diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml index 36eaea165cc..568bd638057 100644 --- a/docs/source/_toctree.yml +++ b/docs/source/_toctree.yml @@ -25,8 +25,12 @@ title: Using LeRobotDataset - local: porting_datasets_v3 title: Porting Large Datasets + - local: using_dataset_tools + title: Using the Dataset Tools title: "Datasets" - sections: + - local: act + title: ACT - local: smolvla title: SmolVLA - local: pi0 diff --git a/docs/source/act.mdx b/docs/source/act.mdx new file mode 100644 index 00000000000..e3294ca6945 --- /dev/null +++ b/docs/source/act.mdx @@ -0,0 +1,92 @@ +# ACT (Action Chunking with Transformers) + +ACT is a **lightweight and efficient policy for imitation learning**, especially well-suited for fine-grained manipulation tasks. It's the **first model we recommend when you're starting out** with LeRobot due to its fast training time, low computational requirements, and strong performance. + +
+ +
+ +_Watch this tutorial from the LeRobot team to learn how ACT works: [LeRobot ACT Tutorial](https://www.youtube.com/watch?v=ft73x0LfGpM)_ + +## Model Overview + +Action Chunking with Transformers (ACT) was introduced in the paper [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://arxiv.org/abs/2304.13705) by Zhao et al. The policy was designed to enable precise, contact-rich manipulation tasks using affordable hardware and minimal demonstration data. + +### Why ACT is Great for Beginners + +ACT stands out as an excellent starting point for several reasons: + +- **Fast Training**: Trains in a few hours on a single GPU +- **Lightweight**: Only ~80M parameters, making it efficient and easy to work with +- **Data Efficient**: Often achieves high success rates with just 50 demonstrations + +### Architecture + +ACT uses a transformer-based architecture with three main components: + +1. **Vision Backbone**: ResNet-18 processes images from multiple camera viewpoints +2. **Transformer Encoder**: Synthesizes information from camera features, joint positions, and a learned latent variable +3. **Transformer Decoder**: Generates coherent action sequences using cross-attention + +The policy takes as input: + +- Multiple RGB images (e.g., from wrist cameras, front/top cameras) +- Current robot joint positions +- A latent style variable `z` (learned during training, set to zero during inference) + +And outputs a chunk of `k` future action sequences. + +## Installation Requirements + +1. Install LeRobot by following our [Installation Guide](./installation). +2. ACT is included in the base LeRobot installation, so no additional dependencies are needed! + +## Training ACT + +ACT works seamlessly with the standard LeRobot training pipeline. Here's a complete example for training ACT on your dataset: + +```bash +lerobot-train \ + --dataset.repo_id=${HF_USER}/your_dataset \ + --policy.type=act \ + --output_dir=outputs/train/act_your_dataset \ + --job_name=act_your_dataset \ + --policy.device=cuda \ + --wandb.enable=true \ + --policy.repo_id=${HF_USER}/act_policy +``` + +### Training Tips + +1. **Start with defaults**: ACT's default hyperparameters work well for most tasks +2. **Training duration**: Expect a few hours for 100k training steps on a single GPU +3. **Batch size**: Start with batch size 8 and adjust based on your GPU memory + +### Train using Google Colab + +If your local computer doesn't have a powerful GPU, you can utilize Google Colab to train your model by following the [ACT training notebook](./notebooks#training-act). + +## Evaluating ACT + +Once training is complete, you can evaluate your ACT policy using the `lerobot-record` command with your trained policy. This will run inference and record evaluation episodes: + +```bash +lerobot-record \ + --robot.type=so100_follower \ + --robot.port=/dev/ttyACM0 \ + --robot.id=my_robot \ + --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \ + --display_data=true \ + --dataset.repo_id=${HF_USER}/eval_act_your_dataset \ + --dataset.num_episodes=10 \ + --dataset.single_task="Your task description" \ + --policy.path=${HF_USER}/act_policy +``` diff --git a/docs/source/il_robots.mdx b/docs/source/il_robots.mdx index 91df14028cd..0d8fd56e5a8 100644 --- a/docs/source/il_robots.mdx +++ b/docs/source/il_robots.mdx @@ -513,13 +513,14 @@ from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig from lerobot.datasets.lerobot_dataset import LeRobotDataset from lerobot.datasets.utils import hw_to_dataset_features from lerobot.policies.act.modeling_act import ACTPolicy +from lerobot.policies.factory import make_pre_post_processors from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig from lerobot.robots.so100_follower.so100_follower import SO100Follower +from lerobot.scripts.lerobot_record import record_loop from lerobot.utils.control_utils import init_keyboard_listener from lerobot.utils.utils import log_say from lerobot.utils.visualization_utils import init_rerun -from lerobot.record import record_loop -from lerobot.policies.factory import make_processor + NUM_EPISODES = 5 FPS = 30 @@ -562,7 +563,7 @@ init_rerun(session_name="recording") # Connect the robot robot.connect() -preprocessor, postprocessor = make_processor( +preprocessor, postprocessor = make_pre_post_processors( policy_cfg=policy, pretrained_path=HF_MODEL_ID, dataset_stats=dataset.meta.stats, diff --git a/docs/source/integrate_hardware.mdx b/docs/source/integrate_hardware.mdx index 7e7fe0bffc7..ed9dc8dd563 100644 --- a/docs/source/integrate_hardware.mdx +++ b/docs/source/integrate_hardware.mdx @@ -8,7 +8,7 @@ To that end, we provide the [`Robot`](https://github.com/huggingface/lerobot/blo - Your own robot which exposes a communication interface (e.g. serial, CAN, TCP) - A way to read sensor data and send motor commands programmatically, e.g. manufacturer's SDK or API, or your own protocol implementation. -- LeRobot installed in your environment. Follow our [Installation Guide](./installation.mdx). +- LeRobot installed in your environment. Follow our [Installation Guide](./installation). ## Choose your motors @@ -65,7 +65,7 @@ class MyCoolRobotConfig(RobotConfig): ``` -[Cameras tutorial](./cameras.mdx) to understand how to detect and add your camera. +[Cameras tutorial](./cameras) to understand how to detect and add your camera. Next, we'll create our actual robot class which inherits from `Robot`. This abstract class defines a contract you must follow for your robot to be usable with the rest of the LeRobot tools. diff --git a/docs/source/introduction_processors.mdx b/docs/source/introduction_processors.mdx index 308edbb3bee..6f376861541 100644 --- a/docs/source/introduction_processors.mdx +++ b/docs/source/introduction_processors.mdx @@ -297,9 +297,9 @@ LeRobot provides many registered processor steps. Here are the most commonly use ### Next Steps -- **[Implement Your Own Processor](implement_your_own_processor.mdx)** - Create custom processor steps -- **[Debug Your Pipeline](debug_processor_pipeline.mdx)** - Troubleshoot and optimize pipelines -- **[Processors for Robots and Teleoperators](processors_robots_teleop.mdx)** - Real-world integration patterns +- **[Implement Your Own Processor](./implement_your_own_processor)** - Create custom processor steps +- **[Debug Your Pipeline](./debug_processor_pipeline)** - Troubleshoot and optimize pipelines +- **[Processors for Robots and Teleoperators](./processors_robots_teleop)** - Real-world integration patterns ## Summary diff --git a/docs/source/lerobot-dataset-v3.mdx b/docs/source/lerobot-dataset-v3.mdx index cf1942fdcfb..3521914f2f2 100644 --- a/docs/source/lerobot-dataset-v3.mdx +++ b/docs/source/lerobot-dataset-v3.mdx @@ -279,3 +279,36 @@ python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id== episode_length - 10) + + dataset_with_success = add_feature( + dataset_with_reward, + feature_name="success", + feature_values=compute_success, + feature_info={ + "dtype": "float32", + "shape": (1,), + "names": None, + }, + repo_id="lerobot/pusht_with_reward_and_success", + ) + + print(f"New features: {list(dataset_with_success.meta.features.keys())}") + + print("\n4. Removing the success feature...") + dataset_cleaned = remove_feature( + dataset_with_success, feature_names="success", repo_id="lerobot/pusht_cleaned" + ) + print(f"Features after removal: {list(dataset_cleaned.meta.features.keys())}") + + print("\n5. 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("\n6. 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() diff --git a/examples/lekiwi/evaluate.py b/examples/lekiwi/evaluate.py index 8a62d92a9c6..4501008d04a 100644 --- a/examples/lekiwi/evaluate.py +++ b/examples/lekiwi/evaluate.py @@ -133,4 +133,6 @@ log_say("Stop recording") robot.disconnect() listener.stop() + +dataset.finalize() dataset.push_to_hub() diff --git a/examples/lekiwi/record.py b/examples/lekiwi/record.py index 9070741bf46..491e1c3869c 100644 --- a/examples/lekiwi/record.py +++ b/examples/lekiwi/record.py @@ -130,4 +130,6 @@ leader_arm.disconnect() keyboard.disconnect() listener.stop() + +dataset.finalize() dataset.push_to_hub() diff --git a/examples/phone_to_so100/evaluate.py b/examples/phone_to_so100/evaluate.py index 0d53f117751..ff8dbddd218 100644 --- a/examples/phone_to_so100/evaluate.py +++ b/examples/phone_to_so100/evaluate.py @@ -194,4 +194,6 @@ log_say("Stop recording") robot.disconnect() listener.stop() + +dataset.finalize() dataset.push_to_hub() diff --git a/examples/phone_to_so100/record.py b/examples/phone_to_so100/record.py index d3ef293a717..880f9c9b482 100644 --- a/examples/phone_to_so100/record.py +++ b/examples/phone_to_so100/record.py @@ -200,4 +200,6 @@ robot.disconnect() phone.disconnect() listener.stop() + +dataset.finalize() dataset.push_to_hub() diff --git a/examples/port_datasets/port_droid.py b/examples/port_datasets/port_droid.py index 4efb131e4c6..a1fb509141e 100644 --- a/examples/port_datasets/port_droid.py +++ b/examples/port_datasets/port_droid.py @@ -362,6 +362,8 @@ def port_droid( lerobot_dataset.save_episode() logging.info("Save_episode") + lerobot_dataset.finalize() + if push_to_hub: lerobot_dataset.push_to_hub( # Add openx tag, since it belongs to the openx collection of datasets diff --git a/examples/so100_to_so100_EE/evaluate.py b/examples/so100_to_so100_EE/evaluate.py index 53a38544249..60489b3cf73 100644 --- a/examples/so100_to_so100_EE/evaluate.py +++ b/examples/so100_to_so100_EE/evaluate.py @@ -195,4 +195,6 @@ log_say("Stop recording") robot.disconnect() listener.stop() + +dataset.finalize() dataset.push_to_hub() diff --git a/examples/so100_to_so100_EE/record.py b/examples/so100_to_so100_EE/record.py index 9ed6e51a992..5ff1c286f72 100644 --- a/examples/so100_to_so100_EE/record.py +++ b/examples/so100_to_so100_EE/record.py @@ -199,4 +199,6 @@ leader.disconnect() follower.disconnect() listener.stop() + +dataset.finalize() dataset.push_to_hub() diff --git a/pyproject.toml b/pyproject.toml index c67b481f09b..a70208cb2e5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -67,7 +67,7 @@ dependencies = [ "cmake>=3.29.0.1,<4.2.0", "einops>=0.8.0,<0.9.0", "opencv-python-headless>=4.9.0,<4.13.0", - "av>=14.2.0,<16.0.0", + "av>=15.0.0,<16.0.0", "jsonlines>=4.0.0,<5.0.0", "packaging>=24.2,<26.0", "pynput>=1.7.7,<1.9.0", @@ -175,6 +175,7 @@ lerobot-dataset-viz="lerobot.scripts.lerobot_dataset_viz:main" lerobot-info="lerobot.scripts.lerobot_info:main" lerobot-find-joint-limits="lerobot.scripts.lerobot_find_joint_limits:main" lerobot-imgtransform-viz="lerobot.scripts.lerobot_imgtransform_viz:main" +lerobot-edit-dataset="lerobot.scripts.lerobot_edit_dataset:main" # ---------------- Tool Configurations ---------------- [tool.setuptools.packages.find] diff --git a/src/lerobot/async_inference/configs.py b/src/lerobot/async_inference/configs.py index 24f889df17d..d1768a323e4 100644 --- a/src/lerobot/async_inference/configs.py +++ b/src/lerobot/async_inference/configs.py @@ -142,11 +142,6 @@ class RobotClientConfig: default=False, metadata={"help": "Visualize the action queue size"} ) - # Verification configuration - verify_robot_cameras: bool = field( - default=True, metadata={"help": "Verify that the robot cameras match the policy cameras"} - ) - @property def environment_dt(self) -> float: """Environment time step, in seconds""" diff --git a/src/lerobot/async_inference/helpers.py b/src/lerobot/async_inference/helpers.py index 54fad8c546a..f73cbc1dabe 100644 --- a/src/lerobot/async_inference/helpers.py +++ b/src/lerobot/async_inference/helpers.py @@ -62,15 +62,6 @@ def visualize_action_queue_size(action_queue_size: list[int]) -> None: plt.show() -def validate_robot_cameras_for_policy( - lerobot_observation_features: dict[str, dict], policy_image_features: dict[str, PolicyFeature] -) -> None: - image_keys = list(filter(is_image_key, lerobot_observation_features)) - assert set(image_keys) == set(policy_image_features.keys()), ( - f"Policy image features must match robot cameras! Received {list(policy_image_features.keys())} != {image_keys}" - ) - - def map_robot_keys_to_lerobot_features(robot: Robot) -> dict[str, dict]: return hw_to_dataset_features(robot.observation_features, OBS_STR, use_video=False) diff --git a/src/lerobot/async_inference/robot_client.py b/src/lerobot/async_inference/robot_client.py index 8c4425c6b5f..f9d70a64ece 100644 --- a/src/lerobot/async_inference/robot_client.py +++ b/src/lerobot/async_inference/robot_client.py @@ -48,7 +48,6 @@ from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig # noqa: F401 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig # noqa: F401 -from lerobot.configs.policies import PreTrainedConfig from lerobot.robots import ( # noqa: F401 Robot, RobotConfig, @@ -76,7 +75,6 @@ TimedObservation, get_logger, map_robot_keys_to_lerobot_features, - validate_robot_cameras_for_policy, visualize_action_queue_size, ) @@ -98,14 +96,6 @@ def __init__(self, config: RobotClientConfig): lerobot_features = map_robot_keys_to_lerobot_features(self.robot) - if config.verify_robot_cameras: - # Load policy config for validation - policy_config = PreTrainedConfig.from_pretrained(config.pretrained_name_or_path) - policy_image_features = policy_config.image_features - - # The cameras specified for inference must match the one supported by the policy chosen - validate_robot_cameras_for_policy(lerobot_features, policy_image_features) - # Use environment variable if server_address is not provided in config self.server_address = config.server_address diff --git a/src/lerobot/datasets/aggregate.py b/src/lerobot/datasets/aggregate.py index 803645f292e..870c9571e83 100644 --- a/src/lerobot/datasets/aggregate.py +++ b/src/lerobot/datasets/aggregate.py @@ -31,15 +31,15 @@ DEFAULT_EPISODES_PATH, DEFAULT_VIDEO_FILE_SIZE_IN_MB, DEFAULT_VIDEO_PATH, + get_file_size_in_mb, get_parquet_file_size_in_mb, - get_video_size_in_mb, to_parquet_with_hf_images, update_chunk_file_indices, write_info, write_stats, write_tasks, ) -from lerobot.datasets.video_utils import concatenate_video_files +from lerobot.datasets.video_utils import concatenate_video_files, get_video_duration_in_s def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]): @@ -130,10 +130,34 @@ def update_meta_data( df["data/chunk_index"] = df["data/chunk_index"] + data_idx["chunk"] df["data/file_index"] = df["data/file_index"] + data_idx["file"] for key, video_idx in videos_idx.items(): - df[f"videos/{key}/chunk_index"] = df[f"videos/{key}/chunk_index"] + video_idx["chunk"] - df[f"videos/{key}/file_index"] = df[f"videos/{key}/file_index"] + video_idx["file"] - df[f"videos/{key}/from_timestamp"] = df[f"videos/{key}/from_timestamp"] + video_idx["latest_duration"] - df[f"videos/{key}/to_timestamp"] = df[f"videos/{key}/to_timestamp"] + video_idx["latest_duration"] + # Store original video file indices before updating + orig_chunk_col = f"videos/{key}/chunk_index" + orig_file_col = f"videos/{key}/file_index" + df["_orig_chunk"] = df[orig_chunk_col].copy() + df["_orig_file"] = df[orig_file_col].copy() + + # Update chunk and file indices to point to destination + df[orig_chunk_col] = video_idx["chunk"] + df[orig_file_col] = video_idx["file"] + + # Apply per-source-file timestamp offsets + src_to_offset = video_idx.get("src_to_offset", {}) + if src_to_offset: + # Apply offset based on original source file + for idx in df.index: + src_key = (df.at[idx, "_orig_chunk"], df.at[idx, "_orig_file"]) + offset = src_to_offset.get(src_key, 0) + df.at[idx, f"videos/{key}/from_timestamp"] += offset + df.at[idx, f"videos/{key}/to_timestamp"] += offset + else: + # Fallback to simple offset (for backward compatibility) + df[f"videos/{key}/from_timestamp"] = ( + df[f"videos/{key}/from_timestamp"] + video_idx["latest_duration"] + ) + df[f"videos/{key}/to_timestamp"] = df[f"videos/{key}/to_timestamp"] + video_idx["latest_duration"] + + # Clean up temporary columns + df = df.drop(columns=["_orig_chunk", "_orig_file"]) df["dataset_from_index"] = df["dataset_from_index"] + dst_meta.info["total_frames"] df["dataset_to_index"] = df["dataset_to_index"] + dst_meta.info["total_frames"] @@ -193,6 +217,10 @@ def aggregate_datasets( robot_type=robot_type, features=features, root=aggr_root, + use_videos=len(video_keys) > 0, + chunks_size=chunk_size, + data_files_size_in_mb=data_files_size_in_mb, + video_files_size_in_mb=video_files_size_in_mb, ) logging.info("Find all tasks") @@ -236,6 +264,11 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu Returns: dict: Updated videos_idx with current chunk and file indices. """ + for key in videos_idx: + videos_idx[key]["episode_duration"] = 0 + # Track offset for each source (chunk, file) pair + videos_idx[key]["src_to_offset"] = {} + for key, video_idx in videos_idx.items(): unique_chunk_file_pairs = { (chunk, file) @@ -249,6 +282,7 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu chunk_idx = video_idx["chunk"] file_idx = video_idx["file"] + current_offset = video_idx["latest_duration"] for src_chunk_idx, src_file_idx in unique_chunk_file_pairs: src_path = src_meta.root / DEFAULT_VIDEO_PATH.format( @@ -263,21 +297,25 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu file_index=file_idx, ) - # If a new file is created, we don't want to increment the latest_duration - update_latest_duration = False + src_duration = get_video_duration_in_s(src_path) if not dst_path.exists(): - # First write to this destination file + # Store offset before incrementing + videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = current_offset dst_path.parent.mkdir(parents=True, exist_ok=True) shutil.copy(str(src_path), str(dst_path)) - continue # not accumulating further, already copied the file in place + videos_idx[key]["episode_duration"] += src_duration + current_offset += src_duration + continue # Check file sizes before appending - src_size = get_video_size_in_mb(src_path) - dst_size = get_video_size_in_mb(dst_path) + src_size = get_file_size_in_mb(src_path) + dst_size = get_file_size_in_mb(dst_path) if dst_size + src_size >= video_files_size_in_mb: - # Rotate to a new chunk/file + # Rotate to a new file, this source becomes start of new destination + # So its offset should be 0 + videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = 0 chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, chunk_size) dst_path = dst_meta.root / DEFAULT_VIDEO_PATH.format( video_key=key, @@ -286,25 +324,22 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu ) dst_path.parent.mkdir(parents=True, exist_ok=True) shutil.copy(str(src_path), str(dst_path)) + # Reset offset for next file + current_offset = src_duration else: - # Get the timestamps shift for this video - timestamps_shift_s = dst_meta.info["total_frames"] / dst_meta.info["fps"] - - # Append to existing video file + # Append to existing video file - use current accumulated offset + videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = current_offset concatenate_video_files( [dst_path, src_path], dst_path, ) - # Update the latest_duration when appending (shifts timestamps!) - update_latest_duration = not update_latest_duration + current_offset += src_duration + + videos_idx[key]["episode_duration"] += src_duration - # Update the videos_idx with the final chunk and file indices for this key videos_idx[key]["chunk"] = chunk_idx videos_idx[key]["file"] = file_idx - if update_latest_duration: - videos_idx[key]["latest_duration"] += timestamps_shift_s - return videos_idx @@ -389,9 +424,6 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx): videos_idx, ) - for k in videos_idx: - videos_idx[k]["latest_duration"] += videos_idx[k]["episode_duration"] - meta_idx = append_or_create_parquet_file( df, src_path, @@ -403,6 +435,10 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx): aggr_root=dst_meta.root, ) + # Increment latest_duration by the total duration added from this source dataset + for k in videos_idx: + videos_idx[k]["latest_duration"] += videos_idx[k]["episode_duration"] + return meta_idx diff --git a/src/lerobot/datasets/dataset_tools.py b/src/lerobot/datasets/dataset_tools.py new file mode 100644 index 00000000000..8ebc4a59dee --- /dev/null +++ b/src/lerobot/datasets/dataset_tools.py @@ -0,0 +1,1015 @@ +#!/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. + +"""Dataset tools utilities for LeRobotDataset. + +This module provides utilities for: +- Deleting episodes from datasets +- Splitting datasets into multiple smaller datasets +- Adding/removing features from datasets +- Merging datasets (wrapper around aggregate functionality) +""" + +import logging +import shutil +from collections.abc import Callable +from pathlib import Path + +import numpy as np +import pandas as pd +import torch +from tqdm import tqdm + +from lerobot.datasets.aggregate import aggregate_datasets +from lerobot.datasets.compute_stats import aggregate_stats +from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata +from lerobot.datasets.utils import ( + DEFAULT_CHUNK_SIZE, + DEFAULT_DATA_FILE_SIZE_IN_MB, + DEFAULT_DATA_PATH, + DEFAULT_EPISODES_PATH, + get_parquet_file_size_in_mb, + load_episodes, + to_parquet_with_hf_images, + update_chunk_file_indices, + write_info, + write_stats, + write_tasks, +) +from lerobot.utils.constants import HF_LEROBOT_HOME + + +def _load_episode_with_stats(src_dataset: LeRobotDataset, episode_idx: int) -> dict: + """Load a single episode's metadata including stats from parquet file. + + Args: + src_dataset: Source dataset + episode_idx: Episode index to load + + Returns: + dict containing episode metadata and stats + """ + ep_meta = src_dataset.meta.episodes[episode_idx] + chunk_idx = ep_meta["meta/episodes/chunk_index"] + file_idx = ep_meta["meta/episodes/file_index"] + + parquet_path = src_dataset.