lerobot-clone/src/lerobot/scripts/lerobot_edit_dataset.py

#!/usr/bin/env python

# Copyright 2025 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.

"""
Edit LeRobot datasets using various transformation tools.

This script allows you to delete episodes, split datasets, merge datasets,
remove features, and convert image datasets to video format.
When new_repo_id is specified, creates a new dataset.

Usage Examples:

Delete episodes 0, 2, and 5 from a dataset:
    python -m lerobot.scripts.lerobot_edit_dataset \
        --repo_id lerobot/pusht \
        --operation.type delete_episodes \
        --operation.episode_indices "[0, 2, 5]"

Delete episodes and save to a new dataset:
    python -m lerobot.scripts.lerobot_edit_dataset \
        --repo_id lerobot/pusht \
        --new_repo_id lerobot/pusht_filtered \
        --operation.type delete_episodes \
        --operation.episode_indices "[0, 2, 5]"

Split dataset by fractions:
    python -m lerobot.scripts.lerobot_edit_dataset \
        --repo_id lerobot/pusht \
        --operation.type split \
        --operation.splits '{"train": 0.8, "val": 0.2}'

Split dataset by episode indices:
    python -m lerobot.scripts.lerobot_edit_dataset \
        --repo_id lerobot/pusht \
        --operation.type split \
        --operation.splits '{"train": [0, 1, 2, 3], "val": [4, 5]}'

Split into more than two splits:
    python -m lerobot.scripts.lerobot_edit_dataset \
        --repo_id lerobot/pusht \
        --operation.type split \
        --operation.splits '{"train": 0.6, "val": 0.2, "test": 0.2}'

Merge multiple datasets:
    python -m lerobot.scripts.lerobot_edit_dataset \
        --repo_id lerobot/pusht_merged \
        --operation.type merge \
        --operation.repo_ids "['lerobot/pusht_train', 'lerobot/pusht_val']"

Remove camera feature:
    python -m lerobot.scripts.lerobot_edit_dataset \
        --repo_id lerobot/pusht \
        --operation.type remove_feature \
        --operation.feature_names "['observation.images.top']"

Convert image dataset to video format and save locally:
    python -m lerobot.scripts.lerobot_edit_dataset \
        --repo_id lerobot/pusht_image \
        --operation.type convert_image_to_video \
        --operation.output_dir /path/to/output/pusht_video

Convert image dataset to video format and save with new repo_id:
    python -m lerobot.scripts.lerobot_edit_dataset \
        --repo_id lerobot/pusht_image \
        --new_repo_id lerobot/pusht_video \
        --operation.type convert_image_to_video

Convert image dataset to video format and push to hub:
    python -m lerobot.scripts.lerobot_edit_dataset \
        --repo_id lerobot/pusht_image \
        --new_repo_id lerobot/pusht_video \
        --operation.type convert_image_to_video \
        --push_to_hub true

Using JSON config file:
    python -m lerobot.scripts.lerobot_edit_dataset \
        --config_path path/to/edit_config.json
"""

import logging
import shutil
from dataclasses import dataclass
from pathlib import Path

from lerobot.configs import parser
from lerobot.datasets.dataset_tools import (
    convert_image_to_video_dataset,
    delete_episodes,
    merge_datasets,
    remove_feature,
    split_dataset,
)
from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.utils.constants import HF_LEROBOT_HOME
from lerobot.utils.utils import init_logging


@dataclass
class DeleteEpisodesConfig:
    type: str = "delete_episodes"
    episode_indices: list[int] | None = None


@dataclass
class SplitConfig:
    type: str = "split"
    splits: dict[str, float | list[int]] | None = None


@dataclass
class MergeConfig:
    type: str = "merge"
    repo_ids: list[str] | None = None


@dataclass
class RemoveFeatureConfig:
    type: str = "remove_feature"
    feature_names: list[str] | None = None


@dataclass
class ConvertImageToVideoConfig:
    type: str = "convert_image_to_video"
    output_dir: str | None = None
    vcodec: str = "libsvtav1"
    pix_fmt: str = "yuv420p"
    g: int = 2
    crf: int = 30
    fast_decode: int = 0
    episode_indices: list[int] | None = None
    num_workers: int = 4
    max_episodes_per_batch: int | None = None
    max_frames_per_batch: int | None = None


