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

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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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30
lerobot/scripts/configure_motor.py
View File

@@ -38,7 +38,12 @@ def get_motor_bus_cls(brand: str) -> tuple:
FeetechMotorsBus,
)
return FeetechMotorsBusConfig, FeetechMotorsBus, MODEL_BAUDRATE_TABLE, SCS_SERIES_BAUDRATE_TABLE
return (
FeetechMotorsBusConfig,
FeetechMotorsBus,
MODEL_BAUDRATE_TABLE,
SCS_SERIES_BAUDRATE_TABLE,
)
elif brand == "dynamixel":
from lerobot.common.robot_devices.motors.configs import DynamixelMotorsBusConfig
@@ -48,7 +53,12 @@ def get_motor_bus_cls(brand: str) -> tuple:
DynamixelMotorsBus,
)
return DynamixelMotorsBusConfig, DynamixelMotorsBus, MODEL_BAUDRATE_TABLE, X_SERIES_BAUDRATE_TABLE
return (
DynamixelMotorsBusConfig,
DynamixelMotorsBus,
MODEL_BAUDRATE_TABLE,
X_SERIES_BAUDRATE_TABLE,
)
else:
raise ValueError(
@@ -57,8 +67,8 @@ def get_motor_bus_cls(brand: str) -> tuple:
def configure_motor(port, brand, model, motor_idx_des, baudrate_des):
motor_bus_config_cls, motor_bus_cls, model_baudrate_table, series_baudrate_table = get_motor_bus_cls(
brand
motor_bus_config_cls, motor_bus_cls, model_baudrate_table, series_baudrate_table = (
get_motor_bus_cls(brand)
)
# Check if the provided model exists in the model_baud_rate_table
@@ -72,7 +82,9 @@ def configure_motor(port, brand, model, motor_idx_des, baudrate_des):
motor_index_arbitrary = motor_idx_des # Use the motor ID passed via argument
motor_model = model # Use the motor model passed via argument
config = motor_bus_config_cls(port=port, motors={motor_name: (motor_index_arbitrary, motor_model)})
config = motor_bus_config_cls(
port=port, motors={motor_name: (motor_index_arbitrary, motor_model)}
)
# Initialize the MotorBus with the correct port and motor configurations
motor_bus = motor_bus_cls(config=config)
@@ -139,8 +151,12 @@ def configure_motor(port, brand, model, motor_idx_des, baudrate_des):
print(f"Setting its index to desired index {motor_idx_des}")
if brand == "feetech":
motor_bus.write_with_motor_ids(motor_bus.motor_models, motor_index, "Lock", 0)
motor_bus.write_with_motor_ids(motor_bus.motor_models, motor_index, "ID", motor_idx_des)
motor_bus.write_with_motor_ids(
motor_bus.motor_models, motor_index, "Lock", 0
)
motor_bus.write_with_motor_ids(
motor_bus.motor_models, motor_index, "ID", motor_idx_des
)
present_idx = motor_bus.read_with_motor_ids(
motor_bus.motor_models, motor_idx_des, "ID", num_retry=2