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx) + df = pd.read_parquet(parquet_path) + + episode_row = df[df["episode_index"] == episode_idx].iloc[0] + + return episode_row.to_dict() + + +def delete_episodes( + dataset: LeRobotDataset, + episode_indices: list[int], + output_dir: str | Path | None = None, + repo_id: str | None = None, +) -> LeRobotDataset: + """Delete episodes from a LeRobotDataset and create a new dataset. + + Args: + dataset: The source LeRobotDataset. + episode_indices: List of episode indices to delete. + output_dir: Directory to save the new dataset. If None, uses default location. + repo_id: Repository ID for the new dataset. If None, appends "_modified" to original. + """ + if not episode_indices: + raise ValueError("No episodes to delete") + + valid_indices = set(range(dataset.meta.total_episodes)) + invalid = set(episode_indices) - valid_indices + if invalid: + raise ValueError(f"Invalid episode indices: {invalid}") + + logging.info(f"Deleting {len(episode_indices)} episodes from dataset") + + if repo_id is None: + repo_id = f"{dataset.repo_id}_modified" + output_dir = Path(output_dir) if output_dir is not None else HF_LEROBOT_HOME / repo_id + + episodes_to_keep = [i for i in range(dataset.meta.total_episodes) if i not in episode_indices] + if not episodes_to_keep: + raise ValueError("Cannot delete all episodes from dataset") + + new_meta = LeRobotDatasetMetadata.create( + repo_id=repo_id, + fps=dataset.meta.fps, + features=dataset.meta.features, + robot_type=dataset.meta.robot_type, + root=output_dir, + use_videos=len(dataset.meta.video_keys) > 0, + ) + + episode_mapping = {old_idx: new_idx for new_idx, old_idx in enumerate(episodes_to_keep)} + + video_metadata = None + if dataset.meta.video_keys: + video_metadata = _copy_and_reindex_videos(dataset, new_meta, episode_mapping) + + data_metadata = _copy_and_reindex_data(dataset, new_meta, episode_mapping) + + _copy_and_reindex_episodes_metadata(dataset, new_meta, episode_mapping, data_metadata, video_metadata) + + new_dataset = LeRobotDataset( + repo_id=repo_id, + root=output_dir, + image_transforms=dataset.image_transforms, + delta_timestamps=dataset.delta_timestamps, + tolerance_s=dataset.tolerance_s, + ) + + logging.info(f"Created new dataset with {len(episodes_to_keep)} episodes") + return new_dataset + + +def split_dataset( + dataset: LeRobotDataset, + splits: dict[str, float | list[int]], + output_dir: str | Path | None = None, +) -> dict[str, LeRobotDataset]: + """Split a LeRobotDataset into multiple smaller datasets. + + Args: + dataset: The source LeRobotDataset to split. + splits: Either a dict mapping split names to episode indices, or a dict mapping + split names to fractions (must sum to <= 1.0). + output_dir: Base directory for output datasets. If None, uses default location. + + Examples: + Split by specific episodes + splits = {"train": [0, 1, 2], "val": [3, 4]} + datasets = split_dataset(dataset, splits) + + Split by fractions + splits = {"train": 0.8, "val": 0.2} + datasets = split_dataset(dataset, splits) + """ + if not splits: + raise ValueError("No splits provided") + + if all(isinstance(v, float) for v in splits.values()): + splits = _fractions_to_episode_indices(dataset.meta.total_episodes, splits) + + all_episodes = set() + for split_name, episodes in splits.items(): + if not episodes: + raise ValueError(f"Split '{split_name}' has no episodes") + episode_set = set(episodes) + if episode_set & all_episodes: + raise ValueError("Episodes cannot appear in multiple splits") + all_episodes.update(episode_set) + + valid_indices = set(range(dataset.meta.total_episodes)) + invalid = all_episodes - valid_indices + if invalid: + raise ValueError(f"Invalid episode indices: {invalid}") + + if output_dir is not None: + output_dir = Path(output_dir) + + result_datasets = {} + + for split_name, episodes in splits.items(): + logging.info(f"Creating split '{split_name}' with {len(episodes)} episodes") + + split_repo_id = f"{dataset.repo_id}_{split_name}" + + split_output_dir = ( + output_dir / split_name if output_dir is not None else HF_LEROBOT_HOME / split_repo_id + ) + + episode_mapping = {old_idx: new_idx for new_idx, old_idx in enumerate(sorted(episodes))} + + new_meta = LeRobotDatasetMetadata.create( + repo_id=split_repo_id, + fps=dataset.meta.fps, + features=dataset.meta.features, + robot_type=dataset.meta.robot_type, + root=split_output_dir, + use_videos=len(dataset.meta.video_keys) > 0, + chunks_size=dataset.meta.chunks_size, + data_files_size_in_mb=dataset.meta.data_files_size_in_mb, + video_files_size_in_mb=dataset.meta.video_files_size_in_mb, + ) + + video_metadata = None + if dataset.meta.video_keys: + video_metadata = _copy_and_reindex_videos(dataset, new_meta, episode_mapping) + + data_metadata = _copy_and_reindex_data(dataset, new_meta, episode_mapping) + + _copy_and_reindex_episodes_metadata(dataset, new_meta, episode_mapping, data_metadata, video_metadata) + + new_dataset = LeRobotDataset( + repo_id=split_repo_id, + root=split_output_dir, + image_transforms=dataset.image_transforms, + delta_timestamps=dataset.delta_timestamps, + tolerance_s=dataset.tolerance_s, + ) + + result_datasets[split_name] = new_dataset + + return result_datasets + + +def merge_datasets( + datasets: list[LeRobotDataset], + output_repo_id: str, + output_dir: str | Path | None = None, +) -> LeRobotDataset: + """Merge multiple LeRobotDatasets into a single dataset. + + This is a wrapper around the aggregate_datasets functionality with a cleaner API. + + Args: + datasets: List of LeRobotDatasets to merge. + output_repo_id: Repository ID for the merged dataset. + output_dir: Directory to save the merged dataset. If None, uses default location. + """ + if not datasets: + raise ValueError("No datasets to merge") + + output_dir = Path(output_dir) if output_dir is not None else HF_LEROBOT_HOME / output_repo_id + + repo_ids = [ds.repo_id for ds in datasets] + roots = [ds.root for ds in datasets] + + aggregate_datasets( + repo_ids=repo_ids, + aggr_repo_id=output_repo_id, + roots=roots, + aggr_root=output_dir, + ) + + merged_dataset = LeRobotDataset( + repo_id=output_repo_id, + root=output_dir, + image_transforms=datasets[0].image_transforms, + delta_timestamps=datasets[0].delta_timestamps, + tolerance_s=datasets[0].tolerance_s, + ) + + return merged_dataset + + +def add_feature( + dataset: LeRobotDataset, + feature_name: str, + feature_values: np.ndarray | torch.Tensor | Callable, + feature_info: dict, + output_dir: str | Path | None = None, + repo_id: str | None = None, +) -> LeRobotDataset: + """Add a new feature to a LeRobotDataset. + + Args: + dataset: The source LeRobotDataset. + feature_name: Name of the new feature. + feature_values: Either: + - Array/tensor of shape (num_frames, ...) with values for each frame + - Callable that takes (frame_dict, episode_index, frame_index) and returns feature value + feature_info: Dictionary with feature metadata (dtype, shape, names). + output_dir: Directory to save the new dataset. If None, uses default location. + repo_id: Repository ID for the new dataset. If None, appends "_modified" to original. + """ + if feature_name in dataset.meta.features: + raise ValueError(f"Feature '{feature_name}' already exists in dataset") + + if repo_id is None: + repo_id = f"{dataset.repo_id}_modified" + output_dir = Path(output_dir) if output_dir is not None else HF_LEROBOT_HOME / repo_id + + required_keys = {"dtype", "shape"} + if not required_keys.issubset(feature_info.keys()): + raise ValueError(f"feature_info must contain keys: {required_keys}") + + new_features = dataset.meta.features.copy() + new_features[feature_name] = feature_info + + new_meta = LeRobotDatasetMetadata.create( + repo_id=repo_id, + fps=dataset.meta.fps, + features=new_features, + robot_type=dataset.meta.robot_type, + root=output_dir, + use_videos=len(dataset.meta.video_keys) > 0, + ) + + _copy_data_with_feature_changes( + dataset=dataset, + new_meta=new_meta, + add_features={feature_name: (feature_values, feature_info)}, + ) + + if dataset.meta.video_keys: + _copy_videos(dataset, new_meta) + + new_dataset = LeRobotDataset( + repo_id=repo_id, + root=output_dir, + image_transforms=dataset.image_transforms, + delta_timestamps=dataset.delta_timestamps, + tolerance_s=dataset.tolerance_s, + ) + + return new_dataset + + +def remove_feature( + dataset: LeRobotDataset, + feature_names: str | list[str], + output_dir: str | Path | None = None, + repo_id: str | None = None, +) -> LeRobotDataset: + """Remove features from a LeRobotDataset. + + Args: + dataset: The source LeRobotDataset. + feature_names: Name(s) of features to remove. Can be a single string or list. + output_dir: Directory to save the new dataset. If None, uses default location. + repo_id: Repository ID for the new dataset. If None, appends "_modified" to original. + + """ + if isinstance(feature_names, str): + feature_names = [feature_names] + + for name in feature_names: + if name not in dataset.meta.features: + raise ValueError(f"Feature '{name}' not found in dataset") + + required_features = {"timestamp", "frame_index", "episode_index", "index", "task_index"} + if any(name in required_features for name in feature_names): + raise ValueError(f"Cannot remove required features: {required_features}") + + if repo_id is None: + repo_id = f"{dataset.repo_id}_modified" + output_dir = Path(output_dir) if output_dir is not None else HF_LEROBOT_HOME / repo_id + + new_features = {k: v for k, v in dataset.meta.features.items() if k not in feature_names} + + video_keys_to_remove = [name for name in feature_names if name in dataset.meta.video_keys] + + remaining_video_keys = [k for k in dataset.meta.video_keys if k not in video_keys_to_remove] + + new_meta = LeRobotDatasetMetadata.create( + repo_id=repo_id, + fps=dataset.meta.fps, + features=new_features, + robot_type=dataset.meta.robot_type, + root=output_dir, + use_videos=len(remaining_video_keys) > 0, + ) + + _copy_data_with_feature_changes( + dataset=dataset, + new_meta=new_meta, + remove_features=feature_names, + ) + + if new_meta.video_keys: + _copy_videos(dataset, new_meta, exclude_keys=video_keys_to_remove) + + new_dataset = LeRobotDataset( + repo_id=repo_id, + root=output_dir, + image_transforms=dataset.image_transforms, + delta_timestamps=dataset.delta_timestamps, + tolerance_s=dataset.tolerance_s, + ) + + return