lerobot/record.py

# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Records a dataset. Actions for the robot can be either generated by teleoperation or by a policy.

Example:

```shell
python -m lerobot.record \
    --robot.type=so100_follower \
    --robot.port=/dev/tty.usbmodem58760431541 \
    --robot.cameras="{laptop: {type: opencv, camera_index: 0, width: 640, height: 480}}" \
    --robot.id=black \
    --teleop.type=so100_leader \
    --teleop.port=/dev/tty.usbmodem58760431551 \
    --teleop.id=blue \
    --dataset.repo_id=aliberts/record-test \
    --dataset.num_episodes=2 \
    --dataset.single_task="Grab the cube"
```
"""

import logging
import time
from dataclasses import asdict, dataclass
from pathlib import Path
from pprint import pformat

import numpy as np
import rerun as rr

from lerobot.common.cameras import (  # noqa: F401
    CameraConfig,  # noqa: F401
)
from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
from lerobot.common.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
from lerobot.common.datasets.image_writer import safe_stop_image_writer
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
from lerobot.common.datasets.utils import build_dataset_frame, hw_to_dataset_features
from lerobot.common.policies.factory import make_policy
from lerobot.common.policies.pretrained import PreTrainedPolicy
from lerobot.common.robots import (  # noqa: F401
    Robot,
    RobotConfig,
    koch_follower,
    make_robot_from_config,
    so100_follower,
    so101_follower,
)
from lerobot.common.teleoperators import (  # noqa: F401
    Teleoperator,
    TeleoperatorConfig,
    make_teleoperator_from_config,
)
from lerobot.common.utils.control_utils import (
    init_keyboard_listener,
    is_headless,
    predict_action,
    sanity_check_dataset_name,
    sanity_check_dataset_robot_compatibility,
)
from lerobot.common.utils.robot_utils import busy_wait
from lerobot.common.utils.utils import (
    get_safe_torch_device,
    init_logging,
    log_say,
)
from lerobot.common.utils.visualization_utils import _init_rerun
from lerobot.configs import parser
from lerobot.configs.policies import PreTrainedConfig

from .common.teleoperators import koch_leader, so100_leader, so101_leader  # noqa: F401


@dataclass
class DatasetRecordConfig:
    # Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).
    repo_id: str
    # A short but accurate description of the task performed during the recording (e.g. "Pick the Lego block and drop it in the box on the right.")
    single_task: str
    # Root directory where the dataset will be stored (e.g. 'dataset/path').
    root: str | Path | None = None
    # Limit the frames per second.
    fps: int = 30
    # Number of seconds for data recording for each episode.
    episode_time_s: int | float = 60
    # Number of seconds for resetting the environment after each episode.
    reset_time_s: int | float = 60
    # Number of episodes to record.
    num_episodes: int = 50
    # Encode frames in the dataset into video
    video: bool = True
    # Upload dataset to Hugging Face hub.
    push_to_hub: bool = True
    # Upload on private repository on the Hugging Face hub.
    private: bool = False
    # Add tags to your dataset on the hub.
    tags: list[str] | None = None
    # Number of subprocesses handling the saving of frames as PNG. Set to 0 to use threads only;
    # set to ≥1 to use subprocesses, each using threads to write images. The best number of processes
    # and threads depends on your system. We recommend 4 threads per camera with 0 processes.
    # If fps is unstable, adjust the thread count. If still unstable, try using 1 or more subprocesses.
    num_image_writer_processes: int = 0
    # Number of threads writing the frames as png images on disk, per camera.
    # Too many threads might cause unstable teleoperation fps due to main thread being blocked.
    # Not enough threads might cause low camera fps.
    num_image_writer_threads_per_camera: int = 4

    def __post_init__(self):
        if self.single_task is None:
            raise ValueError("You need to provide a task as argument in `single_task`.")


@dataclass
class RecordConfig:
    robot: RobotConfig
    dataset: DatasetRecordConfig
    # Whether to control the robot with a teleoperator
    teleop: TeleoperatorConfig | None = None
    # Whether to control the robot with a policy
    policy: PreTrainedConfig | None = None
    # Display all cameras on screen
    display_data: bool = False
    # Use vocal synthesis to read events.
    play_sounds: bool = True
    # Resume recording on an existing dataset.
    resume: bool = False

    def __post_init__(self):
        if bool(self.teleop) == bool(self.policy):
            raise ValueError("Choose either a policy or a teleoperator to control the robot")

        # HACK: We parse again the cli args here to get the pretrained path if there was one.
        policy_path = parser.get_path_arg("policy")
        if policy_path:
            cli_overrides = parser.get_cli_overrides("policy")
            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
            self.policy.pretrained_path = policy_path

    @classmethod
    def __get_path_fields__(cls) -> list[str]:
        """This enables the parser to load config from the policy using `--policy.path=local/dir`"""
        return ["policy"]


@safe_stop_image_writer
def record_loop(
    robot: Robot,
    events: dict,
    fps: int,
    dataset: LeRobotDataset | None = None,
    teleop: Teleoperator | None = None,
    policy: PreTrainedPolicy | None = None,
    control_time_s: int | None = None,
    single_task: str | None = None,
    display_data: bool = False,
):
    if dataset is not None and dataset.fps != fps:
        raise ValueError(f"The dataset fps should be equal to requested fps ({dataset.fps} != {fps}).")

