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[pre-commit.ci] auto fixes from pre-commit.com hooks

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pre-commit-ci[bot]
2025-03-04 13:38:47 +00:00
committed by AdilZouitine
parent 76df8a31b3
commit 38f5fa4523
79 changed files with 2782 additions and 788 deletions
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12
lerobot/common/datasets/image_writer.py
View File

@@ -127,7 +127,9 @@ class AsyncImageWriter:
self._stopped = False
if num_threads <= 0 and num_processes <= 0:
raise ValueError("Number of threads and processes must be greater than zero.")
raise ValueError(
"Number of threads and processes must be greater than zero."
)
if self.num_processes == 0:
# Use threading
@@ -141,12 +143,16 @@ class AsyncImageWriter:
# Use multiprocessing
self.queue = multiprocessing.JoinableQueue()
for _ in range(self.num_processes):
p = multiprocessing.Process(target=worker_process, args=(self.queue, self.num_threads))
p = multiprocessing.Process(
target=worker_process, args=(self.queue, self.num_threads)
)
p.daemon = True
p.start()
self.processes.append(p)
def save_image(self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path):
def save_image(
self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path
):
if isinstance(image, torch.Tensor):
# Convert tensor to numpy array to minimize main process time
image = image.cpu().numpy()

73
lerobot/common/datasets/lerobot_dataset.py
View File

@@ -139,7 +139,9 @@ class LeRobotDatasetMetadata:
def get_video_file_path(self, ep_index: int, vid_key: str) -> Path:
ep_chunk = self.get_episode_chunk(ep_index)
fpath = self.video_path.format(episode_chunk=ep_chunk, video_key=vid_key, episode_index=ep_index)
fpath = self.video_path.format(
episode_chunk=ep_chunk, video_key=vid_key, episode_index=ep_index
)
return Path(fpath)
def get_episode_chunk(self, ep_index: int) -> int:
@@ -183,7 +185,11 @@ class LeRobotDatasetMetadata:
@property
def camera_keys(self) -> list[str]:
"""Keys to access visual modalities (regardless of their storage method)."""
return [key for key, ft in self.features.items() if ft["dtype"] in ["video", "image"]]
return [
key
for key, ft in self.features.items()
if ft["dtype"] in ["video", "image"]
]
@property
def names(self) -> dict[str, list | dict]:
@@ -285,7 +291,9 @@ class LeRobotDatasetMetadata:
"""
for key in self.video_keys:
if not self.features[key].get("info", None):
video_path = self.root / self.get_video_file_path(ep_index=0, vid_key=key)
video_path = self.root / self.get_video_file_path(
ep_index=0, vid_key=key
)
self.info["features"][key]["info"] = get_video_info(video_path)
def __repr__(self):
@@ -619,7 +627,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
path = str(self.root / "data")
hf_dataset = load_dataset("parquet", data_dir=path, split="train")
else:
files = [str(self.root / self.meta.get_data_file_path(ep_idx)) for ep_idx in self.episodes]
files = [
str(self.root / self.meta.get_data_file_path(ep_idx))
for ep_idx in self.episodes
]
hf_dataset = load_dataset("parquet", data_files=files, split="train")
# TODO(aliberts): hf_dataset.set_format("torch")
@@ -643,12 +654,20 @@ class LeRobotDataset(torch.utils.data.Dataset):
@property
def num_frames(self) -> int:
"""Number of frames in selected episodes."""
return len(self.hf_dataset) if self.hf_dataset is not None else self.meta.total_frames
return (
len(self.hf_dataset)
if self.hf_dataset is not None
else self.meta.total_frames
)
@property
def num_episodes(self) -> int:
"""Number of episodes selected."""
return len(self.episodes) if self.episodes is not None else self.meta.total_episodes
return (
len(self.episodes)
if self.episodes is not None
else self.meta.total_episodes
)
@property
def features(self) -> dict[str, dict]:
@@ -662,16 +681,24 @@ class LeRobotDataset(torch.utils.data.Dataset):
else:
return get_hf_features_from_features(self.features)
def _get_query_indices(self, idx: int, ep_idx: int) -> tuple[dict[str, list[int | bool]]]:
def _get_query_indices(
self, idx: int, ep_idx: int
) -> tuple[dict[str, list[int | bool]]]:
ep_start = self.episode_data_index["from"][ep_idx]
ep_end = self.episode_data_index["to"][ep_idx]
query_indices = {
key: [max(ep_start.item(), min(ep_end.item() - 1, idx + delta)) for delta in delta_idx]
key: [
max(ep_start.item(), min(ep_end.item() - 1, idx + delta))
for delta in delta_idx
]
for key, delta_idx in self.delta_indices.items()
}
padding = { # Pad values outside of current episode range
f"{key}_is_pad": torch.BoolTensor(
[(idx + delta < ep_start.item()) | (idx + delta >= ep_end.item()) for delta in delta_idx]
[
(idx + delta < ep_start.item()) | (idx + delta >= ep_end.item())
for delta in delta_idx
]
)
for key, delta_idx in self.delta_indices.items()
}
@@ -771,13 +798,17 @@ class LeRobotDataset(torch.utils.data.Dataset):
ep_buffer[key] = current_ep_idx if key == "episode_index" else []
return ep_buffer
def _get_image_file_path(self, episode_index: int, image_key: str, frame_index: int) -> Path:
def _get_image_file_path(
self, episode_index: int, image_key: str, frame_index: int
) -> Path:
fpath = DEFAULT_IMAGE_PATH.format(
image_key=image_key, episode_index=episode_index, frame_index=frame_index
)
return self.root / fpath
def _save_image(self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path) -> None:
def _save_image(
self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path
) -> None:
if self.image_writer is None:
if isinstance(image, torch.Tensor):
image = image.cpu().numpy()
@@ -803,7 +834,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
# Automatically add frame_index and timestamp to episode buffer
frame_index = self.episode_buffer["size"]
timestamp = frame.pop("timestamp") if "timestamp" in frame else frame_index / self.fps
timestamp = (
frame.pop("timestamp") if "timestamp" in frame else frame_index / self.fps
)
self.episode_buffer["frame_index"].append(frame_index)
self.episode_buffer["timestamp"].append(timestamp)
@@ -821,7 +854,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
if self.features[key]["dtype"] in ["image", "video"]:
img_path = self._get_image_file_path(
episode_index=self.episode_buffer["episode_index"], image_key=key, frame_index=frame_index
episode_index=self.episode_buffer["episode_index"],
image_key=key,
frame_index=frame_index,
)
if frame_index == 0:
img_path.parent.mkdir(parents=True, exist_ok=True)
@@ -1132,7 +1167,13 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
def features(self) -> datasets.Features:
features = {}
for dataset in self._datasets:
features.update({k: v for k, v in dataset.hf_features.items() if k not in self.disabled_features})
features.update(
{
k: v
for k, v in dataset.hf_features.items()
if k not in self.disabled_features
}
)
return features
@property
@@ -1193,7 +1234,9 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
continue
break
else:
raise AssertionError("We expect the loop to break out as long as the index is within bounds.")
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_features:

67
lerobot/common/datasets/online_buffer.py
View File

@@ -131,7 +131,9 @@ class OnlineBuffer(torch.utils.data.Dataset):
else:
self._delta_timestamps = None
def _make_data_spec(self, data_spec: dict[str, Any], buffer_capacity: int) -> dict[str, dict[str, Any]]:
def _make_data_spec(
self, data_spec: dict[str, Any], buffer_capacity: int
) -> dict[str, dict[str, Any]]:
"""Makes the data spec for np.memmap."""
if any(k.startswith("_") for k in data_spec):
raise ValueError(
@@ -154,14 +156,32 @@ class OnlineBuffer(torch.utils.data.Dataset):
OnlineBuffer.NEXT_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": ()},
# Since the memmap is initialized with all-zeros, this keeps track of which indices are occupied
# with real data rather than the dummy initialization.
OnlineBuffer.OCCUPANCY_MASK_KEY: {"dtype": np.dtype("?"), "shape": (buffer_capacity,)},
OnlineBuffer.INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)},
OnlineBuffer.FRAME_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)},
OnlineBuffer.EPISODE_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)},
OnlineBuffer.TIMESTAMP_KEY: {"dtype": np.dtype("float64"), "shape": (buffer_capacity,)},
OnlineBuffer.OCCUPANCY_MASK_KEY: {
"dtype": np.dtype("?"),
"shape": (buffer_capacity,),
},
OnlineBuffer.INDEX_KEY: {
"dtype": np.dtype("int64"),
"shape": (buffer_capacity,),
},
OnlineBuffer.FRAME_INDEX_KEY: {
"dtype": np.dtype("int64"),
"shape": (buffer_capacity,),
},
OnlineBuffer.EPISODE_INDEX_KEY: {
"dtype": np.dtype("int64"),
"shape": (buffer_capacity,),
},
OnlineBuffer.TIMESTAMP_KEY: {
"dtype": np.dtype("float64"),
"shape": (buffer_capacity,),
},
}
for k, v in data_spec.items():
complete_data_spec[k] = {"dtype": v["dtype"], "shape": (buffer_capacity, *v["shape"])}
complete_data_spec[k] = {
"dtype": v["dtype"],
"shape": (buffer_capacity, *v["shape"]),
}
return complete_data_spec
def add_data(self, data: dict[str, np.ndarray]):
@@ -188,7 +208,9 @@ class OnlineBuffer(torch.utils.data.Dataset):
# Shift the incoming indices if necessary.
if self.num_frames > 0:
last_episode_index = self._data[OnlineBuffer.EPISODE_INDEX_KEY][next_index - 1]
last_episode_index = self._data[OnlineBuffer.EPISODE_INDEX_KEY][
next_index - 1
]
last_data_index = self._data[OnlineBuffer.INDEX_KEY][next_index - 1]
data[OnlineBuffer.EPISODE_INDEX_KEY] += last_episode_index + 1
data[OnlineBuffer.INDEX_KEY] += last_data_index + 1
@@ -223,7 +245,11 @@ class OnlineBuffer(torch.utils.data.Dataset):
@property
def num_episodes(self) -> int:
return len(
np.unique(self._data[OnlineBuffer.EPISODE_INDEX_KEY][self._data[OnlineBuffer.OCCUPANCY_MASK_KEY]])
np.unique(
self._data[OnlineBuffer.EPISODE_INDEX_KEY][
self._data[OnlineBuffer.OCCUPANCY_MASK_KEY]
]
)
)
@property
@@ -261,7 +287,9 @@ class OnlineBuffer(torch.utils.data.Dataset):
self._data[OnlineBuffer.OCCUPANCY_MASK_KEY],
)
)[0]
episode_timestamps = self._data[OnlineBuffer.TIMESTAMP_KEY][episode_data_indices]
episode_timestamps = self._data[OnlineBuffer.TIMESTAMP_KEY][
episode_data_indices
]
for data_key in self.delta_timestamps:
# Note: The logic in this loop is copied from `load_previous_and_future_frames`.
@@ -278,7 +306,8 @@ class OnlineBuffer(torch.utils.data.Dataset):
# Check violated query timestamps are all outside the episode range.
assert (
(query_ts[is_pad] < episode_timestamps[0]) | (episode_timestamps[-1] < query_ts[is_pad])
(query_ts[is_pad] < episode_timestamps[0])
| (episode_timestamps[-1] < query_ts[is_pad])
).all(), (
f"One or several timestamps unexpectedly violate the tolerance ({min_} > {self.tolerance_s=}"
") inside the episode range."
@@ -293,7 +322,9 @@ class OnlineBuffer(torch.utils.data.Dataset):
def get_data_by_key(self, key: str) -> torch.Tensor:
"""Returns all data for a given data key as a Tensor."""
return torch.from_numpy(self._data[key][self._data[OnlineBuffer.OCCUPANCY_MASK_KEY]])
return torch.from_numpy(
self._data[key][self._data[OnlineBuffer.OCCUPANCY_MASK_KEY]]
)
def compute_sampler_weights(
@@ -324,13 +355,19 @@ def compute_sampler_weights(
- Options `drop_first_n_frames` and `episode_indices_to_use` can be added easily. They were not
included here to avoid adding complexity.
"""
if len(offline_dataset) == 0 and (online_dataset is None or len(online_dataset) == 0):
raise ValueError("At least one of `offline_dataset` or `online_dataset` should be contain data.")
if len(offline_dataset) == 0 and (
online_dataset is None or len(online_dataset) == 0
):
raise ValueError(
"At least one of `offline_dataset` or `online_dataset` should be contain data."
)
if (online_dataset is None) ^ (online_sampling_ratio is None):
raise ValueError(
"`online_dataset` and `online_sampling_ratio` must be provided together or not at all."
)
offline_sampling_ratio = 0 if online_sampling_ratio is None else 1 - online_sampling_ratio
offline_sampling_ratio = (
0 if online_sampling_ratio is None else 1 - online_sampling_ratio
)
weights = []

16
lerobot/common/datasets/push_dataset_to_hub/utils.py
View File

@@ -45,7 +45,9 @@ def concatenate_episodes(ep_dicts):
return data_dict
def save_images_concurrently(imgs_array: numpy.array, out_dir: Path, max_workers: int = 4):
def save_images_concurrently(
imgs_array: numpy.array, out_dir: Path, max_workers: int = 4
):
out_dir = Path(out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
@@ -55,7 +57,10 @@ def save_images_concurrently(imgs_array: numpy.array, out_dir: Path, max_workers
num_images = len(imgs_array)
with ThreadPoolExecutor(max_workers=max_workers) as executor:
[executor.submit(save_image, imgs_array[i], i, out_dir) for i in range(num_images)]
[
executor.submit(save_image, imgs_array[i], i, out_dir)
for i in range(num_images)
]
def get_default_encoding() -> dict:
@@ -64,7 +69,8 @@ def get_default_encoding() -> dict:
return {
k: v.default
for k, v in signature.parameters.items()
if v.default is not inspect.Parameter.empty and k in ["vcodec", "pix_fmt", "g", "crf"]
if v.default is not inspect.Parameter.empty
and k in ["vcodec", "pix_fmt", "g", "crf"]
}
@@ -77,7 +83,9 @@ def check_repo_id(repo_id: str) -> None:
# TODO(aliberts): remove
def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> Dict[str, torch.Tensor]:
def calculate_episode_data_index(
hf_dataset: datasets.Dataset,
) -> Dict[str, torch.Tensor]:
"""
Calculate episode data index for the provided HuggingFace Dataset. Relies on episode_index column of hf_dataset.

5
lerobot/common/datasets/sampler.py
View File

@@ -43,7 +43,10 @@ class EpisodeAwareSampler:
):
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)
range(
start_index.item() + drop_n_first_frames,
end_index.item() - drop_n_last_frames,
)
)
self.indices = indices

16
lerobot/common/datasets/transforms.py
View File

@@ -58,7 +58,9 @@ class RandomSubsetApply(Transform):
elif not isinstance(n_subset, int):
raise TypeError("n_subset should be an int or None")
elif not (1 <= n_subset <= len(transforms)):
raise ValueError(f"n_subset should be in the interval [1, {len(transforms)}]")
raise ValueError(
f"n_subset should be in the interval [1, {len(transforms)}]"
)
self.transforms = transforms
total = sum(p)
@@ -119,16 +121,22 @@ class SharpnessJitter(Transform):
def _check_input(self, sharpness):
if isinstance(sharpness, (int, float)):
if sharpness < 0:
raise ValueError("If sharpness is a single number, it must be non negative.")
raise ValueError(
"If sharpness is a single number, it must be non negative."
)
sharpness = [1.0 - sharpness, 1.0 + sharpness]
sharpness[0] = max(sharpness[0], 0.0)
elif isinstance(sharpness, collections.abc.Sequence) and len(sharpness) == 2:
sharpness = [float(v) for v in sharpness]
else:
raise TypeError(f"{sharpness=} should be a single number or a sequence with length 2.")
raise TypeError(
f"{sharpness=} should be a single number or a sequence with length 2."
)
if not 0.0 <= sharpness[0] <= sharpness[1]:
raise ValueError(f"sharpnesss values should be between (0., inf), but got {sharpness}.")
raise ValueError(
f"sharpnesss values should be between (0., inf), but got {sharpness}."
)
return float(sharpness[0]), float(sharpness[1])

33
lerobot/common/datasets/utils.py
View File

@@ -52,9 +52,15 @@ STATS_PATH = "meta/stats.json"
EPISODES_STATS_PATH = "meta/episodes_stats.jsonl"
TASKS_PATH = "meta/tasks.jsonl"
DEFAULT_VIDEO_PATH = "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4"
DEFAULT_PARQUET_PATH = "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"
DEFAULT_IMAGE_PATH = "images/{image_key}/episode_{episode_index:06d}/frame_{frame_index:06d}.png"
DEFAULT_VIDEO_PATH = (
"videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4"
)
DEFAULT_PARQUET_PATH = (
"data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"
)
DEFAULT_IMAGE_PATH = (
"images/{image_key}/episode_{episode_index:06d}/frame_{frame_index:06d}.png"
)
DATASET_CARD_TEMPLATE = """
---
@@ -540,7 +546,10 @@ def check_timestamps_sync(
def check_delta_timestamps(
delta_timestamps: dict[str, list[float]], fps: int, tolerance_s: float, raise_value_error: bool = True
delta_timestamps: dict[str, list[float]],
fps: int,
tolerance_s: float,
raise_value_error: bool = True,
) -> bool:
"""This will check if all the values in delta_timestamps are multiples of 1/fps +/- tolerance.
This is to ensure that these delta_timestamps added to any timestamp from a dataset will themselves be
@@ -548,10 +557,14 @@ def check_delta_timestamps(
"""
outside_tolerance = {}
for key, delta_ts in delta_timestamps.items():
within_tolerance = [abs(ts * fps - round(ts * fps)) / fps <= tolerance_s for ts in delta_ts]
within_tolerance = [
abs(ts * fps - round(ts * fps)) / fps <= tolerance_s for ts in delta_ts
]
if not all(within_tolerance):
outside_tolerance[key] = [
ts for ts, is_within in zip(delta_ts, within_tolerance, strict=True) if not is_within
ts
for ts, is_within in zip(delta_ts, within_tolerance, strict=True)
if not is_within
]
if len(outside_tolerance) > 0:
@@ -569,7 +582,9 @@ def check_delta_timestamps(
return True
def get_delta_indices(delta_timestamps: dict[str, list[float]], fps: int) -> dict[str, list[int]]:
def get_delta_indices(
delta_timestamps: dict[str, list[float]], fps: int
) -> dict[str, list[int]]:
delta_indices = {}
for key, delta_ts in delta_timestamps.items():
delta_indices[key] = [round(d * fps) for d in delta_ts]
@@ -634,7 +649,9 @@ def create_lerobot_dataset_card(
],
)
card_template = (importlib.resources.files("lerobot.common.datasets") / "card_template.md").read_text()
card_template = (
importlib.resources.files("lerobot.common.datasets") / "card_template.md"
).read_text()
return DatasetCard.from_template(
card_data=card_data,

65
lerobot/common/datasets/v2/batch_convert_dataset_v1_to_v2.py
View File

@@ -118,7 +118,10 @@ DATASETS = {
"single_task": "Place the battery into the slot of the remote controller.",
**ALOHA_STATIC_INFO,
},
"aloha_static_candy": {"single_task": "Pick up the candy and unwrap it.", **ALOHA_STATIC_INFO},
"aloha_static_candy": {
"single_task": "Pick up the candy and unwrap it.",
**ALOHA_STATIC_INFO,
},
"aloha_static_coffee": {
"single_task": "Place the coffee capsule inside the capsule container, then place the cup onto the center of the cup tray, then push the 'Hot Water' and 'Travel Mug' buttons.",
**ALOHA_STATIC_INFO,
@@ -167,13 +170,22 @@ DATASETS = {
"single_task": "Pick up the plastic cup with the left arm, then pop its lid open with the right arm.",
**ALOHA_STATIC_INFO,
},
"aloha_static_ziploc_slide": {"single_task": "Slide open the ziploc bag.", **ALOHA_STATIC_INFO},
"aloha_sim_insertion_scripted": {"single_task": "Insert the peg into the socket.", **ALOHA_STATIC_INFO},
"aloha_static_ziploc_slide": {
"single_task": "Slide open the ziploc bag.",
**ALOHA_STATIC_INFO,
},
"aloha_sim_insertion_scripted": {
"single_task": "Insert the peg into the socket.",
**ALOHA_STATIC_INFO,
},
"aloha_sim_insertion_scripted_image": {
"single_task": "Insert the peg into the socket.",
**ALOHA_STATIC_INFO,
},
"aloha_sim_insertion_human": {"single_task": "Insert the peg into the socket.", **ALOHA_STATIC_INFO},
"aloha_sim_insertion_human": {
"single_task": "Insert the peg into the socket.",
**ALOHA_STATIC_INFO,
},
"aloha_sim_insertion_human_image": {
"single_task": "Insert the peg into the socket.",
**ALOHA_STATIC_INFO,
@@ -194,10 +206,19 @@ DATASETS = {
"single_task": "Pick up the cube with the right arm and transfer it to the left arm.",
**ALOHA_STATIC_INFO,
},
"pusht": {"single_task": "Push the T-shaped block onto the T-shaped target.", **PUSHT_INFO},
"pusht_image": {"single_task": "Push the T-shaped block onto the T-shaped target.", **PUSHT_INFO},
"pusht": {
"single_task": "Push the T-shaped block onto the T-shaped target.",
**PUSHT_INFO,
},
"pusht_image": {
"single_task": "Push the T-shaped block onto the T-shaped target.",
**PUSHT_INFO,
},
"unitreeh1_fold_clothes": {"single_task": "Fold the sweatshirt.", **UNITREEH_INFO},
"unitreeh1_rearrange_objects": {"single_task": "Put the object into the bin.", **UNITREEH_INFO},
"unitreeh1_rearrange_objects": {
"single_task": "Put the object into the bin.",
**UNITREEH_INFO,
},
"unitreeh1_two_robot_greeting": {
"single_task": "Greet the other robot with a high five.",
**UNITREEH_INFO,
@@ -207,13 +228,31 @@ DATASETS = {
**UNITREEH_INFO,
},
"xarm_lift_medium": {"single_task": "Pick up the cube and lift it.", **XARM_INFO},
"xarm_lift_medium_image": {"single_task": "Pick up the cube and lift it.", **XARM_INFO},
"xarm_lift_medium_replay": {"single_task": "Pick up the cube and lift it.", **XARM_INFO},
"xarm_lift_medium_replay_image": {"single_task": "Pick up the cube and lift it.", **XARM_INFO},
"xarm_lift_medium_image": {
"single_task": "Pick up the cube and lift it.",
**XARM_INFO,
},
"xarm_lift_medium_replay": {
"single_task": "Pick up the cube and lift it.",
**XARM_INFO,
},
"xarm_lift_medium_replay_image": {
"single_task": "Pick up the cube and lift it.",
**XARM_INFO,
},
"xarm_push_medium": {"single_task": "Push the cube onto the target.", **XARM_INFO},
"xarm_push_medium_image": {"single_task": "Push the cube onto the target.", **XARM_INFO},
"xarm_push_medium_replay": {"single_task": "Push the cube onto the target.", **XARM_INFO},
"xarm_push_medium_replay_image": {"single_task": "Push the cube onto the target.", **XARM_INFO},
"xarm_push_medium_image": {
"single_task": "Push the cube onto the target.",
**XARM_INFO,
},
"xarm_push_medium_replay": {
"single_task": "Push the cube onto the target.",
**XARM_INFO,
},
"xarm_push_medium_replay_image": {
"single_task": "Push the cube onto the target.",
**XARM_INFO,
},
"umi_cup_in_the_wild": {
"single_task": "Put the cup on the plate.",
"license": "apache-2.0",