new_dataset + + +def _fractions_to_episode_indices( + total_episodes: int, + splits: dict[str, float], +) -> dict[str, list[int]]: + """Convert split fractions to episode indices.""" + if sum(splits.values()) > 1.0: + raise ValueError("Split fractions must sum to <= 1.0") + + indices = list(range(total_episodes)) + result = {} + start_idx = 0 + + for split_name, fraction in splits.items(): + num_episodes = int(total_episodes * fraction) + if num_episodes == 0: + logging.warning(f"Split '{split_name}' has no episodes, skipping...") + continue + end_idx = start_idx + num_episodes + if split_name == list(splits.keys())[-1]: + end_idx = total_episodes + result[split_name] = indices[start_idx:end_idx] + start_idx = end_idx + + return result + + +def _copy_and_reindex_data( + src_dataset: LeRobotDataset, + dst_meta: LeRobotDatasetMetadata, + episode_mapping: dict[int, int], +) -> dict[int, dict]: + """Copy and filter data files, only modifying files with deleted episodes. + + Args: + src_dataset: Source dataset to copy from + dst_meta: Destination metadata object + episode_mapping: Mapping from old episode indices to new indices + + Returns: + dict mapping episode index to its data file metadata (chunk_index, file_index, etc.) + """ + if src_dataset.meta.episodes is None: + src_dataset.meta.episodes = load_episodes(src_dataset.meta.root) + + file_to_episodes: dict[Path, set[int]] = {} + for old_idx in episode_mapping: + file_path = src_dataset.meta.get_data_file_path(old_idx) + if file_path not in file_to_episodes: + file_to_episodes[file_path] = set() + file_to_episodes[file_path].add(old_idx) + + global_index = 0 + episode_data_metadata: dict[int, dict] = {} + + if dst_meta.tasks is None: + all_task_indices = set() + for src_path in file_to_episodes: + df = pd.read_parquet(src_dataset.root / src_path) + mask = df["episode_index"].isin(list(episode_mapping.keys())) + task_series: pd.Series = df[mask]["task_index"] + all_task_indices.update(task_series.unique().tolist()) + tasks = [src_dataset.meta.tasks.iloc[idx].name for idx in all_task_indices] + dst_meta.save_episode_tasks(list(set(tasks))) + + task_mapping = {} + for old_task_idx in range(len(src_dataset.meta.tasks)): + task_name = src_dataset.meta.tasks.iloc[old_task_idx].name + new_task_idx = dst_meta.get_task_index(task_name) + if new_task_idx is not None: + task_mapping[old_task_idx] = new_task_idx + + for src_path in tqdm(sorted(file_to_episodes.keys()), desc="Processing data files"): + df = pd.read_parquet(src_dataset.root / src_path) + + all_episodes_in_file = set(df["episode_index"].unique()) + episodes_to_keep = file_to_episodes[src_path] + + if all_episodes_in_file == episodes_to_keep: + df["episode_index"] = df["episode_index"].replace(episode_mapping) + df["index"] = range(global_index, global_index + len(df)) + df["task_index"] = df["task_index"].replace(task_mapping) + + first_ep_old_idx = min(episodes_to_keep) + src_ep = src_dataset.meta.episodes[first_ep_old_idx] + chunk_idx = src_ep["data/chunk_index"] + file_idx = src_ep["data/file_index"] + else: + mask = df["episode_index"].isin(list(episode_mapping.keys())) + df = df[mask].copy().reset_index(drop=True) + + if len(df) == 0: + continue + + df["episode_index"] = df["episode_index"].replace(episode_mapping) + df["index"] = range(global_index, global_index + len(df)) + df["task_index"] = df["task_index"].replace(task_mapping) + + first_ep_old_idx = min(episodes_to_keep) + src_ep = src_dataset.meta.episodes[first_ep_old_idx] + chunk_idx = src_ep["data/chunk_index"] + file_idx = src_ep["data/file_index"] + + dst_path = dst_meta.root / DEFAULT_DATA_PATH.format(chunk_index=chunk_idx, file_index=file_idx) + dst_path.parent.mkdir(parents=True, exist_ok=True) + + if len(dst_meta.image_keys) > 0: + to_parquet_with_hf_images(df, dst_path) + else: + df.to_parquet(dst_path, index=False) + + for ep_old_idx in episodes_to_keep: + ep_new_idx = episode_mapping[ep_old_idx] + ep_df = df[df["episode_index"] == ep_new_idx] + episode_data_metadata[ep_new_idx] = { + "data/chunk_index": chunk_idx, + "data/file_index": file_idx, + "dataset_from_index": int(ep_df["index"].min()), + "dataset_to_index": int(ep_df["index"].max() + 1), + } + + global_index += len(df) + + return episode_data_metadata + + +def _keep_episodes_from_video_with_av( + input_path: Path, + output_path: Path, + episodes_to_keep: list[tuple[float, float]], + fps: float, + vcodec: str = "libsvtav1", + pix_fmt: str = "yuv420p", +) -> None: + """Keep only specified episodes from a video file using PyAV. + + This function decodes frames from specified time ranges and re-encodes them with + properly reset timestamps to ensure monotonic progression. + + Args: + input_path: Source video file path. + output_path: Destination video file path. + episodes_to_keep: List of (start_time, end_time) tuples for episodes to keep. + fps: Frame rate of the video. + vcodec: Video codec to use for encoding. + pix_fmt: Pixel format for output video. + """ + from fractions import Fraction + + import av + + if not episodes_to_keep: + raise ValueError("No episodes to keep") + + in_container = av.open(str(input_path)) + + # Check if video stream exists. + if not in_container.streams.video: + raise ValueError( + f"No video streams found in {input_path}. " + "The video file may be corrupted or empty. " + "Try re-downloading the dataset or checking the video file." + ) + + v_in = in_container.streams.video[0] + + out = av.open(str(output_path), mode="w") + + # Convert fps to Fraction for PyAV compatibility. + fps_fraction = Fraction(fps).limit_denominator(1000) + v_out = out.add_stream(vcodec, rate=fps_fraction) + + # PyAV type stubs don't distinguish video streams from audio/subtitle streams. + v_out.width = v_in.codec_context.width + v_out.height = v_in.codec_context.height + v_out.pix_fmt = pix_fmt + + # Set time_base to match the frame rate for proper timestamp handling. + v_out.time_base = Fraction(1, int(fps)) + + out.start_encoding() + + # Create set of (start, end) ranges for fast lookup. + # Convert to a sorted list for efficient checking. + time_ranges = sorted(episodes_to_keep) + + # Track frame index for setting PTS and current range being processed. + frame_count = 0 + range_idx = 0 + + # Read through entire video once and filter frames. + for packet in in_container.demux(v_in): + for frame in packet.decode(): + if frame is None: + continue + + # Get frame timestamp. + frame_time = float(frame.pts * frame.time_base) if frame.pts is not None else 0.0 + + # Check if frame is in any of our desired time ranges. + # Skip ranges that have already passed. + while range_idx < len(time_ranges) and frame_time >= time_ranges[range_idx][1]: + range_idx += 1 + + # If we've passed all ranges, stop processing. + if range_idx >= len(time_ranges): + break + + # Check if frame is in current range. + start_ts, end_ts = time_ranges[range_idx] + if frame_time < start_ts: + continue + + # Frame is in range - create a new frame with reset timestamps. + # We need to create a copy to avoid modifying the original. + new_frame = frame.reformat(width=v_out.width, height=v_out.height, format=v_out.pix_fmt) + new_frame.pts = frame_count + new_frame.time_base = Fraction(1, int(fps)) + + # Encode and mux the frame. + for pkt in v_out.encode(new_frame): + out.mux(pkt) + + frame_count += 1 + + # Flush encoder. + for pkt in v_out.encode(): + out.mux(pkt) + + out.close() + in_container.close() + + +def _copy_and_reindex_videos( + src_dataset: LeRobotDataset, + dst_meta: LeRobotDatasetMetadata, + episode_mapping: dict[int, int], + vcodec: str = "libsvtav1", + pix_fmt: str = "yuv420p", +) -> dict[int, dict]: + """Copy and filter video files, only re-encoding files with deleted episodes. + + For video files that only contain kept episodes, we copy them directly. + For files with mixed kept/deleted episodes, we use PyAV filters to efficiently + re-encode only the desired segments. + + Args: + src_dataset: Source dataset to copy from + dst_meta: Destination metadata object + episode_mapping: Mapping from old episode indices to new indices + + Returns: + dict mapping episode index to its video metadata (chunk_index, file_index, timestamps) + """ + if src_dataset.meta.episodes is None: + src_dataset.meta.episodes = load_episodes(src_dataset.meta.root) + + episodes_video_metadata: dict[int, dict] = {new_idx: {} for new_idx in episode_mapping.values()} + + for video_key in src_dataset.meta.video_keys: + logging.info(f"Processing videos for {video_key}") + + if dst_meta.video_path is None: + raise ValueError("Destination metadata has no video_path defined") + + file_to_episodes: dict[tuple[int, int], list[int]] = {} + for old_idx in episode_mapping: + src_ep = src_dataset.meta.episodes[old_idx] + chunk_idx = src_ep[f"videos/{video_key}/chunk_index"] + file_idx = src_ep[f"videos/{video_key}/file_index"] + file_key = (chunk_idx, file_idx) + if file_key not in file_to_episodes: + file_to_episodes[file_key] = [] + file_to_episodes[file_key].append(old_idx) + + for (src_chunk_idx, src_file_idx), episodes_in_file in tqdm( + sorted(file_to_episodes.items()), desc=f"Processing {video_key} video files" + ): + all_episodes_in_file = [ + ep_idx + for ep_idx in range(src_dataset.meta.total_episodes) + if src_dataset.meta.episodes[ep_idx].get(f"videos/{video_key}/chunk_index") == src_chunk_idx + and src_dataset.meta.episodes[ep_idx].get(f"videos/{video_key}/file_index") == src_file_idx + ] + + episodes_to_keep_set = set(episodes_in_file) + all_in_file_set = set(all_episodes_in_file) + + if all_in_file_set == episodes_to_keep_set: + assert src_dataset.meta.video_path is not None + src_video_path = src_dataset.root / src_dataset.meta.video_path.format( + video_key=video_key, chunk_index=src_chunk_idx, file_index=src_file_idx + ) + dst_video_path = dst_meta.root / dst_meta.video_path.format( + video_key=video_key, chunk_index=src_chunk_idx, file_index=src_file_idx + ) + dst_video_path.parent.mkdir(parents=True, exist_ok=True) + shutil.copy(src_video_path, dst_video_path) + + for old_idx in episodes_in_file: + new_idx = episode_mapping[old_idx] + src_ep = src_dataset.meta.episodes[old_idx] + episodes_video_metadata[new_idx][f"videos/{video_key}/chunk_index"] = src_chunk_idx + episodes_video_metadata[new_idx][f"videos/{video_key}/file_index"] = src_file_idx + episodes_video_metadata[new_idx][f"videos/{video_key}/from_timestamp"] = src_ep[ + f"videos/{video_key}/from_timestamp" + ] + episodes_video_metadata[new_idx][f"videos/{video_key}/to_timestamp"] = src_ep[ + f"videos/{video_key}/to_timestamp" + ] + else: + # Build list of time ranges to keep, in sorted order. + sorted_keep_episodes = sorted(episodes_in_file, key=lambda x: episode_mapping[x]) + episodes_to_keep_ranges: list[tuple[float, float]] = [] + + for old_idx in sorted_keep_episodes: + src_ep = src_dataset.meta.episodes[old_idx] + from_ts = src_ep[f"videos/{video_key}/from_timestamp"] + to_ts = src_ep[f"videos/{video_key}/to_timestamp"] + episodes_to_keep_ranges.append((from_ts, to_ts)) + + # Use PyAV filters to efficiently re-encode only the desired segments. + assert src_dataset.meta.video_path is not None + src_video_path = src_dataset.root / src_dataset.meta.video_path.format( + video_key=video_key, chunk_index=src_chunk_idx, file_index=src_file_idx + ) + dst_video_path = dst_meta.root / dst_meta.video_path.format( + video_key=video_key, chunk_index=src_chunk_idx, file_index=src_file_idx + ) + dst_video_path.parent.mkdir(parents=True, exist_ok=True) + + logging.info( + f"Re-encoding {video_key} (chunk {src_chunk_idx}, file {src_file_idx}) " + f"with {len(episodes_to_keep_ranges)} episodes" + ) + _keep_episodes_from_video_with_av( + src_video_path, + dst_video_path, + episodes_to_keep_ranges, + src_dataset.meta.fps, + vcodec, + pix_fmt, + ) + + cumulative_ts = 0.0 + for old_idx in sorted_keep_episodes: + new_idx = episode_mapping[old_idx] + src_ep = src_dataset.meta.episodes[old_idx] + ep_length = src_ep["length"] + ep_duration = ep_length / src_dataset.meta.fps + + episodes_video_metadata[new_idx][f"videos/{video_key}/chunk_index"] = src_chunk_idx + episodes_video_metadata[new_idx][f"videos/{video_key}/file_index"] = src_file_idx + episodes_video_metadata[new_idx][f"videos/{video_key}/from_timestamp"] = cumulative_ts + episodes_video_metadata[new_idx][f"videos/{video_key}/to_timestamp"] = ( + cumulative_ts + ep_duration + ) + + cumulative_ts += ep_duration + + return episodes_video_metadata + + +def _copy_and_reindex_episodes_metadata( + src_dataset: LeRobotDataset, + dst_meta: LeRobotDatasetMetadata, + episode_mapping: dict[int, int], + data_metadata: dict[int, dict], + video_metadata: dict[int, dict] | None = None, +) -> None: + """Copy and reindex episodes metadata using provided data and video metadata. + + Args: + src_dataset: Source dataset to copy from + dst_meta: Destination metadata object + episode_mapping: Mapping from old episode indices to new indices + data_metadata: Dict mapping new episode index to its data file metadata + video_metadata: Optional dict mapping new episode index to its video metadata + """ + from lerobot.datasets.utils import flatten_dict + + if src_dataset.meta.episodes is None: + src_dataset.meta.episodes = load_episodes(src_dataset.meta.root) + + all_stats = [] + total_frames = 0 + + for old_idx, new_idx in tqdm( + sorted(episode_mapping.items(), key=lambda x: x[1]), desc="Processing episodes metadata" + ): + src_episode_full = _load_episode_with_stats(src_dataset, old_idx) + + src_episode = src_dataset.meta.episodes[old_idx] + + episode_meta = data_metadata[new_idx].copy() + + if video_metadata and new_idx in video_metadata: + episode_meta.update(video_metadata[new_idx]) + + # Extract episode statistics from parquet metadata. + # Note (maractingi): When pandas/pyarrow serializes numpy arrays with shape (3, 1, 1) to parquet, + # they are being deserialized as nested object arrays like: + # array([array([array([0.])]), array([array([0.])]), array([array([0.])])]) + # This happens particularly with image/video statistics. We need to detect and flatten + # these nested structures back to proper (3, 1, 1) arrays so aggregate_stats can process them. + episode_stats = {} + for key in src_episode_full: + if key.startswith("stats/"): + stat_key = key.replace("stats/", "") + parts = stat_key.split("/") + if len(parts) == 2: + feature_name, stat_name = parts + if feature_name not in episode_stats: + episode_stats[feature_name] = {} + + value = src_episode_full[key] + + if feature_name in src_dataset.meta.features: + feature_dtype = src_dataset.meta.features[feature_name]["dtype"] + if feature_dtype in ["image", "video"] and stat_name != "count": + if isinstance(value, np.ndarray) and value.dtype == object: + flat_values = [] + for item in value: + while isinstance(item, np.ndarray): + item = item.flatten()[0] + flat_values.append(item) + value = np.array(flat_values, dtype=np.float64).reshape(3, 1, 1) + elif isinstance(value, np.ndarray) and value.shape == (3,): + value = value.reshape(3, 1, 1) + + episode_stats[feature_name][stat_name] = value + + all_stats.append(episode_stats) + + episode_dict = { + "episode_index": new_idx, + "tasks": src_episode["tasks"], + "length": src_episode["length"], + } + episode_dict.update(episode_meta) + episode_dict.update(flatten_dict({"stats": episode_stats})) + dst_meta._save_episode_metadata(episode_dict) + + total_frames += src_episode["length"] + + dst_meta._close_writer() + + dst_meta.info.update( + { + "total_episodes": len(episode_mapping), + "total_frames": total_frames, + "total_tasks": len(dst_meta.tasks) if dst_meta.tasks is not None else 0, + "splits": {"train": f"0:{len(episode_mapping)}"}, + } + ) + write_info(dst_meta.info, dst_meta.root) + + if not all_stats: + logging.warning("No statistics found to aggregate") + return + + logging.info(f"Aggregating statistics for {len(all_stats)} episodes") + aggregated_stats = aggregate_stats(all_stats) + filtered_stats = {k: v for k, v in aggregated_stats.items() if k in dst_meta.features} + write_stats(filtered_stats, dst_meta.root) + + +def _save_data_chunk( + df: pd.DataFrame, + meta: LeRobotDatasetMetadata, + chunk_idx: int = 0, + file_idx: int = 0, +) -> tuple[int, int, dict[int, dict]]: + """Save a data chunk and return updated indices and episode metadata. + + Returns: + tuple: (next_chunk_idx, next_file_idx, episode_metadata_dict) + where episode_metadata_dict maps episode_index to its data file metadata + """ + path = meta.root / DEFAULT_DATA_PATH.format(chunk_index=chunk_idx, file_index=file_idx) + path.parent.mkdir(parents=True, exist_ok=True) + + if len(meta.image_keys) > 0: + to_parquet_with_hf_images(df, path) + else: + df.to_parquet(path, index=False) + + episode_metadata = {} + for ep_idx in df["episode_index"].unique(): + ep_df = df[df["episode_index"] == ep_idx] + episode_metadata[ep_idx] = { + "data/chunk_index": chunk_idx, + "data/file_index": file_idx, + "dataset_from_index": int(ep_df["index"].min()), + "dataset_to_index": int(ep_df["index"].max() + 1), + } + + file_size = get_parquet_file_size_in_mb(path) + if file_size >= DEFAULT_DATA_FILE_SIZE_IN_MB * 0.9: + chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE) + + return chunk_idx, file_idx, episode_metadata + + +def _copy_data_with_feature_changes( + dataset: LeRobotDataset, + new_meta: LeRobotDatasetMetadata, + add_features: dict[str, tuple] | None = None, + remove_features: list[str] | None = None, +) -> None: + """Copy data while adding or removing features.""" + file_paths = set() + for ep_idx in range(dataset.meta.total_episodes): + file_paths.add(dataset.meta.get_data_file_path(ep_idx)) + + frame_idx = 0 + + for src_path in tqdm(sorted(file_paths), desc="Processing data files"): + df = pd.read_parquet(dataset.root / src_path).reset_index(drop=True) + + if remove_features: + df = df.drop(columns=remove_features, errors="ignore") + + if add_features: + for feature_name, (values, _) in add_features.items(): + if callable(values): + feature_values = [] + for _, row in df.iterrows(): + ep_idx = row["episode_index"] + frame_in_ep = row["frame_index"] + value = values(row.to_dict(), ep_idx, frame_in_ep) + if isinstance(value, np.ndarray) and value.size == 1: + value = value.item() + feature_values.append(value) + df[feature_name] = feature_values + else: + end_idx = frame_idx + len(df) + feature_slice = values[frame_idx:end_idx] + if len(feature_slice.shape) > 1 and feature_slice.shape[1] == 1: + df[feature_name] = feature_slice.flatten() + else: + df[feature_name] = feature_slice + frame_idx = end_idx + + _save_data_chunk(df, new_meta) + + _copy_episodes_metadata_and_stats(dataset, new_meta) + + +def _copy_videos( + src_dataset: LeRobotDataset, + dst_meta: LeRobotDatasetMetadata, + exclude_keys: list[str] | None = None, +) -> None: + """Copy video files, optionally excluding certain keys.""" + if exclude_keys is None: + exclude_keys = [] + + for video_key in src_dataset.meta.video_keys: + if video_key in exclude_keys: + continue + + video_files = set() + for ep_idx in range(len(src_dataset.meta.episodes)): + try: + video_files.add(src_dataset.meta.get_video_file_path(ep_idx, video_key)) + except KeyError: + continue + + for src_path in tqdm(sorted(video_files), desc=f"Copying {video_key} videos"): + dst_path = dst_meta.root / src_path + dst_path.parent.mkdir(parents=True, exist_ok=True) + shutil.copy(src_dataset.root / src_path, dst_path) + + +def _copy_episodes_metadata_and_stats( + src_dataset: LeRobotDataset, + dst_meta: LeRobotDatasetMetadata, +) -> None: + """Copy episodes metadata and recalculate stats.""" + if src_dataset.meta.tasks is not None: + write_tasks(src_dataset.meta.tasks, dst_meta.root) + dst_meta.tasks = src_dataset.meta.tasks.copy() + + episodes_dir = src_dataset.root / "meta/episodes" + dst_episodes_dir = dst_meta.root / "meta/episodes" + if episodes_dir.exists(): + shutil.copytree(episodes_dir, dst_episodes_dir, dirs_exist_ok=True) + + dst_meta.info.update( + { + "total_episodes": src_dataset.meta.total_episodes, + "total_frames": src_dataset.meta.total_frames, + "total_tasks": src_dataset.meta.total_tasks, + "splits": src_dataset.meta.info.get("splits", {"train": f"0:{src_dataset.meta.total_episodes}"}), + } + ) + + if dst_meta.video_keys and src_dataset.meta.video_keys: + for key in dst_meta.video_keys: + if key in src_dataset.meta.features: + dst_meta.info["features"][key]["info"] = src_dataset.meta.info["features"][key].get( + "info", {} + ) + + write_info(dst_meta.info, dst_meta.root) + + if set(dst_meta.features.keys()) != set(src_dataset.meta.features.keys()): + logging.info("Recalculating dataset statistics...") + if src_dataset.meta.stats: + new_stats = {} + for key in dst_meta.features: + if key in src_dataset.meta.stats: + new_stats[key] = src_dataset.meta.stats[key] + write_stats(new_stats, dst_meta.root) + else: + if src_dataset.meta.stats: + write_stats(src_dataset.meta.stats, dst_meta.root) diff --git a/src/lerobot/datasets/image_writer.py b/src/lerobot/datasets/image_writer.py index 4a4e1ab0586..ee10df6e19c 100644 --- a/src/lerobot/datasets/image_writer.py +++ b/src/lerobot/datasets/image_writer.py @@ -68,7 +68,30 @@ def image_array_to_pil_image(image_array: np.ndarray, range_check: bool = True) return PIL.Image.fromarray(image_array) -def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path): +def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path, compress_level: int = 1): + """ + Saves a NumPy array or PIL Image to a file. + + This function handles both NumPy arrays and PIL Image objects, converting + the former to a PIL Image before saving. It includes error handling for + the save operation. + + Args: + image (np.ndarray | PIL.Image.Image): The image data to save. + fpath (Path): The destination file path for the image. + compress_level (int, optional): The compression level for the saved + image, as used by PIL.Image.save(). Defaults to 