fix aloha conversion changes

.github/workflows/nightly-tests.yml

@@ -63,17 +63,12 @@ jobs:
 
       - name: Test
         run: pytest -v --cov=./lerobot --cov-report=xml --disable-warnings tests
-
-      - name: ls
-        run: |
-          ls -glha .
-          ls -glha lerobot
-
-      - name: Upload coverage reports to Codecov with GitHub Action
-        uses: codecov/codecov-action@v3
-        with:
-          verbose: true
-
+      #   TODO(aliberts): Link with HF Codecov account
+      # - name: Upload coverage reports to Codecov with GitHub Action
+      #   uses: codecov/codecov-action@v4
+      #   with:
+      #     files: ./coverage.xml
+      #     verbose: true
       - name: Tests end-to-end
         env:
           DEVICE: cuda

lerobot/__init__.py

@@ -45,9 +45,6 @@ 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",
@@ -55,7 +52,7 @@ available_tasks_per_env = {
     ],
     "pusht": ["PushT-v0"],
     "xarm": ["XarmLift-v0"],
-    "dora_aloha_real": ["DoraAloha-v0", "DoraKoch-v0", "DoraReachy2-v0"],
+    "dora": ["DoraAloha-v0", "DoraKoch-v0", "DoraReachy2-v0"],
 }
 available_envs = list(available_tasks_per_env.keys())
 
@@ -81,7 +78,7 @@ available_datasets_per_env = {
         "lerobot/xarm_push_medium_image",
         "lerobot/xarm_push_medium_replay_image",
     ],
-    "dora_aloha_real": [
+    "dora": [
         "lerobot/aloha_static_battery",
         "lerobot/aloha_static_candy",
         "lerobot/aloha_static_coffee",
@@ -129,19 +126,17 @@ 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/factory.py

@@ -16,9 +16,9 @@
 import logging
 
 import torch
-from omegaconf import ListConfig, OmegaConf
+from omegaconf import OmegaConf
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, MultiLeRobotDataset
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 
 
 def resolve_delta_timestamps(cfg):
@@ -35,54 +35,25 @@ def resolve_delta_timestamps(cfg):
                 cfg.training.delta_timestamps[key] = eval(delta_timestamps[key])
 
 
-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."
+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=})."
         )
 
-    # 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
 
-    if isinstance(cfg.dataset_repo_id, str):
-        dataset = LeRobotDataset(
-            cfg.dataset_repo_id,
-            split=split,
-            delta_timestamps=cfg.training.get("delta_timestamps"),
-        )
-    else:
-        dataset = MultiLeRobotDataset(
-            cfg.dataset_repo_id, split=split, delta_timestamps=cfg.training.get("delta_timestamps")
-        )
+    dataset = LeRobotDataset(
+        cfg.dataset_repo_id,
+        split=split,
+        delta_timestamps=cfg.training.get("delta_timestamps"),
+    )
 
     if cfg.get("override_dataset_stats"):
         for key, stats_dict in cfg.override_dataset_stats.items():

lerobot/common/datasets/lerobot_dataset.py

@@ -13,16 +13,12 @@
 # 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,
@@ -46,7 +42,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         version: str | None = CODEBASE_VERSION,
         root: Path | None = DATA_DIR,
         split: str = "train",
-        transform: Callable | None = None,
+        transform: callable = None,
         delta_timestamps: dict[list[float]] | None = None,
     ):
         super().__init__()
@@ -175,7 +171,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
     @classmethod
     def from_preloaded(
         cls,
-        repo_id: str = "from_preloaded",
+        repo_id: str,
         version: str | None = CODEBASE_VERSION,
         root: Path | None = None,
         split: str = "train",
@@ -187,15 +183,7 @@ 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
@@ -207,193 +195,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.hf_dataset = hf_dataset
         obj.episode_data_index = episode_data_index
         obj.stats = stats
-        obj.info = info if info is not None else {}
+        obj.info = info
         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",
-        transform: 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,
-                transform=transform,
-            )
-            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.transform = transform
-        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.transform},\n"
-            f")"
-        )

lerobot/common/datasets/push_dataset_to_hub/aloha_dora_format.py

@@ -78,15 +78,29 @@ 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}"
@@ -111,11 +125,24 @@ def load_from_raw(raw_dir: Path, out_dir: Path, fps: int):
     del df["timestamp_utc"]
 
     # sanity check
-    has_nan = df.isna().any().any()
-    if has_nan:
-        raise ValueError("Dataset contains Nan values.")
+    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)
 
     # 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:
@@ -214,8 +241,6 @@ 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/push_dataset_to_hub/compute_stats.py