169
lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py
View File

@@ -218,7 +218,9 @@ def get_features_from_hf_dataset(
dtype = ft.feature.dtype
shape = (ft.length,)
motor_names = (
robot_config["names"][key] if robot_config else [f"motor_{i}" for i in range(ft.length)]
robot_config["names"][key]
if robot_config
else [f"motor_{i}" for i in range(ft.length)]
)
assert len(motor_names) == shape[0]
names = {"motors": motor_names}
@@ -242,11 +244,15 @@ def get_features_from_hf_dataset(
return features
def add_task_index_by_episodes(dataset: Dataset, tasks_by_episodes: dict) -> tuple[Dataset, list[str]]:
def add_task_index_by_episodes(
dataset: Dataset, tasks_by_episodes: dict
) -> tuple[Dataset, list[str]]:
df = dataset.to_pandas()
tasks = list(set(tasks_by_episodes.values()))
tasks_to_task_index = {task: task_idx for task_idx, task in enumerate(tasks)}
episodes_to_task_index = {ep_idx: tasks_to_task_index[task] for ep_idx, task in tasks_by_episodes.items()}
episodes_to_task_index = {
ep_idx: tasks_to_task_index[task] for ep_idx, task in tasks_by_episodes.items()
}
df["task_index"] = df["episode_index"].map(episodes_to_task_index).astype(int)
features = dataset.features
@@ -263,10 +269,19 @@ def add_task_index_from_tasks_col(
# HACK: This is to clean some of the instructions in our version of Open X datasets
prefix_to_clean = "tf.Tensor(b'"
suffix_to_clean = "', shape=(), dtype=string)"
df[tasks_col] = df[tasks_col].str.removeprefix(prefix_to_clean).str.removesuffix(suffix_to_clean)
df[tasks_col] = (
df[tasks_col]
.str.removeprefix(prefix_to_clean)
.str.removesuffix(suffix_to_clean)
)
# Create task_index col
tasks_by_episode = df.groupby("episode_index")[tasks_col].unique().apply(lambda x: x.tolist()).to_dict()
tasks_by_episode = (
df.groupby("episode_index")[tasks_col]
.unique()
.apply(lambda x: x.tolist())
.to_dict()
)
tasks = df[tasks_col].unique().tolist()
tasks_to_task_index = {task: idx for idx, task in enumerate(tasks)}
df["task_index"] = df[tasks_col].map(tasks_to_task_index).astype(int)
@@ -291,7 +306,9 @@ def split_parquet_by_episodes(
for ep_chunk in range(total_chunks):
ep_chunk_start = DEFAULT_CHUNK_SIZE * ep_chunk
ep_chunk_end = min(DEFAULT_CHUNK_SIZE * (ep_chunk + 1), total_episodes)
chunk_dir = "/".join(DEFAULT_PARQUET_PATH.split("/")[:-1]).format(episode_chunk=ep_chunk)
chunk_dir = "/".join(DEFAULT_PARQUET_PATH.split("/")[:-1]).format(
episode_chunk=ep_chunk
)
(output_dir / chunk_dir).mkdir(parents=True, exist_ok=True)
for ep_idx in range(ep_chunk_start, ep_chunk_end):
ep_table = table.filter(pc.equal(table["episode_index"], ep_idx))
@@ -323,7 +340,9 @@ def move_videos(
videos_moved = False
video_files = [str(f.relative_to(work_dir)) for f in work_dir.glob("videos*/*.mp4")]
if len(video_files) == 0:
video_files = [str(f.relative_to(work_dir)) for f in work_dir.glob("videos*/*/*/*.mp4")]
video_files = [
str(f.relative_to(work_dir)) for f in work_dir.glob("videos*/*/*/*.mp4")
]
videos_moved = True # Videos have already been moved
assert len(video_files) == total_episodes * len(video_keys)
@@ -354,7 +373,9 @@ def move_videos(
target_path = DEFAULT_VIDEO_PATH.format(
episode_chunk=ep_chunk, video_key=vid_key, episode_index=ep_idx
)
video_file = V1_VIDEO_FILE.format(video_key=vid_key, episode_index=ep_idx)
video_file = V1_VIDEO_FILE.format(
video_key=vid_key, episode_index=ep_idx
)
if len(video_dirs) == 1:
video_path = video_dirs[0] / video_file
else:
@@ -371,7 +392,9 @@ def move_videos(
subprocess.run(["git", "push"], cwd=work_dir, check=True)
def fix_lfs_video_files_tracking(work_dir: Path, lfs_untracked_videos: list[str]) -> None:
def fix_lfs_video_files_tracking(
work_dir: Path, lfs_untracked_videos: list[str]
) -> None:
"""
HACK: This function fixes the tracking by git lfs which was not properly set on some repos. In that case,
there's no other option than to download the actual files and reupload them with lfs tracking.
@@ -379,7 +402,12 @@ def fix_lfs_video_files_tracking(work_dir: Path, lfs_untracked_videos: list[str]
for i in range(0, len(lfs_untracked_videos), 100):
files = lfs_untracked_videos[i : i + 100]
try:
subprocess.run(["git", "rm", "--cached", *files], cwd=work_dir, capture_output=True, check=True)
subprocess.run(
["git", "rm", "--cached", *files],
cwd=work_dir,
capture_output=True,
check=True,
)
except subprocess.CalledProcessError as e:
print("git rm --cached ERROR:")
print(e.stderr)
@@ -390,10 +418,14 @@ def fix_lfs_video_files_tracking(work_dir: Path, lfs_untracked_videos: list[str]
subprocess.run(["git", "push"], cwd=work_dir, check=True)
def fix_gitattributes(work_dir: Path, current_gittatributes: Path, clean_gittatributes: Path) -> None:
def fix_gitattributes(
work_dir: Path, current_gittatributes: Path, clean_gittatributes: Path
) -> None:
shutil.copyfile(clean_gittatributes, current_gittatributes)
subprocess.run(["git", "add", ".gitattributes"], cwd=work_dir, check=True)
subprocess.run(["git", "commit", "-m", "Fix .gitattributes"], cwd=work_dir, check=True)
subprocess.run(
["git", "commit", "-m", "Fix .gitattributes"], cwd=work_dir, check=True
)
subprocess.run(["git", "push"], cwd=work_dir, check=True)
@@ -402,7 +434,17 @@ def _lfs_clone(repo_id: str, work_dir: Path, branch: str) -> None:
repo_url = f"https://huggingface.co/datasets/{repo_id}"
env = {"GIT_LFS_SKIP_SMUDGE": "1"} # Prevent downloading LFS files
subprocess.run(
["git", "clone", "--branch", branch, "--single-branch", "--depth", "1", repo_url, str(work_dir)],
[
"git",
"clone",
"--branch",
branch,
"--single-branch",
"--depth",
"1",
repo_url,
str(work_dir),
],
check=True,
env=env,
)
@@ -410,13 +452,19 @@ def _lfs_clone(repo_id: str, work_dir: Path, branch: str) -> None:
def _get_lfs_untracked_videos(work_dir: Path, video_files: list[str]) -> list[str]:
lfs_tracked_files = subprocess.run(
["git", "lfs", "ls-files", "-n"], cwd=work_dir, capture_output=True, text=True, check=True
["git", "lfs", "ls-files", "-n"],
cwd=work_dir,
capture_output=True,
text=True,
check=True,
)
lfs_tracked_files = set(lfs_tracked_files.stdout.splitlines())
return [f for f in video_files if f not in lfs_tracked_files]
def get_videos_info(repo_id: str, local_dir: Path, video_keys: list[str], branch: str) -> dict:
def get_videos_info(
repo_id: str, local_dir: Path, video_keys: list[str], branch: str
) -> dict:
# Assumes first episode
video_files = [
DEFAULT_VIDEO_PATH.format(episode_chunk=0, video_key=vid_key, episode_index=0)
@@ -424,7 +472,11 @@ def get_videos_info(repo_id: str, local_dir: Path, video_keys: list[str], branch
]
hub_api = HfApi()
hub_api.snapshot_download(
repo_id=repo_id, repo_type="dataset", local_dir=local_dir, revision=branch, allow_patterns=video_files
repo_id=repo_id,
repo_type="dataset",
local_dir=local_dir,
revision=branch,
allow_patterns=video_files,
)
videos_info_dict = {}
for vid_key, vid_path in zip(video_keys, video_files, strict=True):
@@ -451,7 +503,11 @@ def convert_dataset(
hub_api = HfApi()
hub_api.snapshot_download(
repo_id=repo_id, repo_type="dataset", revision=v1, local_dir=v1x_dir, ignore_patterns="videos*/"
repo_id=repo_id,
repo_type="dataset",
revision=v1,
local_dir=v1x_dir,
ignore_patterns="videos*/",
)
branch = "main"
if test_branch:
@@ -483,19 +539,31 @@ def convert_dataset(
if single_task:
tasks_by_episodes = {ep_idx: single_task for ep_idx in episode_indices}
dataset, tasks = add_task_index_by_episodes(dataset, tasks_by_episodes)
tasks_by_episodes = {ep_idx: [task] for ep_idx, task in tasks_by_episodes.items()}
tasks_by_episodes = {
ep_idx: [task] for ep_idx, task in tasks_by_episodes.items()
}
elif tasks_path:
tasks_by_episodes = load_json(tasks_path)
tasks_by_episodes = {int(ep_idx): task for ep_idx, task in tasks_by_episodes.items()}
tasks_by_episodes = {
int(ep_idx): task for ep_idx, task in tasks_by_episodes.items()
}
dataset, tasks = add_task_index_by_episodes(dataset, tasks_by_episodes)
tasks_by_episodes = {ep_idx: [task] for ep_idx, task in tasks_by_episodes.items()}
tasks_by_episodes = {
ep_idx: [task] for ep_idx, task in tasks_by_episodes.items()
}
elif tasks_col:
dataset, tasks, tasks_by_episodes = add_task_index_from_tasks_col(dataset, tasks_col)
dataset, tasks, tasks_by_episodes = add_task_index_from_tasks_col(
dataset, tasks_col
)
else:
raise ValueError
assert set(tasks) == {task for ep_tasks in tasks_by_episodes.values() for task in ep_tasks}
tasks = [{"task_index": task_idx, "task": task} for task_idx, task in enumerate(tasks)]
assert set(tasks) == {
task for ep_tasks in tasks_by_episodes.values() for task in ep_tasks
}
tasks = [
{"task_index": task_idx, "task": task} for task_idx, task in enumerate(tasks)
]
write_jsonlines(tasks, v20_dir / TASKS_PATH)
features["task_index"] = {
"dtype": "int64",
@@ -509,14 +577,25 @@ def convert_dataset(
dataset = dataset.remove_columns(video_keys)
clean_gitattr = Path(
hub_api.hf_hub_download(
repo_id=GITATTRIBUTES_REF, repo_type="dataset", local_dir=local_dir, filename=".gitattributes"
repo_id=GITATTRIBUTES_REF,
repo_type="dataset",
local_dir=local_dir,
filename=".gitattributes",
)
).absolute()
with tempfile.TemporaryDirectory() as tmp_video_dir:
move_videos(
repo_id, video_keys, total_episodes, total_chunks, Path(tmp_video_dir), clean_gitattr, branch
repo_id,
video_keys,
total_episodes,
total_chunks,
Path(tmp_video_dir),
clean_gitattr,
branch,
)
videos_info = get_videos_info(repo_id, v1x_dir, video_keys=video_keys, branch=branch)
videos_info = get_videos_info(
repo_id, v1x_dir, video_keys=video_keys, branch=branch
)
for key in video_keys:
features[key]["shape"] = (
videos_info[key].pop("video.height"),
@@ -524,15 +603,22 @@ def convert_dataset(
videos_info[key].pop("video.channels"),
)
features[key]["video_info"] = videos_info[key]
assert math.isclose(videos_info[key]["video.fps"], metadata_v1["fps"], rel_tol=1e-3)
assert math.isclose(
videos_info[key]["video.fps"], metadata_v1["fps"], rel_tol=1e-3
)
if "encoding" in metadata_v1:
assert videos_info[key]["video.pix_fmt"] == metadata_v1["encoding"]["pix_fmt"]
assert (
videos_info[key]["video.pix_fmt"]
== metadata_v1["encoding"]["pix_fmt"]
)
else:
assert metadata_v1.get("video", 0) == 0
videos_info = None
# Split data into 1 parquet file by episode
episode_lengths = split_parquet_by_episodes(dataset, total_episodes, total_chunks, v20_dir)
episode_lengths = split_parquet_by_episodes(
dataset, total_episodes, total_chunks, v20_dir
)
if robot_config is not None:
robot_type = robot_config.type
@@ -543,7 +629,11 @@ def convert_dataset(
# Episodes
episodes = [
{"episode_index": ep_idx, "tasks": tasks_by_episodes[ep_idx], "length": episode_lengths[ep_idx]}
{
"episode_index": ep_idx,
"tasks": tasks_by_episodes[ep_idx],
"length": episode_lengths[ep_idx],
}
for ep_idx in episode_indices
]
write_jsonlines(episodes, v20_dir / EPISODES_PATH)
@@ -566,16 +656,27 @@ def convert_dataset(
}
write_json(metadata_v2_0, v20_dir / INFO_PATH)
convert_stats_to_json(v1x_dir, v20_dir)
card = create_lerobot_dataset_card(tags=repo_tags, dataset_info=metadata_v2_0, **card_kwargs)
card = create_lerobot_dataset_card(
tags=repo_tags, dataset_info=metadata_v2_0, **card_kwargs
)
with contextlib.suppress(EntryNotFoundError, HfHubHTTPError):
hub_api.delete_folder(repo_id=repo_id, path_in_repo="data", repo_type="dataset", revision=branch)
hub_api.delete_folder(
repo_id=repo_id, path_in_repo="data", repo_type="dataset", revision=branch
)
with contextlib.suppress(EntryNotFoundError, HfHubHTTPError):
hub_api.delete_folder(repo_id=repo_id, path_in_repo="meta_data", repo_type="dataset", revision=branch)
hub_api.delete_folder(
repo_id=repo_id,
path_in_repo="meta_data",
repo_type="dataset",
revision=branch,
)
with contextlib.suppress(EntryNotFoundError, HfHubHTTPError):
hub_api.delete_folder(repo_id=repo_id, path_in_repo="meta", repo_type="dataset", revision=branch)
hub_api.delete_folder(
repo_id=repo_id, path_in_repo="meta", repo_type="dataset", revision=branch
)
hub_api.upload_folder(
repo_id=repo_id,

12
lerobot/common/datasets/video_utils.py
View File

@@ -344,7 +344,9 @@ def get_audio_info(video_path: Path | str) -> dict:
"json",
str(video_path),
]
result = subprocess.run(ffprobe_audio_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
result = subprocess.run(
ffprobe_audio_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True
)
if result.returncode != 0:
raise RuntimeError(f"Error running ffprobe: {result.stderr}")
@@ -358,7 +360,9 @@ def get_audio_info(video_path: Path | str) -> dict:
"has_audio": True,
"audio.channels": audio_stream_info.get("channels", None),
"audio.codec": audio_stream_info.get("codec_name", None),
"audio.bit_rate": int(audio_stream_info["bit_rate"]) if audio_stream_info.get("bit_rate") else None,
"audio.bit_rate": int(audio_stream_info["bit_rate"])
if audio_stream_info.get("bit_rate")
else None,
"audio.sample_rate": int(audio_stream_info["sample_rate"])
if audio_stream_info.get("sample_rate")
else None,
@@ -380,7 +384,9 @@ def get_video_info(video_path: Path | str) -> dict:
"json",
str(video_path),
]
result = subprocess.run(ffprobe_video_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
result = subprocess.run(
ffprobe_video_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True
)
if result.returncode != 0:
raise RuntimeError(f"Error running ffprobe: {result.stderr}")

18
lerobot/common/envs/factory.py
View File

@@ -70,7 +70,9 @@ def make_env(cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False) -> g
return env
def make_maniskill_env(cfg: DictConfig, n_envs: int | None = None) -> gym.vector.VectorEnv | None:
def make_maniskill_env(
cfg: DictConfig, n_envs: int | None = None
) -> gym.vector.VectorEnv | None:
"""Make ManiSkill3 gym environment"""
from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv
@@ -87,7 +89,9 @@ def make_maniskill_env(cfg: DictConfig, n_envs: int | None = None) -> gym.vector
# state should have the size of 25
# env = ConvertToLeRobotEnv(env, n_envs)
# env = PixelWrapper(cfg, env, n_envs)
env._max_episode_steps = env.max_episode_steps = 50 # gym_utils.find_max_episode_steps_value(env)
env._max_episode_steps = env.max_episode_steps = (
50 # gym_utils.find_max_episode_steps_value(env)
)
env.unwrapped.metadata["render_fps"] = 20
return env
@@ -114,7 +118,11 @@ class PixelWrapper(gym.Wrapper):
def _get_obs(self, obs):
frame = obs["sensor_data"]["base_camera"]["rgb"].cpu().permute(0, 3, 1, 2)
self._frames.append(frame)
return {"pixels": torch.from_numpy(np.concatenate(self._frames, axis=1)).to(self.env.device)}
return {
"pixels": torch.from_numpy(np.concatenate(self._frames, axis=1)).to(
self.env.device
)
}
def reset(self, seed):
obs, info = self.env.reset() # (seed=seed)
@@ -148,7 +156,9 @@ class ConvertToLeRobotEnv(gym.Wrapper):
images = torch.concat(images, axis=-1)
# flatten the rest of the data which should just be state data
observation = common.flatten_state_dict(observation, use_torch=True, device=self.base_env.device)
observation = common.flatten_state_dict(
observation, use_torch=True, device=self.base_env.device
)
ret = dict()
ret["state"] = observation
ret["pixels"] = images

62
lerobot/common/logger.py
View File

@@ -84,7 +84,9 @@ class Logger:
pretrained_model_dir_name = "pretrained_model"
training_state_file_name = "training_state.pth"
def __init__(self, cfg: DictConfig, log_dir: str, wandb_job_name: str | None = None):
def __init__(
self, cfg: DictConfig, log_dir: str, wandb_job_name: str | None = None
):
"""
Args:
log_dir: The directory to save all logs and training outputs to.
@@ -104,7 +106,9 @@ class Logger:
enable_wandb = cfg.get("wandb", {}).get("enable", False)
run_offline = not enable_wandb or not project
if run_offline:
logging.info(colored("Logs will be saved locally.", "yellow", attrs=["bold"]))
logging.info(
colored("Logs will be saved locally.", "yellow", attrs=["bold"])
)
self._wandb = None
else:
os.environ["WANDB_SILENT"] = "true"
@@ -130,7 +134,9 @@ class Logger:
# Handle custom step key for rl asynchronous training.
self._wandb_custom_step_key: set[str] | None = None
print(colored("Logs will be synced with wandb.", "blue", attrs=["bold"]))
logging.info(f"Track this run --> {colored(wandb.run.get_url(), 'yellow', attrs=['bold'])}")
logging.info(
f"Track this run --> {colored(wandb.run.get_url(), 'yellow', attrs=['bold'])}"
)
self._wandb = wandb
@classmethod
@@ -151,7 +157,9 @@ class Logger:
"""
return cls.get_last_checkpoint_dir(log_dir) / cls.pretrained_model_dir_name
def save_model(self, save_dir: Path, policy: Policy, wandb_artifact_name: str | None = None):
def save_model(
self, save_dir: Path, policy: Policy, wandb_artifact_name: str | None = None
):
"""Save the weights of the Policy model using PyTorchModelHubMixin.
The weights are saved in a folder called "pretrained_model" under the checkpoint directory.
@@ -221,22 +229,30 @@ class Logger:
else f"{self._group.replace(':', '_').replace('/', '_')}-{self._cfg.seed}-{identifier}"
)
self.save_model(
checkpoint_dir / self.pretrained_model_dir_name, policy, wandb_artifact_name=wandb_artifact_name
checkpoint_dir / self.pretrained_model_dir_name,
policy,
wandb_artifact_name=wandb_artifact_name,
)
self.save_training_state(
checkpoint_dir, train_step, optimizer, scheduler, interaction_step
)
self.save_training_state(checkpoint_dir, train_step, optimizer, scheduler, interaction_step)
os.symlink(checkpoint_dir.absolute(), self.last_checkpoint_dir)
def load_last_training_state(self, optimizer: Optimizer | dict, scheduler: LRScheduler | None) -> int:
def load_last_training_state(
self, optimizer: Optimizer | dict, scheduler: LRScheduler | None
) -> int:
"""
Given the last checkpoint in the logging directory, load the optimizer state, scheduler state, and
random state, and return the global training step.
"""
training_state = torch.load(self.last_checkpoint_dir / self.training_state_file_name)
training_state = torch.load(
self.last_checkpoint_dir / self.training_state_file_name
)
# For the case where the optimizer is a dictionary of optimizers (e.g., sac)
if type(training_state["optimizer"]) is dict:
assert set(training_state["optimizer"].keys()) == set(optimizer.keys()), (
"Optimizer dictionaries do not have the same keys during resume!"
)
assert set(training_state["optimizer"].keys()) == set(
optimizer.keys()
), "Optimizer dictionaries do not have the same keys during resume!"
for k, v in training_state["optimizer"].items():
optimizer[k].load_state_dict(v)
else:
@@ -248,10 +264,18 @@ class Logger:
"The checkpoint contains a scheduler state_dict, but no LRScheduler was provided."
)
# Small hack to get the expected keys: use `get_global_random_state`.
set_global_random_state({k: training_state[k] for k in get_global_random_state()})
set_global_random_state(
{k: training_state[k] for k in get_global_random_state()}
)
return training_state["step"]
def log_dict(self, d, step: int | None = None, mode="train", custom_step_key: str | None = None):
def log_dict(
self,
d,
step: int | None = None,
mode="train",
custom_step_key: str | None = None,
):
"""Log a dictionary of metrics to WandB."""
assert mode in {"train", "eval"}
# TODO(alexander-soare): Add local text log.
@@ -280,12 +304,20 @@ class Logger:
continue
# Do not log the custom step key itself.
if self._wandb_custom_step_key is not None and k in self._wandb_custom_step_key:
if (
self._wandb_custom_step_key is not None
and k in self._wandb_custom_step_key
):
continue
if custom_step_key is not None:
value_custom_step = d[custom_step_key]
self._wandb.log({f"{mode}/{k}": v, f"{mode}/{custom_step_key}": value_custom_step})
self._wandb.log(
{
f"{mode}/{k}": v,
f"{mode}/{custom_step_key}": value_custom_step,
}
)
continue
self._wandb.log(data={f"{mode}/{k}": v}, step=step)