1. + Refer to: https://github.com/huggingface/lerobot/pull/2135 + for more details on the default value rationale. + + Raises: + TypeError: If the input 'image' is not a NumPy array or a + PIL.Image.Image object. + + Side Effects: + Prints an error message to the console if the image writing process + fails for any reason. + """ try: if isinstance(image, np.ndarray): img = image_array_to_pil_image(image) @@ -76,7 +99,7 @@ def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path): img = image else: raise TypeError(f"Unsupported image type: {type(image)}") - img.save(fpath) + img.save(fpath, compress_level=compress_level) except Exception as e: print(f"Error writing image {fpath}: {e}") diff --git a/src/lerobot/datasets/lerobot_dataset.py b/src/lerobot/datasets/lerobot_dataset.py index b661b21b038..ae142c1e8ff 100644 --- a/src/lerobot/datasets/lerobot_dataset.py +++ b/src/lerobot/datasets/lerobot_dataset.py @@ -14,7 +14,6 @@ # See the License for the specific language governing permissions and # limitations under the License. import contextlib -import gc import logging import shutil import tempfile @@ -26,6 +25,8 @@ import packaging.version import pandas as pd import PIL.Image +import pyarrow as pa +import pyarrow.parquet as pq import torch import torch.utils from huggingface_hub import HfApi, snapshot_download @@ -46,13 +47,9 @@ embed_images, flatten_dict, get_delta_indices, - get_hf_dataset_cache_dir, - get_hf_dataset_size_in_mb, + get_file_size_in_mb, get_hf_features_from_features, - get_parquet_file_size_in_mb, - get_parquet_num_frames, get_safe_version, - get_video_size_in_mb, hf_transform_to_torch, is_valid_version, load_episodes, @@ -60,7 +57,6 @@ load_nested_dataset, load_stats, load_tasks, - to_parquet_with_hf_images, update_chunk_file_indices, validate_episode_buffer, validate_frame, @@ -90,10 +86,15 @@ def __init__( root: str | Path | None = None, revision: str | None = None, force_cache_sync: bool = False, + metadata_buffer_size: int = 10, ): self.repo_id = repo_id self.revision = revision if revision else CODEBASE_VERSION self.root = Path(root) if root is not None else HF_LEROBOT_HOME / repo_id + self.writer = None + self.latest_episode = None + self.metadata_buffer: list[dict] = [] + self.metadata_buffer_size = metadata_buffer_size try: if force_cache_sync: @@ -107,6 +108,54 @@ def __init__( self.pull_from_repo(allow_patterns="meta/") self.load_metadata() + def _flush_metadata_buffer(self) -> None: + """Write all buffered episode metadata to parquet file.""" + if not hasattr(self, "metadata_buffer") or len(self.metadata_buffer) == 0: + return + + combined_dict = {} + for episode_dict in self.metadata_buffer: + for key, value in episode_dict.items(): + if key not in combined_dict: + combined_dict[key] = [] + # Extract value and serialize numpy arrays + # because PyArrow's from_pydict function doesn't support numpy arrays + val = value[0] if isinstance(value, list) else value + combined_dict[key].append(val.tolist() if isinstance(val, np.ndarray) else val) + + first_ep = self.metadata_buffer[0] + chunk_idx = first_ep["meta/episodes/chunk_index"][0] + file_idx = first_ep["meta/episodes/file_index"][0] + + table = pa.Table.from_pydict(combined_dict) + + if not self.writer: + path = Path(self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx)) + path.parent.mkdir(parents=True, exist_ok=True) + self.writer = pq.ParquetWriter( + path, schema=table.schema, compression="snappy", use_dictionary=True + ) + + self.writer.write_table(table) + + self.latest_episode = self.metadata_buffer[-1] + self.metadata_buffer.clear() + + def _close_writer(self) -> None: + """Close and cleanup the parquet writer if it exists.""" + self._flush_metadata_buffer() + + writer = getattr(self, "writer", None) + if writer is not None: + writer.close() + self.writer = None + + def __del__(self): + """ + Trust the user to call .finalize() but as an added safety check call the parquet writer to stop when calling the destructor + """ + self._close_writer() + def load_metadata(self): self.info = load_info(self.root) check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION) @@ -138,6 +187,12 @@ def _version(self) -> packaging.version.Version: return packaging.version.parse(self.info["codebase_version"]) def get_data_file_path(self, ep_index: int) -> Path: + if self.episodes is None: + self.episodes = load_episodes(self.root) + if ep_index >= len(self.episodes): + raise IndexError( + f"Episode index {ep_index} out of range. Episodes: {len(self.episodes) if self.episodes else 0}" + ) ep = self.episodes[ep_index] chunk_idx = ep["data/chunk_index"] file_idx = ep["data/file_index"] @@ -145,6 +200,12 @@ def get_data_file_path(self, ep_index: int) -> Path: return Path(fpath) def get_video_file_path(self, ep_index: int, vid_key: str) -> Path: + if self.episodes is None: + self.episodes = load_episodes(self.root) + if ep_index >= len(self.episodes): + raise IndexError( + f"Episode index {ep_index} out of range. Episodes: {len(self.episodes) if self.episodes else 0}" + ) ep = self.episodes[ep_index] chunk_idx = ep[f"videos/{vid_key}/chunk_index"] file_idx = ep[f"videos/{vid_key}/file_index"] @@ -260,72 +321,75 @@ def save_episode_tasks(self, tasks: list[str]): write_tasks(self.tasks, self.root) def _save_episode_metadata(self, episode_dict: dict) -> None: - """Save episode metadata to a parquet file and update the Hugging Face dataset of episodes metadata. + """Buffer episode metadata and write to parquet in batches for efficiency. - This function processes episodes metadata from a dictionary, converts it into a Hugging Face dataset, - and saves it as a parquet file. It handles both the creation of new parquet files and the - updating of existing ones based on size constraints. After saving the metadata, it reloads - the Hugging Face dataset to ensure it is up-to-date. + This function accumulates episode metadata in a buffer and flushes it when the buffer + reaches the configured size. This reduces I/O overhead by writing multiple episodes + at once instead of one row at a time. Notes: We both need to update parquet files and HF dataset: - `pandas` loads parquet file in RAM - `datasets` relies on a memory mapping from pyarrow (no RAM). It either converts parquet files to a pyarrow cache on disk, or loads directly from pyarrow cache. """ - # Convert buffer into HF Dataset + # Convert to list format for each value episode_dict = {key: [value] for key, value in episode_dict.items()} - ep_dataset = datasets.Dataset.from_dict(episode_dict) - ep_size_in_mb = get_hf_dataset_size_in_mb(ep_dataset) - df = pd.DataFrame(ep_dataset) num_frames = episode_dict["length"][0] - if self.episodes is None: + if self.latest_episode is None: # Initialize indices and frame count for a new dataset made of the first episode data chunk_idx, file_idx = 0, 0 - df["meta/episodes/chunk_index"] = [chunk_idx] - df["meta/episodes/file_index"] = [file_idx] - df["dataset_from_index"] = [0] - df["dataset_to_index"] = [num_frames] - else: - # Retrieve information from the latest parquet file - latest_ep = self.episodes[-1] - chunk_idx = latest_ep["meta/episodes/chunk_index"] - file_idx = latest_ep["meta/episodes/file_index"] + if self.episodes is not None and len(self.episodes) > 0: + # It means we are resuming recording, so we need to load the latest episode + # Update the indices to avoid overwriting the latest episode + chunk_idx = self.episodes[-1]["meta/episodes/chunk_index"] + file_idx = self.episodes[-1]["meta/episodes/file_index"] + latest_num_frames = self.episodes[-1]["dataset_to_index"] + episode_dict["dataset_from_index"] = [latest_num_frames] + episode_dict["dataset_to_index"] = [latest_num_frames + num_frames] + + # When resuming, move to the next file + chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.chunks_size) + else: + episode_dict["dataset_from_index"] = [0] + episode_dict["dataset_to_index"] = [num_frames] - latest_path = self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx) - latest_size_in_mb = get_parquet_file_size_in_mb(latest_path) + episode_dict["meta/episodes/chunk_index"] = [chunk_idx] + episode_dict["meta/episodes/file_index"] = [file_idx] + else: + chunk_idx = self.latest_episode["meta/episodes/chunk_index"][0] + file_idx = self.latest_episode["meta/episodes/file_index"][0] - if latest_size_in_mb + ep_size_in_mb >= self.data_files_size_in_mb: - # Size limit is reached, prepare new parquet file - chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.chunks_size) + latest_path = ( + self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx) + if self.writer is None + else self.writer.where + ) - # Update the existing pandas dataframe with new row - df["meta/episodes/chunk_index"] = [chunk_idx] - df["meta/episodes/file_index"] = [file_idx] - df["dataset_from_index"] = [latest_ep["dataset_to_index"]] - df["dataset_to_index"] = [latest_ep["dataset_to_index"] + num_frames] + if Path(latest_path).exists(): + latest_size_in_mb = get_file_size_in_mb(Path(latest_path)) + latest_num_frames = self.latest_episode["episode_index"][0] - if latest_size_in_mb + ep_size_in_mb < self.data_files_size_in_mb: - # Size limit wasnt reached, concatenate latest dataframe with new one - latest_df = pd.read_parquet(latest_path) - df = pd.concat([latest_df, df], ignore_index=True) + av_size_per_frame = latest_size_in_mb / latest_num_frames if latest_num_frames > 0 else 0.0 - # Memort optimization - del latest_df - gc.collect() + if latest_size_in_mb + av_size_per_frame * num_frames >= self.data_files_size_in_mb: + # Size limit is reached, flush buffer and prepare new parquet file + self._flush_metadata_buffer() + chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.chunks_size) + self._close_writer() - # Write the resulting dataframe from RAM to disk - path = self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx) - path.parent.mkdir(parents=True, exist_ok=True) - df.to_parquet(path, index=False) + # Update the existing pandas dataframe with new row + episode_dict["meta/episodes/chunk_index"] = [chunk_idx] + episode_dict["meta/episodes/file_index"] = [file_idx] + episode_dict["dataset_from_index"] = [self.latest_episode["dataset_to_index"][0]] + episode_dict["dataset_to_index"] = [self.latest_episode["dataset_to_index"][0] + num_frames] - if self.episodes is not None: - # Remove the episodes cache directory, necessary to avoid cache bloat - cached_dir = get_hf_dataset_cache_dir(self.episodes) - if cached_dir is not None: - shutil.rmtree(cached_dir) + # Add to buffer + self.metadata_buffer.append(episode_dict) + self.latest_episode = episode_dict - self.episodes = load_episodes(self.root) + if len(self.metadata_buffer) >= self.metadata_buffer_size: + self._flush_metadata_buffer() def save_episode( self, @@ -438,6 +502,10 @@ def create( robot_type: str | None = None, root: str | Path | None = None, use_videos: bool = True, + metadata_buffer_size: int = 10, + chunks_size: int | None = None, + data_files_size_in_mb: int | None = None, + video_files_size_in_mb: int | None = None, ) -> "LeRobotDatasetMetadata": """Creates metadata for a LeRobotDataset.""" obj = cls.