40
lerobot/scripts/control_robot.py
View File

@@ -161,7 +161,6 @@ from lerobot.common.robot_devices.control_utils import (
log_control_info,
record_episode,
reset_environment,
reset_follower_position,
sanity_check_dataset_name,
sanity_check_dataset_robot_compatibility,
stop_recording,
@@ -256,7 +255,8 @@ def record(
if len(robot.cameras) > 0:
dataset.start_image_writer(
num_processes=cfg.num_image_writer_processes,
num_threads=cfg.num_image_writer_threads_per_camera * len(robot.cameras),
num_threads=cfg.num_image_writer_threads_per_camera
* len(robot.cameras),
)
sanity_check_dataset_robot_compatibility(dataset, robot, cfg.fps, cfg.video)
else:
@@ -269,14 +269,19 @@ def record(
robot=robot,
use_videos=cfg.video,
image_writer_processes=cfg.num_image_writer_processes,
image_writer_threads=cfg.num_image_writer_threads_per_camera * len(robot.cameras),
image_writer_threads=cfg.num_image_writer_threads_per_camera
* len(robot.cameras),
)
# Load pretrained policy
policy = None if cfg.policy is None else make_policy(cfg.policy, ds_meta=dataset.meta)
policy = (
None if cfg.policy is None else make_policy(cfg.policy, ds_meta=dataset.meta)
)
# Load pretrained policy
policy = None if cfg.policy is None else make_policy(cfg.policy, ds_meta=dataset.meta)
policy = (
None if cfg.policy is None else make_policy(cfg.policy, ds_meta=dataset.meta)
)
if not robot.is_connected:
robot.connect()
@@ -291,7 +296,14 @@ def record(
# 3. place the cameras windows on screen
enable_teleoperation = policy is None
log_say("Warmup record", cfg.play_sounds)
warmup_record(robot, events, enable_teleoperation, cfg.warmup_time_s, cfg.display_data, cfg.fps)
warmup_record(
robot,
events,
enable_teleoperation,
cfg.warmup_time_s,
cfg.display_data,
cfg.fps,
)
if has_method(robot, "teleop_safety_stop"):
robot.teleop_safety_stop()
@@ -376,7 +388,9 @@ def replay(
log_control_info(robot, dt_s, fps=cfg.fps)
def _init_rerun(control_config: ControlConfig, session_name: str = "lerobot_control_loop") -> None:
def _init_rerun(
control_config: ControlConfig, session_name: str = "lerobot_control_loop"
) -> None:
"""Initializes the Rerun SDK for visualizing the control loop.
Args:
@@ -422,17 +436,23 @@ def control_robot(cfg: ControlPipelineConfig):
if isinstance(cfg.control, CalibrateControlConfig):
calibrate(robot, cfg.control)
elif isinstance(cfg.control, TeleoperateControlConfig):
_init_rerun(control_config=cfg.control, session_name="lerobot_control_loop_teleop")
_init_rerun(
control_config=cfg.control, session_name="lerobot_control_loop_teleop"
)
teleoperate(robot, cfg.control)
elif isinstance(cfg.control, RecordControlConfig):
_init_rerun(control_config=cfg.control, session_name="lerobot_control_loop_record")
_init_rerun(
control_config=cfg.control, session_name="lerobot_control_loop_record"
)
record(robot, cfg.control)
elif isinstance(cfg.control, ReplayControlConfig):
replay(robot, cfg.control)
elif isinstance(cfg.control, RemoteRobotConfig):
from lerobot.common.robot_devices.robots.lekiwi_remote import run_lekiwi
_init_rerun(control_config=cfg.control, session_name="lerobot_control_loop_remote")
_init_rerun(
control_config=cfg.control, session_name="lerobot_control_loop_remote"
)
run_lekiwi(cfg.robot)
if robot.is_connected:

6
lerobot/scripts/control_sim_robot.py
View File

@@ -262,7 +262,11 @@ def record(
shape = env.observation_space[key].shape
if not key.startswith("observation.image."):
key = "observation.image." + key
features[key] = {"dtype": "video", "names": ["channels", "height", "width"], "shape": shape}
features[key] = {
"dtype": "video",
"names": ["channels", "height", "width"],
"shape": shape,
}
for key, obs_key in state_keys_dict.items():
features[key] = {

18
lerobot/scripts/eval.py
View File

@@ -152,7 +152,8 @@ def rollout(
all_observations.append(deepcopy(observation))
observation = {
key: observation[key].to(device, non_blocking=device.type == "cuda") for key in observation
key: observation[key].to(device, non_blocking=device.type == "cuda")
for key in observation
}
# Infer "task" from attributes of environments.
@@ -515,10 +516,14 @@ def eval_main(cfg: EvalPipelineConfig):
torch.backends.cuda.matmul.allow_tf32 = True
set_seed(cfg.seed)
logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}")
logging.info(
colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}"
)
logging.info("Making environment.")
env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
env = make_env(
cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs
)
logging.info("Making policy.")
@@ -528,7 +533,12 @@ def eval_main(cfg: EvalPipelineConfig):
)
policy.eval()
with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext():
with (
torch.no_grad(),
torch.autocast(device_type=device.type)
if cfg.policy.use_amp
else nullcontext(),
):
info = eval_policy(
env,
policy,