160
lerobot/common/policies/act/modeling_act.py
View File

@@ -74,7 +74,9 @@ class ACTPolicy(PreTrainedPolicy):
self.model = ACT(config)
if config.temporal_ensemble_coeff is not None:
self.temporal_ensembler = ACTTemporalEnsembler(config.temporal_ensemble_coeff, config.chunk_size)
self.temporal_ensembler = ACTTemporalEnsembler(
config.temporal_ensemble_coeff, config.chunk_size
)
self.reset()
@@ -153,7 +155,8 @@ class ACTPolicy(PreTrainedPolicy):
actions_hat, (mu_hat, log_sigma_x2_hat) = self.model(batch)
l1_loss = (
F.l1_loss(batch["action"], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1)
F.l1_loss(batch["action"], actions_hat, reduction="none")
* ~batch["action_is_pad"].unsqueeze(-1)
).mean()
loss_dict = {"l1_loss": l1_loss.item()}
@@ -163,7 +166,12 @@ class ACTPolicy(PreTrainedPolicy):
# KL-divergence per batch element, then take the mean over the batch.
# (See App. B of https://arxiv.org/abs/1312.6114 for more details).
mean_kld = (
(-0.5 * (1 + log_sigma_x2_hat - mu_hat.pow(2) - (log_sigma_x2_hat).exp())).sum(-1).mean()
(
-0.5
* (1 + log_sigma_x2_hat - mu_hat.pow(2) - (log_sigma_x2_hat).exp())
)
.sum(-1)
.mean()
)
loss_dict["kld_loss"] = mean_kld.item()
loss = l1_loss + mean_kld * self.config.kl_weight
@@ -217,7 +225,9 @@ class ACTTemporalEnsembler:
```
"""
self.chunk_size = chunk_size
self.ensemble_weights = torch.exp(-temporal_ensemble_coeff * torch.arange(chunk_size))
self.ensemble_weights = torch.exp(
-temporal_ensemble_coeff * torch.arange(chunk_size)
)
self.ensemble_weights_cumsum = torch.cumsum(self.ensemble_weights, dim=0)
self.reset()
@@ -233,7 +243,9 @@ class ACTTemporalEnsembler:
time steps, and pop/return the next batch of actions in the sequence.
"""
self.ensemble_weights = self.ensemble_weights.to(device=actions.device)
self.ensemble_weights_cumsum = self.ensemble_weights_cumsum.to(device=actions.device)
self.ensemble_weights_cumsum = self.ensemble_weights_cumsum.to(
device=actions.device
)
if self.ensembled_actions is None:
# Initializes `self._ensembled_action` to the sequence of actions predicted during the first
# time step of the episode.
@@ -241,19 +253,34 @@ class ACTTemporalEnsembler:
# Note: The last dimension is unsqueeze to make sure we can broadcast properly for tensor
# operations later.
self.ensembled_actions_count = torch.ones(
(self.chunk_size, 1), dtype=torch.long, device=self.ensembled_actions.device
(self.chunk_size, 1),
dtype=torch.long,
device=self.ensembled_actions.device,
)
else:
# self.ensembled_actions will have shape (batch_size, chunk_size - 1, action_dim). Compute
# the online update for those entries.
self.ensembled_actions *= self.ensemble_weights_cumsum[self.ensembled_actions_count - 1]
self.ensembled_actions += actions[:, :-1] * self.ensemble_weights[self.ensembled_actions_count]
self.ensembled_actions /= self.ensemble_weights_cumsum[self.ensembled_actions_count]
self.ensembled_actions_count = torch.clamp(self.ensembled_actions_count + 1, max=self.chunk_size)
self.ensembled_actions *= self.ensemble_weights_cumsum[
self.ensembled_actions_count - 1
]
self.ensembled_actions += (
actions[:, :-1] * self.ensemble_weights[self.ensembled_actions_count]
)
self.ensembled_actions /= self.ensemble_weights_cumsum[
self.ensembled_actions_count
]
self.ensembled_actions_count = torch.clamp(
self.ensembled_actions_count + 1, max=self.chunk_size
)
# The last action, which has no prior online average, needs to get concatenated onto the end.
self.ensembled_actions = torch.cat([self.ensembled_actions, actions[:, -1:]], dim=1)
self.ensembled_actions = torch.cat(
[self.ensembled_actions, actions[:, -1:]], dim=1
)
self.ensembled_actions_count = torch.cat(
[self.ensembled_actions_count, torch.ones_like(self.ensembled_actions_count[-1:])]
[
self.ensembled_actions_count,
torch.ones_like(self.ensembled_actions_count[-1:]),
]
)
# "Consume" the first action.
action, self.ensembled_actions, self.ensembled_actions_count = (
@@ -319,7 +346,9 @@ class ACT(nn.Module):
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)
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 + config.chunk_size
@@ -327,20 +356,28 @@ class ACT(nn.Module):
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),
create_sinusoidal_pos_embedding(
num_input_token_encoder, config.dim_model
).unsqueeze(0),
)
# Backbone for image feature extraction.
if self.config.image_features:
backbone_model = getattr(torchvision.models, config.vision_backbone)(
replace_stride_with_dilation=[False, False, config.replace_final_stride_with_dilation],
replace_stride_with_dilation=[
False,
False,
config.replace_final_stride_with_dilation,
],
weights=config.pretrained_backbone_weights,
norm_layer=FrozenBatchNorm2d,
)
# Note: The assumption here is that we are using a ResNet model (and hence layer4 is the final
# feature map).
# Note: The forward method of this returns a dict: {"feature_map": output}.
self.backbone = IntermediateLayerGetter(backbone_model, return_layers={"layer4": "feature_map"})
self.backbone = IntermediateLayerGetter(
backbone_model, return_layers={"layer4": "feature_map"}
)
# Transformer (acts as VAE decoder when training with the variational objective).
self.encoder = ACTEncoder(config)
@@ -386,7 +423,9 @@ class ACT(nn.Module):
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, tuple[Tensor, Tensor] | tuple[None, None]]:
def forward(
self, batch: dict[str, Tensor]
) -> tuple[Tensor, tuple[Tensor, Tensor] | tuple[None, None]]:
"""A forward pass through the Action Chunking Transformer (with optional VAE encoder).
`batch` should have the following structure:
@@ -424,7 +463,9 @@ class ACT(nn.Module):
if self.config.robot_state_feature:
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)
action_embed = self.vae_encoder_action_input_proj(
batch["action"]
) # (B, S, D)
if self.config.robot_state_feature:
vae_encoder_input = [cls_embed, robot_state_embed, action_embed] # (B, S+2, D)
@@ -465,20 +506,24 @@ 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(
batch["observation.state"].device
)
latent_sample = torch.zeros(
[batch_size, self.config.latent_dim], dtype=torch.float32
).to(batch["observation.state"].device)
# Prepare transformer encoder inputs.
encoder_in_tokens = [self.encoder_latent_input_proj(latent_sample)]
encoder_in_pos_embed = list(self.encoder_1d_feature_pos_embed.weight.unsqueeze(1))
encoder_in_pos_embed = list(
self.encoder_1d_feature_pos_embed.weight.unsqueeze(1)
)
# Robot state token.
if self.config.robot_state_feature:
encoder_in_tokens.append(self.encoder_robot_state_input_proj(batch["observation.state"]))
# Environment state token.
if self.config.env_state_feature:
encoder_in_tokens.append(
self.encoder_env_state_input_proj(batch["observation.environment_state"])
self.encoder_env_state_input_proj(
batch["observation.environment_state"]
)
)
# Camera observation features and positional embeddings.
@@ -535,12 +580,21 @@ class ACTEncoder(nn.Module):
def __init__(self, config: ACTConfig, is_vae_encoder: bool = False):
super().__init__()
self.is_vae_encoder = is_vae_encoder
num_layers = config.n_vae_encoder_layers if self.is_vae_encoder else config.n_encoder_layers
self.layers = nn.ModuleList([ACTEncoderLayer(config) for _ in range(num_layers)])
num_layers = (
config.n_vae_encoder_layers
if self.is_vae_encoder
else config.n_encoder_layers
)
self.layers = nn.ModuleList(
[ACTEncoderLayer(config) for _ in range(num_layers)]
)
self.norm = nn.LayerNorm(config.dim_model) if config.pre_norm else nn.Identity()
def forward(
self, x: Tensor, pos_embed: Tensor | None = None, key_padding_mask: Tensor | None = None
self,
x: Tensor,
pos_embed: Tensor | None = None,
key_padding_mask: Tensor | None = None,
) -> Tensor:
for layer in self.layers:
x = layer(x, pos_embed=pos_embed, key_padding_mask=key_padding_mask)
@@ -551,7 +605,9 @@ class ACTEncoder(nn.Module):
class ACTEncoderLayer(nn.Module):
def __init__(self, config: ACTConfig):
super().__init__()
self.self_attn = nn.MultiheadAttention(config.dim_model, config.n_heads, dropout=config.dropout)
self.self_attn = nn.MultiheadAttention(
config.dim_model, config.n_heads, dropout=config.dropout
)
# Feed forward layers.
self.linear1 = nn.Linear(config.dim_model, config.dim_feedforward)
@@ -566,7 +622,9 @@ class ACTEncoderLayer(nn.Module):
self.activation = get_activation_fn(config.feedforward_activation)
self.pre_norm = config.pre_norm
def forward(self, x, pos_embed: Tensor | None = None, key_padding_mask: Tensor | None = None) -> Tensor:
def forward(
self, x, pos_embed: Tensor | None = None, key_padding_mask: Tensor | None = None
) -> Tensor:
skip = x
if self.pre_norm:
x = self.norm1(x)
@@ -591,7 +649,9 @@ class ACTDecoder(nn.Module):
def __init__(self, config: ACTConfig):
"""Convenience module for running multiple decoder layers followed by normalization."""
super().__init__()
self.layers = nn.ModuleList([ACTDecoderLayer(config) for _ in range(config.n_decoder_layers)])
self.layers = nn.ModuleList(
[ACTDecoderLayer(config) for _ in range(config.n_decoder_layers)]
)
self.norm = nn.LayerNorm(config.dim_model)
def forward(
@@ -603,7 +663,10 @@ class ACTDecoder(nn.Module):
) -> Tensor:
for layer in self.layers:
x = layer(
x, encoder_out, decoder_pos_embed=decoder_pos_embed, encoder_pos_embed=encoder_pos_embed
x,
encoder_out,
decoder_pos_embed=decoder_pos_embed,
encoder_pos_embed=encoder_pos_embed,
)
if self.norm is not None:
x = self.norm(x)
@@ -613,8 +676,12 @@ class ACTDecoder(nn.Module):
class ACTDecoderLayer(nn.Module):
def __init__(self, config: ACTConfig):
super().__init__()
self.self_attn = nn.MultiheadAttention(config.dim_model, config.n_heads, dropout=config.dropout)
self.multihead_attn = nn.MultiheadAttention(config.dim_model, config.n_heads, dropout=config.dropout)
self.self_attn = nn.MultiheadAttention(
config.dim_model, config.n_heads, dropout=config.dropout
)
self.multihead_attn = nn.MultiheadAttention(
config.dim_model, config.n_heads, dropout=config.dropout
)
# Feed forward layers.
self.linear1 = nn.Linear(config.dim_model, config.dim_feedforward)
@@ -655,7 +722,9 @@ class ACTDecoderLayer(nn.Module):
if self.pre_norm:
x = self.norm1(x)
q = k = self.maybe_add_pos_embed(x, decoder_pos_embed)
x = self.self_attn(q, k, value=x)[0] # select just the output, not the attention weights
x = self.self_attn(q, k, value=x)[
0
] # select just the output, not the attention weights
x = skip + self.dropout1(x)
if self.pre_norm:
skip = x
@@ -692,9 +761,14 @@ def create_sinusoidal_pos_embedding(num_positions: int, dimension: int) -> Tenso
"""
def get_position_angle_vec(position):
return [position / np.power(10000, 2 * (hid_j // 2) / dimension) for hid_j in range(dimension)]
return [
position / np.power(10000, 2 * (hid_j // 2) / dimension)
for hid_j in range(dimension)
]
sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(num_positions)])
sinusoid_table = np.array(
[get_position_angle_vec(pos_i) for pos_i in range(num_positions)]
)
sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
return torch.from_numpy(sinusoid_table).float()
@@ -739,7 +813,9 @@ class ACTSinusoidalPositionEmbedding2d(nn.Module):
x_range = x_range / (x_range[:, :, -1:] + self._eps) * self._two_pi
inverse_frequency = self._temperature ** (
2 * (torch.arange(self.dimension, dtype=torch.float32, device=x.device) // 2) / self.dimension
2
* (torch.arange(self.dimension, dtype=torch.float32, device=x.device) // 2)
/ self.dimension
)
x_range = x_range.unsqueeze(-1) / inverse_frequency # (1, H, W, 1)
@@ -747,9 +823,15 @@ class ACTSinusoidalPositionEmbedding2d(nn.Module):
# Note: this stack then flatten operation results in interleaved sine and cosine terms.
# pos_embed_x and pos_embed_y are (1, H, W, C // 2).
pos_embed_x = torch.stack((x_range[..., 0::2].sin(), x_range[..., 1::2].cos()), dim=-1).flatten(3)
pos_embed_y = torch.stack((y_range[..., 0::2].sin(), y_range[..., 1::2].cos()), dim=-1).flatten(3)
pos_embed = torch.cat((pos_embed_y, pos_embed_x), dim=3).permute(0, 3, 1, 2) # (1, C, H, W)
pos_embed_x = torch.stack(
(x_range[..., 0::2].sin(), x_range[..., 1::2].cos()), dim=-1
).flatten(3)
pos_embed_y = torch.stack(
(y_range[..., 0::2].sin(), y_range[..., 1::2].cos()), dim=-1
).flatten(3)
pos_embed = torch.cat((pos_embed_y, pos_embed_x), dim=3).permute(
0, 3, 1, 2
) # (1, C, H, W)
return pos_embed

127
lerobot/common/policies/diffusion/modeling_diffusion.py
View File

@@ -132,7 +132,11 @@ class DiffusionPolicy(PreTrainedPolicy):
if len(self._queues["action"]) == 0:
# stack n latest observations from the queue
batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch if k in self._queues}
batch = {
k: torch.stack(list(self._queues[k]), dim=1)
for k in batch
if k in self._queues
}
actions = self.diffusion.generate_actions(batch)
# TODO(rcadene): make above methods return output dictionary?
@@ -189,7 +193,9 @@ class DiffusionModel(nn.Module):
if self.config.env_state_feature:
global_cond_dim += self.config.env_state_feature.shape[0]
self.unet = DiffusionConditionalUnet1d(config, global_cond_dim=global_cond_dim * config.n_obs_steps)
self.unet = DiffusionConditionalUnet1d(
config, global_cond_dim=global_cond_dim * config.n_obs_steps
)
self.noise_scheduler = _make_noise_scheduler(
config.noise_scheduler_type,
@@ -209,7 +215,10 @@ class DiffusionModel(nn.Module):
# ========= inference ============
def conditional_sample(
self, batch_size: int, global_cond: Tensor | None = None, generator: torch.Generator | None = None
self,
batch_size: int,
global_cond: Tensor | None = None,
generator: torch.Generator | None = None,
) -> Tensor:
device = get_device_from_parameters(self)
dtype = get_dtype_from_parameters(self)
@@ -232,7 +241,9 @@ class DiffusionModel(nn.Module):
global_cond=global_cond,
)
# Compute previous image: x_t -> x_t-1
sample = self.noise_scheduler.step(model_output, t, sample, generator=generator).prev_sample
sample = self.noise_scheduler.step(
model_output, t, sample, generator=generator
).prev_sample
return sample
@@ -244,27 +255,39 @@ class DiffusionModel(nn.Module):
if self.config.image_features:
if self.config.use_separate_rgb_encoder_per_camera:
# Combine batch and sequence dims while rearranging to make the camera index dimension first.
images_per_camera = einops.rearrange(batch["observation.images"], "b s n ... -> n (b s) ...")
images_per_camera = einops.rearrange(
batch["observation.images"], "b s n ... -> n (b s) ..."
)
img_features_list = torch.cat(
[
encoder(images)
for encoder, images in zip(self.rgb_encoder, images_per_camera, strict=True)
for encoder, images in zip(
self.rgb_encoder, images_per_camera, strict=True
)
]
)
# Separate batch and sequence dims back out. The camera index dim gets absorbed into the
# feature dim (effectively concatenating the camera features).
img_features = einops.rearrange(
img_features_list, "(n b s) ... -> b s (n ...)", b=batch_size, s=n_obs_steps
img_features_list,
"(n b s) ... -> b s (n ...)",
b=batch_size,
s=n_obs_steps,
)
else:
# Combine batch, sequence, and "which camera" dims before passing to shared encoder.
img_features = self.rgb_encoder(
einops.rearrange(batch["observation.images"], "b s n ... -> (b s n) ...")
einops.rearrange(
batch["observation.images"], "b s n ... -> (b s n) ..."
)
)
# Separate batch dim and sequence dim back out. The camera index dim gets absorbed into the
# feature dim (effectively concatenating the camera features).
img_features = einops.rearrange(
img_features, "(b s n) ... -> b s (n ...)", b=batch_size, s=n_obs_steps
img_features,
"(b s n) ... -> b s (n ...)",
b=batch_size,
s=n_obs_steps,
)
global_cond_feats.append(img_features)
@@ -350,7 +373,9 @@ class DiffusionModel(nn.Module):
elif self.config.prediction_type == "sample":
target = batch["action"]
else:
raise ValueError(f"Unsupported prediction type {self.config.prediction_type}")
raise ValueError(
f"Unsupported prediction type {self.config.prediction_type}"
)
loss = F.mse_loss(pred, target, reduction="none")
@@ -410,7 +435,9 @@ class SpatialSoftmax(nn.Module):
# we could use torch.linspace directly but that seems to behave slightly differently than numpy
# and causes a small degradation in pc_success of pre-trained models.
pos_x, pos_y = np.meshgrid(np.linspace(-1.0, 1.0, self._in_w), np.linspace(-1.0, 1.0, self._in_h))
pos_x, pos_y = np.meshgrid(
np.linspace(-1.0, 1.0, self._in_w), np.linspace(-1.0, 1.0, self._in_h)
)
pos_x = torch.from_numpy(pos_x.reshape(self._in_h * self._in_w, 1)).float()
pos_y = torch.from_numpy(pos_y.reshape(self._in_h * self._in_w, 1)).float()
# register as buffer so it's moved to the correct device.
@@ -452,7 +479,9 @@ class DiffusionRgbEncoder(nn.Module):
# Always use center crop for eval
self.center_crop = torchvision.transforms.CenterCrop(config.crop_shape)
if config.crop_is_random:
self.maybe_random_crop = torchvision.transforms.RandomCrop(config.crop_shape)
self.maybe_random_crop = torchvision.transforms.RandomCrop(
config.crop_shape
)
else:
self.maybe_random_crop = self.center_crop
else:
@@ -473,7 +502,9 @@ class DiffusionRgbEncoder(nn.Module):
self.backbone = _replace_submodules(
root_module=self.backbone,
predicate=lambda x: isinstance(x, nn.BatchNorm2d),
func=lambda x: nn.GroupNorm(num_groups=x.num_features // 16, num_channels=x.num_features),
func=lambda x: nn.GroupNorm(
num_groups=x.num_features // 16, num_channels=x.num_features
),
)
# Set up pooling and final layers.
@@ -515,7 +546,9 @@ class DiffusionRgbEncoder(nn.Module):
def _replace_submodules(
root_module: nn.Module, predicate: Callable[[nn.Module], bool], func: Callable[[nn.Module], nn.Module]
root_module: nn.Module,
predicate: Callable[[nn.Module], bool],
func: Callable[[nn.Module], nn.Module],
) -> nn.Module:
"""
Args:
@@ -528,7 +561,11 @@ def _replace_submodules(
if predicate(root_module):
return func(root_module)
replace_list = [k.split(".") for k, m in root_module.named_modules(remove_duplicate=True) if predicate(m)]
replace_list = [
k.split(".")
for k, m in root_module.named_modules(remove_duplicate=True)
if predicate(m)
]
for *parents, k in replace_list:
parent_module = root_module
if len(parents) > 0:
@@ -543,7 +580,9 @@ def _replace_submodules(
else:
setattr(parent_module, k, tgt_module)
# verify that all BN are replaced
assert not any(predicate(m) for _, m in root_module.named_modules(remove_duplicate=True))
assert not any(
predicate(m) for _, m in root_module.named_modules(remove_duplicate=True)
)
return root_module
@@ -571,7 +610,9 @@ class DiffusionConv1dBlock(nn.Module):
super().__init__()
self.block = nn.Sequential(
nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2),
nn.Conv1d(
inp_channels, out_channels, kernel_size, padding=kernel_size // 2
),
nn.GroupNorm(n_groups, out_channels),
nn.Mish(),
)
@@ -594,9 +635,13 @@ class DiffusionConditionalUnet1d(nn.Module):
# Encoder for the diffusion timestep.
self.diffusion_step_encoder = nn.Sequential(
DiffusionSinusoidalPosEmb(config.diffusion_step_embed_dim),
nn.Linear(config.diffusion_step_embed_dim, config.diffusion_step_embed_dim * 4),
nn.Linear(
config.diffusion_step_embed_dim, config.diffusion_step_embed_dim * 4
),
nn.Mish(),
nn.Linear(config.diffusion_step_embed_dim * 4, config.diffusion_step_embed_dim),
nn.Linear(
config.diffusion_step_embed_dim * 4, config.diffusion_step_embed_dim
),
)
# The FiLM conditioning dimension.
@@ -621,10 +666,16 @@ class DiffusionConditionalUnet1d(nn.Module):
self.down_modules.append(
nn.ModuleList(
[
DiffusionConditionalResidualBlock1d(dim_in, dim_out, **common_res_block_kwargs),
DiffusionConditionalResidualBlock1d(dim_out, dim_out, **common_res_block_kwargs),
DiffusionConditionalResidualBlock1d(
dim_in, dim_out, **common_res_block_kwargs
),
DiffusionConditionalResidualBlock1d(
dim_out, dim_out, **common_res_block_kwargs
),
# Downsample as long as it is not the last block.
nn.Conv1d(dim_out, dim_out, 3, 2, 1) if not is_last else nn.Identity(),
nn.Conv1d(dim_out, dim_out, 3, 2, 1)
if not is_last
else nn.Identity(),
]
)
)
@@ -633,10 +684,14 @@ class DiffusionConditionalUnet1d(nn.Module):
self.mid_modules = nn.ModuleList(
[
DiffusionConditionalResidualBlock1d(
config.down_dims[-1], config.down_dims[-1], **common_res_block_kwargs
config.down_dims[-1],
config.down_dims[-1],
**common_res_block_kwargs,
),
DiffusionConditionalResidualBlock1d(
config.down_dims[-1], config.down_dims[-1], **common_res_block_kwargs
config.down_dims[-1],
config.down_dims[-1],
**common_res_block_kwargs,
),
]
)
@@ -649,10 +704,16 @@ class DiffusionConditionalUnet1d(nn.Module):
nn.ModuleList(
[
# dim_in * 2, because it takes the encoder's skip connection as well
DiffusionConditionalResidualBlock1d(dim_in * 2, dim_out, **common_res_block_kwargs),
DiffusionConditionalResidualBlock1d(dim_out, dim_out, **common_res_block_kwargs),
DiffusionConditionalResidualBlock1d(
dim_in * 2, dim_out, **common_res_block_kwargs
),
DiffusionConditionalResidualBlock1d(
dim_out, dim_out, **common_res_block_kwargs
),
# Upsample as long as it is not the last block.
nn.ConvTranspose1d(dim_out, dim_out, 4, 2, 1) if not is_last else nn.Identity(),
nn.ConvTranspose1d(dim_out, dim_out, 4, 2, 1)
if not is_last
else nn.Identity(),
]
)
)
@@ -726,17 +787,23 @@ class DiffusionConditionalResidualBlock1d(nn.Module):