@@ -16,15 +16,17 @@
 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, num_workers=0):
+def get_stats_einops_patterns(dataset: LeRobotDataset | datasets.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
@@ -64,8 +66,9 @@ def get_stats_einops_patterns(dataset, num_workers=0):
     return stats_patterns
 
 
-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."""
+def compute_stats(
+    dataset: LeRobotDataset | datasets.Dataset, batch_size=32, num_workers=16, max_num_samples=None
+):
     if max_num_samples is None:
         max_num_samples = len(dataset)
 
@@ -156,54 +159,3 @@ def compute_stats(dataset, batch_size=32, num_workers=16, max_num_samples=None):
             "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/sampler.py

@@ -1,61 +0,0 @@
-#!/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/utils.py

@@ -59,7 +59,7 @@ def unflatten_dict(d, sep="/"):
     return outdict
 
 
-def hf_transform_to_torch(items_dict: dict[torch.Tensor | None]):
+def hf_transform_to_torch(items_dict):
     """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
@@ -73,8 +73,6 @@ def hf_transform_to_torch(items_dict: dict[torch.Tensor | None]):
         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
@@ -320,7 +318,8 @@ 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.
@@ -339,7 +338,6 @@ 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

@@ -26,10 +26,11 @@ 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.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.
+        - 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.
         - "action" is required as an output key.
 
     Args:

lerobot/common/policies/act/modeling_act.py

@@ -198,14 +198,15 @@ class ACT(nn.Module):
     def __init__(self, config: ACTConfig):
         super().__init__()
         self.config = config
-        # BERT style VAE encoder with input tokens [cls, robot_state, *action_sequence].
+        # BERT style VAE encoder with input [cls, *joint_space_configuration, *action_sequence].
         # The cls token forms parameters of the latent's distribution (like this [*means, *log_variances]).
-        self.use_input_state = "observation.state" in config.input_shapes
+        self.has_state = "observation.state" in config.input_shapes
+        self.latent_dim = config.latent_dim
         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.use_input_state:
+            if self.has_state:
                 self.vae_encoder_robot_state_input_proj = nn.Linear(
                     config.input_shapes["observation.state"][0], config.dim_model
                 )
@@ -214,12 +215,10 @@ 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, config.latent_dim * 2)
-            # Fixed sinusoidal positional embedding for the input to the VAE encoder. Unsqueeze for batch
+            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
             # dimension.
-            num_input_token_encoder = 1 + config.chunk_size
-            if self.use_input_state:
-                num_input_token_encoder += 1
+            num_input_token_encoder = 1 + 1 + config.chunk_size if self.has_state else 1 + config.chunk_size
             self.register_buffer(
                 "vae_encoder_pos_enc",
                 create_sinusoidal_pos_embedding(num_input_token_encoder, config.dim_model).unsqueeze(0),
@@ -242,16 +241,16 @@ class ACT(nn.Module):
 
         # Transformer encoder input projections. The tokens will be structured like
         # [latent, robot_state, image_feature_map_pixels].
-        if self.use_input_state:
+        if self.has_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(config.latent_dim, config.dim_model)
+        self.encoder_latent_input_proj = nn.Linear(self.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.use_input_state else 1
+        num_input_token_decoder = 2 if self.has_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)
 
@@ -299,12 +298,12 @@ 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.use_input_state:
+            if self.has_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.use_input_state:
+            if self.has_state:
                 vae_encoder_input = [cls_embed, robot_state_embed, action_embed]  # (B, S+2, D)
             else:
                 vae_encoder_input = [cls_embed, action_embed]
@@ -319,9 +318,9 @@ 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.config.latent_dim]
+            mu = latent_pdf_params[:, : self.latent_dim]
             # This is 2log(sigma). Done this way to match the original implementation.
-            log_sigma_x2 = latent_pdf_params[:, self.config.latent_dim :]
+            log_sigma_x2 = latent_pdf_params[:, self.latent_dim :]
 
             # Sample the latent with the reparameterization trick.
             latent_sample = mu + log_sigma_x2.div(2).exp() * torch.randn_like(mu)
@@ -329,7 +328,7 @@ 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.config.latent_dim], dtype=torch.float32).to(
+            latent_sample = torch.zeros([batch_size, self.latent_dim], dtype=torch.float32).to(
                 batch["observation.state"].device
             )
 
@@ -351,12 +350,12 @@ 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.use_input_state:
+        if self.has_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.use_input_state else [latent_embed]
+        encoder_in_feats = [latent_embed, robot_state_embed] if self.has_state else [latent_embed]
         encoder_in = torch.cat(
             [
                 torch.stack(encoder_in_feats, axis=0),

lerobot/common/policies/diffusion/configuration_diffusion.py

@@ -28,7 +28,10 @@ class DiffusionConfig:
 
     Notes on the inputs and outputs:
         - "observation.state" is required as an input key.
-        - A key starting with "observation.image is required as an input.
+        - 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.
         - "action" is required as an output key.
 
