 logger.log_video(eval_info["video_paths"][0], step, mode="eval")
             logging.info("Resume training")
@@ -344,29 +355,40 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
                 policy,
                 optimizer,
                 lr_scheduler,
-                identifier=str(step).zfill(
-                    max(6, len(str(cfg.training.offline_steps + cfg.training.online_steps)))
-                ),
+                identifier=step_identifier,
             )
             logging.info("Resume training")
 
     # create dataloader for offline training
+    if cfg.training.get("drop_n_last_frames"):
+        shuffle = False
+        sampler = EpisodeAwareSampler(
+            offline_dataset.episode_data_index,
+            drop_n_last_frames=cfg.training.drop_n_last_frames,
+            shuffle=True,
+        )
+    else:
+        shuffle = True
+        sampler = None
     dataloader = torch.utils.data.DataLoader(
         offline_dataset,
         num_workers=cfg.training.num_workers,
         batch_size=cfg.training.batch_size,
-        shuffle=True,
+        shuffle=shuffle,
+        sampler=sampler,
         pin_memory=device.type != "cpu",
         drop_last=False,
     )
     dl_iter = cycle(dataloader)
 
     policy.train()
-    is_offline = True
     for _ in range(step, cfg.training.offline_steps):
         if step == 0:
             logging.info("Start offline training on a fixed dataset")
+
+        start_time = time.perf_counter()
         batch = next(dl_iter)
+        dataloading_s = time.perf_counter() - start_time
 
         for key in batch:
             batch[key] = batch[key].to(device, non_blocking=True)
@@ -381,8 +403,10 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
             use_amp=cfg.use_amp,
         )
 
+        train_info["dataloading_s"] = dataloading_s
+
         if step % cfg.training.log_freq == 0:
-            log_train_info(logger, train_info, step, cfg, offline_dataset, is_offline)
+            log_train_info(logger, train_info, step, cfg, offline_dataset, is_offline=True)
 
         # Note: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed,
         # so we pass in step + 1.
@@ -390,41 +414,8 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
 
         step += 1
 
-    logging.info("End of offline training")
-
-    if cfg.training.online_steps == 0:
-        if cfg.training.eval_freq > 0:
-            eval_env.close()
-        return
-
-    # create an env dedicated to online episodes collection from policy rollout
-    online_training_env = make_env(cfg, n_envs=1)
-
-    # create an empty online dataset similar to offline dataset
-    online_dataset = deepcopy(offline_dataset)
-    online_dataset.hf_dataset = {}
-    online_dataset.episode_data_index = {}
-
-    # create dataloader for online training
-    concat_dataset = torch.utils.data.ConcatDataset([offline_dataset, online_dataset])
-    weights = [1.0] * len(concat_dataset)
-    sampler = torch.utils.data.WeightedRandomSampler(
-        weights, num_samples=len(concat_dataset), replacement=True
-    )
-    dataloader = torch.utils.data.DataLoader(
-        concat_dataset,
-        num_workers=4,
-        batch_size=cfg.training.batch_size,
-        sampler=sampler,
-        pin_memory=device.type != "cpu",
-        drop_last=False,
-    )
-
-    logging.info("End of online training")
-
-    if cfg.training.eval_freq > 0:
-        eval_env.close()
-    online_training_env.close()
+    eval_env.close()
+    logging.info("End of training")
 
 
 @hydra.main(version_base="1.2", config_name="default", config_path="../configs")

lerobot/scripts/visualize_dataset.py

@@ -224,7 +224,8 @@ def main():
         help=(
             "Mode of viewing between 'local' or 'distant'. "
             "'local' requires data to be on a local machine. It spawns a viewer to visualize the data locally. "
-            "'distant' creates a server on the distant machine where the data is stored. Visualize the data by connecting to the server with `rerun ws://localhost:PORT` on the local machine."
+            "'distant' creates a server on the distant machine where the data is stored. "
+            "Visualize the data by connecting to the server with `rerun ws://localhost:PORT` on the local machine."
         ),
     )
     parser.add_argument(
@@ -245,8 +246,8 @@ def main():
         default=0,
         help=(
             "Save a .rrd file in the directory provided by `--output-dir`. "
-            "It also deactivates the spawning of a viewer. ",
-            "Visualize the data by running `rerun path/to/file.rrd` on your local machine.",
+            "It also deactivates the spawning of a viewer. "
+            "Visualize the data by running `rerun path/to/file.rrd` on your local machine."
         ),
     )
     parser.add_argument(