self.use_film_scale_modulation = use_film_scale_modulation
self.out_channels = out_channels
self.conv1 = DiffusionConv1dBlock(in_channels, out_channels, kernel_size, n_groups=n_groups)
self.conv1 = DiffusionConv1dBlock(
in_channels, out_channels, kernel_size, n_groups=n_groups
)
# FiLM modulation (https://arxiv.org/abs/1709.07871) outputs per-channel bias and (maybe) scale.
cond_channels = out_channels * 2 if use_film_scale_modulation else out_channels
self.cond_encoder = nn.Sequential(nn.Mish(), nn.Linear(cond_dim, cond_channels))
self.conv2 = DiffusionConv1dBlock(out_channels, out_channels, kernel_size, n_groups=n_groups)
self.conv2 = DiffusionConv1dBlock(
out_channels, out_channels, kernel_size, n_groups=n_groups
)
# A final convolution for dimension matching the residual (if needed).
self.residual_conv = (
nn.Conv1d(in_channels, out_channels, 1) if in_channels != out_channels else nn.Identity()
nn.Conv1d(in_channels, out_channels, 1)
if in_channels != out_channels
else nn.Identity()
)
def forward(self, x: Tensor, cond: Tensor) -> Tensor:

37
lerobot/common/policies/hilserl/classifier/modeling_classifier.py
View File

@@ -7,7 +7,9 @@ from torch import Tensor, nn
from .configuration_classifier import ClassifierConfig
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)
@@ -15,7 +17,10 @@ class ClassifierOutput:
"""Wrapper for classifier outputs with additional metadata."""
def __init__(
self, logits: Tensor, probabilities: Optional[Tensor] = None, hidden_states: Optional[Tensor] = None
self,
logits: Tensor,
probabilities: Optional[Tensor] = None,
hidden_states: Optional[Tensor] = None,
):
self.logits = logits
self.probabilities = probabilities
@@ -43,12 +48,14 @@ class Classifier(
name = "classifier"
def __init__(self, config: ClassifierConfig):
from transformers import AutoImageProcessor, AutoModel
from transformers import AutoModel
super().__init__()
self.config = config
# self.processor = AutoImageProcessor.from_pretrained(self.config.model_name, trust_remote_code=True)
encoder = AutoModel.from_pretrained(self.config.model_name, trust_remote_code=True)
encoder = AutoModel.from_pretrained(
self.config.model_name, trust_remote_code=True
)
# Extract vision model if we're given a multimodal model
if hasattr(encoder, "vision_model"):
logging.info("Multimodal model detected - using vision encoder only")
@@ -74,7 +81,9 @@ class Classifier(
self.feature_dim = self.encoder.fc.in_features
self.encoder = nn.Sequential(*list(self.encoder.children())[:-1])
elif hasattr(self.encoder.config, "hidden_sizes"):
self.feature_dim = self.encoder.config.hidden_sizes[-1] # Last channel dimension
self.feature_dim = self.encoder.config.hidden_sizes[
-1
] # Last channel dimension
else:
raise ValueError("Unsupported CNN architecture")
@@ -94,14 +103,19 @@ class Classifier(
if hasattr(self.encoder.config, "hidden_size"):
input_dim = self.encoder.config.hidden_size
else:
raise ValueError("Unsupported transformer architecture since hidden_size is not found")
raise ValueError(
"Unsupported transformer architecture since hidden_size is not found"
)
self.classifier_head = nn.Sequential(
nn.Linear(input_dim * self.config.num_cameras, self.config.hidden_dim),
nn.Dropout(self.config.dropout_rate),
nn.LayerNorm(self.config.hidden_dim),
nn.ReLU(),
nn.Linear(self.config.hidden_dim, 1 if self.config.num_classes == 2 else self.config.num_classes),
nn.Linear(
self.config.hidden_dim,
1 if self.config.num_classes == 2 else self.config.num_classes,
),
)
self.classifier_head = self.classifier_head.to(self.config.device)
@@ -127,7 +141,10 @@ class Classifier(
return features
else: # Transformer models
outputs = self.encoder(processed)
if hasattr(outputs, "pooler_output") and outputs.pooler_output is not None:
if (
hasattr(outputs, "pooler_output")
and outputs.pooler_output is not None
):
return outputs.pooler_output
return outputs.last_hidden_state[:, 0, :]
@@ -143,7 +160,9 @@ class Classifier(
else:
probabilities = torch.softmax(logits, dim=-1)
return ClassifierOutput(logits=logits, probabilities=probabilities, hidden_states=encoder_outputs)
return ClassifierOutput(
logits=logits, probabilities=probabilities, hidden_states=encoder_outputs
)
def predict_reward(self, x, threshold=0.6):
if self.config.num_classes == 2:

161
lerobot/common/policies/sac/modeling_sac.py
View File

@@ -59,7 +59,9 @@ class SACPolicy(
config.input_normalization_params
)
self.normalize_inputs = Normalize(
config.input_shapes, config.input_normalization_modes, input_normalization_params
config.input_shapes,
config.input_normalization_modes,
input_normalization_params,
)
else:
self.normalize_inputs = nn.Identity()
@@ -90,7 +92,8 @@ class SACPolicy(
ensemble=Ensemble(
[
CriticHead(
input_dim=encoder_critic.output_dim + config.output_shapes["action"][0],
input_dim=encoder_critic.output_dim
+ config.output_shapes["action"][0],
**config.critic_network_kwargs,
)
for _ in range(config.num_critics)
@@ -104,7 +107,8 @@ class SACPolicy(
ensemble=Ensemble(
[
CriticHead(
input_dim=encoder_critic.output_dim + config.output_shapes["action"][0],
input_dim=encoder_critic.output_dim
+ config.output_shapes["action"][0],
**config.critic_network_kwargs,
)
for _ in range(config.num_critics)
@@ -120,13 +124,17 @@ class SACPolicy(
self.actor = Policy(
encoder=encoder_actor,
network=MLP(input_dim=encoder_actor.output_dim, **config.actor_network_kwargs),
network=MLP(
input_dim=encoder_actor.output_dim, **config.actor_network_kwargs
),
action_dim=config.output_shapes["action"][0],
encoder_is_shared=config.shared_encoder,
**config.policy_kwargs,
)
if config.target_entropy is None:
config.target_entropy = -np.prod(config.output_shapes["action"][0]) / 2 # (-dim(A)/2)
config.target_entropy = (
-np.prod(config.output_shapes["action"][0]) / 2
) # (-dim(A)/2)
# TODO (azouitine): Handle the case where the temparameter is a fixed
# TODO (michel-aractingi): Put the log_alpha in cuda by default because otherwise
@@ -153,7 +161,11 @@ class SACPolicy(
return actions
def critic_forward(
self, observations: dict[str, Tensor], actions: Tensor, use_target: bool = False, observation_features: Tensor | None = None
self,
observations: dict[str, Tensor],
actions: Tensor,
use_target: bool = False,
observation_features: Tensor | None = None,
) -> Tensor:
"""Forward pass through a critic network ensemble
@@ -173,21 +185,37 @@ class SACPolicy(
def update_target_networks(self):
"""Update target networks with exponential moving average"""
for target_param, param in zip(
self.critic_target.parameters(), self.critic_ensemble.parameters(), strict=False
self.critic_target.parameters(),
self.critic_ensemble.parameters(),
strict=False,
):
target_param.data.copy_(
param.data * self.config.critic_target_update_weight
+ target_param.data * (1.0 - self.config.critic_target_update_weight)
)
def compute_loss_critic(self, observations, actions, rewards, next_observations, done, observation_features: Tensor | None = None, next_observation_features: Tensor | None = None) -> Tensor:
def compute_loss_critic(
self,
observations,
actions,
rewards,
next_observations,
done,
observation_features: Tensor | None = None,
next_observation_features: Tensor | None = None,
) -> Tensor:
temperature = self.log_alpha.exp().item()
with torch.no_grad():
next_action_preds, next_log_probs, _ = self.actor(next_observations, next_observation_features)
next_action_preds, next_log_probs, _ = self.actor(
next_observations, next_observation_features
)
# 2- compute q targets
q_targets = self.critic_forward(
observations=next_observations, actions=next_action_preds, use_target=True, observation_features=next_observation_features
observations=next_observations,
actions=next_action_preds,
use_target=True,
observation_features=next_observation_features,
)
# subsample critics to prevent overfitting if use high UTD (update to date)
@@ -204,7 +232,12 @@ class SACPolicy(
td_target = rewards + (1 - done) * self.config.discount * min_q
# 3- compute predicted qs
q_preds = self.critic_forward(observations, actions, use_target=False, observation_features=observation_features)
q_preds = self.critic_forward(
observations,
actions,
use_target=False,
observation_features=observation_features,
)
# 4- Calculate loss
# Compute state-action value loss (TD loss) for all of the Q functions in the ensemble.
@@ -219,20 +252,31 @@ class SACPolicy(
).sum()
return critics_loss
def compute_loss_temperature(self, observations, observation_features: Tensor | None = None) -> Tensor:
def compute_loss_temperature(
self, observations, observation_features: Tensor | None = None
) -> Tensor:
"""Compute the temperature loss"""
# calculate temperature loss
with torch.no_grad():
_, log_probs, _ = self.actor(observations, observation_features)
temperature_loss = (-self.log_alpha.exp() * (log_probs + self.config.target_entropy)).mean()
temperature_loss = (
-self.log_alpha.exp() * (log_probs + self.config.target_entropy)
).mean()
return temperature_loss
def compute_loss_actor(self, observations, observation_features: Tensor | None = None) -> Tensor:
def compute_loss_actor(
self, observations, observation_features: Tensor | None = None
) -> Tensor:
temperature = self.log_alpha.exp().item()
actions_pi, log_probs, _ = self.actor(observations, observation_features)
q_preds = self.critic_forward(observations, actions_pi, use_target=False, observation_features=observation_features)
q_preds = self.critic_forward(
observations,
actions_pi,
use_target=False,
observation_features=observation_features,
)
min_q_preds = q_preds.min(dim=0)[0]
actor_loss = ((temperature * log_probs) - min_q_preds).mean()
@@ -259,7 +303,11 @@ class MLP(nn.Module):
if dropout_rate is not None and dropout_rate > 0:
layers.append(nn.Dropout(p=dropout_rate))
layers.append(nn.LayerNorm(hidden_dims[0]))
layers.append(activations if isinstance(activations, nn.Module) else getattr(nn, activations)())
layers.append(
activations
if isinstance(activations, nn.Module)
else getattr(nn, activations)()
)
# Rest of the layers
for i in range(1, len(hidden_dims)):
@@ -270,7 +318,9 @@ class MLP(nn.Module):
layers.append(nn.Dropout(p=dropout_rate))
layers.append(nn.LayerNorm(hidden_dims[i]))
layers.append(
activations if isinstance(activations, nn.Module) else getattr(nn, activations)()
activations
if isinstance(activations, nn.Module)
else getattr(nn, activations)()
)
self.net = nn.Sequential(*layers)
@@ -381,7 +431,11 @@ class CriticEnsemble(nn.Module):
actions = self.output_normalization(actions)["action"]
actions = actions.to(device)
obs_enc = observation_features if observation_features is not None else (observations if self.encoder is None else self.encoder(observations))
obs_enc = (
observation_features
if observation_features is not None
else (observations if self.encoder is None else self.encoder(observations))
)
inputs = torch.cat([obs_enc, actions], dim=-1)
q_values = self.ensemble(inputs) # [num_critics, B, 1]
@@ -445,7 +499,11 @@ class Policy(nn.Module):
observation_features: torch.Tensor | None = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
# Encode observations if encoder exists
obs_enc = observation_features if observation_features is not None else (observations if self.encoder is None else self.encoder(observations))
obs_enc = (
observation_features
if observation_features is not None
else (observations if self.encoder is None else self.encoder(observations))
)
# Get network outputs
outputs = self.network(obs_enc)
@@ -454,11 +512,15 @@ class Policy(nn.Module):
# Compute standard deviations
if self.fixed_std is None:
log_std = self.std_layer(outputs)
assert not torch.isnan(log_std).any(), "[ERROR] log_std became NaN after std_layer!"
assert not torch.isnan(
log_std
).any(), "[ERROR] log_std became NaN after std_layer!"
if self.use_tanh_squash:
log_std = torch.tanh(log_std)
log_std = self.log_std_min + 0.5 * (self.log_std_max - self.log_std_min) * (log_std + 1.0)
log_std = self.log_std_min + 0.5 * (
self.log_std_max - self.log_std_min
) * (log_std + 1.0)
else:
log_std = torch.clamp(log_std, self.log_std_min, self.log_std_max)
else:
@@ -471,7 +533,9 @@ class Policy(nn.Module):
if self.use_tanh_squash:
actions = torch.tanh(x_t)
log_probs -= torch.log((1 - actions.pow(2)) + 1e-6) # Adjust log-probs for Tanh
log_probs -= torch.log(
(1 - actions.pow(2)) + 1e-6
) # Adjust log-probs for Tanh
else:
actions = x_t # No Tanh; raw Gaussian sample
@@ -518,12 +582,15 @@ class SACObservationEncoder(nn.Module):
freeze_image_encoder(self.image_enc_layers)
else:
self.parameters_to_optimize += list(self.image_enc_layers.parameters())
self.all_image_keys = [k for k in config.input_shapes if k.startswith("observation.image")]
self.all_image_keys = [
k for k in config.input_shapes if k.startswith("observation.image")
]
if "observation.state" in config.input_shapes:
self.state_enc_layers = nn.Sequential(
nn.Linear(
in_features=config.input_shapes["observation.state"][0], out_features=config.latent_dim
in_features=config.input_shapes["observation.state"][0],
out_features=config.latent_dim,
),
nn.LayerNorm(normalized_shape=config.latent_dim),
nn.Tanh(),
@@ -544,7 +611,9 @@ class SACObservationEncoder(nn.Module):
self.aggregation_size += config.latent_dim
self.parameters_to_optimize += list(self.env_state_enc_layers.parameters())
self.aggregation_layer = nn.Linear(in_features=self.aggregation_size, out_features=config.latent_dim)
self.aggregation_layer = nn.Linear(
in_features=self.aggregation_size, out_features=config.latent_dim
)
self.parameters_to_optimize += list(self.aggregation_layer.parameters())
def forward(self, obs_dict: dict[str, Tensor]) -> Tensor:
@@ -557,13 +626,19 @@ class SACObservationEncoder(nn.Module):
obs_dict = self.input_normalization(obs_dict)
# Batch all images along the batch dimension, then encode them.
if len(self.all_image_keys) > 0:
images_batched = torch.cat([obs_dict[key] for key in self.all_image_keys], dim=0)
images_batched = torch.cat(
[obs_dict[key] for key in self.all_image_keys], dim=0
)
images_batched = self.image_enc_layers(images_batched)
embeddings_chunks = torch.chunk(images_batched, dim=0, chunks=len(self.all_image_keys))
embeddings_chunks = torch.chunk(
images_batched, dim=0, chunks=len(self.all_image_keys)
)
feat.extend(embeddings_chunks)
if "observation.environment_state" in self.config.input_shapes:
feat.append(self.env_state_enc_layers(obs_dict["observation.environment_state"]))
feat.append(
self.env_state_enc_layers(obs_dict["observation.environment_state"])
)
if "observation.state" in self.config.input_shapes:
feat.append(self.state_enc_layers(obs_dict["observation.state"]))
@@ -631,7 +706,9 @@ class PretrainedImageEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.image_enc_layers, self.image_enc_out_shape = self._load_pretrained_vision_encoder(config)
self.image_enc_layers, self.image_enc_out_shape = (
self._load_pretrained_vision_encoder(config)
)
self.image_enc_proj = nn.Sequential(
nn.Linear(np.prod(self.image_enc_out_shape), config.latent_dim),
nn.LayerNorm(config.latent_dim),
@@ -642,15 +719,21 @@ class PretrainedImageEncoder(nn.Module):
"""Set up CNN encoder"""
from transformers import AutoModel
self.image_enc_layers = AutoModel.from_pretrained(config.vision_encoder_name, trust_remote_code=True)
self.image_enc_layers = AutoModel.from_pretrained(
config.vision_encoder_name, trust_remote_code=True
)
# self.image_enc_layers.pooler = Identity()
if hasattr(self.image_enc_layers.config, "hidden_sizes"):
self.image_enc_out_shape = self.image_enc_layers.config.hidden_sizes[-1] # Last channel dimension
self.image_enc_out_shape = self.image_enc_layers.config.hidden_sizes[
-1
] # Last channel dimension
elif hasattr(self.image_enc_layers, "fc"):
self.image_enc_out_shape = self.image_enc_layers.fc.in_features
else:
raise ValueError("Unsupported vision encoder architecture, make sure you are using a CNN")
raise ValueError(
"Unsupported vision encoder architecture, make sure you are using a CNN"
)
return self.image_enc_layers, self.image_enc_out_shape
def forward(self, x):
@@ -673,7 +756,7 @@ def orthogonal_init():
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
super().__init__()
def forward(self, x):
return x
@@ -701,7 +784,9 @@ class Ensemble(nn.Module):
return self.module(*args, **kwargs)
def forward(self, *args, **kwargs):
return torch.vmap(self._call, (0, None), randomness="different")(self.params, *args, **kwargs)
return torch.vmap(self._call, (0, None), randomness="different")(
self.params, *args, **kwargs
)
def __repr__(self):
return f"Vectorized {len(self)}x " + self._repr
@@ -710,7 +795,9 @@ class Ensemble(nn.Module):
# TODO (azouitine): I think in our case this function is not usefull we should remove it
# after some investigation
# borrowed from tdmpc
def flatten_forward_unflatten(fn: Callable[[Tensor], Tensor], image_tensor: Tensor) -> Tensor:
def flatten_forward_unflatten(
fn: Callable[[Tensor], Tensor], image_tensor: Tensor
) -> Tensor:
"""Helper to temporarily flatten extra dims at the start of the image tensor.
Args:
@@ -736,7 +823,9 @@ def _convert_normalization_params_to_tensor(normalization_params: dict) -> dict:
for key, value in inner_dict.items():
converted_params[outer_key][key] = torch.tensor(value)
if "image" in outer_key:
converted_params[outer_key][key] = converted_params[outer_key][key].view(3, 1, 1)
converted_params[outer_key][key] = converted_params[outer_key][
key
].view(3, 1, 1)
return converted_params

4
lerobot/common/policies/tdmpc/configuration_tdmpc.py
View File

@@ -183,7 +183,9 @@ class TDMPCConfig(PreTrainedConfig):
"If `n_action_steps > 1`, `n_action_repeats` must be left to its default value of 1."
)
if not self.use_mpc:
raise ValueError("If `n_action_steps > 1`, `use_mpc` must be set to `True`.")
raise ValueError(
"If `n_action_steps > 1`, `use_mpc` must be set to `True`."
)
if self.n_action_steps > self.horizon:
raise ValueError("`n_action_steps` must be less than or equal to `horizon`.")

139
lerobot/common/policies/tdmpc/modeling_tdmpc.py
View File

@@ -100,7 +100,9 @@ class TDMPCPolicy(PreTrainedPolicy):
"""
self._queues = {
"observation.state": deque(maxlen=1),
"action": deque(maxlen=max(self.config.n_action_steps, self.config.n_action_repeats)),
"action": deque(
maxlen=max(self.config.n_action_steps, self.config.n_action_repeats)
),
}
if self.config.image_features:
self._queues["observation.image"] = deque(maxlen=1)
@@ -189,7 +191,11 @@ class TDMPCPolicy(PreTrainedPolicy):
# In the CEM loop we will need this for a call to estimate_value with the gaussian sampled
# trajectories.
z = einops.repeat(z, "b d -> n b d", n=self.config.n_gaussian_samples + self.config.n_pi_samples)
z = einops.repeat(
z,
"b d -> n b d",
n=self.config.n_gaussian_samples + self.config.n_pi_samples,
)
# Model Predictive Path Integral (MPPI) with the cross-entropy method (CEM) as the optimization
# algorithm.
@@ -211,35 +217,47 @@ class TDMPCPolicy(PreTrainedPolicy):
self.config.action_feature.shape[0],
device=std.device,
)
gaussian_actions = torch.clamp(mean.unsqueeze(1) + std.unsqueeze(1) * std_normal_noise, -1, 1)
gaussian_actions = torch.clamp(
mean.unsqueeze(1) + std.unsqueeze(1) * std_normal_noise, -1, 1
)
# Compute elite actions.
actions = torch.cat([gaussian_actions, pi_actions], dim=1)
value = self.estimate_value(z, actions).nan_to_num_(0)
elite_idxs = torch.topk(value, self.config.n_elites, dim=0).indices # (n_elites, batch)
elite_idxs = torch.topk(
value, self.config.n_elites, dim=0
).indices # (n_elites, batch)
elite_value = value.take_along_dim(elite_idxs, dim=0) # (n_elites, batch)
# (horizon, n_elites, batch, action_dim)
elite_actions = actions.take_along_dim(einops.rearrange(elite_idxs, "n b -> 1 n b 1"), dim=1)
elite_actions = actions.take_along_dim(
einops.rearrange(elite_idxs, "n b -> 1 n b 1"), dim=1
)
# Update gaussian PDF parameters to be the (weighted) mean and standard deviation of the elites.
max_value = elite_value.max(0, keepdim=True)[0] # (1, batch)
# The weighting is a softmax over trajectory values. Note that this is not the same as the usage
# of Ω in eqn 4 of the TD-MPC paper. Instead it is the normalized version of it: s = Ω/ΣΩ. This
# makes the equations: μ = Σ(s⋅Γ), σ = Σ(s⋅(Γ-μ)²).
score = torch.exp(self.config.elite_weighting_temperature * (elite_value - max_value))
score = torch.exp(
self.config.elite_weighting_temperature * (elite_value - max_value)
)
score /= score.sum(axis=0, keepdim=True)
# (horizon, batch, action_dim)
_mean = torch.sum(einops.rearrange(score, "n b -> n b 1") * elite_actions, dim=1)
_mean = torch.sum(
einops.rearrange(score, "n b -> n b 1") * elite_actions, dim=1
)
_std = torch.sqrt(
torch.sum(
einops.rearrange(score, "n b -> n b 1")
* (elite_actions - einops.rearrange(_mean, "h b d -> h 1 b d")) ** 2,
* (elite_actions - einops.rearrange(_mean, "h b d -> h 1 b d"))
** 2,
dim=1,
)
)
# Update mean with an exponential moving average, and std with a direct replacement.
mean = (
self.config.gaussian_mean_momentum * mean + (1 - self.config.gaussian_mean_momentum) * _mean
self.config.gaussian_mean_momentum * mean
+ (1 - self.config.gaussian_mean_momentum) * _mean
)
std = _std.clamp_(self.config.min_std, self.config.max_std)