__new__(cls) @@ -452,11 +520,24 @@ def create( obj.tasks = None obj.episodes = None obj.stats = None - obj.info = create_empty_dataset_info(CODEBASE_VERSION, fps, features, use_videos, robot_type) + obj.info = create_empty_dataset_info( + CODEBASE_VERSION, + fps, + features, + use_videos, + robot_type, + chunks_size, + data_files_size_in_mb, + video_files_size_in_mb, + ) if len(obj.video_keys) > 0 and not use_videos: raise ValueError() write_json(obj.info, obj.root / INFO_PATH) obj.revision = None + obj.writer = None + obj.latest_episode = None + obj.metadata_buffer = [] + obj.metadata_buffer_size = metadata_buffer_size return obj @@ -603,6 +684,8 @@ def __init__( # Unused attributes self.image_writer = None self.episode_buffer = None + self.writer = None + self.latest_episode = None self.root.mkdir(exist_ok=True, parents=True) @@ -611,6 +694,11 @@ def __init__( self.repo_id, self.root, self.revision, force_cache_sync=force_cache_sync ) + # Track dataset state for efficient incremental writing + self._lazy_loading = False + self._recorded_frames = self.meta.total_frames + self._writer_closed_for_reading = False + # Load actual data try: if force_cache_sync: @@ -629,6 +717,19 @@ def __init__( check_delta_timestamps(self.delta_timestamps, self.fps, self.tolerance_s) self.delta_indices = get_delta_indices(self.delta_timestamps, self.fps) + def _close_writer(self) -> None: + """Close and cleanup the parquet writer if it exists.""" + writer = getattr(self, "writer", None) + if writer is not None: + writer.close() + self.writer = None + + def __del__(self): + """ + Trust the user to call .finalize() but as an added safety check call the parquet writer to stop when calling the destructor + """ + self._close_writer() + def push_to_hub( self, branch: str | None = None, @@ -769,8 +870,15 @@ def fps(self) -> int: @property def num_frames(self) -> int: - """Number of frames in selected episodes.""" - return len(self.hf_dataset) if self.hf_dataset is not None else self.meta.total_frames + """Number of frames in selected episodes. + + Note: When episodes a subset of the full dataset is requested, we must return the + actual loaded data length (len(self.hf_dataset)) rather than metadata total_frames. + self.meta.total_frames is the total number of frames in the full dataset. + """ + if self.episodes is not None and self.hf_dataset is not None: + return len(self.hf_dataset) + return self.meta.total_frames @property def num_episodes(self) -> int: @@ -848,10 +956,22 @@ def _query_videos(self, query_timestamps: dict[str, list[float]], ep_idx: int) - return item + def _ensure_hf_dataset_loaded(self): + """Lazy load the HF dataset only when needed for reading.""" + if self._lazy_loading or self.hf_dataset is None: + # Close the writer before loading to ensure parquet file is properly finalized + if self.writer is not None: + self._close_writer() + self._writer_closed_for_reading = True + self.hf_dataset = self.load_hf_dataset() + self._lazy_loading = False + def __len__(self): return self.num_frames def __getitem__(self, idx) -> dict: + # Ensure dataset is loaded when we actually need to read from it + self._ensure_hf_dataset_loaded() item = self.hf_dataset[idx] ep_idx = item["episode_index"].item() @@ -890,6 +1010,14 @@ def __repr__(self): "})',\n" ) + def finalize(self): + """ + Close the parquet writers. This function needs to be called after data collection/conversion, else footer metadata won't be written to the parquet files. + The dataset won't be valid and can't be loaded as ds = LeRobotDataset(repo_id=repo, root=HF_LEROBOT_HOME.joinpath(repo)) + """ + self._close_writer() + self.meta._close_writer() + def create_episode_buffer(self, episode_index: int | None = None) -> dict: current_ep_idx = self.meta.total_episodes if episode_index is None else episode_index ep_buffer = {} @@ -1097,74 +1225,101 @@ def _save_episode_data(self, episode_buffer: dict) -> dict: ep_dict = {key: episode_buffer[key] for key in self.hf_features} ep_dataset = datasets.Dataset.from_dict(ep_dict, features=self.hf_features, split="train") ep_dataset = embed_images(ep_dataset) - ep_size_in_mb = get_hf_dataset_size_in_mb(ep_dataset) ep_num_frames = len(ep_dataset) - df = pd.DataFrame(ep_dataset) - if self.meta.episodes is None: + if self.latest_episode is None: # Initialize indices and frame count for a new dataset made of the first episode data chunk_idx, file_idx = 0, 0 - latest_num_frames = 0 + global_frame_index = 0 + # However, if the episodes already exists + # It means we are resuming recording, so we need to load the latest episode + # Update the indices to avoid overwriting the latest episode + if self.meta.episodes is not None and len(self.meta.episodes) > 0: + latest_ep = self.meta.episodes[-1] + global_frame_index = latest_ep["dataset_to_index"] + chunk_idx = latest_ep["data/chunk_index"] + file_idx = latest_ep["data/file_index"] + + # When resuming, move to the next file + chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.meta.chunks_size) else: # Retrieve information from the latest parquet file - latest_ep = self.meta.episodes[-1] + latest_ep = self.latest_episode chunk_idx = latest_ep["data/chunk_index"] file_idx = latest_ep["data/file_index"] + global_frame_index = latest_ep["index"][-1] + 1 latest_path = self.root / self.meta.data_path.format(chunk_index=chunk_idx, file_index=file_idx) - latest_size_in_mb = get_parquet_file_size_in_mb(latest_path) - latest_num_frames = get_parquet_num_frames(latest_path) + latest_size_in_mb = get_file_size_in_mb(latest_path) + + frames_in_current_file = global_frame_index - latest_ep["dataset_from_index"] + av_size_per_frame = ( + latest_size_in_mb / frames_in_current_file if frames_in_current_file > 0 else 0 + ) # Determine if a new parquet file is needed - if latest_size_in_mb + ep_size_in_mb >= self.meta.data_files_size_in_mb: - # Size limit is reached, prepare new parquet file + if ( + latest_size_in_mb + av_size_per_frame * ep_num_frames >= self.meta.data_files_size_in_mb + or self._writer_closed_for_reading + ): + # Size limit is reached or writer was closed for reading, prepare new parquet file chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.meta.chunks_size) - latest_num_frames = 0 - else: - # Update the existing parquet file with new rows - latest_df = pd.read_parquet(latest_path) - df = pd.concat([latest_df, df], ignore_index=True) + self._close_writer() + self._writer_closed_for_reading = False - # Memort optimization - del latest_df - gc.collect() + ep_dict["data/chunk_index"] = chunk_idx + ep_dict["data/file_index"] = file_idx # Write the resulting dataframe from RAM to disk path = self.root / self.meta.data_path.format(chunk_index=chunk_idx, file_index=file_idx) path.parent.mkdir(parents=True, exist_ok=True) - if len(self.meta.image_keys) > 0: - to_parquet_with_hf_images(df, path) - else: - df.to_parquet(path) - if self.hf_dataset is not None: - # Remove hf dataset cache directory, necessary to avoid cache bloat - cached_dir = get_hf_dataset_cache_dir(self.hf_dataset) - if cached_dir is not None: - shutil.rmtree(cached_dir) - - self.hf_dataset = self.load_hf_dataset() + table = ep_dataset.with_format("arrow")[:] + if not self.writer: + self.writer = pq.ParquetWriter( + path, schema=table.schema, compression="snappy", use_dictionary=True + ) + self.writer.write_table(table) metadata = { "data/chunk_index": chunk_idx, "data/file_index": file_idx, - "dataset_from_index": latest_num_frames, - "dataset_to_index": latest_num_frames + ep_num_frames, + "dataset_from_index": global_frame_index, + "dataset_to_index": global_frame_index + ep_num_frames, } + + # Store metadata with episode data for next episode + self.latest_episode = {**ep_dict, **metadata} + + # Mark that the HF dataset needs reloading (lazy loading approach) + # This avoids expensive reloading during sequential recording + self._lazy_loading = True + # Update recorded frames count for efficient length tracking + self._recorded_frames += ep_num_frames + return metadata def _save_episode_video(self, video_key: str, episode_index: int) -> dict: # Encode episode frames into a temporary video ep_path = self._encode_temporary_episode_video(video_key, episode_index) - ep_size_in_mb = get_video_size_in_mb(ep_path) + ep_size_in_mb = get_file_size_in_mb(ep_path) ep_duration_in_s = get_video_duration_in_s(ep_path) - if self.meta.episodes is None or ( - f"videos/{video_key}/chunk_index" not in self.meta.episodes.column_names - or f"videos/{video_key}/file_index" not in self.meta.episodes.column_names + if ( + episode_index == 0 + or self.meta.latest_episode is None + or f"videos/{video_key}/chunk_index" not in self.meta.latest_episode ): # Initialize indices for a new dataset made of the first episode data chunk_idx, file_idx = 0, 0 + if self.meta.episodes is not None and len(self.meta.episodes) > 0: + # It means we are resuming recording, so we need to load the latest episode + # Update the indices to avoid overwriting the latest episode + old_chunk_idx = self.meta.episodes[-1][f"videos/{video_key}/chunk_index"] + old_file_idx = self.meta.episodes[-1][f"videos/{video_key}/file_index"] + chunk_idx, file_idx = update_chunk_file_indices( + old_chunk_idx, old_file_idx, self.meta.chunks_size + ) latest_duration_in_s = 0.0 new_path = self.root / self.meta.video_path.format( video_key=video_key, chunk_index=chunk_idx, file_index=file_idx @@ -1172,16 +1327,16 @@ def _save_episode_video(self, video_key: str, episode_index: int) -> dict: new_path.parent.mkdir(parents=True, exist_ok=True) shutil.move(str(ep_path), str(new_path)) else: - # Retrieve information from the latest updated video file (possibly several episodes ago) - latest_ep = self.meta.episodes[episode_index - 1] - chunk_idx = latest_ep[f"videos/{video_key}/chunk_index"] - file_idx = latest_ep[f"videos/{video_key}/file_index"] + # Retrieve information from the latest updated video file using latest_episode + latest_ep = self.meta.latest_episode + chunk_idx = latest_ep[f"videos/{video_key}/chunk_index"][0] + file_idx = latest_ep[f"videos/{video_key}/file_index"][0] latest_path = self.root / self.meta.video_path.format( video_key=video_key, chunk_index=chunk_idx, file_index=file_idx ) - latest_size_in_mb = get_video_size_in_mb(latest_path) - latest_duration_in_s = get_video_duration_in_s(latest_path) + latest_size_in_mb = get_file_size_in_mb(latest_path) + latest_duration_in_s = latest_ep[f"videos/{video_key}/to_timestamp"][0] if latest_size_in_mb + ep_size_in_mb >= self.meta.video_files_size_in_mb: # Move temporary episode video to a new video file in the dataset @@ -1315,6 +1470,12 @@ def create( obj.delta_timestamps = None obj.delta_indices = None obj.video_backend = video_backend if video_backend is not None else get_safe_default_codec() + obj.writer = None + obj.latest_episode = None + # Initialize tracking for incremental