@dataclass
class EditDatasetConfig:
    repo_id: str
    operation: (
        DeleteEpisodesConfig | SplitConfig | MergeConfig | RemoveFeatureConfig | ConvertImageToVideoConfig
    )
    root: str | None = None
    new_repo_id: str | None = None
    push_to_hub: bool = False


def get_output_path(repo_id: str, new_repo_id: str | None, root: Path | None) -> tuple[str, Path]:
    if new_repo_id:
        output_repo_id = new_repo_id
        output_dir = root / new_repo_id if root else HF_LEROBOT_HOME / new_repo_id
    else:
        output_repo_id = repo_id
        dataset_path = root / repo_id if root else HF_LEROBOT_HOME / repo_id
        old_path = Path(str(dataset_path) + "_old")

        if dataset_path.exists():
            if old_path.exists():
                shutil.rmtree(old_path)
            shutil.move(str(dataset_path), str(old_path))

        output_dir = dataset_path

    return output_repo_id, output_dir


def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
    if not isinstance(cfg.operation, DeleteEpisodesConfig):
        raise ValueError("Operation config must be DeleteEpisodesConfig")

    if not cfg.operation.episode_indices:
        raise ValueError("episode_indices must be specified for delete_episodes operation")

    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
    output_repo_id, output_dir = get_output_path(
        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
    )

    if cfg.new_repo_id is None:
        dataset.root = Path(str(dataset.root) + "_old")

    logging.info(f"Deleting episodes {cfg.operation.episode_indices} from {cfg.repo_id}")
    new_dataset = delete_episodes(
        dataset,
        episode_indices=cfg.operation.episode_indices,
        output_dir=output_dir,
        repo_id=output_repo_id,
    )

    logging.info(f"Dataset saved to {output_dir}")
    logging.info(f"Episodes: {new_dataset.meta.total_episodes}, Frames: {new_dataset.meta.total_frames}")

    if cfg.push_to_hub:
        logging.info(f"Pushing to hub as {output_repo_id}")
        LeRobotDataset(output_repo_id, root=output_dir).push_to_hub()


def handle_split(cfg: EditDatasetConfig) -> None:
    if not isinstance(cfg.operation, SplitConfig):
        raise ValueError("Operation config must be SplitConfig")

    if not cfg.operation.splits:
        raise ValueError(
            "splits dict must be specified with split names as keys and fractions/episode lists as values"
        )

    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)

    logging.info(f"Splitting dataset {cfg.repo_id} with splits: {cfg.operation.splits}")
    split_datasets = split_dataset(dataset, splits=cfg.operation.splits)

    for split_name, split_ds in split_datasets.items():
        split_repo_id = f"{cfg.repo_id}_{split_name}"
        logging.info(
            f"{split_name}: {split_ds.meta.total_episodes} episodes, {split_ds.meta.total_frames} frames"
        )

        if cfg.push_to_hub:
            logging.info(f"Pushing {split_name} split to hub as {split_repo_id}")
            LeRobotDataset(split_ds.repo_id, root=split_ds.root).push_to_hub()


def handle_merge(cfg: EditDatasetConfig) -> None:
    if not isinstance(cfg.operation, MergeConfig):
        raise ValueError("Operation config must be MergeConfig")

    if not cfg.operation.repo_ids:
        raise ValueError("repo_ids must be specified for merge operation")

    if not cfg.repo_id:
        raise ValueError("repo_id must be specified as the output repository for merged dataset")

    logging.info(f"Loading {len(cfg.operation.repo_ids)} datasets to merge")
    datasets = [LeRobotDataset(repo_id, root=cfg.root) for repo_id in cfg.operation.repo_ids]

    output_dir = Path(cfg.root) / cfg.repo_id if cfg.root else HF_LEROBOT_HOME / cfg.repo_id

    logging.info(f"Merging datasets into {cfg.repo_id}")
    merged_dataset = merge_datasets(
        datasets,
        output_repo_id=cfg.repo_id,
        output_dir=output_dir,
    )

    logging.info(f"Merged dataset saved to {output_dir}")
    logging.info(
        f"Episodes: {merged_dataset.meta.total_episodes}, Frames: {merged_dataset.meta.total_frames}"
    )

    if cfg.push_to_hub:
        logging.info(f"Pushing to hub as {cfg.repo_id}")
        LeRobotDataset(merged_dataset.repo_id, root=output_dir).push_to_hub()


def handle_remove_feature(cfg: EditDatasetConfig) -> None:
    if not isinstance(cfg.operation, RemoveFeatureConfig):
        raise ValueError("Operation config must be RemoveFeatureConfig")

    if not cfg.operation.feature_names:
        raise ValueError("feature_names must be specified for remove_feature operation")