lerobot/scripts/visualize_image_transforms.py

@@ -0,0 +1,46 @@
+from pathlib import Path
+
+import hydra
+from torchvision.transforms import ToPILImage
+
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.transforms import make_image_transforms
+
+to_pil = ToPILImage()
+
+
+def main(cfg, output_dir=Path("outputs/image_transforms")):
+    dataset = LeRobotDataset(cfg.dataset_repo_id, image_transforms=None)
+
+    output_dir = Path(output_dir) / Path(cfg.dataset_repo_id.split("/")[-1])
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Get first frame of 1st episode
+    first_idx = dataset.episode_data_index["from"][0].item()
+    frame = dataset[first_idx][dataset.camera_keys[0]]
+    to_pil(frame).save(output_dir / "original_frame.png", quality=100)
+
+    transforms = ["brightness", "contrast", "saturation", "hue", "sharpness"]
+
+    # Apply each single transformation
+    for transform_name in transforms:
+        for t in transforms:
+            if t == transform_name:
+                cfg.image_transforms[t].weight = 1
+            else:
+                cfg.image_transforms[t].weight = 0
+
+        transform = make_image_transforms(cfg.image_transforms)
+        img = transform(frame)
+        to_pil(img).save(output_dir / f"{transform_name}.png", quality=100)
+
+
+@hydra.main(version_base="1.2", config_name="default", config_path="../configs")
+def visualize_transforms_cli(cfg: dict):
+    main(
+        cfg,
+    )
+
+
+if __name__ == "__main__":
+    visualize_transforms_cli()

poetry.lock

@@ -444,63 +444,63 @@ files = [
 
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 name = "coverage"
-version = "7.5.1"
+version = "7.5.3"
 description = "Code coverage measurement for Python"
 optional = true
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -1104,7 +1104,7 @@ pyarrow = ">=12.0.0"
 type = "git"
 url = "https://github.com/dora-rs/dora-lerobot.git"
 reference = "HEAD"
-resolved_reference = "1c6c2a401c3a2967d41444be6286ca9a28893abf"
+resolved_reference = "ed0c00a4fdc6ec856c9842551acd7dc7ee776f79"
 subdirectory = "gym_dora"
 
 [[package]]
@@ -1310,13 +1310,13 @@ files = [
 
 [[package]]
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+version = "0.23.2"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -2102,18 +2102,15 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
 name = "nodeenv"
-version = "1.8.0"
+version = "1.9.0"
 description = "Node.js virtual environment builder"
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 ]
 
-[package.dependencies]
-setuptools = "*"
-
 [[package]]
 name = "numba"
 version = "0.59.1"
@@ -3231,13 +3228,13 @@ files = [
 
 [[package]]
 name = "requests"
-version = "2.32.2"
+version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3253,16 +3250,16 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "rerun-sdk"
-version = "0.16.0"
+version = "0.16.1"
 description = "The Rerun Logging SDK"
 optional = false
 python-versions = "<3.13,>=3.8"
 files = [
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-    {file = "rerun_sdk-0.16.0-cp38-abi3-manylinux_2_31_aarch64.whl", hash = "sha256:860a6394380d3e9b9e48bf34423bd56dda54d5b0158d2ae0e433698659b34198"},
-    {file = "rerun_sdk-0.16.0-cp38-abi3-manylinux_2_31_x86_64.whl", hash = "sha256:5b8d1476f73a3ad1a5d3f21b61c633f3ab62aa80fa0b049f5ad10bf1227681ab"},
-    {file = "rerun_sdk-0.16.0-cp38-abi3-win_amd64.whl", hash = "sha256:aff0051a263b8c3067243c0126d319845baf4fe640899f04aeef7daf151f35e4"},
+    {file = "rerun_sdk-0.16.1-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:170c6976634008611753e10dfef8cdc395ce8180e634c169e7c61cef2f89a277"},
+    {file = "rerun_sdk-0.16.1-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:c9a76eab7eb5559276737dad655200e9350df0837158dbc5a896970ab4201454"},
+    {file = "rerun_sdk-0.16.1-cp38-abi3-manylinux_2_31_aarch64.whl", hash = "sha256:4d6436752d57e8b8038489a0e7e37f0c760b088e96db5fb81667d3a376d63fea"},
+    {file = "rerun_sdk-0.16.1-cp38-abi3-manylinux_2_31_x86_64.whl", hash = "sha256:37b7b47948471873e84f224b16f417a94a91c7cbd6c72c68281eeff1ba414b8f"},
+    {file = "rerun_sdk-0.16.1-cp38-abi3-win_amd64.whl", hash = "sha256:be88799c8afdf68eafa99e64e2e4f0a484e187e017a180219abbe6bb988acd4e"},
 ]
 