@@ -248,7 +266,9 @@ class TDMPCPolicy(PreTrainedPolicy):
# Randomly select one of the elite actions from the last iteration of MPPI/CEM using the softmax
# scores from the last iteration.
actions = elite_actions[:, torch.multinomial(score.T, 1).squeeze(), torch.arange(batch_size)]
actions = elite_actions[
:, torch.multinomial(score.T, 1).squeeze(), torch.arange(batch_size)
]
return actions
@@ -271,7 +291,8 @@ class TDMPCPolicy(PreTrainedPolicy):
# of the FOWM paper.
if self.config.uncertainty_regularizer_coeff > 0:
regularization = -(
self.config.uncertainty_regularizer_coeff * self.model.Qs(z, actions[t]).std(0)
self.config.uncertainty_regularizer_coeff
* self.model.Qs(z, actions[t]).std(0)
)
else:
regularization = 0
@@ -291,15 +312,22 @@ class TDMPCPolicy(PreTrainedPolicy):
if self.config.q_ensemble_size > 2:
G += (
running_discount
* torch.min(terminal_values[torch.randint(0, self.config.q_ensemble_size, size=(2,))], dim=0)[
0
]
* torch.min(
terminal_values[
torch.randint(0, self.config.q_ensemble_size, size=(2,))
],
dim=0,
)[0]
)
else:
G += running_discount * torch.min(terminal_values, dim=0)[0]
# Finally, also regularize the terminal value.
if self.config.uncertainty_regularizer_coeff > 0:
G -= running_discount * self.config.uncertainty_regularizer_coeff * terminal_values.std(0)
G -= (
running_discount
* self.config.uncertainty_regularizer_coeff
* terminal_values.std(0)
)
return G
def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
@@ -329,7 +357,10 @@ class TDMPCPolicy(PreTrainedPolicy):
# Apply random image augmentations.
if self.config.image_features and self.config.max_random_shift_ratio > 0:
observations["observation.image"] = flatten_forward_unflatten(
partial(random_shifts_aug, max_random_shift_ratio=self.config.max_random_shift_ratio),
partial(
random_shifts_aug,
max_random_shift_ratio=self.config.max_random_shift_ratio,
),
observations["observation.image"],
)
@@ -347,14 +378,20 @@ class TDMPCPolicy(PreTrainedPolicy):
# Note this has shape `horizon+1` because there are `horizon` actions and a current `z`. Each action
# gives us a next `z`.
batch_size = batch["index"].shape[0]
z_preds = torch.empty(horizon + 1, batch_size, self.config.latent_dim, device=device)
z_preds = torch.empty(
horizon + 1, batch_size, self.config.latent_dim, device=device
)
z_preds[0] = self.model.encode(current_observation)
reward_preds = torch.empty_like(reward, device=device)
for t in range(horizon):
z_preds[t + 1], reward_preds[t] = self.model.latent_dynamics_and_reward(z_preds[t], action[t])
z_preds[t + 1], reward_preds[t] = self.model.latent_dynamics_and_reward(
z_preds[t], action[t]
)
# Compute Q and V value predictions based on the latent rollout.
q_preds_ensemble = self.model.Qs(z_preds[:-1], action) # (ensemble, horizon, batch)
q_preds_ensemble = self.model.Qs(
z_preds[:-1], action
) # (ensemble, horizon, batch)
v_preds = self.model.V(z_preds[:-1])
info.update({"Q": q_preds_ensemble.mean().item(), "V": v_preds.mean().item()})
@@ -368,10 +405,14 @@ class TDMPCPolicy(PreTrainedPolicy):
# actions (not actions estimated by π).
# Note: Here we do not use self.model_target, but self.model. This is to follow the original code
# and the FOWM paper.
q_targets = reward + self.config.discount * self.model.V(self.model.encode(next_observations))
q_targets = reward + self.config.discount * self.model.V(
self.model.encode(next_observations)
)
# From eqn 3 of FOWM. These appear as Q(z, a). Here we call them v_targets to emphasize that we
# are using them to compute loss for V.
v_targets = self.model_target.Qs(z_preds[:-1].detach(), action, return_min=True)
v_targets = self.model_target.Qs(
z_preds[:-1].detach(), action, return_min=True
)
# Compute losses.
# Exponentially decay the loss weight with respect to the timestep. Steps that are more distant in the
@@ -414,7 +455,9 @@ class TDMPCPolicy(PreTrainedPolicy):
temporal_loss_coeffs
* F.mse_loss(
q_preds_ensemble,
einops.repeat(q_targets, "t b -> e t b", e=q_preds_ensemble.shape[0]),
einops.repeat(
q_targets, "t b -> e t b", e=q_preds_ensemble.shape[0]
),
reduction="none",
).sum(0) # sum over ensemble
# `q_preds_ensemble` depends on the first observation and the actions.
@@ -452,12 +495,14 @@ class TDMPCPolicy(PreTrainedPolicy):
z_preds = z_preds.detach()
# Use stopgrad for the advantage calculation.
with torch.no_grad():
advantage = self.model_target.Qs(z_preds[:-1], action, return_min=True) - self.model.V(
z_preds[:-1]
)
advantage = self.model_target.Qs(
z_preds[:-1], action, return_min=True
) - self.model.V(z_preds[:-1])
info["advantage"] = advantage[0]
# (t, b)
exp_advantage = torch.clamp(torch.exp(advantage * self.config.advantage_scaling), max=100.0)
exp_advantage = torch.clamp(
torch.exp(advantage * self.config.advantage_scaling), max=100.0
)
action_preds = self.model.pi(z_preds[:-1]) # (t, b, a)
# Calculate the MSE between the actions and the action predictions.
# Note: FOWM's original code calculates the log probability (wrt to a unit standard deviation
@@ -511,7 +556,9 @@ class TDMPCPolicy(PreTrainedPolicy):
# Note a minor variation with respect to the original FOWM code. Here they do this based on an EMA
# update frequency parameter which is set to 2 (every 2 steps an update is done). To simplify the code
# we update every step and adjust the decay parameter `alpha` accordingly (0.99 -> 0.995)
update_ema_parameters(self.model_target, self.model, self.config.target_model_momentum)
update_ema_parameters(
self.model_target, self.model, self.config.target_model_momentum
)
class TDMPCTOLD(nn.Module):
@@ -598,7 +645,9 @@ class TDMPCTOLD(nn.Module):
"Sanity check. The last linear layer needs 0 initialization on weights."
)
nn.init.zeros_(m[-1].weight)
nn.init.zeros_(m[-1].bias) # this has already been done, but keep this line here for good measure
nn.init.zeros_(
m[-1].bias
) # this has already been done, but keep this line here for good measure
def encode(self, obs: dict[str, Tensor]) -> Tensor:
"""Encodes an observation into its latent representation."""
@@ -702,11 +751,26 @@ class TDMPCObservationEncoder(nn.Module):
stride=2,
),
nn.ReLU(),
nn.Conv2d(config.image_encoder_hidden_dim, config.image_encoder_hidden_dim, 5, stride=2),
nn.Conv2d(
config.image_encoder_hidden_dim,
config.image_encoder_hidden_dim,
5,
stride=2,
),
nn.ReLU(),
nn.Conv2d(config.image_encoder_hidden_dim, config.image_encoder_hidden_dim, 3, stride=2),
nn.Conv2d(
config.image_encoder_hidden_dim,
config.image_encoder_hidden_dim,
3,
stride=2,
),
nn.ReLU(),
nn.Conv2d(config.image_encoder_hidden_dim, config.image_encoder_hidden_dim, 3, stride=2),
nn.Conv2d(
config.image_encoder_hidden_dim,
config.image_encoder_hidden_dim,
3,
stride=2,
),
nn.ReLU(),
)
dummy_shape = (1, *next(iter(config.image_features.values())).shape)
@@ -796,12 +860,17 @@ def update_ema_parameters(ema_net: nn.Module, net: nn.Module, alpha: float):
"""Update EMA parameters in place with ema_param <- alpha * ema_param + (1 - alpha) * param."""
for ema_module, module in zip(ema_net.modules(), net.modules(), strict=True):
for (n_p_ema, p_ema), (n_p, p) in zip(
ema_module.named_parameters(recurse=False), module.named_parameters(recurse=False), strict=True
ema_module.named_parameters(recurse=False),
module.named_parameters(recurse=False),
strict=True,
):
assert n_p_ema == n_p, "Parameter names don't match for EMA model update"
if isinstance(p, dict):
raise RuntimeError("Dict parameter not supported")
if isinstance(module, nn.modules.batchnorm._BatchNorm) or not p.requires_grad:
if (
isinstance(module, nn.modules.batchnorm._BatchNorm)
or not p.requires_grad
):
# Copy BatchNorm parameters, and non-trainable parameters directly.
p_ema.copy_(p.to(dtype=p_ema.dtype).data)
with torch.no_grad():
@@ -809,7 +878,9 @@ def update_ema_parameters(ema_net: nn.Module, net: nn.Module, alpha: float):
p_ema.add_(p.to(dtype=p_ema.dtype).data, alpha=1 - alpha)
def flatten_forward_unflatten(fn: Callable[[Tensor], Tensor], image_tensor: Tensor) -> Tensor:
def flatten_forward_unflatten(
fn: Callable[[Tensor], Tensor], image_tensor: Tensor
) -> Tensor:
"""Helper to temporarily flatten extra dims at the start of the image tensor.
Args:

144
lerobot/common/policies/vqbet/modeling_vqbet.py
View File

@@ -145,8 +145,14 @@ class VQBeTPolicy(PreTrainedPolicy):
)
if len(self._queues["action"]) == 0:
batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch if k in self._queues}
actions = self.vqbet(batch, rollout=True)[:, : self.config.action_chunk_size]
batch = {
k: torch.stack(list(self._queues[k]), dim=1)
for k in batch
if k in self._queues
}
actions = self.vqbet(batch, rollout=True)[
:, : self.config.action_chunk_size
]
# the dimension of returned action is (batch_size, action_chunk_size, action_dim)
actions = self.unnormalize_outputs({"action": actions})["action"]
@@ -168,7 +174,9 @@ class VQBeTPolicy(PreTrainedPolicy):
# n_different_codes: how many of the total possible VQ codes are being used in single batch (how many of them have at least one encoder embedding as a nearest neighbor). This can be at most `vqvae_n_embed * number of layers of RVQ (=2)`.
# n_different_combinations: how many different code combinations are being used out of all possible combinations in single batch. This can be at most `vqvae_n_embed ^ number of layers of RVQ (=2)` (hint consider the RVQ as a decision tree).
loss, n_different_codes, n_different_combinations, recon_l1_error = (
self.vqbet.action_head.discretize(self.config.n_vqvae_training_steps, batch["action"])
self.vqbet.action_head.discretize(
self.config.n_vqvae_training_steps, batch["action"]
)
)
return loss, {
"n_different_codes": n_different_codes,
@@ -225,7 +233,9 @@ class SpatialSoftmax(nn.Module):
# we could use torch.linspace directly but that seems to behave slightly differently than numpy
# and causes a small degradation in pc_success of pre-trained models.
pos_x, pos_y = np.meshgrid(np.linspace(-1.0, 1.0, self._in_w), np.linspace(-1.0, 1.0, self._in_h))
pos_x, pos_y = np.meshgrid(
np.linspace(-1.0, 1.0, self._in_w), np.linspace(-1.0, 1.0, self._in_h)
)
pos_x = torch.from_numpy(pos_x.reshape(self._in_h * self._in_w, 1)).float()
pos_y = torch.from_numpy(pos_y.reshape(self._in_h * self._in_w, 1)).float()
# register as buffer so it's moved to the correct device.
@@ -339,7 +349,12 @@ class VQBeTModel(nn.Module):
num_tokens = self.config.n_action_pred_token + self.config.n_obs_steps - 1
self.register_buffer(
"select_target_actions_indices",
torch.row_stack([torch.arange(i, i + self.config.action_chunk_size) for i in range(num_tokens)]),
torch.row_stack(
[
torch.arange(i, i + self.config.action_chunk_size)
for i in range(num_tokens)
]
),
)
def forward(self, batch: dict[str, Tensor], rollout: bool) -> tuple[dict, dict]:
@@ -354,7 +369,11 @@ class VQBeTModel(nn.Module):
)
# Separate batch and sequence dims.
img_features = einops.rearrange(
img_features, "(b s n) ... -> b s n ...", b=batch_size, s=n_obs_steps, n=self.num_images
img_features,
"(b s n) ... -> b s n ...",
b=batch_size,
s=n_obs_steps,
n=self.num_images,
)
# Arrange prior and current observation step tokens as shown in the class docstring.
@@ -366,13 +385,19 @@ class VQBeTModel(nn.Module):
input_tokens.append(
self.state_projector(batch["observation.state"])
) # (batch, obs_step, projection dims)
input_tokens.append(einops.repeat(self.action_token, "1 1 d -> b n d", b=batch_size, n=n_obs_steps))
input_tokens.append(
einops.repeat(
self.action_token, "1 1 d -> b n d", b=batch_size, n=n_obs_steps
)
)
# Interleave tokens by stacking and rearranging.
input_tokens = torch.stack(input_tokens, dim=2)
input_tokens = einops.rearrange(input_tokens, "b n t d -> b (n t) d")
len_additional_action_token = self.config.n_action_pred_token - 1
future_action_tokens = self.action_token.repeat(batch_size, len_additional_action_token, 1)
future_action_tokens = self.action_token.repeat(
batch_size, len_additional_action_token, 1
)
# add additional action query tokens for predicting future action chunks
input_tokens = torch.cat([input_tokens, future_action_tokens], dim=1)
@@ -391,7 +416,11 @@ class VQBeTModel(nn.Module):
# Thus, it predicts a historical action sequence, in addition to current and future actions (predicting future actions : optional).
if len_additional_action_token > 0:
features = torch.cat(
[features[:, historical_act_pred_index], features[:, -len_additional_action_token:]], dim=1
[
features[:, historical_act_pred_index],
features[:, -len_additional_action_token:],
],
dim=1,
)
else:
features = features[:, historical_act_pred_index]
@@ -399,13 +428,15 @@ class VQBeTModel(nn.Module):
action_head_output = self.action_head(features)
# if rollout, VQ-BeT don't calculate loss
if rollout:
return action_head_output["predicted_action"][:, n_obs_steps - 1, :].reshape(
batch_size, self.config.action_chunk_size, -1
)
return action_head_output["predicted_action"][
:, n_obs_steps - 1, :
].reshape(batch_size, self.config.action_chunk_size, -1)
# else, it calculate overall loss (bin prediction loss, and offset loss)
else:
output = batch["action"][:, self.select_target_actions_indices]
loss = self.action_head.loss_fn(action_head_output, output, reduction="mean")
loss = self.action_head.loss_fn(
action_head_output, output, reduction="mean"
)
return action_head_output, loss
@@ -440,7 +471,9 @@ class VQBeTHead(nn.Module):
else:
self.map_to_cbet_preds_bin = MLP(
in_channels=config.gpt_output_dim,
hidden_channels=[self.vqvae_model.vqvae_num_layers * self.config.vqvae_n_embed],
hidden_channels=[
self.vqvae_model.vqvae_num_layers * self.config.vqvae_n_embed
],
)
self.map_to_cbet_preds_offset = MLP(
in_channels=config.gpt_output_dim,
@@ -467,7 +500,10 @@ class VQBeTHead(nn.Module):
loss, metric = self.vqvae_model.vqvae_forward(actions)
n_different_codes = sum(
[len(torch.unique(metric[2][:, i])) for i in range(self.vqvae_model.vqvae_num_layers)]
[
len(torch.unique(metric[2][:, i]))
for i in range(self.vqvae_model.vqvae_num_layers)
]
)
n_different_combinations = len(torch.unique(metric[2], dim=0))
recon_l1_error = metric[0].detach().cpu().item()
@@ -514,7 +550,13 @@ class VQBeTHead(nn.Module):
cbet_secondary_logits = self.map_to_cbet_preds_secondary_bin(
torch.cat(
(x, F.one_hot(sampled_primary_centers, num_classes=self.config.vqvae_n_embed)),
(
x,
F.one_hot(
sampled_primary_centers,
num_classes=self.config.vqvae_n_embed,
),
),
axis=1,
)
)
@@ -522,19 +564,29 @@ class VQBeTHead(nn.Module):
cbet_secondary_logits / self.config.bet_softmax_temperature, dim=-1
)
sampled_secondary_centers = einops.rearrange(
torch.multinomial(cbet_secondary_probs.view(-1, choices), num_samples=1),
torch.multinomial(
cbet_secondary_probs.view(-1, choices), num_samples=1
),
"(NT) 1 -> NT",
NT=NT,
)
sampled_centers = torch.stack((sampled_primary_centers, sampled_secondary_centers), axis=1)
cbet_logits = torch.stack([cbet_primary_logits, cbet_secondary_logits], dim=1)
sampled_centers = torch.stack(
(sampled_primary_centers, sampled_secondary_centers), axis=1
)
cbet_logits = torch.stack(
[cbet_primary_logits, cbet_secondary_logits], dim=1
)
# if self.config.sequentially_select is False, bin prediction head samples primary and secondary code at once.
else:
cbet_logits = self.map_to_cbet_preds_bin(x)
cbet_logits = einops.rearrange(
cbet_logits, "(NT) (G C) -> (NT) G C", G=self.vqvae_model.vqvae_num_layers
cbet_logits,
"(NT) (G C) -> (NT) G C",
G=self.vqvae_model.vqvae_num_layers,
)
cbet_probs = torch.softmax(
cbet_logits / self.config.bet_softmax_temperature, dim=-1
)
cbet_probs = torch.softmax(cbet_logits / self.config.bet_softmax_temperature, dim=-1)
NT, G, choices = cbet_probs.shape
sampled_centers = einops.rearrange(
torch.multinomial(cbet_probs.view(-1, choices), num_samples=1),
@@ -554,9 +606,17 @@ class VQBeTHead(nn.Module):
sampled_offsets = sampled_offsets.sum(dim=1)
with torch.no_grad():
# Get the centroids (= vectors corresponding to the codes) of each layer to pass it through RVQ decoder
return_decoder_input = self.vqvae_model.get_embeddings_from_code(sampled_centers).clone().detach()
return_decoder_input = (
self.vqvae_model.get_embeddings_from_code(sampled_centers)
.clone()
.detach()
)
# pass the centroids through decoder to get actions.
decoded_action = self.vqvae_model.get_action_from_latent(return_decoder_input).clone().detach()
decoded_action = (
self.vqvae_model.get_action_from_latent(return_decoder_input)
.clone()
.detach()
)
# reshaped extracted offset to match with decoded centroids
sampled_offsets = einops.rearrange(
sampled_offsets, "NT (W A) -> NT W A", W=self.config.action_chunk_size
@@ -605,7 +665,9 @@ class VQBeTHead(nn.Module):
# Figure out the loss for the actions.
# First, we need to find the closest cluster center for each ground truth action.
with torch.no_grad():
state_vq, action_bins = self.vqvae_model.get_code(action_seq) # action_bins: NT, G
state_vq, action_bins = self.vqvae_model.get_code(
action_seq
) # action_bins: NT, G
# Now we can compute the loss.
@@ -628,8 +690,12 @@ class VQBeTHead(nn.Module):
+ cbet_loss2 * self.config.secondary_code_loss_weight
)
equal_primary_code_rate = torch.sum((action_bins[:, 0] == sampled_centers[:, 0]).int()) / (NT)
equal_secondary_code_rate = torch.sum((action_bins[:, 1] == sampled_centers[:, 1]).int()) / (NT)
equal_primary_code_rate = torch.sum(
(action_bins[:, 0] == sampled_centers[:, 0]).int()
) / (NT)
equal_secondary_code_rate = torch.sum(
(action_bins[:, 1] == sampled_centers[:, 1]).int()
) / (NT)
action_mse_error = torch.mean((action_seq - predicted_action) ** 2)
vq_action_error = torch.mean(torch.abs(action_seq - decoded_action))
@@ -643,7 +709,9 @@ class VQBeTHead(nn.Module):
"classification_loss": cbet_loss.detach().cpu().item(),
"offset_loss": offset_loss.detach().cpu().item(),
"equal_primary_code_rate": equal_primary_code_rate.detach().cpu().item(),
"equal_secondary_code_rate": equal_secondary_code_rate.detach().cpu().item(),
"equal_secondary_code_rate": equal_secondary_code_rate.detach()
.cpu()
.item(),
"vq_action_error": vq_action_error.detach().cpu().item(),
"offset_action_error": offset_action_error.detach().cpu().item(),
"action_error_max": action_error_max.detach().cpu().item(),
@@ -668,7 +736,9 @@ class VQBeTRgbEncoder(nn.Module):
# Always use center crop for eval
self.center_crop = torchvision.transforms.CenterCrop(config.crop_shape)
if config.crop_is_random:
self.maybe_random_crop = torchvision.transforms.RandomCrop(config.crop_shape)
self.maybe_random_crop = torchvision.transforms.RandomCrop(
config.crop_shape
)
else:
self.maybe_random_crop = self.center_crop
else:
@@ -689,7 +759,9 @@ class VQBeTRgbEncoder(nn.Module):
self.backbone = _replace_submodules(
root_module=self.backbone,
predicate=lambda x: isinstance(x, nn.BatchNorm2d),
func=lambda x: nn.GroupNorm(num_groups=x.num_features // 16, num_channels=x.num_features),
func=lambda x: nn.GroupNorm(
num_groups=x.num_features // 16, num_channels=x.num_features
),
)
# Set up pooling and final layers.
@@ -730,7 +802,9 @@ class VQBeTRgbEncoder(nn.Module):
def _replace_submodules(
root_module: nn.Module, predicate: Callable[[nn.Module], bool], func: Callable[[nn.Module], nn.Module]
root_module: nn.Module,
predicate: Callable[[nn.Module], bool],
func: Callable[[nn.Module], nn.Module],
) -> nn.Module:
"""
Args:
@@ -743,7 +817,11 @@ def _replace_submodules(
if predicate(root_module):
return func(root_module)
replace_list = [k.split(".") for k, m in root_module.named_modules(remove_duplicate=True) if predicate(m)]
replace_list = [
k.split(".")
for k, m in root_module.named_modules(remove_duplicate=True)
if predicate(m)
]
for *parents, k in replace_list:
parent_module = root_module
if len(parents) > 0:
@@ -758,7 +836,9 @@ def _replace_submodules(
else:
setattr(parent_module, k, tgt_module)
# verify that all BN are replaced
assert not any(predicate(m) for _, m in root_module.named_modules(remove_duplicate=True))
assert not any(
predicate(m) for _, m in root_module.named_modules(remove_duplicate=True)
)
return root_module

184
lerobot/common/policies/vqbet/vqbet_utils.py
View File

@@ -123,9 +123,15 @@ class CausalSelfAttention(nn.Module):
# calculate query, key, values for all heads in batch and move head forward to be the batch dim
q, k, v = self.c_attn(x).split(self.gpt_hidden_dim, dim=2)
k = k.view(B, T, self.gpt_n_head, C // self.gpt_n_head).transpose(1, 2) # (B, nh, T, hs)
q = q.view(B, T, self.gpt_n_head, C // self.gpt_n_head).transpose(1, 2) # (B, nh, T, hs)
v = v.view(B, T, self.gpt_n_head, C // self.gpt_n_head).transpose(1, 2) # (B, nh, T, hs)
k = k.view(B, T, self.gpt_n_head, C // self.gpt_n_head).transpose(
1, 2
) # (B, nh, T, hs)
q = q.view(B, T, self.gpt_n_head, C // self.gpt_n_head).transpose(
1, 2
) # (B, nh, T, hs)
v = v.view(B, T, self.gpt_n_head, C // self.gpt_n_head).transpose(
1, 2
) # (B, nh, T, hs)
# causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
@@ -133,7 +139,9 @@ class CausalSelfAttention(nn.Module):
att = F.softmax(att, dim=-1)
att = self.attn_dropout(att)
y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
y = (
y.transpose(1, 2).contiguous().view(B, T, C)
) # re-assemble all head outputs side by side
# output projection
y = self.resid_dropout(self.c_proj(y))
