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#!/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.
"""
Example script demonstrating dataset tools utilities.
This script shows how to:
1. Delete episodes from a dataset
2. Split a dataset into train/val sets
3. Add/remove features
4. Merge datasets
Usage:
python examples/dataset/use_dataset_tools.py
"""
import numpy as np
from lerobot.datasets.dataset_tools import (
add_features,
delete_episodes,
merge_datasets,
modify_features,
remove_feature,
split_dataset,
)
from lerobot.datasets.lerobot_dataset import LeRobotDataset
def main():
dataset = LeRobotDataset("lerobot/pusht")
print(f"Original dataset: {dataset.meta.total_episodes} episodes, {dataset.meta.total_frames} frames")
print(f"Features: {list(dataset.meta.features.keys())}")
print("\n1. Deleting episodes 0 and 2...")
filtered_dataset = delete_episodes(dataset, episode_indices=[0, 2], repo_id="lerobot/pusht_filtered")
print(f"Filtered dataset: {filtered_dataset.meta.total_episodes} episodes")
print("\n2. Splitting dataset into train/val...")
splits = split_dataset(
dataset,
splits={"train": 0.8, "val": 0.2},
)
print(f"Train split: {splits['train'].meta.total_episodes} episodes")
print(f"Val split: {splits['val'].meta.total_episodes} episodes")
print("\n3. Adding features...")
reward_values = np.random.randn(dataset.meta.total_frames).astype(np.float32)
def compute_success(row_dict, episode_index, frame_index):
episode_length = 10
return float(frame_index >= episode_length - 10)
dataset_with_features = add_features(
dataset,
features={
"reward": (
reward_values,
{"dtype": "float32", "shape": (1,), "names": None},
),
"success": (
compute_success,
{"dtype": "float32", "shape": (1,), "names": None},
),
},
repo_id="lerobot/pusht_with_features",
)
print(f"New features: {list(dataset_with_features.meta.features.keys())}")
print("\n4. Removing the success feature...")
dataset_cleaned = remove_feature(
dataset_with_features, feature_names="success", repo_id="lerobot/pusht_cleaned"
)
print(f"Features after removal: {list(dataset_cleaned.meta.features.keys())}")
print("\n5. Using modify_features to add and remove features simultaneously...")
dataset_modified = modify_features(
dataset_with_features,
add_features={
"discount": (
np.ones(dataset.meta.total_frames, dtype=np.float32) * 0.99,
{"dtype": "float32", "shape": (1,), "names": None},
),
},
remove_features="reward",
repo_id="lerobot/pusht_modified",
)
print(f"Modified features: {list(dataset_modified.meta.features.keys())}")
print("\n6. Merging train and val splits back together...")
merged = merge_datasets([splits["train"], splits["val"]], output_repo_id="lerobot/pusht_merged")
print(f"Merged dataset: {merged.meta.total_episodes} episodes")
print("\n7. Complex workflow example...")
if len(dataset.meta.camera_keys) > 1:
camera_to_remove = dataset.meta.camera_keys[0]
print(f"Removing camera: {camera_to_remove}")
dataset_no_cam = remove_feature(
dataset, feature_names=camera_to_remove, repo_id="pusht_no_first_camera"
)
print(f"Remaining cameras: {dataset_no_cam.meta.camera_keys}")
print("\nDone! Check ~/.cache/huggingface/lerobot/ for the created datasets.")
if __name__ == "__main__":
main()
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