 [package.dependencies]
@@ -3739,17 +3736,17 @@ files = [
 
 [[package]]
 name = "sympy"
-version = "1.12"
+version = "1.12.1"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "sympy-1.12-py3-none-any.whl", hash = "sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5"},
-    {file = "sympy-1.12.tar.gz", hash = "sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8"},
+    {file = "sympy-1.12.1-py3-none-any.whl", hash = "sha256:9b2cbc7f1a640289430e13d2a56f02f867a1da0190f2f99d8968c2f74da0e515"},
+    {file = "sympy-1.12.1.tar.gz", hash = "sha256:2877b03f998cd8c08f07cd0de5b767119cd3ef40d09f41c30d722f6686b0fb88"},
 ]
 
 [package.dependencies]
-mpmath = ">=0.19"
+mpmath = ">=1.1.0,<1.4.0"
 
 [[package]]
 name = "tbb"
@@ -4263,13 +4260,13 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zarr"
-version = "2.18.1"
+version = "2.18.2"
 description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "zarr-2.18.1-py3-none-any.whl", hash = "sha256:a1770d194eec4ec0a41a01295a6f724e1c3471d704d3aca906d3b3a7f8830245"},
-    {file = "zarr-2.18.1.tar.gz", hash = "sha256:28c360ed123e606c425a694a83300227a907cb86a995fc9eef620ecafbe5f92d"},
+    {file = "zarr-2.18.2-py3-none-any.whl", hash = "sha256:a638754902f97efa99b406083fdc807a0e2ccf12a949117389d2a4ba9b05df38"},
+    {file = "zarr-2.18.2.tar.gz", hash = "sha256:9bb393b8a0a38fb121dbb913b047d75db28de9890f6d644a217a73cf4ae74f47"},
 ]
 
 [package.dependencies]
@@ -4284,13 +4281,13 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
 
 [[package]]
 name = "zipp"
-version = "3.18.2"
+version = "3.19.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"},
-    {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"},
+    {file = "zipp-3.19.0-py3-none-any.whl", hash = "sha256:96dc6ad62f1441bcaccef23b274ec471518daf4fbbc580341204936a5a3dddec"},
+    {file = "zipp-3.19.0.tar.gz", hash = "sha256:952df858fb3164426c976d9338d3961e8e8b3758e2e059e0f754b8c4262625ee"},
 ]
 
 [package.extras]

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/actions.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:ebd21273f6048b66c806f92035352843a9069908b3296863fd55d34cf71cd0ef
-size 51248
+oid sha256:2fff6294b94cf42d4dd1249dcc5c3b0269d6d9c697f894e61b867d7ab81a94e4
+size 5104

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/grad_stats.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b9bbf951891077320a5da27e77ddb580a6e833e8d3162b62a2f887a1989585cc
+oid sha256:4aa23e51607604a18b70fa42edbbe1af34f119d985628fc27cc1bbb0efbc8901
 size 31688

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/output_dict.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:d4070bd1f1cd8c72bc2daf628088e42b8ef113f6df0bfd9e91be052bc90038c3
+oid sha256:6fd368406c93cb562a69ff11cf7adf34a4b223507dcb2b9e9b8f44ee1036988a
 size 68

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/param_stats.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:42f92239223bb4df32d5c3016bc67450159f1285a7ab046307b645f699ccc34e
+oid sha256:5663ee79a13bb70a1604b887dd21bf89d18482287442419c6cc6c5bf0e753e99
 size 34928

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real_no_state/actions.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:52f85d6262ad1dd0b66578b25829fed96aaaca3c7458cb73ac75111350d17fcf
-size 51248
+oid sha256:fb1a45463efd860af2ca22c16c77d55a18bd96fef080ae77978845a2f22ef716
+size 5104

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real_no_state/grad_stats.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5ba7c910618f0f3ca69f82f3d70c880d2b2e432456524a2a63dfd5c50efa45f0
+oid sha256:aa5a43e22f01d8e2f8d19f31753608794f1edbd74aaf71660091ab80ea58dc9b
 size 30808