@@ -189,12 +197,16 @@ class GPT(nn.Module):
"ln_f": nn.LayerNorm(config.gpt_hidden_dim),
}
)
self.lm_head = nn.Linear(config.gpt_hidden_dim, config.gpt_output_dim, bias=False)
self.lm_head = nn.Linear(
config.gpt_hidden_dim, config.gpt_output_dim, bias=False
)
# init all weights, and apply a special scaled init to the residual projections, per GPT-2 paper
self.apply(self._init_weights)
for pn, p in self.named_parameters():
if pn.endswith("c_proj.weight"):
torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.gpt_n_layer))
torch.nn.init.normal_(
p, mean=0.0, std=0.02 / math.sqrt(2 * config.gpt_n_layer)
)
# report number of parameters
n_params = sum(p.numel() for p in self.parameters())
@@ -208,11 +220,17 @@ class GPT(nn.Module):
)
# positional encodings that are added to the input embeddings
pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0) # shape (1, t)
pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(
0
) # shape (1, t)
# forward the GPT model itself
tok_emb = self.transformer.wte(input) # token embeddings of shape (b, t, gpt_hidden_dim)
pos_emb = self.transformer.wpe(pos) # position embeddings of shape (1, t, gpt_hidden_dim)
tok_emb = self.transformer.wte(
input
) # token embeddings of shape (b, t, gpt_hidden_dim)
pos_emb = self.transformer.wpe(
pos
) # position embeddings of shape (1, t, gpt_hidden_dim)
x = self.transformer.drop(tok_emb + pos_emb)
for block in self.transformer.h:
x = block(x)
@@ -237,7 +255,9 @@ class GPT(nn.Module):
# but want to use a smaller block size for some smaller, simpler model
assert gpt_block_size <= self.config.gpt_block_size
self.config.gpt_block_size = gpt_block_size
self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:gpt_block_size])
self.transformer.wpe.weight = nn.Parameter(
self.transformer.wpe.weight[:gpt_block_size]
)
for block in self.transformer.h:
block.attn.bias = block.attn.bias[:, :, :gpt_block_size, :gpt_block_size]
@@ -270,7 +290,9 @@ class GPT(nn.Module):
param_dict = dict(self.named_parameters())
inter_params = decay & no_decay
union_params = decay | no_decay
assert len(inter_params) == 0, "parameters {} made it into both decay/no_decay sets!".format(
assert (
len(inter_params) == 0
), "parameters {} made it into both decay/no_decay sets!".format(
str(inter_params)
)
assert len(param_dict.keys() - union_params) == 0, (
@@ -368,8 +390,12 @@ class ResidualVQ(nn.Module):
codebook_input_dim = codebook_dim * heads
requires_projection = codebook_input_dim != dim
self.project_in = nn.Linear(dim, codebook_input_dim) if requires_projection else nn.Identity()
self.project_out = nn.Linear(codebook_input_dim, dim) if requires_projection else nn.Identity()
self.project_in = (
nn.Linear(dim, codebook_input_dim) if requires_projection else nn.Identity()
)
self.project_out = (
nn.Linear(codebook_input_dim, dim) if requires_projection else nn.Identity()
)
self.num_quantizers = num_quantizers
@@ -377,7 +403,10 @@ class ResidualVQ(nn.Module):
self.layers = nn.ModuleList(
[
VectorQuantize(
dim=codebook_dim, codebook_dim=codebook_dim, accept_image_fmap=accept_image_fmap, **kwargs
dim=codebook_dim,
codebook_dim=codebook_dim,
accept_image_fmap=accept_image_fmap,
**kwargs,
)
for _ in range(num_quantizers)
]
@@ -448,7 +477,9 @@ class ResidualVQ(nn.Module):
return all_codes
def forward(self, x, indices=None, return_all_codes=False, sample_codebook_temp=None):
def forward(
self, x, indices=None, return_all_codes=False, sample_codebook_temp=None
):
"""
For given input tensor x, this function will return the quantized output, the indices of the quantized output, and the loss.
First, the input tensor x is projected to the codebook dimension. Then, the input tensor x is passed through Nq layers of VectorQuantize.
@@ -477,13 +508,17 @@ class ResidualVQ(nn.Module):
)
ce_losses = []
should_quantize_dropout = self.training and self.quantize_dropout and not return_loss
should_quantize_dropout = (
self.training and self.quantize_dropout and not return_loss
)
# sample a layer index at which to dropout further residual quantization
# also prepare null indices and loss
if should_quantize_dropout:
rand_quantize_dropout_index = randrange(self.quantize_dropout_cutoff_index, num_quant)
rand_quantize_dropout_index = randrange(
self.quantize_dropout_cutoff_index, num_quant
)
if quant_dropout_multiple_of != 1:
rand_quantize_dropout_index = (
@@ -492,14 +527,23 @@ class ResidualVQ(nn.Module):
- 1
)
null_indices_shape = (x.shape[0], *x.shape[-2:]) if self.accept_image_fmap else tuple(x.shape[:2])
null_indices = torch.full(null_indices_shape, -1.0, device=device, dtype=torch.long)
null_indices_shape = (
(x.shape[0], *x.shape[-2:])
if self.accept_image_fmap
else tuple(x.shape[:2])
)
null_indices = torch.full(
null_indices_shape, -1.0, device=device, dtype=torch.long
)
null_loss = torch.full((1,), 0.0, device=device, dtype=x.dtype)
# go through the layers
for quantizer_index, layer in enumerate(self.layers):
if should_quantize_dropout and quantizer_index > rand_quantize_dropout_index:
if (
should_quantize_dropout
and quantizer_index > rand_quantize_dropout_index
):
all_indices.append(null_indices)
all_losses.append(null_loss)
continue
@@ -539,7 +583,9 @@ class ResidualVQ(nn.Module):
# stack all losses and indices
all_losses, all_indices = map(partial(torch.stack, dim=-1), (all_losses, all_indices))
all_losses, all_indices = map(
partial(torch.stack, dim=-1), (all_losses, all_indices)
)
ret = (quantized_out, all_indices, all_losses)
@@ -599,8 +645,12 @@ class VectorQuantize(nn.Module):
codebook_input_dim = codebook_dim * heads
requires_projection = codebook_input_dim != dim
self.project_in = nn.Linear(dim, codebook_input_dim) if requires_projection else nn.Identity()
self.project_out = nn.Linear(codebook_input_dim, dim) if requires_projection else nn.Identity()
self.project_in = (
nn.Linear(dim, codebook_input_dim) if requires_projection else nn.Identity()
)
self.project_out = (
nn.Linear(codebook_input_dim, dim) if requires_projection else nn.Identity()
)
self.eps = eps
self.commitment_weight = commitment_weight
@@ -614,10 +664,14 @@ class VectorQuantize(nn.Module):
self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
assert not (ema_update and learnable_codebook), "learnable codebook not compatible with EMA update"
assert not (
ema_update and learnable_codebook
), "learnable codebook not compatible with EMA update"
assert 0 <= sync_update_v <= 1.0
assert not (sync_update_v > 0.0 and not learnable_codebook), "learnable codebook must be turned on"
assert not (
sync_update_v > 0.0 and not learnable_codebook
), "learnable codebook must be turned on"
self.sync_update_v = sync_update_v
@@ -629,7 +683,9 @@ class VectorQuantize(nn.Module):
)
if sync_codebook is None:
sync_codebook = distributed.is_initialized() and distributed.get_world_size() > 1
sync_codebook = (
distributed.is_initialized() and distributed.get_world_size() > 1
)
codebook_kwargs = {
"dim": codebook_dim,
@@ -794,11 +850,17 @@ class VectorQuantize(nn.Module):
# quantize again
quantize, embed_ind, distances = self._codebook(x, **codebook_forward_kwargs)
quantize, embed_ind, distances = self._codebook(
x, **codebook_forward_kwargs
)
if self.training:
# determine code to use for commitment loss
maybe_detach = torch.detach if not self.learnable_codebook or freeze_codebook else identity
maybe_detach = (
torch.detach
if not self.learnable_codebook or freeze_codebook
else identity
)
commit_quantize = maybe_detach(quantize)
@@ -808,7 +870,9 @@ class VectorQuantize(nn.Module):
if self.sync_update_v > 0.0:
# (21) in https://minyoungg.github.io/vqtorch/assets/draft_050523.pdf
quantize = quantize + self.sync_update_v * (quantize - quantize.detach())
quantize = quantize + self.sync_update_v * (
quantize - quantize.detach()
)
# function for calculating cross entropy loss to distance matrix
# used for (1) naturalspeech2 training residual vq latents to be close to the correct codes and (2) cross-entropy based commitment loss
@@ -841,7 +905,9 @@ class VectorQuantize(nn.Module):
embed_ind = rearrange(embed_ind, "1 (b h) n -> b n h", h=heads)
if self.accept_image_fmap:
embed_ind = rearrange(embed_ind, "b (h w) ... -> b h w ...", h=height, w=width)
embed_ind = rearrange(
embed_ind, "b (h w) ... -> b h w ...", h=height, w=width
)
if only_one:
embed_ind = rearrange(embed_ind, "b 1 -> b")
@@ -895,8 +961,12 @@ class VectorQuantize(nn.Module):
num_codes = codebook.shape[-2]
if (self.orthogonal_reg_max_codes is not None) and num_codes > self.orthogonal_reg_max_codes:
rand_ids = torch.randperm(num_codes, device=device)[: self.orthogonal_reg_max_codes]
if (
self.orthogonal_reg_max_codes is not None
) and num_codes > self.orthogonal_reg_max_codes:
rand_ids = torch.randperm(num_codes, device=device)[
: self.orthogonal_reg_max_codes
]
codebook = codebook[:, rand_ids]
orthogonal_reg_loss = orthogonal_loss_fn(codebook)
@@ -928,7 +998,9 @@ class VectorQuantize(nn.Module):
# if masking, only return quantized for where mask has True
if mask is not None:
quantize = torch.where(rearrange(mask, "... -> ... 1"), quantize, orig_input)
quantize = torch.where(
rearrange(mask, "... -> ... 1"), quantize, orig_input
)
return quantize, embed_ind, loss
@@ -1038,7 +1110,9 @@ def sample_vectors(samples, num):
def batched_sample_vectors(samples, num):
return torch.stack([sample_vectors(sample, num) for sample in samples.unbind(dim=0)], dim=0)
return torch.stack(
[sample_vectors(sample, num) for sample in samples.unbind(dim=0)], dim=0
)
def pad_shape(shape, size, dim=0):
@@ -1089,7 +1163,9 @@ def sample_vectors_distributed(local_samples, num):
all_num_samples = all_gather_sizes(local_samples, dim=0)
if rank == 0:
samples_per_rank = sample_multinomial(num, all_num_samples / all_num_samples.sum())
samples_per_rank = sample_multinomial(
num, all_num_samples / all_num_samples.sum()
)
else:
samples_per_rank = torch.empty_like(all_num_samples)
@@ -1202,7 +1278,9 @@ class EuclideanCodebook(nn.Module):
self.eps = eps
self.threshold_ema_dead_code = threshold_ema_dead_code
self.reset_cluster_size = (
reset_cluster_size if (reset_cluster_size is not None) else threshold_ema_dead_code
reset_cluster_size
if (reset_cluster_size is not None)
else threshold_ema_dead_code
)
assert callable(gumbel_sample)
@@ -1213,8 +1291,14 @@ class EuclideanCodebook(nn.Module):
"kmeans init is not compatible with multiple codebooks in distributed environment for now"
)
self.sample_fn = sample_vectors_distributed if use_ddp and sync_kmeans else batched_sample_vectors
self.kmeans_all_reduce_fn = distributed.all_reduce if use_ddp and sync_kmeans else noop
self.sample_fn = (
sample_vectors_distributed
if use_ddp and sync_kmeans
else batched_sample_vectors
)
self.kmeans_all_reduce_fn = (
distributed.all_reduce if use_ddp and sync_kmeans else noop
)
self.all_reduce_fn = distributed.all_reduce if use_ddp else noop
self.register_buffer("initted", torch.Tensor([not kmeans_init]))
@@ -1353,7 +1437,9 @@ class EuclideanCodebook(nn.Module):
distributed.all_reduce(variance_number)
batch_variance = variance_number / num_vectors
self.update_with_decay("batch_variance", batch_variance, self.affine_param_batch_decay)
self.update_with_decay(
"batch_variance", batch_variance, self.affine_param_batch_decay
)
def replace(self, batch_samples, batch_mask):
for ind, (samples, mask) in enumerate(
@@ -1362,7 +1448,9 @@ class EuclideanCodebook(nn.Module):
if not torch.any(mask):
continue
sampled = self.sample_fn(rearrange(samples, "... -> 1 ..."), mask.sum().item())
sampled = self.sample_fn(
rearrange(samples, "... -> 1 ..."), mask.sum().item()
)
sampled = rearrange(sampled, "1 ... -> ...")
self.embed.data[ind][mask] = sampled
@@ -1386,7 +1474,9 @@ class EuclideanCodebook(nn.Module):
def forward(self, x, sample_codebook_temp=None, mask=None, freeze_codebook=False):
needs_codebook_dim = x.ndim < 4
sample_codebook_temp = (
sample_codebook_temp if (sample_codebook_temp is not None) else self.sample_codebook_temp
sample_codebook_temp
if (sample_codebook_temp is not None)
else self.sample_codebook_temp
)
x = x.float()
@@ -1414,7 +1504,9 @@ class EuclideanCodebook(nn.Module):
if self.affine_param:
codebook_std = self.codebook_variance.clamp(min=1e-5).sqrt()
batch_std = self.batch_variance.clamp(min=1e-5).sqrt()
embed = (embed - self.codebook_mean) * (batch_std / codebook_std) + self.batch_mean
embed = (embed - self.codebook_mean) * (
batch_std / codebook_std
) + self.batch_mean
dist = -cdist(flatten, embed)
@@ -1432,7 +1524,9 @@ class EuclideanCodebook(nn.Module):
if self.training and self.ema_update and not freeze_codebook:
if self.affine_param:
flatten = (flatten - self.batch_mean) * (codebook_std / batch_std) + self.codebook_mean
flatten = (flatten - self.batch_mean) * (
codebook_std / batch_std
) + self.codebook_mean
if mask is not None:
embed_onehot[~mask] = 0.0
@@ -1455,7 +1549,9 @@ class EuclideanCodebook(nn.Module):
self.expire_codes_(x)
if needs_codebook_dim:
quantize, embed_ind = tuple(rearrange(t, "1 ... -> ...") for t in (quantize, embed_ind))
quantize, embed_ind = tuple(
rearrange(t, "1 ... -> ...") for t in (quantize, embed_ind)
)
dist = unpack_one(dist, ps, "h * d")

36
lerobot/common/robot_devices/cameras/intelrealsense.py
View File

@@ -79,7 +79,9 @@ def save_image(img_array, serial_number, frame_index, images_dir):
img.save(str(path), quality=100)
logging.info(f"Saved image: {path}")
except Exception as e:
logging.error(f"Failed to save image for camera {serial_number} frame {frame_index}: {e}")
logging.error(
f"Failed to save image for camera {serial_number} frame {frame_index}: {e}"
)
def save_images_from_cameras(
@@ -157,7 +159,9 @@ def save_images_from_cameras(
if time.perf_counter() - start_time > record_time_s:
break
print(f"Frame: {frame_index:04d}\tLatency (ms): {(time.perf_counter() - now) * 1000:.2f}")
print(
f"Frame: {frame_index:04d}\tLatency (ms): {(time.perf_counter() - now) * 1000:.2f}"
)
frame_index += 1
finally:
@@ -275,7 +279,9 @@ class IntelRealSenseCamera:
f"Multiple {name} cameras have been detected. Please use their serial number to instantiate them."
)
name_to_serial_dict = {cam["name"]: cam["serial_number"] for cam in camera_infos}
name_to_serial_dict = {
cam["name"]: cam["serial_number"] for cam in camera_infos
}
cam_sn = name_to_serial_dict[name]
return cam_sn
@@ -339,7 +345,9 @@ class IntelRealSenseCamera:
actual_height = color_profile.height()
# Using `math.isclose` since actual fps can be a float (e.g. 29.9 instead of 30)
if self.fps is not None and not math.isclose(self.fps, actual_fps, rel_tol=1e-3):
if self.fps is not None and not math.isclose(
self.fps, actual_fps, rel_tol=1e-3
):
# Using `OSError` since it's a broad that encompasses issues related to device communication
raise OSError(
f"Can't set {self.fps=} for IntelRealSenseCamera({self.serial_number}). Actual value is {actual_fps}."
@@ -359,7 +367,9 @@ class IntelRealSenseCamera:
self.is_connected = True
def read(self, temporary_color: str | None = None) -> np.ndarray | tuple[np.ndarray, np.ndarray]:
def read(
self, temporary_color: str | None = None
) -> np.ndarray | tuple[np.ndarray, np.ndarray]:
"""Read a frame from the camera returned in the format height x width x channels (e.g. 480 x 640 x 3)
of type `np.uint8`, contrarily to the pytorch format which is float channel first.
@@ -386,11 +396,15 @@ class IntelRealSenseCamera:
color_frame = frame.get_color_frame()
if not color_frame:
raise OSError(f"Can't capture color image from IntelRealSenseCamera({self.serial_number}).")
raise OSError(
f"Can't capture color image from IntelRealSenseCamera({self.serial_number})."
)
color_image = np.asanyarray(color_frame.get_data())
requested_color_mode = self.color_mode if temporary_color is None else temporary_color
requested_color_mode = (
self.color_mode if temporary_color is None else temporary_color
)
if requested_color_mode not in ["rgb", "bgr"]:
raise ValueError(
f"Expected color values are 'rgb' or 'bgr', but {requested_color_mode} is provided."
@@ -418,7 +432,9 @@ class IntelRealSenseCamera:
if self.use_depth:
depth_frame = frame.get_depth_frame()
if not depth_frame:
raise OSError(f"Can't capture depth image from IntelRealSenseCamera({self.serial_number}).")
raise OSError(
f"Can't capture depth image from IntelRealSenseCamera({self.serial_number})."
)
depth_map = np.asanyarray(depth_frame.get_data())
@@ -460,7 +476,9 @@ class IntelRealSenseCamera:
# TODO(rcadene, aliberts): intelrealsense has diverged compared to opencv over here
num_tries += 1
time.sleep(1 / self.fps)
if num_tries > self.fps and (self.thread.ident is None or not self.thread.is_alive()):
if num_tries > self.fps and (
self.thread.ident is None or not self.thread.is_alive()
):
raise Exception(
"The thread responsible for `self.async_read()` took too much time to start. There might be an issue. Verify that `self.thread.start()` has been called."
)

28
lerobot/common/robot_devices/cameras/opencv.py
View File

@@ -45,10 +45,14 @@ from lerobot.common.utils.utils import capture_timestamp_utc
MAX_OPENCV_INDEX = 60
def find_cameras(raise_when_empty=False, max_index_search_range=MAX_OPENCV_INDEX, mock=False) -> list[dict]:
def find_cameras(
raise_when_empty=False, max_index_search_range=MAX_OPENCV_INDEX, mock=False
) -> list[dict]:
cameras = []
if platform.system() == "Linux":
print("Linux detected. Finding available camera indices through scanning '/dev/video*' ports")
print(
"Linux detected. Finding available camera indices through scanning '/dev/video*' ports"
)
possible_ports = [str(port) for port in Path("/dev").glob("video*")]
ports = _find_cameras(possible_ports, mock=mock)
for port in ports:
@@ -180,7 +184,9 @@ def save_images_from_cameras(
dt_s = time.perf_counter() - now
busy_wait(1 / fps - dt_s)
print(f"Frame: {frame_index:04d}\tLatency (ms): {(time.perf_counter() - now) * 1000:.2f}")
print(
f"Frame: {frame_index:04d}\tLatency (ms): {(time.perf_counter() - now) * 1000:.2f}"
)
if time.perf_counter() - start_time > record_time_s:
break
@@ -237,7 +243,9 @@ class OpenCVCamera:
if platform.system() == "Linux":
if isinstance(self.camera_index, int):
self.port = Path(f"/dev/video{self.camera_index}")
elif isinstance(self.camera_index, str) and is_valid_unix_path(self.camera_index):
elif isinstance(self.camera_index, str) and is_valid_unix_path(
self.camera_index
):
self.port = Path(self.camera_index)
# Retrieve the camera index from a potentially symlinked path
self.camera_index = get_camera_index_from_unix_port(self.port)
@@ -283,7 +291,9 @@ class OpenCVCamera:
def connect(self):
if self.is_connected:
raise RobotDeviceAlreadyConnectedError(f"OpenCVCamera({self.camera_index}) is already connected.")
raise RobotDeviceAlreadyConnectedError(
f"OpenCVCamera({self.camera_index}) is already connected."
)
if self.mock:
import tests.cameras.mock_cv2 as cv2
@@ -344,7 +354,9 @@ class OpenCVCamera:
actual_height = self.camera.get(cv2.CAP_PROP_FRAME_HEIGHT)
# Using `math.isclose` since actual fps can be a float (e.g. 29.9 instead of 30)
if self.fps is not None and not math.isclose(self.fps, actual_fps, rel_tol=1e-3):
if self.fps is not None and not math.isclose(
self.fps, actual_fps, rel_tol=1e-3
):
# Using `OSError` since it's a broad that encompasses issues related to device communication
raise OSError(
f"Can't set {self.fps=} for OpenCVCamera({self.camera_index}). Actual value is {actual_fps}."
@@ -386,7 +398,9 @@ class OpenCVCamera:
if not ret:
raise OSError(f"Can't capture color image from camera {self.camera_index}.")
requested_color_mode = self.color_mode if temporary_color_mode is None else temporary_color_mode
requested_color_mode = (
self.color_mode if temporary_color_mode is None else temporary_color_mode
)
if requested_color_mode not in ["rgb", "bgr"]:
raise ValueError(

33
lerobot/common/robot_devices/control_utils.py
View File

@@ -39,7 +39,9 @@ from lerobot.common.robot_devices.utils import busy_wait
from lerobot.common.utils.utils import get_safe_torch_device, has_method
def log_control_info(robot: Robot, dt_s, episode_index=None, frame_index=None, fps=None):
def log_control_info(
robot: Robot, dt_s, episode_index=None, frame_index=None, fps=None
):
log_items = []
if episode_index is not None:
log_items.append(f"ep:{episode_index}")
@@ -106,7 +108,9 @@ def predict_action(observation, policy, device, use_amp):
observation = copy(observation)
with (
torch.inference_mode(),
torch.autocast(device_type=device.type) if device.type == "cuda" and use_amp else nullcontext(),
torch.autocast(device_type=device.type)
if device.type == "cuda" and use_amp
else nullcontext(),
):
# Convert to pytorch format: channel first and float32 in [0,1] with batch dimension
for name in observation:
@@ -162,7 +166,9 @@ def init_keyboard_listener(assign_rewards=False):
print("Right arrow key pressed. Exiting loop...")
events["exit_early"] = True
elif key == keyboard.Key.left:
print("Left arrow key pressed. Exiting loop and rerecord the last episode...")
print(
"Left arrow key pressed. Exiting loop and rerecord the last episode..."
)
events["rerecord_episode"] = True
events["exit_early"] = True
elif key == keyboard.Key.esc:
@@ -256,7 +262,9 @@ def control_loop(