    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
    output_repo_id, output_dir = get_output_path(
        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
    )

    if cfg.new_repo_id is None:
        dataset.root = Path(str(dataset.root) + "_old")

    logging.info(f"Removing features {cfg.operation.feature_names} from {cfg.repo_id}")
    new_dataset = remove_feature(
        dataset,
        feature_names=cfg.operation.feature_names,
        output_dir=output_dir,
        repo_id=output_repo_id,
    )

    logging.info(f"Dataset saved to {output_dir}")
    logging.info(f"Remaining features: {list(new_dataset.meta.features.keys())}")

    if cfg.push_to_hub:
        logging.info(f"Pushing to hub as {output_repo_id}")
        LeRobotDataset(output_repo_id, root=output_dir).push_to_hub()


def handle_convert_image_to_video(cfg: EditDatasetConfig) -> None:
    # Note: Parser may create any config type with the right fields, so we access fields directly
    # instead of checking isinstance()
    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)

    # Determine output directory and repo_id
    # Priority: 1) new_repo_id, 2) operation.output_dir, 3) auto-generated name
    output_dir_config = getattr(cfg.operation, "output_dir", None)

    if cfg.new_repo_id:
        # Use new_repo_id for both local storage and hub push
        output_repo_id = cfg.new_repo_id
        # Place new dataset as a sibling to the original dataset
        # Get the parent of the actual dataset root (not cfg.root which might be the lerobot cache dir)
        # Extract just the dataset name (after last slash) for the local directory
        local_dir_name = cfg.new_repo_id.split("/")[-1]
        output_dir = dataset.root.parent / local_dir_name
        logging.info(f"Saving to new dataset: {cfg.new_repo_id} at {output_dir}")
    elif output_dir_config:
        # Use custom output directory for local-only storage
        output_dir = Path(output_dir_config)
        # Extract repo name from output_dir for the dataset
        output_repo_id = output_dir.name
        logging.info(f"Saving to local directory: {output_dir}")
    else:
        # Auto-generate name: append "_video" to original repo_id
        output_repo_id = f"{cfg.repo_id}_video"
        # Place new dataset as a sibling to the original dataset
        # Extract just the dataset name (after last slash) for the local directory
        local_dir_name = output_repo_id.split("/")[-1]
        output_dir = dataset.root.parent / local_dir_name
        logging.info(f"Saving to auto-generated location: {output_dir}")

    logging.info(f"Converting dataset {cfg.repo_id} to video format")

    new_dataset = convert_image_to_video_dataset(
        dataset=dataset,
        output_dir=output_dir,
        repo_id=output_repo_id,
        vcodec=getattr(cfg.operation, "vcodec", "libsvtav1"),
        pix_fmt=getattr(cfg.operation, "pix_fmt", "yuv420p"),
        g=getattr(cfg.operation, "g", 2),
        crf=getattr(cfg.operation, "crf", 30),
        fast_decode=getattr(cfg.operation, "fast_decode", 0),
        episode_indices=getattr(cfg.operation, "episode_indices", None),
        num_workers=getattr(cfg.operation, "num_workers", 4),
        max_episodes_per_batch=getattr(cfg.operation, "max_episodes_per_batch", None),
        max_frames_per_batch=getattr(cfg.operation, "max_frames_per_batch", None),
    )

    logging.info("Video dataset created successfully!")
    logging.info(f"Location: {output_dir}")
    logging.info(f"Episodes: {new_dataset.meta.total_episodes}")
    logging.info(f"Frames: {new_dataset.meta.total_frames}")

    if cfg.push_to_hub:
        logging.info(f"Pushing to hub as {output_repo_id}...")
        new_dataset.push_to_hub()
        logging.info("✓ Successfully pushed to hub!")
    else:
        logging.info("Dataset saved locally (not pushed to hub)")


@parser.wrap()
def edit_dataset(cfg: EditDatasetConfig) -> None:
    operation_type = cfg.operation.type

    if operation_type == "delete_episodes":
        handle_delete_episodes(cfg)
    elif operation_type == "split":
        handle_split(cfg)
    elif operation_type == "merge":
        handle_merge(cfg)
    elif operation_type == "remove_feature":
        handle_remove_feature(cfg)
    elif operation_type == "convert_image_to_video":
        handle_convert_image_to_video(cfg)
    else:
        raise ValueError(
            f"Unknown operation type: {operation_type}\n"
            f"Available operations: delete_episodes, split, merge, remove_feature, convert_to_video"
        )


def main() -> None:
    init_logging()
    edit_dataset()


if __name__ == "__main__":
    main()