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real_no_state/param_stats.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:53ad410f43855254438790f54aa7c895a052776acdd922906ae430684f659b53
+oid sha256:54d1f75cf67a7b1d7a7c6865ecb9b1cc86a2f032d1890245f8996789ab6e0df6
 size 33608

tests/scripts/save_image_transforms.py

@@ -0,0 +1,47 @@
+from pathlib import Path
+
+import torch
+from torchvision.transforms import v2
+from safetensors.torch import save_file
+
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.transforms import RangeRandomSharpness
+from lerobot.common.utils.utils import seeded_context
+
+DEFAULT_CONFIG_PATH = "lerobot/configs/default.yaml"
+ARTIFACT_DIR = "tests/data/save_image_transforms"
+SEED = 1336
+to_pil = v2.ToPILImage()
+
+
+def main(repo_id):
+    dataset = LeRobotDataset(repo_id, image_transforms=None)
+    output_dir = Path(ARTIFACT_DIR)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Get first frame of given episode
+    from_idx = dataset.episode_data_index["from"][0].item()
+    original_frame = dataset[from_idx][dataset.camera_keys[0]]
+    to_pil(original_frame).save(output_dir / "original_frame.png", quality=100)
+
+    transforms = {
+        "brightness": v2.ColorJitter(brightness=(0.0, 2.0)),
+        "contrast": v2.ColorJitter(contrast=(0.0, 2.0)),
+        "saturation": v2.ColorJitter(saturation=(0.0, 2.0)),
+        "hue": v2.ColorJitter(hue=(-0.5, 0.5)),
+        "sharpness": RangeRandomSharpness(0.0, 2.0),
+    }
+
+    # frames = {"original_frame": original_frame}
+    for name, transform in transforms.items():
+        with seeded_context(SEED):
+            # transform = v2.Compose([transform, v2.ToDtype(torch.float32, scale=True)])
+            transformed_frame = transform(original_frame)
+            # frames[name] = transform(original_frame)
+            to_pil(transformed_frame).save(output_dir / f"{SEED}_{name}.png", quality=100)
+
+    # save_file(frames, output_dir / f"transformed_frames_{SEED}.safetensors")
+
+if __name__ == "__main__":
+    repo_id = "lerobot/aloha_mobile_shrimp"
+    main(repo_id)

tests/scripts/save_policy_to_safetensors.py

@@ -77,7 +77,7 @@ def get_policy_stats(env_name, policy_name, extra_overrides):
     batch = next(iter(dataloader))
     obs = {}
     for k in batch:
-        if "observation" in k:
+        if k.startswith("observation"):
             obs[k] = batch[k]
 
     if "n_action_steps" in cfg.policy:
@@ -115,8 +115,8 @@ if __name__ == "__main__":
             ["policy.n_action_steps=8", "policy.num_inference_steps=10", "policy.down_dims=[128, 256, 512]"],
         ),
         ("aloha", "act", ["policy.n_action_steps=10"]),
-        ("dora_aloha_real", "act_real", []),
-        ("dora_aloha_real", "act_real_no_state", []),
+        ("dora_aloha_real", "act_real", ["policy.n_action_steps=10"]),
+        ("dora_aloha_real", "act_real_no_state", ["policy.n_action_steps=10"]),
     ]
     for env, policy, extra_overrides in env_policies:
         save_policy_to_safetensors("tests/data/save_policy_to_safetensors", env, policy, extra_overrides)

tests/test_datasets.py

@@ -16,6 +16,7 @@
 import json
 import logging
 from copy import deepcopy
+from itertools import chain
 from pathlib import Path
 
 import einops
@@ -25,26 +26,34 @@ from datasets import Dataset
 from safetensors.torch import load_file
 
 import lerobot
-from lerobot.common.datasets.factory import make_dataset
-from lerobot.common.datasets.lerobot_dataset import (
-    LeRobotDataset,
-)
-from lerobot.common.datasets.push_dataset_to_hub.compute_stats import (
+from lerobot.common.datasets.compute_stats import (
+    aggregate_stats,
     compute_stats,
     get_stats_einops_patterns,
 )
+from lerobot.common.datasets.factory import make_dataset
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, MultiLeRobotDataset
 from lerobot.common.datasets.utils import (
     flatten_dict,
     hf_transform_to_torch,
     load_previous_and_future_frames,
     unflatten_dict,
 )
-from lerobot.common.utils.utils import init_hydra_config
+from lerobot.common.utils.utils import init_hydra_config, seeded_context
 from tests.utils import DEFAULT_CONFIG_PATH, DEVICE
 
 
-@pytest.mark.parametrize("env_name, repo_id, policy_name", lerobot.env_dataset_policy_triplets)
+@pytest.mark.parametrize(
+    "env_name, repo_id, policy_name",
+    lerobot.env_dataset_policy_triplets
+    + [("aloha", ["lerobot/aloha_sim_insertion_human", "lerobot/aloha_sim_transfer_cube_human"], "act")],
+)
 def test_factory(env_name, repo_id, policy_name):
+    """
+    Tests that:
+        - we can create a dataset with the factory.
+        - for a commonly used set of data keys, the data dimensions are correct.
+    """
     cfg = init_hydra_config(
         DEFAULT_CONFIG_PATH,
         overrides=[
@@ -105,6 +114,39 @@ def test_factory(env_name, repo_id, policy_name):
             assert key in item, f"{key}"
 