raise ValueError("You need to provide a task as argument in `single_task`.")
if dataset is not None and fps is not None and dataset.fps != fps:
raise ValueError(f"The dataset fps should be equal to requested fps ({dataset['fps']} != {fps}).")
raise ValueError(
f"The dataset fps should be equal to requested fps ({dataset['fps']} != {fps})."
)
timestamp = 0
start_episode_t = time.perf_counter()
@@ -291,7 +299,9 @@ def control_loop(
if display_cameras and not is_headless():
image_keys = [key for key in observation if "image" in key]
for key in image_keys:
cv2.imshow(key, cv2.cvtColor(observation[key].numpy(), cv2.COLOR_RGB2BGR))
cv2.imshow(
key, cv2.cvtColor(observation[key].numpy(), cv2.COLOR_RGB2BGR)
)
cv2.waitKey(1)
if fps is not None:
@@ -361,7 +371,11 @@ def sanity_check_dataset_name(repo_id, policy_cfg):
def sanity_check_dataset_robot_compatibility(
dataset: LeRobotDataset, robot: Robot, fps: int, use_videos: bool, extra_features: dict = None
dataset: LeRobotDataset,
robot: Robot,
fps: int,
use_videos: bool,
extra_features: dict = None,
) -> None:
features_from_robot = get_features_from_robot(robot, use_videos)
if extra_features is not None:
@@ -375,11 +389,14 @@ def sanity_check_dataset_robot_compatibility(
mismatches = []
for field, dataset_value, present_value in fields:
diff = DeepDiff(dataset_value, present_value, exclude_regex_paths=[r".*\['info'\]$"])
diff = DeepDiff(
dataset_value, present_value, exclude_regex_paths=[r".*\['info'\]$"]
)
if diff:
mismatches.append(f"{field}: expected {present_value}, got {dataset_value}")
if mismatches:
raise ValueError(
"Dataset metadata compatibility check failed with mismatches:\n" + "\n".join(mismatches)
"Dataset metadata compatibility check failed with mismatches:\n"
+ "\n".join(mismatches)
)

87
lerobot/common/robot_devices/motors/dynamixel.py
View File

@@ -158,7 +158,9 @@ NUM_READ_RETRY = 10
NUM_WRITE_RETRY = 10
def convert_degrees_to_steps(degrees: float | np.ndarray, models: str | list[str]) -> np.ndarray:
def convert_degrees_to_steps(
degrees: float | np.ndarray, models: str | list[str]
) -> np.ndarray:
"""This function converts the degree range to the step range for indicating motors rotation.
It assumes a motor achieves a full rotation by going from -180 degree position to +180.
The motor resolution (e.g. 4096) corresponds to the number of steps needed to achieve a full rotation.
@@ -384,7 +386,9 @@ class DynamixelMotorsBus:
indices = []
for idx in tqdm.tqdm(possible_ids):
try:
present_idx = self.read_with_motor_ids(self.motor_models, [idx], "ID", num_retry=num_retry)[0]
present_idx = self.read_with_motor_ids(
self.motor_models, [idx], "ID", num_retry=num_retry
)[0]
except ConnectionError:
continue
@@ -400,7 +404,9 @@ class DynamixelMotorsBus:
def set_bus_baudrate(self, baudrate):
present_bus_baudrate = self.port_handler.getBaudRate()
if present_bus_baudrate != baudrate:
print(f"Setting bus baud rate to {baudrate}. Previously {present_bus_baudrate}.")
print(
f"Setting bus baud rate to {baudrate}. Previously {present_bus_baudrate}."
)
self.port_handler.setBaudRate(baudrate)
if self.port_handler.getBaudRate() != baudrate:
@@ -421,7 +427,9 @@ class DynamixelMotorsBus:
def set_calibration(self, calibration: dict[str, list]):
self.calibration = calibration
def apply_calibration_autocorrect(self, values: np.ndarray | list, motor_names: list[str] | None):
def apply_calibration_autocorrect(
self, values: np.ndarray | list, motor_names: list[str] | None
):
"""This function applies the calibration, automatically detects out of range errors for motors values and attempts to correct.
For more info, see docstring of `apply_calibration` and `autocorrect_calibration`.
@@ -434,7 +442,9 @@ class DynamixelMotorsBus:
values = self.apply_calibration(values, motor_names)
return values
def apply_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
def apply_calibration(
self, values: np.ndarray | list, motor_names: list[str] | None
):
"""Convert from unsigned int32 joint position range [0, 2**32[ to the universal float32 nominal degree range ]-180.0, 180.0[ with
a "zero position" at 0 degree.
@@ -509,7 +519,9 @@ class DynamixelMotorsBus:
return values
def autocorrect_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
def autocorrect_calibration(
self, values: np.ndarray | list, motor_names: list[str] | None
):
"""This function automatically detects issues with values of motors after calibration, and correct for these issues.
Some motors might have values outside of expected maximum bounds after calibration.
@@ -551,15 +563,23 @@ class DynamixelMotorsBus:
values[i] *= -1
# Convert from initial range to range [-180, 180] degrees
calib_val = (values[i] + homing_offset) / (resolution // 2) * HALF_TURN_DEGREE
in_range = (calib_val > LOWER_BOUND_DEGREE) and (calib_val < UPPER_BOUND_DEGREE)
calib_val = (
(values[i] + homing_offset) / (resolution // 2) * HALF_TURN_DEGREE
)
in_range = (calib_val > LOWER_BOUND_DEGREE) and (
calib_val < UPPER_BOUND_DEGREE
)
# Solve this inequality to find the factor to shift the range into [-180, 180] degrees
# values[i] = (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE
# - HALF_TURN_DEGREE <= (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE <= HALF_TURN_DEGREE
# (- (resolution // 2) - values[i] - homing_offset) / resolution <= factor <= ((resolution // 2) - values[i] - homing_offset) / resolution
low_factor = (-(resolution // 2) - values[i] - homing_offset) / resolution
upp_factor = ((resolution // 2) - values[i] - homing_offset) / resolution
low_factor = (
-(resolution // 2) - values[i] - homing_offset
) / resolution
upp_factor = (
(resolution // 2) - values[i] - homing_offset
) / resolution
elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
start_pos = self.calibration["start_pos"][calib_idx]
@@ -567,7 +587,9 @@ class DynamixelMotorsBus:
# Convert from initial range to range [0, 100] in %
calib_val = (values[i] - start_pos) / (end_pos - start_pos) * 100
in_range = (calib_val > LOWER_BOUND_LINEAR) and (calib_val < UPPER_BOUND_LINEAR)
in_range = (calib_val > LOWER_BOUND_LINEAR) and (
calib_val < UPPER_BOUND_LINEAR
)
# Solve this inequality to find the factor to shift the range into [0, 100] %
# values[i] = (values[i] - start_pos + resolution * factor) / (end_pos + resolution * factor - start_pos - resolution * factor) * 100
@@ -583,19 +605,27 @@ class DynamixelMotorsBus:
factor = math.ceil(low_factor)
if factor > upp_factor:
raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
raise ValueError(
f"No integer found between bounds [{low_factor=}, {upp_factor=}]"
)
else:
factor = math.ceil(upp_factor)
if factor > low_factor:
raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
raise ValueError(
f"No integer found between bounds [{low_factor=}, {upp_factor=}]"
)
if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
out_of_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
in_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
out_of_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
in_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
out_of_range_str = (
f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
)
in_range_str = (
f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
)
logging.warning(
f"Auto-correct calibration of motor '{name}' by shifting value by {abs(factor)} full turns, "
@@ -605,7 +635,9 @@ class DynamixelMotorsBus:
# A full turn corresponds to 360 degrees but also to 4096 steps for a motor resolution of 4096.
self.calibration["homing_offset"][calib_idx] += resolution * factor
def revert_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
def revert_calibration(
self, values: np.ndarray | list, motor_names: list[str] | None
):
"""Inverse of `apply_calibration`."""
if motor_names is None:
motor_names = self.motor_names
@@ -644,7 +676,9 @@ class DynamixelMotorsBus:
values = np.round(values).astype(np.int32)
return values
def read_with_motor_ids(self, motor_models, motor_ids, data_name, num_retry=NUM_READ_RETRY):
def read_with_motor_ids(
self, motor_models, motor_ids, data_name, num_retry=NUM_READ_RETRY
):
if self.mock:
import tests.motors.mock_dynamixel_sdk as dxl
else:
@@ -746,7 +780,9 @@ class DynamixelMotorsBus:
values = self.apply_calibration_autocorrect(values, motor_names)
# log the number of seconds it took to read the data from the motors
delta_ts_name = get_log_name("delta_timestamp_s", "read", data_name, motor_names)
delta_ts_name = get_log_name(
"delta_timestamp_s", "read", data_name, motor_names
)
self.logs[delta_ts_name] = time.perf_counter() - start_time
# log the utc time at which the data was received
@@ -755,7 +791,9 @@ class DynamixelMotorsBus:
return values
def write_with_motor_ids(self, motor_models, motor_ids, data_name, values, num_retry=NUM_WRITE_RETRY):
def write_with_motor_ids(
self, motor_models, motor_ids, data_name, values, num_retry=NUM_WRITE_RETRY
):
if self.mock:
import tests.motors.mock_dynamixel_sdk as dxl
else:
@@ -784,7 +822,12 @@ class DynamixelMotorsBus:
f"{self.packet_handler.getTxRxResult(comm)}"
)
def write(self, data_name, values: int | float | np.ndarray, motor_names: str | list[str] | None = None):
def write(
self,
data_name,
values: int | float | np.ndarray,
motor_names: str | list[str] | None = None,
):
if not self.is_connected:
raise RobotDeviceNotConnectedError(
f"DynamixelMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
@@ -845,7 +888,9 @@ class DynamixelMotorsBus:
)
# log the number of seconds it took to write the data to the motors
delta_ts_name = get_log_name("delta_timestamp_s", "write", data_name, motor_names)
delta_ts_name = get_log_name(
"delta_timestamp_s", "write", data_name, motor_names
)
self.logs[delta_ts_name] = time.perf_counter() - start_time
# TODO(rcadene): should we log the time before sending the write command?

87
lerobot/common/robot_devices/motors/feetech.py
View File

@@ -137,7 +137,9 @@ NUM_READ_RETRY = 20
NUM_WRITE_RETRY = 20
def convert_degrees_to_steps(degrees: float | np.ndarray, models: str | list[str]) -> np.ndarray:
def convert_degrees_to_steps(
degrees: float | np.ndarray, models: str | list[str]
) -> np.ndarray:
"""This function converts the degree range to the step range for indicating motors rotation.
It assumes a motor achieves a full rotation by going from -180 degree position to +180.
The motor resolution (e.g. 4096) corresponds to the number of steps needed to achieve a full rotation.
@@ -365,7 +367,9 @@ class FeetechMotorsBus:
indices = []
for idx in tqdm.tqdm(possible_ids):
try:
present_idx = self.read_with_motor_ids(self.motor_models, [idx], "ID", num_retry=num_retry)[0]
present_idx = self.read_with_motor_ids(
self.motor_models, [idx], "ID", num_retry=num_retry
)[0]
except ConnectionError:
continue
@@ -381,7 +385,9 @@ class FeetechMotorsBus:
def set_bus_baudrate(self, baudrate):
present_bus_baudrate = self.port_handler.getBaudRate()
if present_bus_baudrate != baudrate:
print(f"Setting bus baud rate to {baudrate}. Previously {present_bus_baudrate}.")
print(
f"Setting bus baud rate to {baudrate}. Previously {present_bus_baudrate}."
)
self.port_handler.setBaudRate(baudrate)
if self.port_handler.getBaudRate() != baudrate:
@@ -402,7 +408,9 @@ class FeetechMotorsBus:
def set_calibration(self, calibration: dict[str, list]):
self.calibration = calibration
def apply_calibration_autocorrect(self, values: np.ndarray | list, motor_names: list[str] | None):
def apply_calibration_autocorrect(
self, values: np.ndarray | list, motor_names: list[str] | None
):
"""This function apply the calibration, automatically detects out of range errors for motors values and attempt to correct.
For more info, see docstring of `apply_calibration` and `autocorrect_calibration`.
@@ -415,7 +423,9 @@ class FeetechMotorsBus:
values = self.apply_calibration(values, motor_names)
return values
def apply_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
def apply_calibration(
self, values: np.ndarray | list, motor_names: list[str] | None
):
"""Convert from unsigned int32 joint position range [0, 2**32[ to the universal float32 nominal degree range ]-180.0, 180.0[ with
a "zero position" at 0 degree.
@@ -489,7 +499,9 @@ class FeetechMotorsBus:
return values
def autocorrect_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
def autocorrect_calibration(
self, values: np.ndarray | list, motor_names: list[str] | None
):
"""This function automatically detects issues with values of motors after calibration, and correct for these issues.
Some motors might have values outside of expected maximum bounds after calibration.
@@ -528,18 +540,26 @@ class FeetechMotorsBus:
values[i] *= -1
# Convert from initial range to range [-180, 180] degrees
calib_val = (values[i] + homing_offset) / (resolution // 2) * HALF_TURN_DEGREE
in_range = (calib_val > LOWER_BOUND_DEGREE) and (calib_val < UPPER_BOUND_DEGREE)
calib_val = (
(values[i] + homing_offset) / (resolution // 2) * HALF_TURN_DEGREE
)
in_range = (calib_val > LOWER_BOUND_DEGREE) and (
calib_val < UPPER_BOUND_DEGREE
)
# Solve this inequality to find the factor to shift the range into [-180, 180] degrees
# values[i] = (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE
# - HALF_TURN_DEGREE <= (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE <= HALF_TURN_DEGREE
# (- HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2) - values[i] - homing_offset) / resolution <= factor <= (HALF_TURN_DEGREE / 180 * (resolution // 2) - values[i] - homing_offset) / resolution
low_factor = (
-HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2) - values[i] - homing_offset
-HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2)
- values[i]
- homing_offset
) / resolution
upp_factor = (
HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2) - values[i] - homing_offset
HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2)
- values[i]
- homing_offset
) / resolution
elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
@@ -548,7 +568,9 @@ class FeetechMotorsBus:
# Convert from initial range to range [0, 100] in %
calib_val = (values[i] - start_pos) / (end_pos - start_pos) * 100
in_range = (calib_val > LOWER_BOUND_LINEAR) and (calib_val < UPPER_BOUND_LINEAR)
in_range = (calib_val > LOWER_BOUND_LINEAR) and (
calib_val < UPPER_BOUND_LINEAR
)
# Solve this inequality to find the factor to shift the range into [0, 100] %
# values[i] = (values[i] - start_pos + resolution * factor) / (end_pos + resolution * factor - start_pos - resolution * factor) * 100
@@ -564,19 +586,27 @@ class FeetechMotorsBus:
factor = math.ceil(low_factor)
if factor > upp_factor:
raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
raise ValueError(
f"No integer found between bounds [{low_factor=}, {upp_factor=}]"
)
else:
factor = math.ceil(upp_factor)
if factor > low_factor:
raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
raise ValueError(
f"No integer found between bounds [{low_factor=}, {upp_factor=}]"
)
if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
out_of_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
in_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
out_of_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
in_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
out_of_range_str = (
f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
)
in_range_str = (
f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
)
logging.warning(
f"Auto-correct calibration of motor '{name}' by shifting value by {abs(factor)} full turns, "
@@ -586,7 +616,9 @@ class FeetechMotorsBus:
# A full turn corresponds to 360 degrees but also to 4096 steps for a motor resolution of 4096.
self.calibration["homing_offset"][calib_idx] += resolution * factor
def revert_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
def revert_calibration(
self, values: np.ndarray | list, motor_names: list[str] | None
):
"""Inverse of `apply_calibration`."""
if motor_names is None:
motor_names = self.motor_names
@@ -662,7 +694,9 @@ class FeetechMotorsBus:
return values
def read_with_motor_ids(self, motor_models, motor_ids, data_name, num_retry=NUM_READ_RETRY):
def read_with_motor_ids(
self, motor_models, motor_ids, data_name, num_retry=NUM_READ_RETRY
):
if self.mock:
import tests.motors.mock_scservo_sdk as scs
else:
@@ -771,7 +805,9 @@ class FeetechMotorsBus:
values = self.apply_calibration_autocorrect(values, motor_names)
# log the number of seconds it took to read the data from the motors
delta_ts_name = get_log_name("delta_timestamp_s", "read", data_name, motor_names)
delta_ts_name = get_log_name(
"delta_timestamp_s", "read", data_name, motor_names
)
self.logs[delta_ts_name] = time.perf_counter() - start_time
# log the utc time at which the data was received
@@ -780,7 +816,9 @@ class FeetechMotorsBus:
return values
def write_with_motor_ids(self, motor_models, motor_ids, data_name, values, num_retry=NUM_WRITE_RETRY):
def write_with_motor_ids(
self, motor_models, motor_ids, data_name, values, num_retry=NUM_WRITE_RETRY
):
if self.mock:
import tests.motors.mock_scservo_sdk as scs
else:
@@ -809,7 +847,12 @@ class FeetechMotorsBus:
f"{self.packet_handler.getTxRxResult(comm)}"
)
def write(self, data_name, values: int | float | np.ndarray, motor_names: str | list[str] | None = None):
def write(
self,
data_name,
values: int | float | np.ndarray,
motor_names: str | list[str] | None = None,
):
if not self.is_connected:
raise RobotDeviceNotConnectedError(
f"FeetechMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
@@ -870,7 +913,9 @@ class FeetechMotorsBus:
)
# log the number of seconds it took to write the data to the motors
delta_ts_name = get_log_name("delta_timestamp_s", "write", data_name, motor_names)
delta_ts_name = get_log_name(
"delta_timestamp_s", "write", data_name, motor_names
)
self.logs[delta_ts_name] = time.perf_counter() - start_time
# TODO(rcadene): should we log the time before sending the write command?

33
lerobot/common/robot_devices/robots/dynamixel_calibration.py
View File

@@ -24,9 +24,7 @@ from lerobot.common.robot_devices.motors.dynamixel import (
)
from lerobot.common.robot_devices.motors.utils import MotorsBus
URL_TEMPLATE = (
"https://raw.githubusercontent.com/huggingface/lerobot/main/media/{robot}/{arm}_{position}.webp"
)
URL_TEMPLATE = "https://raw.githubusercontent.com/huggingface/lerobot/main/media/{robot}/{arm}_{position}.webp"
# The following positions are provided in nominal degree range ]-180, +180[
# For more info on these constants, see comments in the code where they get used.
@@ -37,7 +35,9 @@ ROTATED_POSITION_DEGREE = 90
def assert_drive_mode(drive_mode):
# `drive_mode` is in [0,1] with 0 means original rotation direction for the motor, and 1 means inverted.
if not np.all(np.isin(drive_mode, [0, 1])):
raise ValueError(f"`drive_mode` contains values other than 0 or 1: ({drive_mode})")
raise ValueError(
f"`drive_mode` contains values other than 0 or 1: ({drive_mode})"
)
def apply_drive_mode(position, drive_mode):
@@ -78,12 +78,16 @@ def run_arm_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type
```
"""
if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
raise ValueError("To run calibration, the torque must be disabled on all motors.")
raise ValueError(
"To run calibration, the torque must be disabled on all motors."
)
print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
print("\nMove arm to zero position")
print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="zero"))
print(
"See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="zero")
)
input("Press Enter to continue...")
# We arbitrarily chose our zero target position to be a straight horizontal position with gripper upwards and closed.
@@ -104,10 +108,15 @@ def run_arm_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type
# corresponds to opening the gripper. When the rotation direction is ambiguous, we arbitrarily rotate clockwise from the point of view
# of the previous motor in the kinetic chain.
print("\nMove arm to rotated target position")
print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rotated"))
print(
"See: "
+ URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rotated")
)
input("Press Enter to continue...")
rotated_target_pos = convert_degrees_to_steps(ROTATED_POSITION_DEGREE, arm.motor_models)
rotated_target_pos = convert_degrees_to_steps(
ROTATED_POSITION_DEGREE, arm.motor_models
)
# Find drive mode by rotating each motor by a quarter of a turn.
# Drive mode indicates if the motor rotation direction should be inverted (=1) or not (=0).
@@ -116,11 +125,15 @@ def run_arm_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type
# Re-compute homing offset to take into account drive mode
rotated_drived_pos = apply_drive_mode(rotated_pos, drive_mode)
rotated_nearest_pos = compute_nearest_rounded_position(rotated_drived_pos, arm.motor_models)
rotated_nearest_pos = compute_nearest_rounded_position(
rotated_drived_pos, arm.motor_models
)
homing_offset = rotated_target_pos - rotated_nearest_pos
print("\nMove arm to rest position")
print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rest"))
print(
"See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rest")
)
input("Press Enter to continue...")
print()

