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

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for more information, see https://pre-commit.ci
This commit is contained in:
pre-commit-ci[bot]
2025-03-24 13:16:38 +00:00
committed by Michel Aractingi
parent cdcf346061
commit 1c8daf11fd
95 changed files with 1592 additions and 491 deletions
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24
lerobot/common/envs/configs.py
View File

@@ -61,10 +61,16 @@ class AlohaEnv(EnvConfig):
def __post_init__(self):
if self.obs_type == "pixels":
self.features["top"] = PolicyFeature(type=FeatureType.VISUAL, shape=(480, 640, 3))
self.features["top"] = PolicyFeature(
type=FeatureType.VISUAL, shape=(480, 640, 3)
)
elif self.obs_type == "pixels_agent_pos":
self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(14,))
self.features["pixels/top"] = PolicyFeature(type=FeatureType.VISUAL, shape=(480, 640, 3))
self.features["agent_pos"] = PolicyFeature(
type=FeatureType.STATE, shape=(14,)
)
self.features["pixels/top"] = PolicyFeature(
type=FeatureType.VISUAL, shape=(480, 640, 3)
)
@property
def gym_kwargs(self) -> dict:
@@ -102,9 +108,13 @@ class PushtEnv(EnvConfig):
def __post_init__(self):
if self.obs_type == "pixels_agent_pos":
self.features["pixels"] = PolicyFeature(type=FeatureType.VISUAL, shape=(384, 384, 3))
self.features["pixels"] = PolicyFeature(
type=FeatureType.VISUAL, shape=(384, 384, 3)
)
elif self.obs_type == "environment_state_agent_pos":
self.features["environment_state"] = PolicyFeature(type=FeatureType.ENV, shape=(16,))
self.features["environment_state"] = PolicyFeature(
type=FeatureType.ENV, shape=(16,)
)
@property
def gym_kwargs(self) -> dict:
@@ -143,7 +153,9 @@ class XarmEnv(EnvConfig):
def __post_init__(self):
if self.obs_type == "pixels_agent_pos":
self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(4,))
self.features["agent_pos"] = PolicyFeature(
type=FeatureType.STATE, shape=(4,)
)
@property
def gym_kwargs(self) -> dict:

13
lerobot/common/envs/factory.py
View File

@@ -32,7 +32,9 @@ def make_env_config(env_type: str, **kwargs) -> EnvConfig:
raise ValueError(f"Policy type '{env_type}' is not available.")
def make_env(cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False) -> gym.vector.VectorEnv | None:
def make_env(
cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False
) -> gym.vector.VectorEnv | None:
"""Makes a gym vector environment according to the config.
Args:
@@ -56,7 +58,9 @@ def make_env(cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False) -> g
try:
importlib.import_module(package_name)
except ModuleNotFoundError as e:
print(f"{package_name} is not installed. Please install it with `pip install 'lerobot[{cfg.type}]'`")
print(
f"{package_name} is not installed. Please install it with `pip install 'lerobot[{cfg.type}]'`"
)
raise e
gym_handle = f"{package_name}/{cfg.task}"
@@ -64,7 +68,10 @@ def make_env(cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False) -> g
# batched version of the env that returns an observation of shape (b, c)
env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
env = env_cls(
[lambda: gym.make(gym_handle, disable_env_checker=True, **cfg.gym_kwargs) for _ in range(n_envs)]
[
lambda: gym.make(gym_handle, disable_env_checker=True, **cfg.gym_kwargs)
for _ in range(n_envs)
]
)
return env

16
lerobot/common/envs/utils.py
View File

@@ -50,7 +50,9 @@ def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Ten
# sanity check that images are channel last
_, h, w, c = img.shape
assert c < h and c < w, f"expect channel last images, but instead got {img.shape=}"
assert c < h and c < w, (
f"expect channel last images, but instead got {img.shape=}"
)
# sanity check that images are uint8
assert img.dtype == torch.uint8, f"expect torch.uint8, but instead {img.dtype=}"
@@ -83,7 +85,9 @@ def env_to_policy_features(env_cfg: EnvConfig) -> dict[str, PolicyFeature]:
for key, ft in env_cfg.features.items():
if ft.type is FeatureType.VISUAL:
if len(ft.shape) != 3:
raise ValueError(f"Number of dimensions of {key} != 3 (shape={ft.shape})")
raise ValueError(
f"Number of dimensions of {key} != 3 (shape={ft.shape})"
)
shape = get_channel_first_image_shape(ft.shape)
feature = PolicyFeature(type=ft.type, shape=shape)
@@ -105,7 +109,9 @@ def check_env_attributes_and_types(env: gym.vector.VectorEnv) -> None:
with warnings.catch_warnings():
warnings.simplefilter("once", UserWarning) # Apply filter only in this function
if not (hasattr(env.envs[0], "task_description") and hasattr(env.envs[0], "task")):
if not (
hasattr(env.envs[0], "task_description") and hasattr(env.envs[0], "task")
):
warnings.warn(
"The environment does not have 'task_description' and 'task'. Some policies require these features.",
UserWarning,
@@ -119,7 +125,9 @@ def check_env_attributes_and_types(env: gym.vector.VectorEnv) -> None:
)
def add_envs_task(env: gym.vector.VectorEnv, observation: dict[str, Any]) -> dict[str, Any]:
def add_envs_task(
env: gym.vector.VectorEnv, observation: dict[str, Any]
) -> dict[str, Any]:
"""Adds task feature to the observation dict with respect to the first environment attribute."""
if hasattr(env.envs[0], "task_description"):
observation["task"] = env.call("task_description")