8
lerobot/scripts/server/end_effector_control_utils.py
View File

@@ -480,7 +480,7 @@ def test_forward_kinematics(robot, fps=10):
obs = robot.capture_observation()
joint_positions = obs["observation.state"].cpu().numpy()
ee_pos = RobotKinematics.fk_gripper_tip(joint_positions)
logging.info(f"EE Position: {ee_pos[:3,3]}")
logging.info(f"EE Position: {ee_pos[:3, 3]}")
busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
@@ -519,7 +519,7 @@ def teleoperate_inverse_kinematics_with_leader(robot, fps=10):
joint_positions, desired_ee_pos, position_only=True, fk_func=fk_func
)
robot.send_action(torch.from_numpy(target_joint_state))
logging.info(f"Leader EE: {leader_ee[:3,3]}, Follower EE: {ee_pos[:3,3]}")
logging.info(f"Leader EE: {leader_ee[:3, 3]}, Follower EE: {ee_pos[:3, 3]}")
busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
@@ -574,9 +574,9 @@ def teleoperate_delta_inverse_kinematics_with_leader(robot, fps=10):
# Logging
logging.info(
f"Current EE: {current_ee_pos[:3,3]}, Desired EE: {desired_ee_pos[:3,3]}"
f"Current EE: {current_ee_pos[:3, 3]}, Desired EE: {desired_ee_pos[:3, 3]}"
)
logging.info(f"Delta EE: {ee_delta[:3,3]}")
logging.info(f"Delta EE: {ee_delta[:3, 3]}")
busy_wait(1 / fps - (time.perf_counter() - loop_start_time))

8
lerobot/scripts/server/gym_manipulator.py
View File

@@ -1087,9 +1087,9 @@ class GamepadControlWrapper(gym.Wrapper):
class ActionScaleWrapper(gym.ActionWrapper):
def __init__(self, env, ee_action_space_params=None):
super().__init__(env)
assert (
ee_action_space_params is not None
), "TODO: method implemented for ee action space only so far"
assert ee_action_space_params is not None, (
"TODO: method implemented for ee action space only so far"
)
self.scale_vector = np.array(
[
[
@@ -1546,4 +1546,4 @@ if __name__ == "__main__":
busy_wait(1 / args.fps - dt_s)
logging.info(f"Success after 20 steps {sucesses}")
logging.info(f"success rate {sum(sucesses)/ len(sucesses)}")
logging.info(f"success rate {sum(sucesses) / len(sucesses)}")

4
lerobot/scripts/server/network_utils.py
View File

@@ -41,7 +41,7 @@ def send_bytes_in_chunks(
logging_method = logging.info if not silent else logging.debug
logging_method(f"{log_prefix} Buffer size {size_in_bytes/1024/1024} MB with")
logging_method(f"{log_prefix} Buffer size {size_in_bytes / 1024 / 1024} MB with")
while sent_bytes < size_in_bytes:
transfer_state = hilserl_pb2.TransferState.TRANSFER_MIDDLE
@@ -60,7 +60,7 @@ def send_bytes_in_chunks(
f"{log_prefix} Sent {sent_bytes}/{size_in_bytes} bytes with state {transfer_state}"
)
logging_method(f"{log_prefix} Published {sent_bytes/1024/1024} MB")
logging_method(f"{log_prefix} Published {sent_bytes / 1024 / 1024} MB")
def receive_bytes_in_chunks(

24
lerobot/scripts/train.py
View File

@@ -223,14 +223,18 @@ def train(cfg: TrainPipelineConfig):
step = 0 # number of policy updates (forward + backward + optim)
if cfg.resume:
step, optimizer, lr_scheduler = load_training_state(cfg.checkpoint_path, optimizer, lr_scheduler)
step, optimizer, lr_scheduler = load_training_state(
cfg.checkpoint_path, optimizer, lr_scheduler
)
num_learnable_params = sum(
p.numel() for p in policy.parameters() if p.requires_grad
)
num_total_params = sum(p.numel() for p in policy.parameters())
logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}")
logging.info(
colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}"
)
if cfg.env is not None:
logging.info(f"{cfg.env.task=}")
logging.info(f"{cfg.steps=} ({format_big_number(cfg.steps)})")
@@ -273,7 +277,11 @@ def train(cfg: TrainPipelineConfig):
}
train_tracker = MetricsTracker(
cfg.batch_size, dataset.num_frames, dataset.num_episodes, train_metrics, initial_step=step
cfg.batch_size,
dataset.num_frames,
dataset.num_episodes,
train_metrics,
initial_step=step,
)
logging.info("Start offline training on a fixed dataset")
@@ -327,7 +335,9 @@ def train(cfg: TrainPipelineConfig):
logging.info(f"Eval policy at step {step}")
with (
torch.no_grad(),
torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext(),
torch.autocast(device_type=device.type)
if cfg.policy.use_amp
else nullcontext(),
):
eval_info = eval_policy(
eval_env,
@@ -344,7 +354,11 @@ def train(cfg: TrainPipelineConfig):
"eval_s": AverageMeter("eval_s", ":.3f"),
}
eval_tracker = MetricsTracker(
cfg.batch_size, dataset.num_frames, dataset.num_episodes, eval_metrics, initial_step=step
cfg.batch_size,
dataset.num_frames,
dataset.num_episodes,
eval_metrics,
initial_step=step,
)
eval_tracker.eval_s = eval_info["aggregated"].pop("eval_s")
eval_tracker.avg_sum_reward = eval_info["aggregated"].pop("avg_sum_reward")