     Args:

lerobot/common/utils/utils.py

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

lerobot/configs/default.yaml

@@ -23,10 +23,6 @@ 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:

lerobot/configs/policy/act_real.yaml

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

lerobot/configs/policy/diffusion.yaml

@@ -44,10 +44,6 @@ 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/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

@@ -16,6 +16,7 @@
 import logging
 import time
 from contextlib import nullcontext
+from copy import deepcopy
 from pathlib import Path
 from pprint import pformat
 
@@ -27,8 +28,6 @@ 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
@@ -281,11 +280,6 @@ 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,
@@ -336,7 +330,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
                     max_episodes_rendered=4,
                     start_seed=cfg.seed,
                 )
-            log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_offline=True)
+            log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_offline)
             if cfg.wandb.enable:
                 logger.log_video(eval_info["video_paths"][0], step, mode="eval")
             logging.info("Resume training")
@@ -357,28 +351,18 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
             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=shuffle,
-        sampler=sampler,
+        shuffle=True,
         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")
@@ -398,7 +382,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
         )
 
         if step % cfg.training.log_freq == 0:
-            log_train_info(logger, train_info, step, cfg, offline_dataset, is_offline=True)
+            log_train_info(logger, train_info, step, cfg, offline_dataset, is_offline)
 
         # Note: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed,
         # so we pass in step + 1.
@@ -406,8 +390,41 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
 
         step += 1
 
-    eval_env.close()
-    logging.info("End of training")
+    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()
 
 
 @hydra.main(version_base="1.2", config_name="default", config_path="../configs")

poetry.lock

@@ -444,63 +444,63 @@ files = [
 
 [[package]]
 name = "coverage"
-version = "7.5.3"
+version = "7.5.1"
 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 = "ed0c00a4fdc6ec856c9842551acd7dc7ee776f79"
+resolved_reference = "1c6c2a401c3a2967d41444be6286ca9a28893abf"
 subdirectory = "gym_dora"
 
 [[package]]
@@ -1310,13 +1310,13 @@ files = [
 
 [[package]]
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-version = "0.23.2"
+version = "0.23.1"
 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]
@@ -2102,15 +2102,18 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
 name = "nodeenv"
-version = "1.9.0"
+version = "1.8.0"
 description = "Node.js virtual environment builder"
 optional = true
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 files = [
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 ]
 
+[package.dependencies]
+setuptools = "*"
+
 [[package]]
 name = "numba"
 version = "0.59.1"
@@ -3228,13 +3231,13 @@ files = [
 
 [[package]]
 name = "requests"
-version = "2.32.3"
+version = "2.32.2"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
-    {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
+    {file = "requests-2.32.2-py3-none-any.whl", hash = "sha256:fc06670dd0ed212426dfeb94fc1b983d917c4f9847c863f313c9dfaaffb7c23c"},
+    {file = "requests-2.32.2.tar.gz", hash = "sha256:dd951ff5ecf3e3b3aa26b40703ba77495dab41da839ae72ef3c8e5d8e2433289"},
 ]
 
 [package.dependencies]
@@ -3250,16 +3253,16 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "rerun-sdk"
-version = "0.16.1"
+version = "0.16.0"
 description = "The Rerun Logging SDK"
 optional = false
 python-versions = "<3.13,>=3.8"
 files = [
-    {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"},
+    {file = "rerun_sdk-0.16.0-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:1cc6dc66d089e296f945dc238301889efb61dd6d338b5d00f76981cf7aed0a74"},
+    {file = "rerun_sdk-0.16.0-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:faf231897655e46eb975695df2b0ace07db362d697e697f9a3dff52f81c0dc5d"},
+    {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"},
 ]
 
 [package.dependencies]
@@ -3736,17 +3739,17 @@ files = [
 
 [[package]]
 name = "sympy"
-version = "1.12.1"
+version = "1.12"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "sympy-1.12.1-py3-none-any.whl", hash = "sha256:9b2cbc7f1a640289430e13d2a56f02f867a1da0190f2f99d8968c2f74da0e515"},
-    {file = "sympy-1.12.1.tar.gz", hash = "sha256:2877b03f998cd8c08f07cd0de5b767119cd3ef40d09f41c30d722f6686b0fb88"},
+    {file = "sympy-1.12-py3-none-any.whl", hash = "sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5"},
+    {file = "sympy-1.12.tar.gz", hash = "sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8"},
 ]
 
 [package.dependencies]
-mpmath = ">=1.1.0,<1.4.0"
+mpmath = ">=0.19"
 