 
+# TODO(alexander-soare): If you're hunting for savings on testing time, this takes about 5 seconds.
+def test_multilerobotdataset_frames():
+    """Check that all dataset frames are incorporated."""
+    # Note: use the image variants of the dataset to make the test approx 3x faster.
+    # Note: We really do need three repo_ids here as at some point this caught an issue with the chaining
+    # logic that wouldn't be caught with two repo IDs.
+    repo_ids = [
+        "lerobot/aloha_sim_insertion_human_image",
+        "lerobot/aloha_sim_transfer_cube_human_image",
+        "lerobot/aloha_sim_insertion_scripted_image",
+    ]
+    sub_datasets = [LeRobotDataset(repo_id) for repo_id in repo_ids]
+    dataset = MultiLeRobotDataset(repo_ids)
+    assert len(dataset) == sum(len(d) for d in sub_datasets)
+    assert dataset.num_samples == sum(d.num_samples for d in sub_datasets)
+    assert dataset.num_episodes == sum(d.num_episodes for d in sub_datasets)
+
+    # Run through all items of the LeRobotDatasets in parallel with the items of the MultiLerobotDataset and
+    # check they match.
+    expected_dataset_indices = []
+    for i, sub_dataset in enumerate(sub_datasets):
+        expected_dataset_indices.extend([i] * len(sub_dataset))
+
+    for expected_dataset_index, sub_dataset_item, dataset_item in zip(
+        expected_dataset_indices, chain(*sub_datasets), dataset, strict=True
+    ):
+        dataset_index = dataset_item.pop("dataset_index")
+        assert dataset_index == expected_dataset_index
+        assert sub_dataset_item.keys() == dataset_item.keys()
+        for k in sub_dataset_item:
+            assert torch.equal(sub_dataset_item[k], dataset_item[k])
+
+
 def test_compute_stats_on_xarm():
     """Check that the statistics are computed correctly according to the stats_patterns property.
 
@@ -315,3 +357,31 @@ def test_backward_compatibility(repo_id):
     # i = dataset.episode_data_index["to"][-1].item()
     # load_and_compare(i - 2)
     # load_and_compare(i - 1)
+
+
+def test_aggregate_stats():
+    """Makes 3 basic datasets and checks that aggregate stats are computed correctly."""
+    with seeded_context(0):
+        data_a = torch.rand(30, dtype=torch.float32)
+        data_b = torch.rand(20, dtype=torch.float32)
+        data_c = torch.rand(20, dtype=torch.float32)
+
+    hf_dataset_1 = Dataset.from_dict(
+        {"a": data_a[:10], "b": data_b[:10], "c": data_c[:10], "index": torch.arange(10)}
+    )
+    hf_dataset_1.set_transform(hf_transform_to_torch)
+    hf_dataset_2 = Dataset.from_dict({"a": data_a[10:20], "b": data_b[10:], "index": torch.arange(10)})
+    hf_dataset_2.set_transform(hf_transform_to_torch)
+    hf_dataset_3 = Dataset.from_dict({"a": data_a[20:], "c": data_c[10:], "index": torch.arange(10)})
+    hf_dataset_3.set_transform(hf_transform_to_torch)
+    dataset_1 = LeRobotDataset.from_preloaded("d1", hf_dataset=hf_dataset_1)
+    dataset_1.stats = compute_stats(dataset_1, batch_size=len(hf_dataset_1), num_workers=0)
+    dataset_2 = LeRobotDataset.from_preloaded("d2", hf_dataset=hf_dataset_2)
+    dataset_2.stats = compute_stats(dataset_2, batch_size=len(hf_dataset_2), num_workers=0)
+    dataset_3 = LeRobotDataset.from_preloaded("d3", hf_dataset=hf_dataset_3)
+    dataset_3.stats = compute_stats(dataset_3, batch_size=len(hf_dataset_3), num_workers=0)
+    stats = aggregate_stats([dataset_1, dataset_2, dataset_3])
+    for data_key, data in zip(["a", "b", "c"], [data_a, data_b, data_c], strict=True):
+        for agg_fn in ["mean", "min", "max"]:
+            assert torch.allclose(stats[data_key][agg_fn], einops.reduce(data, "n -> 1", agg_fn))
+        assert torch.allclose(stats[data_key]["std"], torch.std(data, correction=0))