112
lerobot/common/robot_devices/robots/feetech_calibration.py
View File

@@ -26,9 +26,7 @@ from lerobot.common.robot_devices.motors.feetech import (
)
from lerobot.common.robot_devices.motors.utils import MotorsBus
URL_TEMPLATE = (
"https://raw.githubusercontent.com/huggingface/lerobot/main/media/{robot}/{arm}_{position}.webp"
)
URL_TEMPLATE = "https://raw.githubusercontent.com/huggingface/lerobot/main/media/{robot}/{arm}_{position}.webp"
# The following positions are provided in nominal degree range ]-180, +180[
# For more info on these constants, see comments in the code where they get used.
@@ -39,7 +37,9 @@ ROTATED_POSITION_DEGREE = 90
def assert_drive_mode(drive_mode):
# `drive_mode` is in [0,1] with 0 means original rotation direction for the motor, and 1 means inverted.
if not np.all(np.isin(drive_mode, [0, 1])):
raise ValueError(f"`drive_mode` contains values other than 0 or 1: ({drive_mode})")
raise ValueError(
f"`drive_mode` contains values other than 0 or 1: ({drive_mode})"
)
def apply_drive_mode(position, drive_mode):
@@ -140,7 +140,9 @@ def apply_offset(calib, offset):
return calib
def run_arm_auto_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
def run_arm_auto_calibration(
arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str
):
if robot_type == "so100":
return run_arm_auto_calibration_so100(arm, robot_type, arm_name, arm_type)
elif robot_type == "moss":
@@ -149,18 +151,27 @@ def run_arm_auto_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm
raise ValueError(robot_type)
def run_arm_auto_calibration_so100(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
def run_arm_auto_calibration_so100(
arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str
):
"""All the offsets and magic numbers are hand tuned, and are unique to SO-100 follower arms"""
if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
raise ValueError("To run calibration, the torque must be disabled on all motors.")
raise ValueError(
"To run calibration, the torque must be disabled on all motors."
)
if not (robot_type == "so100" and arm_type == "follower"):
raise NotImplementedError("Auto calibration only supports the follower of so100 arms for now.")
raise NotImplementedError(
"Auto calibration only supports the follower of so100 arms for now."
)
print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
print("\nMove arm to initial position")
print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="initial"))
print(
"See: "
+ URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="initial")
)
input("Press Enter to continue...")
# Lower the acceleration of the motors (in [0,254])
@@ -207,11 +218,16 @@ def run_arm_auto_calibration_so100(arm: MotorsBus, robot_type: str, arm_name: st
print("Calibrate elbow_flex")
calib["elbow_flex"] = move_to_calibrate(
arm, "elbow_flex", positive_first=False, in_between_move_hook=in_between_move_hook
arm,
"elbow_flex",
positive_first=False,
in_between_move_hook=in_between_move_hook,
)
calib["elbow_flex"] = apply_offset(calib["elbow_flex"], offset=80 - 1024)
arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] + 1024 + 512, "elbow_flex")
arm.write(
"Goal_Position", calib["elbow_flex"]["zero_pos"] + 1024 + 512, "elbow_flex"
)
time.sleep(1)
def in_between_move_hook():
@@ -239,18 +255,30 @@ def run_arm_auto_calibration_so100(arm: MotorsBus, robot_type: str, arm_name: st
}
arm.write("Goal_Position", list(positions.values()), list(positions.keys()))
arm.write("Goal_Position", round(calib["shoulder_lift"]["zero_pos"] - 1600), "shoulder_lift")
arm.write(
"Goal_Position",
round(calib["shoulder_lift"]["zero_pos"] - 1600),
"shoulder_lift",
)
time.sleep(2)
arm.write("Goal_Position", round(calib["elbow_flex"]["zero_pos"] + 1700), "elbow_flex")
arm.write(
"Goal_Position", round(calib["elbow_flex"]["zero_pos"] + 1700), "elbow_flex"
)
time.sleep(2)
arm.write("Goal_Position", round(calib["wrist_flex"]["zero_pos"] + 800), "wrist_flex")
arm.write(
"Goal_Position", round(calib["wrist_flex"]["zero_pos"] + 800), "wrist_flex"
)
time.sleep(2)
arm.write("Goal_Position", round(calib["gripper"]["end_pos"]), "gripper")
time.sleep(2)
print("Calibrate wrist_roll")
calib["wrist_roll"] = move_to_calibrate(
arm, "wrist_roll", invert_drive_mode=True, positive_first=False, while_move_hook=while_move_hook
arm,
"wrist_roll",
invert_drive_mode=True,
positive_first=False,
while_move_hook=while_move_hook,
)
arm.write("Goal_Position", calib["wrist_roll"]["zero_pos"], "wrist_roll")
@@ -260,7 +288,9 @@ def run_arm_auto_calibration_so100(arm: MotorsBus, robot_type: str, arm_name: st
arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"], "wrist_flex")
time.sleep(1)
arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] + 2048, "elbow_flex")
arm.write("Goal_Position", calib["shoulder_lift"]["zero_pos"] - 2048, "shoulder_lift")
arm.write(
"Goal_Position", calib["shoulder_lift"]["zero_pos"] - 2048, "shoulder_lift"
)
time.sleep(1)
arm.write("Goal_Position", calib["shoulder_pan"]["zero_pos"], "shoulder_pan")
time.sleep(1)
@@ -289,18 +319,27 @@ def run_arm_auto_calibration_so100(arm: MotorsBus, robot_type: str, arm_name: st
return calib_dict
def run_arm_auto_calibration_moss(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
def run_arm_auto_calibration_moss(
arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str
):
"""All the offsets and magic numbers are hand tuned, and are unique to SO-100 follower arms"""
if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
raise ValueError("To run calibration, the torque must be disabled on all motors.")
raise ValueError(
"To run calibration, the torque must be disabled on all motors."
)
if not (robot_type == "moss" and arm_type == "follower"):
raise NotImplementedError("Auto calibration only supports the follower of moss arms for now.")
raise NotImplementedError(
"Auto calibration only supports the follower of moss arms for now."
)
print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
print("\nMove arm to initial position")
print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="initial"))
print(
"See: "
+ URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="initial")
)
input("Press Enter to continue...")
# Lower the acceleration of the motors (in [0,254])
@@ -384,8 +423,12 @@ def run_arm_auto_calibration_moss(arm: MotorsBus, robot_type: str, arm_name: str
arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 1024, "wrist_flex")
time.sleep(1)
arm.write("Goal_Position", calib["shoulder_lift"]["zero_pos"] + 2048, "shoulder_lift")
arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] - 1024 - 400, "elbow_flex")
arm.write(
"Goal_Position", calib["shoulder_lift"]["zero_pos"] + 2048, "shoulder_lift"
)
arm.write(
"Goal_Position", calib["elbow_flex"]["zero_pos"] - 1024 - 400, "elbow_flex"
)
time.sleep(2)
calib_modes = []
@@ -412,7 +455,9 @@ def run_arm_auto_calibration_moss(arm: MotorsBus, robot_type: str, arm_name: str
return calib_dict
def run_arm_manual_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
def run_arm_manual_calibration(
arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str
):
"""This function ensures that a neural network trained on data collected on a given robot
can work on another robot. For instance before calibration, setting a same goal position
for each motor of two different robots will get two very different positions. But after calibration,
@@ -435,12 +480,16 @@ def run_arm_manual_calibration(arm: MotorsBus, robot_type: str, arm_name: str, a
```
"""
if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
raise ValueError("To run calibration, the torque must be disabled on all motors.")
raise ValueError(
"To run calibration, the torque must be disabled on all motors."
)
print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
print("\nMove arm to zero position")
print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="zero"))
print(
"See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="zero")
)
input("Press Enter to continue...")
# We arbitrarily chose our zero target position to be a straight horizontal position with gripper upwards and closed.
@@ -460,10 +509,15 @@ def run_arm_manual_calibration(arm: MotorsBus, robot_type: str, arm_name: str, a
# corresponds to opening the gripper. When the rotation direction is ambiguous, we arbitrarily rotate clockwise from the point of view
# of the previous motor in the kinetic chain.
print("\nMove arm to rotated target position")
print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rotated"))
print(
"See: "
+ URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rotated")
)
input("Press Enter to continue...")
rotated_target_pos = convert_degrees_to_steps(ROTATED_POSITION_DEGREE, arm.motor_models)
rotated_target_pos = convert_degrees_to_steps(
ROTATED_POSITION_DEGREE, arm.motor_models
)
# Find drive mode by rotating each motor by a quarter of a turn.
# Drive mode indicates if the motor rotation direction should be inverted (=1) or not (=0).
@@ -475,7 +529,9 @@ def run_arm_manual_calibration(arm: MotorsBus, robot_type: str, arm_name: str, a
homing_offset = rotated_target_pos - rotated_drived_pos
print("\nMove arm to rest position")
print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rest"))
print(
"See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rest")
)
input("Press Enter to continue...")
print()

89
lerobot/common/robot_devices/robots/manipulator.py
View File

@@ -31,11 +31,16 @@ from lerobot.common.robot_devices.cameras.utils import make_cameras_from_configs
from lerobot.common.robot_devices.motors.utils import MotorsBus, make_motors_buses_from_configs
from lerobot.common.robot_devices.robots.configs import ManipulatorRobotConfig
from lerobot.common.robot_devices.robots.utils import get_arm_id
from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
from lerobot.common.robot_devices.utils import (
RobotDeviceAlreadyConnectedError,
RobotDeviceNotConnectedError,
)
def ensure_safe_goal_position(
goal_pos: torch.Tensor, present_pos: torch.Tensor, max_relative_target: float | list[float]
goal_pos: torch.Tensor,
present_pos: torch.Tensor,
max_relative_target: float | list[float],
):
# Cap relative action target magnitude for safety.
diff = goal_pos - present_pos
@@ -277,7 +282,9 @@ class ManipulatorRobot:
# to squeeze the gripper and have it spring back to an open position on its own.
for name in self.leader_arms:
self.leader_arms[name].write("Torque_Enable", 1, "gripper")
self.leader_arms[name].write("Goal_Position", self.config.gripper_open_degree, "gripper")
self.leader_arms[name].write(
"Goal_Position", self.config.gripper_open_degree, "gripper"
)
# Check both arms can be read
for name in self.follower_arms:
@@ -309,18 +316,26 @@ class ManipulatorRobot:
print(f"Missing calibration file '{arm_calib_path}'")
if self.robot_type in ["koch", "koch_bimanual", "aloha"]:
from lerobot.common.robot_devices.robots.dynamixel_calibration import run_arm_calibration
from lerobot.common.robot_devices.robots.dynamixel_calibration import (
run_arm_calibration,
)
calibration = run_arm_calibration(arm, self.robot_type, name, arm_type)
calibration = run_arm_calibration(
arm, self.robot_type, name, arm_type
)
elif self.robot_type in ["so100", "moss", "lekiwi"]:
from lerobot.common.robot_devices.robots.feetech_calibration import (
run_arm_manual_calibration,
)
calibration = run_arm_manual_calibration(arm, self.robot_type, name, arm_type)
calibration = run_arm_manual_calibration(
arm, self.robot_type, name, arm_type
)
print(f"Calibration is done! Saving calibration file '{arm_calib_path}'")
print(
f"Calibration is done! Saving calibration file '{arm_calib_path}'"
)
arm_calib_path.parent.mkdir(parents=True, exist_ok=True)
with open(arm_calib_path, "w") as f:
json.dump(calibration, f)
@@ -339,13 +354,17 @@ class ManipulatorRobot:
from lerobot.common.robot_devices.motors.dynamixel import TorqueMode
if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
raise ValueError("To run set robot preset, the torque must be disabled on all motors.")
raise ValueError(
"To run set robot preset, the torque must be disabled on all motors."
)
# Use 'extended position mode' for all motors except gripper, because in joint mode the servos can't
# rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while assembling the arm,
# you could end up with a servo with a position 0 or 4095 at a crucial point See [
# https://emanual.robotis.com/docs/en/dxl/x/x_series/#operating-mode11]
all_motors_except_gripper = [name for name in arm.motor_names if name != "gripper"]
all_motors_except_gripper = [
name for name in arm.motor_names if name != "gripper"
]
if len(all_motors_except_gripper) > 0:
# 4 corresponds to Extended Position on Koch motors
arm.write("Operating_Mode", 4, all_motors_except_gripper)
@@ -374,7 +393,9 @@ class ManipulatorRobot:
# Enable torque on the gripper of the leader arms, and move it to 45 degrees,
# so that we can use it as a trigger to close the gripper of the follower arms.
self.leader_arms[name].write("Torque_Enable", 1, "gripper")
self.leader_arms[name].write("Goal_Position", self.config.gripper_open_degree, "gripper")
self.leader_arms[name].write(
"Goal_Position", self.config.gripper_open_degree, "gripper"
)
def set_aloha_robot_preset(self):
def set_shadow_(arm):
@@ -404,11 +425,15 @@ class ManipulatorRobot:
# you could end up with a servo with a position 0 or 4095 at a crucial point See [
# https://emanual.robotis.com/docs/en/dxl/x/x_series/#operating-mode11]
all_motors_except_gripper = [
name for name in self.follower_arms[name].motor_names if name != "gripper"
name
for name in self.follower_arms[name].motor_names
if name != "gripper"
]
if len(all_motors_except_gripper) > 0:
# 4 corresponds to Extended Position on Aloha motors
self.follower_arms[name].write("Operating_Mode", 4, all_motors_except_gripper)
self.follower_arms[name].write(
"Operating_Mode", 4, all_motors_except_gripper
)
# Use 'position control current based' for follower gripper to be limited by the limit of the current.
# It can grasp an object without forcing too much even tho,
@@ -456,7 +481,9 @@ class ManipulatorRobot:
before_lread_t = time.perf_counter()
leader_pos[name] = self.leader_arms[name].read("Present_Position")
leader_pos[name] = torch.from_numpy(leader_pos[name])
self.logs[f"read_leader_{name}_pos_dt_s"] = time.perf_counter() - before_lread_t
self.logs[f"read_leader_{name}_pos_dt_s"] = (
time.perf_counter() - before_lread_t
)
# Send goal position to the follower
follower_goal_pos = {}
@@ -477,14 +504,18 @@ class ManipulatorRobot:
if self.config.max_relative_target is not None:
present_pos = self.follower_arms[name].read("Present_Position")
present_pos = torch.from_numpy(present_pos)
goal_pos = ensure_safe_goal_position(goal_pos, present_pos, self.config.max_relative_target)
goal_pos = ensure_safe_goal_position(
goal_pos, present_pos, self.config.max_relative_target
)
# Used when record_data=True
follower_goal_pos[name] = goal_pos
goal_pos = goal_pos.numpy().astype(np.float32)
self.follower_arms[name].write("Goal_Position", goal_pos)
self.logs[f"write_follower_{name}_goal_pos_dt_s"] = time.perf_counter() - before_fwrite_t
self.logs[f"write_follower_{name}_goal_pos_dt_s"] = (
time.perf_counter() - before_fwrite_t
)
# Early exit when recording data is not requested
if not record_data:
@@ -497,7 +528,9 @@ class ManipulatorRobot:
before_fread_t = time.perf_counter()
follower_pos[name] = self.follower_arms[name].read("Present_Position")
follower_pos[name] = torch.from_numpy(follower_pos[name])
self.logs[f"read_follower_{name}_pos_dt_s"] = time.perf_counter() - before_fread_t
self.logs[f"read_follower_{name}_pos_dt_s"] = (
time.perf_counter() - before_fread_t
)
# Create state by concatenating follower current position
state = []
@@ -519,8 +552,12 @@ class ManipulatorRobot:
before_camread_t = time.perf_counter()
images[name] = self.cameras[name].async_read()
images[name] = torch.from_numpy(images[name])
self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs[
"delta_timestamp_s"
]
self.logs[f"async_read_camera_{name}_dt_s"] = (
time.perf_counter() - before_camread_t
)
# Populate output dictionaries
obs_dict, action_dict = {}, {}
@@ -544,7 +581,9 @@ class ManipulatorRobot:
before_fread_t = time.perf_counter()
follower_pos[name] = self.follower_arms[name].read("Present_Position")
follower_pos[name] = torch.from_numpy(follower_pos[name])
self.logs[f"read_follower_{name}_pos_dt_s"] = time.perf_counter() - before_fread_t
self.logs[f"read_follower_{name}_pos_dt_s"] = (
time.perf_counter() - before_fread_t
)
# Create state by concatenating follower current position
state = []
@@ -559,8 +598,12 @@ class ManipulatorRobot:
before_camread_t = time.perf_counter()
images[name] = self.cameras[name].async_read()
images[name] = torch.from_numpy(images[name])
self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs[
"delta_timestamp_s"
]
self.logs[f"async_read_camera_{name}_dt_s"] = (
time.perf_counter() - before_camread_t
)
# Populate output dictionaries and format to pytorch
obs_dict = {}
@@ -606,7 +649,9 @@ class ManipulatorRobot:
if self.config.max_relative_target is not None:
present_pos = self.follower_arms[name].read("Present_Position")
present_pos = torch.from_numpy(present_pos)
goal_pos = ensure_safe_goal_position(goal_pos, present_pos, self.config.max_relative_target)
goal_pos = ensure_safe_goal_position(
goal_pos, present_pos, self.config.max_relative_target
)
# Save tensor to concat and return
action_sent.append(goal_pos)

24
lerobot/common/robot_devices/robots/stretch.py
View File

@@ -52,7 +52,9 @@ class StretchRobot(StretchAPI):
def connect(self) -> None:
self.is_connected = self.startup()
if not self.is_connected:
print("Another process is already using Stretch. Try running 'stretch_free_robot_process.py'")
print(
"Another process is already using Stretch. Try running 'stretch_free_robot_process.py'"
)
raise ConnectionError()
for name in self.cameras:
@@ -60,7 +62,9 @@ class StretchRobot(StretchAPI):
self.is_connected = self.is_connected and self.cameras[name].is_connected
if not self.is_connected:
print("Could not connect to the cameras, check that all cameras are plugged-in.")
print(
"Could not connect to the cameras, check that all cameras are plugged-in."
)
raise ConnectionError()
self.run_calibration()
@@ -105,8 +109,12 @@ class StretchRobot(StretchAPI):
before_camread_t = time.perf_counter()
images[name] = self.cameras[name].async_read()
images[name] = torch.from_numpy(images[name])
self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs[
"delta_timestamp_s"
]
self.logs[f"async_read_camera_{name}_dt_s"] = (
time.perf_counter() - before_camread_t
)
# Populate output dictionaries
obs_dict, action_dict = {}, {}
@@ -150,8 +158,12 @@ class StretchRobot(StretchAPI):
before_camread_t = time.perf_counter()
images[name] = self.cameras[name].async_read()
images[name] = torch.from_numpy(images[name])
self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs[
"delta_timestamp_s"
]
self.logs[f"async_read_camera_{name}_dt_s"] = (
time.perf_counter() - before_camread_t
)
# Populate output dictionaries
obs_dict = {}

3
lerobot/common/robot_devices/utils.py
View File

@@ -48,7 +48,8 @@ class RobotDeviceNotConnectedError(Exception):
"""Exception raised when the robot device is not connected."""
def __init__(
self, message="This robot device is not connected. Try calling `robot_device.connect()` first."
self,
message="This robot device is not connected. Try calling `robot_device.connect()` first.",
):
self.message = message
super().__init__(self.message)

4
lerobot/common/utils/import_utils.py
View File

@@ -17,7 +17,9 @@ import importlib
import logging
def is_package_available(pkg_name: str, return_version: bool = False) -> tuple[bool, str] | bool:
def is_package_available(
pkg_name: str, return_version: bool = False
) -> tuple[bool, str] | bool:
"""Copied from https://github.com/huggingface/transformers/blob/main/src/transformers/utils/import_utils.py
Check if the package spec exists and grab its version to avoid importing a local directory.
**Note:** this doesn't work for all packages.

4
lerobot/common/utils/io_utils.py
View File

@@ -28,7 +28,9 @@ def write_video(video_path, stacked_frames, fps):
# Filter out DeprecationWarnings raised from pkg_resources
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", "pkg_resources is deprecated as an API", category=DeprecationWarning
"ignore",
"pkg_resources is deprecated as an API",
category=DeprecationWarning,
)
imageio.mimsave(video_path, stacked_frames, fps=fps)

36
lerobot/common/utils/utils.py
View File

@@ -148,7 +148,10 @@ def _relative_path_between(path1: Path, path2: Path) -> Path:
except ValueError: # most likely because path1 is not a subpath of path2
common_parts = Path(osp.commonpath([path1, path2])).parts
return Path(
"/".join([".."] * (len(path2.parts) - len(common_parts)) + list(path1.parts[len(common_parts) :]))
"/".join(
[".."] * (len(path2.parts) - len(common_parts))
+ list(path1.parts[len(common_parts) :])
)
)
@@ -159,10 +162,26 @@ def print_cuda_memory_usage():
gc.collect()
# Also clear the cache if you want to fully release the memory
torch.cuda.empty_cache()
print("Current GPU Memory Allocated: {:.2f} MB".format(torch.cuda.memory_allocated(0) / 1024**2))
print("Maximum GPU Memory Allocated: {:.2f} MB".format(torch.cuda.max_memory_allocated(0) / 1024**2))
print("Current GPU Memory Reserved: {:.2f} MB".format(torch.cuda.memory_reserved(0) / 1024**2))
print("Maximum GPU Memory Reserved: {:.2f} MB".format(torch.cuda.max_memory_reserved(0) / 1024**2))
print(
"Current GPU Memory Allocated: {:.2f} MB".format(
torch.cuda.memory_allocated(0) / 1024**2
)
)
print(
"Maximum GPU Memory Allocated: {:.2f} MB".format(
torch.cuda.max_memory_allocated(0) / 1024**2
)
)
print(
"Current GPU Memory Reserved: {:.2f} MB".format(
torch.cuda.memory_reserved(0) / 1024**2
)
)
print(
"Maximum GPU Memory Reserved: {:.2f} MB".format(
torch.cuda.max_memory_reserved(0) / 1024**2
)
)
def capture_timestamp_utc():
@@ -232,7 +251,12 @@ def is_valid_numpy_dtype_string(dtype_str: str) -> bool:
class TimerManager:
def __init__(self, elapsed_time_list: list[float] | None = None, label="Elapsed time", log=True):
def __init__(
self,
elapsed_time_list: list[float] | None = None,
label="Elapsed time",
log=True,
):
self.label = label
self.elapsed_time_list = elapsed_time_list
self.log = log