 [[package]]
 name = "tbb"
@@ -4260,13 +4263,13 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zarr"
-version = "2.18.2"
+version = "2.18.1"
 description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "zarr-2.18.2-py3-none-any.whl", hash = "sha256:a638754902f97efa99b406083fdc807a0e2ccf12a949117389d2a4ba9b05df38"},
-    {file = "zarr-2.18.2.tar.gz", hash = "sha256:9bb393b8a0a38fb121dbb913b047d75db28de9890f6d644a217a73cf4ae74f47"},
+    {file = "zarr-2.18.1-py3-none-any.whl", hash = "sha256:a1770d194eec4ec0a41a01295a6f724e1c3471d704d3aca906d3b3a7f8830245"},
+    {file = "zarr-2.18.1.tar.gz", hash = "sha256:28c360ed123e606c425a694a83300227a907cb86a995fc9eef620ecafbe5f92d"},
 ]
 
 [package.dependencies]
@@ -4281,13 +4284,13 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
 
 [[package]]
 name = "zipp"
-version = "3.19.0"
+version = "3.18.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.19.0-py3-none-any.whl", hash = "sha256:96dc6ad62f1441bcaccef23b274ec471518daf4fbbc580341204936a5a3dddec"},
-    {file = "zipp-3.19.0.tar.gz", hash = "sha256:952df858fb3164426c976d9338d3961e8e8b3758e2e059e0f754b8c4262625ee"},
+    {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"},
+    {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"},
 ]
 
 [package.extras]

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/actions.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2fff6294b94cf42d4dd1249dcc5c3b0269d6d9c697f894e61b867d7ab81a94e4
-size 5104
+oid sha256:ebd21273f6048b66c806f92035352843a9069908b3296863fd55d34cf71cd0ef
+size 51248

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 sha256:4aa23e51607604a18b70fa42edbbe1af34f119d985628fc27cc1bbb0efbc8901
+oid sha256:b9bbf951891077320a5da27e77ddb580a6e833e8d3162b62a2f887a1989585cc
 size 31688

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/output_dict.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6fd368406c93cb562a69ff11cf7adf34a4b223507dcb2b9e9b8f44ee1036988a
+oid sha256:d4070bd1f1cd8c72bc2daf628088e42b8ef113f6df0bfd9e91be052bc90038c3
 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 sha256:5663ee79a13bb70a1604b887dd21bf89d18482287442419c6cc6c5bf0e753e99
+oid sha256:42f92239223bb4df32d5c3016bc67450159f1285a7ab046307b645f699ccc34e
 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:fb1a45463efd860af2ca22c16c77d55a18bd96fef080ae77978845a2f22ef716
-size 5104
+oid sha256:52f85d6262ad1dd0b66578b25829fed96aaaca3c7458cb73ac75111350d17fcf
+size 51248

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:aa5a43e22f01d8e2f8d19f31753608794f1edbd74aaf71660091ab80ea58dc9b
+oid sha256:5ba7c910618f0f3ca69f82f3d70c880d2b2e432456524a2a63dfd5c50efa45f0
 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 sha256:54d1f75cf67a7b1d7a7c6865ecb9b1cc86a2f032d1890245f8996789ab6e0df6
+oid sha256:53ad410f43855254438790f54aa7c895a052776acdd922906ae430684f659b53
 size 33608

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 k.startswith("observation"):
+        if "observation" in k:
             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", ["policy.n_action_steps=10"]),
-        ("dora_aloha_real", "act_real_no_state", ["policy.n_action_steps=10"]),
+        ("dora_aloha_real", "act_real", []),
+        ("dora_aloha_real", "act_real_no_state", []),
     ]
     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

@@ -16,7 +16,6 @@
 import json
 import logging
 from copy import deepcopy
-from itertools import chain
 from pathlib import Path
 
 import einops
@@ -26,34 +25,26 @@ from datasets import Dataset
 from safetensors.torch import load_file
 
 import lerobot
-from lerobot.common.datasets.compute_stats import (
-    aggregate_stats,
+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 (
     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, seeded_context
+from lerobot.common.utils.utils import init_hydra_config
 from tests.utils import DEFAULT_CONFIG_PATH, DEVICE
 
 
-@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")],
-)
+@pytest.mark.parametrize("env_name, repo_id, policy_name", lerobot.env_dataset_policy_triplets)
 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=[
@@ -114,39 +105,6 @@ 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.
 
@@ -357,31 +315,3 @@ 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

@@ -86,9 +86,6 @@ 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,
@@ -140,7 +137,7 @@ def test_policy(env_name, policy_name, extra_overrides):
 
     dataloader = torch.utils.data.DataLoader(
         dataset,
-        num_workers=0,
+        num_workers=4,
         batch_size=2,
         shuffle=True,
         pin_memory=DEVICE != "cpu",

tests/test_sampler.py

@@ -1,90 +0,0 @@
-#!/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}