tests/test_policies.py

@@ -86,6 +86,9 @@ def test_policy(env_name, policy_name, extra_overrides):
         - Updating the policy.
         - Using the policy to select actions at inference time.
         - Test the action can be applied to the policy
+
+    Note: We test various combinations of policy and dataset. The combinations are by no means exhaustive,
+          and for now we add tests as we see fit.
     """
     cfg = init_hydra_config(
         DEFAULT_CONFIG_PATH,
@@ -137,7 +140,7 @@ def test_policy(env_name, policy_name, extra_overrides):
 
     dataloader = torch.utils.data.DataLoader(
         dataset,
-        num_workers=4,
+        num_workers=0,
         batch_size=2,
         shuffle=True,
         pin_memory=DEVICE != "cpu",

tests/test_sampler.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from datasets import Dataset
+
+from lerobot.common.datasets.sampler import EpisodeAwareSampler
+from lerobot.common.datasets.utils import (
+    calculate_episode_data_index,
+    hf_transform_to_torch,
+)
+
+
+def test_drop_n_first_frames():
+    dataset = Dataset.from_dict(
+        {
+            "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
+            "index": [0, 1, 2, 3, 4, 5],
+            "episode_index": [0, 0, 1, 2, 2, 2],
+        },
+    )
+    dataset.set_transform(hf_transform_to_torch)
+    episode_data_index = calculate_episode_data_index(dataset)
+    sampler = EpisodeAwareSampler(episode_data_index, drop_n_first_frames=1)
+    assert sampler.indices == [1, 4, 5]
+    assert len(sampler) == 3
+    assert list(sampler) == [1, 4, 5]
+
+
+def test_drop_n_last_frames():
+    dataset = Dataset.from_dict(
+        {
+            "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
+            "index": [0, 1, 2, 3, 4, 5],
+            "episode_index": [0, 0, 1, 2, 2, 2],
+        },
+    )
+    dataset.set_transform(hf_transform_to_torch)
+    episode_data_index = calculate_episode_data_index(dataset)
+    sampler = EpisodeAwareSampler(episode_data_index, drop_n_last_frames=1)
+    assert sampler.indices == [0, 3, 4]
+    assert len(sampler) == 3
+    assert list(sampler) == [0, 3, 4]
+
+
+def test_episode_indices_to_use():
+    dataset = Dataset.from_dict(
+        {
+            "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
+            "index": [0, 1, 2, 3, 4, 5],
+            "episode_index": [0, 0, 1, 2, 2, 2],
+        },
+    )
+    dataset.set_transform(hf_transform_to_torch)
+    episode_data_index = calculate_episode_data_index(dataset)
+    sampler = EpisodeAwareSampler(episode_data_index, episode_indices_to_use=[0, 2])
+    assert sampler.indices == [0, 1, 3, 4, 5]
+    assert len(sampler) == 5
+    assert list(sampler) == [0, 1, 3, 4, 5]
+
+
+def test_shuffle():
+    dataset = Dataset.from_dict(
+        {
+            "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
+            "index": [0, 1, 2, 3, 4, 5],
+            "episode_index": [0, 0, 1, 2, 2, 2],
+        },
+    )
+    dataset.set_transform(hf_transform_to_torch)
+    episode_data_index = calculate_episode_data_index(dataset)
+    sampler = EpisodeAwareSampler(episode_data_index, shuffle=False)
+    assert sampler.indices == [0, 1, 2, 3, 4, 5]
+    assert len(sampler) == 6
+    assert list(sampler) == [0, 1, 2, 3, 4, 5]
+    sampler = EpisodeAwareSampler(episode_data_index, shuffle=True)
+    assert sampler.indices == [0, 1, 2, 3, 4, 5]
+    assert len(sampler) == 6
+    assert set(sampler) == {0, 1, 2, 3, 4, 5}