    # if policy is given it needs cleaning up
    if policy is not None:
        policy.reset()

    timestamp = 0
    start_episode_t = time.perf_counter()
    while timestamp < control_time_s:
        start_loop_t = time.perf_counter()

        observation = robot.get_observation()

        if policy is not None or dataset is not None:
            observation_frame = build_dataset_frame(dataset.features, observation, prefix="observation")

        if policy is not None:
            action_values = predict_action(
                observation_frame,
                policy,
                get_safe_torch_device(policy.config.device),
                policy.config.use_amp,
                task=single_task,
                robot_type=robot.robot_type,
            )
            action = {key: action_values[i].item() for i, key in enumerate(robot.action_features)}
        else:
            action = teleop.get_action()

        # Action can eventually be clipped using `max_relative_target`,
        # so action actually sent is saved in the dataset.
        sent_action = robot.send_action(action)

        if dataset is not None:
            action_frame = build_dataset_frame(dataset.features, sent_action, prefix="action")
            frame = {**observation_frame, **action_frame}
            dataset.add_frame(frame, task=single_task)

        if display_data:
            for obs, val in observation.items():
                if isinstance(val, float):
                    rr.log(f"observation.{obs}", rr.Scalar(val))
                elif isinstance(val, np.ndarray):
                    rr.log(f"observation.{obs}", rr.Image(val), static=True)
            for act, val in action.items():
                if isinstance(val, float):
                    rr.log(f"action.{act}", rr.Scalar(val))

        dt_s = time.perf_counter() - start_loop_t
        busy_wait(1 / fps - dt_s)

        timestamp = time.perf_counter() - start_episode_t
        if events["exit_early"]:
            events["exit_early"] = False
            break


@parser.wrap()
def record(cfg: RecordConfig) -> LeRobotDataset:
    init_logging()
    logging.info(pformat(asdict(cfg)))
    if cfg.display_data:
        _init_rerun(session_name="recording")

    robot = make_robot_from_config(cfg.robot)
    teleop = make_teleoperator_from_config(cfg.teleop) if cfg.teleop is not None else None

    action_features = hw_to_dataset_features(robot.action_features, "action", cfg.dataset.video)
    obs_features = hw_to_dataset_features(robot.observation_features, "observation", cfg.dataset.video)
    dataset_features = {**action_features, **obs_features}

    if cfg.resume:
        dataset = LeRobotDataset(
            cfg.dataset.repo_id,
            root=cfg.dataset.root,
        )

        if hasattr(robot, "cameras") and len(robot.cameras) > 0:
            dataset.start_image_writer(
                num_processes=cfg.dataset.num_image_writer_processes,
                num_threads=cfg.dataset.num_image_writer_threads_per_camera * len(robot.cameras),
            )
        sanity_check_dataset_robot_compatibility(dataset, robot, cfg.dataset.fps, dataset_features)
    else:
        # Create empty dataset or load existing saved episodes
        sanity_check_dataset_name(cfg.dataset.repo_id, cfg.policy)
        dataset = LeRobotDataset.create(
            cfg.dataset.repo_id,
            cfg.dataset.fps,
            root=cfg.dataset.root,
            robot_type=robot.name,
            features=dataset_features,
            use_videos=cfg.dataset.video,
            image_writer_processes=cfg.dataset.num_image_writer_processes,
            image_writer_threads=cfg.dataset.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)

    robot.connect()
    if teleop is not None:
        teleop.connect()

    listener, events = init_keyboard_listener()

    for recorded_episodes in range(cfg.dataset.num_episodes):
        log_say(f"Recording episode {dataset.num_episodes}", cfg.play_sounds)
        record_loop(
            robot=robot,
            events=events,
            fps=cfg.dataset.fps,
            teleop=teleop,
            policy=policy,
            dataset=dataset,
            control_time_s=cfg.dataset.episode_time_s,
            single_task=cfg.dataset.single_task,
            display_data=cfg.display_data,
        )

        # Execute a few seconds without recording to give time to manually reset the environment
        # Skip reset for the last episode to be recorded
        if not events["stop_recording"] and (
            (recorded_episodes < cfg.dataset.num_episodes - 1) or events["rerecord_episode"]
        ):
            log_say("Reset the environment", cfg.play_sounds)
            record_loop(
                robot=robot,
                events=events,
                fps=cfg.dataset.fps,
                teleop=teleop,
                control_time_s=cfg.dataset.reset_time_s,
                single_task=cfg.dataset.single_task,
                display_data=cfg.display_data,
            )

        if events["rerecord_episode"]:
            log_say("Re-record episode", cfg.play_sounds)
            events["rerecord_episode"] = False
            events["exit_early"] = False
            dataset.clear_episode_buffer()
            continue

        dataset.save_episode()

        if events["stop_recording"]:
            break

    log_say("Stop recording", cfg.play_sounds, blocking=True)

    robot.disconnect()
    teleop.disconnect()

    if not is_headless() and listener is not None:
        listener.stop()

    if cfg.dataset.push_to_hub:
        dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)

    log_say("Exiting", cfg.play_sounds)
    return dataset


if __name__ == "__main__":
    record()