4
lerobot/scripts/train_hilserl_classifier.py
View File

@@ -426,7 +426,7 @@ def train(
# Training loop with validation and checkpointing
for epoch in range(cfg.training.num_epochs):
logging.info(f"\nEpoch {epoch+1}/{cfg.training.num_epochs}")
logging.info(f"\nEpoch {epoch + 1}/{cfg.training.num_epochs}")
train_epoch(
model,
@@ -470,7 +470,7 @@ def train(
policy=model,
optimizer=optimizer,
scheduler=None,
identifier=f"{epoch+1:06d}",
identifier=f"{epoch + 1:06d}",
)
step += len(train_loader)

6
lerobot/scripts/visualize_dataset.py
View File

@@ -94,9 +94,9 @@ def to_hwc_uint8_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
assert chw_float32_torch.dtype == torch.float32
assert chw_float32_torch.ndim == 3
c, h, w = chw_float32_torch.shape
assert (
c < h and c < w
), f"expect channel first images, but instead {chw_float32_torch.shape}"
assert c < h and c < w, (
f"expect channel first images, but instead {chw_float32_torch.shape}"
)
hwc_uint8_numpy = (
(chw_float32_torch * 255).type(torch.uint8).permute(1, 2, 0).numpy()
)

24
lerobot/scripts/visualize_image_transforms.py
View File

@@ -47,7 +47,9 @@ OUTPUT_DIR = Path("outputs/image_transforms")
to_pil = ToPILImage()
def save_all_transforms(cfg: ImageTransformsConfig, original_frame, output_dir, n_examples):
def save_all_transforms(
cfg: ImageTransformsConfig, original_frame, output_dir, n_examples
):
output_dir_all = output_dir / "all"
output_dir_all.mkdir(parents=True, exist_ok=True)
@@ -60,7 +62,9 @@ def save_all_transforms(cfg: ImageTransformsConfig, original_frame, output_dir,
print(f" {output_dir_all}")
def save_each_transform(cfg: ImageTransformsConfig, original_frame, output_dir, n_examples):
def save_each_transform(
cfg: ImageTransformsConfig, original_frame, output_dir, n_examples
):
if not cfg.enable:
logging.warning(
"No single transforms will be saved, because `image_transforms.enable=False`. To enable, set `enable` to True in `ImageTransformsConfig` or in the command line with `--image_transforms.enable=True`."
@@ -89,9 +93,15 @@ def save_each_transform(cfg: ImageTransformsConfig, original_frame, output_dir,
tf_cfg_kwgs_max[key] = [max_, max_]
tf_cfg_kwgs_avg[key] = [avg, avg]
tf_min = make_transform_from_config(replace(tf_cfg, **{"kwargs": tf_cfg_kwgs_min}))
tf_max = make_transform_from_config(replace(tf_cfg, **{"kwargs": tf_cfg_kwgs_max}))
tf_avg = make_transform_from_config(replace(tf_cfg, **{"kwargs": tf_cfg_kwgs_avg}))
tf_min = make_transform_from_config(
replace(tf_cfg, **{"kwargs": tf_cfg_kwgs_min})
)
tf_max = make_transform_from_config(
replace(tf_cfg, **{"kwargs": tf_cfg_kwgs_max})
)
tf_avg = make_transform_from_config(
replace(tf_cfg, **{"kwargs": tf_cfg_kwgs_avg})
)
tf_frame_min = tf_min(original_frame)
tf_frame_max = tf_max(original_frame)
@@ -105,7 +115,9 @@ def save_each_transform(cfg: ImageTransformsConfig, original_frame, output_dir,
@draccus.wrap()
def visualize_image_transforms(cfg: DatasetConfig, output_dir: Path = OUTPUT_DIR, n_examples: int = 5):
def visualize_image_transforms(
cfg: DatasetConfig, output_dir: Path = OUTPUT_DIR, n_examples: int = 5
):
dataset = LeRobotDataset(
repo_id=cfg.repo_id,
episodes=cfg.episodes,