tests/test_transforms.py

@@ -0,0 +1,253 @@
+from pathlib import Path
+
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+import numpy as np
+import pytest
+import torch
+from omegaconf import OmegaConf
+from PIL import Image
+from torchvision.transforms import v2
+from torchvision.transforms.v2 import functional as F  # noqa: N812
+
+from lerobot.common.datasets.transforms import RandomSubsetApply, RangeRandomSharpness, get_image_transforms
+from lerobot.common.utils.utils import seeded_context
+
+
+# test_make_image_transforms 
+# - 
+
+# test backward compatibility torchvision
+# - save artifacts
+
+# test backward compatibility default yaml (enable false, enable true)
+# - save artifacts
+
+
+def test_get_image_transforms_no_transform():
+    get_image_transforms()
+    get_image_transforms(sharpness_weight=0.0)
+    get_image_transforms(max_num_transforms=0)
+
+
+@pytest.fixture
+def img():
+    # dataset = LeRobotDataset("lerobot/pusht")
+    # item = dataset[0]
+    # return item["observation.image"]
+    path = "tests/data/save_image_transforms/original_frame.png"
+    img_chw = torch.from_numpy(np.array(Image.open(path).convert("RGB"))).permute(2, 0, 1)
+    return img_chw
+
+def test_get_image_transforms_brightness(img):
+    brightness_min_max = (0.5, 0.5)
+    tf_actual = get_image_transforms(brightness_weight=1., brightness_min_max=brightness_min_max)
+    tf_expected = v2.ColorJitter(brightness=brightness_min_max)
+    torch.testing.assert_close(tf_actual(img), tf_expected(img))
+
+def test_get_image_transforms_contrast(img):
+    contrast_min_max = (0.5, 0.5)
+    tf_actual = get_image_transforms(contrast_weight=1., contrast_min_max=contrast_min_max)
+    tf_expected = v2.ColorJitter(contrast=contrast_min_max)
+    torch.testing.assert_close(tf_actual(img), tf_expected(img))
+
+def test_get_image_transforms_saturation(img):
+    saturation_min_max = (0.5, 0.5)
+    tf_actual = get_image_transforms(saturation_weight=1., saturation_min_max=saturation_min_max)
+    tf_expected = v2.ColorJitter(saturation=saturation_min_max)
+    torch.testing.assert_close(tf_actual(img), tf_expected(img))
+
+def test_get_image_transforms_hue(img):
+    hue_min_max = (0.5, 0.5)
+    tf_actual = get_image_transforms(hue_weight=1., hue_min_max=hue_min_max)
+    tf_expected = v2.ColorJitter(hue=hue_min_max)
+    torch.testing.assert_close(tf_actual(img), tf_expected(img))
+
+def test_get_image_transforms_sharpness(img):
+    sharpness_min_max = (0.5, 0.5)
+    tf_actual = get_image_transforms(sharpness_weight=1., sharpness_min_max=sharpness_min_max)
+    tf_expected = RangeRandomSharpness(**sharpness_min_max)
+    torch.testing.assert_close(tf_actual(img), tf_expected(img))
+
+def test_get_image_transforms_max_num_transforms(img):
+    tf_actual = get_image_transforms(
+        saturation_min_max=(0.5, 0.5),
+        constrast_min_max=(0.5, 0.5),
+        saturation_min_max=(0.5, 0.5),
+        hue_min_max=(0.5, 0.5),
+        sharpness_min_max=(0.5, 0.5),
+        random_order=False,
+    )
+    tf_expected = v2.Compose([
+        v2.ColorJitter(brightness=(0.5, 0.5)),
+        v2.ColorJitter(contrast=(0.5, 0.5)),
+        v2.ColorJitter(saturation=(0.5, 0.5)),
+        v2.ColorJitter(hue=(0.5, 0.5)),
+        RangeRandomSharpness(sharpness=(0.5, 0.5)),
+    ])
+    torch.testing.assert_close(tf_actual(img), tf_expected(img))
+
+
+def test_get_image_transforms_random_order(img):
+    out_imgs = []
+    with seeded_context(1337):
+        for _ in range(20):
+            tf = get_image_transforms(
+                saturation_min_max=(0.5, 0.5),
+                constrast_min_max=(0.5, 0.5),
+                saturation_min_max=(0.5, 0.5),
+                hue_min_max=(0.5, 0.5),
+                sharpness_min_max=(0.5, 0.5),
+                random_order=False,
+            )
+            out_imgs.append(tf(img))
+    
+    for i in range(1,10):
+        with pytest.raises(ValueError):
+            torch.testing.assert_close(out_imgs[0], out_imgs[i])
+
+
+
+def test_backward_compatibility_torchvision():
+    pass
+
+def test_backward_compatibility_default_yaml():
+    pass
+
+
+# class TestRandomSubsetApply:
+#     @pytest.fixture(autouse=True)
+#     def setup(self):
+#         self.jitters = [
+#             v2.ColorJitter(brightness=0.5),
+#             v2.ColorJitter(contrast=0.5),
+#             v2.ColorJitter(saturation=0.5),
+#         ]
+#         self.flips = [v2.RandomHorizontalFlip(p=1), v2.RandomVerticalFlip(p=1)]
+#         self.img = torch.rand(3, 224, 224)
+
+#     @pytest.mark.parametrize("p", [[0, 1], [1, 0]])
+#     def test_random_choice(self, p):
