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https://github.com/huggingface/lerobot.git
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fix(examples): wrap all of them into a main function (#2524)
This commit is contained in:
Steven Palma
+86
-80
@@ -33,83 +33,68 @@ TASK_DESCRIPTION = "My task description"
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HF_MODEL_ID = "<hf_username>/<model_repo_id>"
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HF_DATASET_ID = "<hf_username>/<eval_dataset_repo_id>"
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# Create the robot configuration & robot
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robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
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robot = LeKiwiClient(robot_config)
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def main():
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# Create the robot configuration & robot
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robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
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# Create policy
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policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
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robot = LeKiwiClient(robot_config)
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# Configure the dataset features
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action_features = hw_to_dataset_features(robot.action_features, ACTION)
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obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR)
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dataset_features = {**action_features, **obs_features}
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# Create policy
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policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
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# Create the dataset
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dataset = LeRobotDataset.create(
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repo_id=HF_DATASET_ID,
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fps=FPS,
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features=dataset_features,
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robot_type=robot.name,
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use_videos=True,
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image_writer_threads=4,
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)
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# Configure the dataset features
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action_features = hw_to_dataset_features(robot.action_features, ACTION)
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obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR)
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dataset_features = {**action_features, **obs_features}
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# Build Policy Processors
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preprocessor, postprocessor = make_pre_post_processors(
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policy_cfg=policy,
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pretrained_path=HF_MODEL_ID,
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dataset_stats=dataset.meta.stats,
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# The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility.
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preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
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)
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# Connect the robot
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# To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
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robot.connect()
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# TODO(Steven): Update this example to use pipelines
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teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
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# Initialize the keyboard listener and rerun visualization
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listener, events = init_keyboard_listener()
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init_rerun(session_name="lekiwi_evaluate")
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if not robot.is_connected:
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raise ValueError("Robot is not connected!")
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print("Starting evaluate loop...")
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recorded_episodes = 0
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while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
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log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")
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# Main record loop
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record_loop(
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robot=robot,
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events=events,
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# Create the dataset
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dataset = LeRobotDataset.create(
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repo_id=HF_DATASET_ID,
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fps=FPS,
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policy=policy,
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preprocessor=preprocessor, # Pass the pre and post policy processors
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postprocessor=postprocessor,
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dataset=dataset,
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control_time_s=EPISODE_TIME_SEC,
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single_task=TASK_DESCRIPTION,
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display_data=True,
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teleop_action_processor=teleop_action_processor,
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robot_action_processor=robot_action_processor,
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robot_observation_processor=robot_observation_processor,
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features=dataset_features,
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robot_type=robot.name,
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use_videos=True,
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image_writer_threads=4,
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)
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# Reset the environment if not stopping or re-recording
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if not events["stop_recording"] and (
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(recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
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):
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log_say("Reset the environment")
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# Build Policy Processors
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preprocessor, postprocessor = make_pre_post_processors(
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policy_cfg=policy,
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pretrained_path=HF_MODEL_ID,
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dataset_stats=dataset.meta.stats,
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# The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility.
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preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
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)
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# Connect the robot
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# To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
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robot.connect()
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# TODO(Steven): Update this example to use pipelines
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teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
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# Initialize the keyboard listener and rerun visualization
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listener, events = init_keyboard_listener()
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init_rerun(session_name="lekiwi_evaluate")
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if not robot.is_connected:
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raise ValueError("Robot is not connected!")
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print("Starting evaluate loop...")
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recorded_episodes = 0
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while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
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log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")
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# Main record loop
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record_loop(
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robot=robot,
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events=events,
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fps=FPS,
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policy=policy,
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preprocessor=preprocessor, # Pass the pre and post policy processors
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postprocessor=postprocessor,
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dataset=dataset,
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control_time_s=EPISODE_TIME_SEC,
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single_task=TASK_DESCRIPTION,
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display_data=True,
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@@ -118,21 +103,42 @@ while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
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robot_observation_processor=robot_observation_processor,
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)
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if events["rerecord_episode"]:
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log_say("Re-record episode")
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events["rerecord_episode"] = False
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events["exit_early"] = False
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dataset.clear_episode_buffer()
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continue
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# Reset the environment if not stopping or re-recording
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if not events["stop_recording"] and (
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(recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
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):
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log_say("Reset the environment")
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record_loop(
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robot=robot,
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events=events,
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fps=FPS,
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control_time_s=EPISODE_TIME_SEC,
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single_task=TASK_DESCRIPTION,
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display_data=True,
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teleop_action_processor=teleop_action_processor,
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robot_action_processor=robot_action_processor,
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robot_observation_processor=robot_observation_processor,
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)
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# Save episode
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dataset.save_episode()
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recorded_episodes += 1
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if events["rerecord_episode"]:
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log_say("Re-record episode")
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events["rerecord_episode"] = False
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events["exit_early"] = False
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dataset.clear_episode_buffer()
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continue
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# Clean up
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log_say("Stop recording")
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robot.disconnect()
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listener.stop()
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# Save episode
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dataset.save_episode()
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recorded_episodes += 1
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dataset.finalize()
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dataset.push_to_hub()
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# Clean up
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log_say("Stop recording")
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robot.disconnect()
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listener.stop()
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dataset.finalize()
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dataset.push_to_hub()
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if __name__ == "__main__":
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main()
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