+#         random_choice = RandomSubsetApply(self.flips, p=p, n_subset=1, random_order=False)
+#         output = random_choice(self.img)
+
+#         p_horz, _ = p
+#         if p_horz:
+#             torch.testing.assert_close(output, F.horizontal_flip(self.img))
+#         else:
+#             torch.testing.assert_close(output, F.vertical_flip(self.img))
+
+#     def test_transform_all(self):
+#         transform = RandomSubsetApply(self.jitters)
+#         output = transform(self.img)
+#         assert output.shape == self.img.shape
+
+#     def test_transform_subset(self):
+#         transform = RandomSubsetApply(self.jitters, n_subset=2)
+#         output = transform(self.img)
+#         assert output.shape == self.img.shape
+
+#     def test_random_order(self):
+#         random_order = RandomSubsetApply(self.flips, p=[0.5, 0.5], n_subset=2, random_order=True)
+#         # We can't really check whether the transforms are actually applied in random order. However,
+#         # horizontal and vertical flip are commutative. Meaning, even under the assumption that the transform
+#         # applies them in random order, we can use a fixed order to compute the expected value.
+#         actual = random_order(self.img)
+#         expected = v2.Compose(self.flips)(self.img)
+#         torch.testing.assert_close(actual, expected)
+
+#     def test_probability_length_mismatch(self):
+#         with pytest.raises(ValueError):
+#             RandomSubsetApply(self.jitters, p=[0.5, 0.5])
+
+#     def test_invalid_n_subset(self):
+#         with pytest.raises(ValueError):
+#             RandomSubsetApply(self.jitters, n_subset=5)
+
+
+# class TestRangeRandomSharpness:
+#     @pytest.fixture(autouse=True)
+#     def setup(self):
+#         self.img = torch.rand(3, 224, 224)
+
+#     def test_valid_range(self):
+#         transform = RangeRandomSharpness(0.1, 2.0)
+#         output = transform(self.img)
+#         assert output.shape == self.img.shape
+
+#     def test_invalid_range_min_negative(self):
+#         with pytest.raises(ValueError):
+#             RangeRandomSharpness(-0.1, 2.0)
+
+#     def test_invalid_range_max_smaller(self):
+#         with pytest.raises(ValueError):
+#             RangeRandomSharpness(2.0, 0.1)
+
+
+# class TestMakeImageTransforms:
+#     @pytest.fixture(autouse=True)
+#     def setup(self):
+#         """Seed should be the same as the one that was used to generate artifacts"""
+#         self.config = {
+#             "enable": True,
+#             "max_num_transforms": 1,
+#             "random_order": False,
+#             "brightness": {"weight": 0, "min": 2.0, "max": 2.0},
+#             "contrast": {
+#                 "weight": 0,
+#                 "min": 2.0,
+#                 "max": 2.0,
+#             },
+#             "saturation": {
+#                 "weight": 0,
+#                 "min": 2.0,
+#                 "max": 2.0,
+#             },
+#             "hue": {
+#                 "weight": 0,
+#                 "min": 0.5,
+#                 "max": 0.5,
+#             },
+#             "sharpness": {
+#                 "weight": 0,
+#                 "min": 2.0,
+#                 "max": 2.0,
+#             },
+#         }
+#         self.path = Path("tests/data/save_image_transforms")
+#         self.original_frame = self.load_png_to_tensor(self.path / "original_frame.png")
+#         self.transforms = {
+#             "brightness": v2.ColorJitter(brightness=(2.0, 2.0)),
+#             "contrast": v2.ColorJitter(contrast=(2.0, 2.0)),
+#             "saturation": v2.ColorJitter(saturation=(2.0, 2.0)),
+#             "hue": v2.ColorJitter(hue=(0.5, 0.5)),
+#             "sharpness": RangeRandomSharpness(2.0, 2.0),
+#         }
+
+#     @staticmethod
+#     def load_png_to_tensor(path: Path):
+#         return torch.from_numpy(np.array(Image.open(path).convert("RGB"))).permute(2, 0, 1)
+
+#     @pytest.mark.parametrize(
+#         "transform_key, seed",
+#         [
+#             ("brightness", 1336),
+#             ("contrast", 1336),
+#             ("saturation", 1336),
+#             ("hue", 1336),
+#             ("sharpness", 1336),
+#         ],
+#     )
+#     def test_single_transform(self, transform_key, seed):
+#         config = self.config
+#         config[transform_key]["weight"] = 1
+#         cfg = OmegaConf.create(config)
+
+#         actual_t = make_image_transforms(cfg, to_dtype=torch.uint8)
+#         with seeded_context(1336):
+#             actual = actual_t(self.original_frame)
+
+#         expected_t = self.transforms[transform_key]
+#         with seeded_context(1336):
+#             expected = expected_t(self.original_frame)
+
+#         torch.testing.assert_close(actual, expected)