recording + obj._lazy_loading = False + obj._recorded_frames = 0 + obj._writer_closed_for_reading = False return obj diff --git a/src/lerobot/datasets/utils.py b/src/lerobot/datasets/utils.py index a2f2850141a..37d8432b2ba 100644 --- a/src/lerobot/datasets/utils.py +++ b/src/lerobot/datasets/utils.py @@ -30,7 +30,7 @@ import pandas as pd import pyarrow.parquet as pq import torch -from datasets import Dataset, concatenate_datasets +from datasets import Dataset from datasets.table import embed_table_storage from huggingface_hub import DatasetCard, DatasetCardData, HfApi from huggingface_hub.errors import RevisionNotFoundError @@ -44,7 +44,7 @@ ForwardCompatibilityError, ) from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_STR -from lerobot.utils.utils import is_valid_numpy_dtype_string +from lerobot.utils.utils import SuppressProgressBars, is_valid_numpy_dtype_string DEFAULT_CHUNK_SIZE = 1000 # Max number of files per chunk DEFAULT_DATA_FILE_SIZE_IN_MB = 100 # Max size per file @@ -94,12 +94,6 @@ def get_hf_dataset_size_in_mb(hf_ds: Dataset) -> int: return hf_ds.data.nbytes // (1024**2) -def get_hf_dataset_cache_dir(hf_ds: Dataset) -> Path | None: - if hf_ds.cache_files is None or len(hf_ds.cache_files) == 0: - return None - return Path(hf_ds.cache_files[0]["filename"]).parents[2] - - def update_chunk_file_indices(chunk_idx: int, file_idx: int, chunks_size: int) -> tuple[int, int]: if file_idx == chunks_size - 1: file_idx = 0 @@ -123,8 +117,9 @@ def load_nested_dataset(pq_dir: Path, features: datasets.Features | None = None) raise FileNotFoundError(f"Provided directory does not contain any parquet file: {pq_dir}") # TODO(rcadene): set num_proc to accelerate conversion to pyarrow - datasets = [Dataset.from_parquet(str(path), features=features) for path in paths] - return concatenate_datasets(datasets) + with SuppressProgressBars(): + datasets = Dataset.from_parquet([str(path) for path in paths], features=features) + return datasets def get_parquet_num_frames(parquet_path: str | Path) -> int: @@ -132,10 +127,14 @@ def get_parquet_num_frames(parquet_path: str | Path) -> int: return metadata.num_rows -def get_video_size_in_mb(mp4_path: Path) -> float: - file_size_bytes = mp4_path.stat().st_size - file_size_mb = file_size_bytes / (1024**2) - return file_size_mb +def get_file_size_in_mb(file_path: Path) -> float: + """Get file size on disk in megabytes. + + Args: + file_path (Path): Path to the file. + """ + file_size_bytes = file_path.stat().st_size + return file_size_bytes / (1024**2) def flatten_dict(d: dict, parent_key: str = "", sep: str = "/") -> dict: diff --git a/src/lerobot/datasets/video_utils.py b/src/lerobot/datasets/video_utils.py index 1d4f07c769a..740cdb6020d 100644 --- a/src/lerobot/datasets/video_utils.py +++ b/src/lerobot/datasets/video_utils.py @@ -452,6 +452,9 @@ def concatenate_video_files( template=input_stream, opaque=True ) + # set the time base to the input stream time base (missing in the codec context) + stream_map[input_stream.index].time_base = input_stream.time_base + # Demux + remux packets (no re-encode) for packet in input_container.demux(): # Skip packets from un-mapped streams @@ -639,6 +642,9 @@ def __exit__(self, exc_type, exc_val, exc_tb): ) self.dataset._batch_save_episode_video(start_ep, end_ep) + # Finalize the dataset to properly close all writers + self.dataset.finalize() + # Clean up episode images if recording was interrupted if exc_type is not None: interrupted_episode_index = self.dataset.num_episodes diff --git a/src/lerobot/envs/configs.py b/src/lerobot/envs/configs.py index 0daaaf9fd1f..7a979b8645d 100644 --- a/src/lerobot/envs/configs.py +++ b/src/lerobot/envs/configs.py @@ -50,6 +50,8 @@ class AlohaEnv(EnvConfig): fps: int = 50 episode_length: int = 400 obs_type: str = "pixels_agent_pos" + observation_height: int = 480 + observation_width: int = 640 render_mode: str = "rgb_array" features: dict[str, PolicyFeature] = field( default_factory=lambda: { @@ -67,10 +69,14 @@ class AlohaEnv(EnvConfig): def __post_init__(self): if self.obs_type == "pixels": - self.features["top"] = PolicyFeature(type=FeatureType.VISUAL, shape=(480, 640, 3)) + self.features["top"] = PolicyFeature( + type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3) + ) elif self.obs_type == "pixels_agent_pos": self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(14,)) - self.features["pixels/top"] = PolicyFeature(type=FeatureType.VISUAL, shape=(480, 640, 3)) + self.features["pixels/top"] = PolicyFeature( + type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3) + ) @property def gym_kwargs(self) -> dict: @@ -91,6 +97,8 @@ class PushtEnv(EnvConfig): render_mode: str = "rgb_array" visualization_width: int = 384 visualization_height: int = 384 + observation_height: int = 384 + observation_width: int = 384 features: dict[str, PolicyFeature] = field( default_factory=lambda: { ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(2,)), @@ -108,7 +116,9 @@ class PushtEnv(EnvConfig): def __post_init__(self): if self.obs_type == "pixels_agent_pos": - self.features["pixels"] = PolicyFeature(type=FeatureType.VISUAL, shape=(384, 384, 3)) + self.features["pixels"] = PolicyFeature( + type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3) + ) elif self.obs_type == "environment_state_agent_pos": self.features["environment_state"] = PolicyFeature(type=FeatureType.ENV, shape=(16,)) @@ -255,6 +265,8 @@ class LiberoEnv(EnvConfig): camera_name: str = "agentview_image,robot0_eye_in_hand_image" init_states: bool = True camera_name_mapping: dict[str, str] | None = None + observation_height: int = 360 + observation_width: int = 360 features: dict[str, PolicyFeature] = field( default_factory=lambda: { ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(7,)), @@ -272,18 +284,18 @@ class LiberoEnv(EnvConfig): def __post_init__(self): if self.obs_type == "pixels": self.features["pixels/agentview_image"] = PolicyFeature( - type=FeatureType.VISUAL, shape=(360, 360, 3) + type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3) ) self.features["pixels/robot0_eye_in_hand_image"] = PolicyFeature( - type=FeatureType.VISUAL, shape=(360, 360, 3) + type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3) ) elif self.obs_type == "pixels_agent_pos": self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(8,)) self.features["pixels/agentview_image"] = PolicyFeature( - type=FeatureType.VISUAL, shape=(360, 360, 3) + type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3) ) self.features["pixels/robot0_eye_in_hand_image"] = PolicyFeature( - type=FeatureType.VISUAL, shape=(360, 360, 3) + type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3) ) else: raise ValueError(f"Unsupported obs_type: {self.obs_type}") diff --git a/src/lerobot/motors/__init__.py b/src/lerobot/motors/__init__.py index dfbfbaee8fc..850ef33d74e 100644 --- a/src/lerobot/motors/__init__.py +++ b/src/lerobot/motors/__init__.py @@ -1 +1,17 @@ +#!/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. + from .motors_bus import Motor, MotorCalibration, MotorNormMode, MotorsBus diff --git a/src/lerobot/policies/pi0/modeling_pi0.py b/src/lerobot/policies/pi0/modeling_pi0.py index a2dcdaea37f..596b273d5fb 100644 --- a/src/lerobot/policies/pi0/modeling_pi0.py +++ b/src/lerobot/policies/pi0/modeling_pi0.py @@ -897,7 +897,7 @@ def from_pretrained( ) -> T: """Override the from_pretrained method to handle key remapping and display important disclaimer.""" print( - "The PI05 model is a direct port of the OpenPI implementation. \n" + "The PI0 model is a direct port of the OpenPI implementation. \n" "This implementation follows the original OpenPI structure for compatibility. \n" "Original implementation: https://github.com/Physical-Intelligence/openpi" ) diff --git a/src/lerobot/processor/policy_robot_bridge.py b/src/lerobot/processor/policy_robot_bridge.py index 845ee065a64..25887d414ee 100644 --- a/src/lerobot/processor/policy_robot_bridge.py +++ b/src/lerobot/processor/policy_robot_bridge.py @@ -1,3 +1,19 @@ +#!/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. + from dataclasses import asdict, dataclass from typing import Any diff --git a/src/lerobot/rl/buffer.py b/src/lerobot/rl/buffer.py index 917e4e2cc96..81aa29c4803 100644 --- a/src/lerobot/rl/buffer.py +++ b/src/lerobot/rl/buffer.py @@ -607,6 +607,7 @@ def to_lerobot_dataset( lerobot_dataset.save_episode() lerobot_dataset.stop_image_writer() + lerobot_dataset.finalize() return lerobot_dataset diff --git a/src/lerobot/rl/gym_manipulator.py b/src/lerobot/rl/gym_manipulator.py index ad36f1b3641..f9c9d0d7a71 100644 --- a/src/lerobot/rl/gym_manipulator.py +++ b/src/lerobot/rl/gym_manipulator.py @@ -696,7 +696,7 @@ def control_loop( episode_idx += 1 if dataset is not None: - if transition[TransitionKey.INFO].get("rerecord_episode", False): + if transition[TransitionKey.INFO].get(TeleopEvents.RERECORD_EPISODE, False): logging.info(f"Re-recording episode {episode_idx}") dataset.clear_episode_buffer() episode_idx -= 1 diff --git a/src/lerobot/robots/__init__.py b/src/lerobot/robots/__init__.py index d8fd0de9311..1dba0f1b089 100644 --- a/src/lerobot/robots/__init__.py +++ b/src/lerobot/robots/__init__.py @@ -1,3 +1,19 @@ +#!/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. + from .config import RobotConfig from .robot import Robot from .utils import make_robot_from_config diff --git a/src/lerobot/scripts/lerobot_edit_dataset.py b/src/lerobot/scripts/lerobot_edit_dataset.py new file mode 100644 index 00000000000..83ba027bcce --- /dev/null +++ b/src/lerobot/scripts/lerobot_edit_dataset.py @@ -0,0 +1,286 @@ +#!/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, +and remove features. 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']" + +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 ( + 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 EditDatasetConfig: + repo_id: str + operation: DeleteEpisodesConfig | SplitConfig | MergeConfig | RemoveFeatureConfig + 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() + + +@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) + else: + raise ValueError( + f"Unknown operation type: {operation_type}\n" + f"Available operations: delete_episodes, split, merge, remove_feature" + ) + + +def main() -> None: + init_logging() + edit_dataset() + + +if __name__ == "__main__": + main() diff --git a/src/lerobot/utils/utils.py b/src/lerobot/utils/utils.py index 8777d5a9db8..dfcd4a6b105 100644 --- a/src/lerobot/utils/utils.py +++ b/src/lerobot/utils/utils.py @@ -27,6 +27,7 @@ import numpy as np import torch +from datasets.utils.logging import disable_progress_bar, enable_progress_bar def inside_slurm(): @@ -247,6 +248,25 @@ def get_elapsed_time_in_days_hours_minutes_seconds(elapsed_time_s: float): return days, hours, minutes, seconds +class SuppressProgressBars: + """ + Context manager to suppress progress bars. + + Example + -------- + ```python + with SuppressProgressBars(): + # Code that would normally show progress bars + ``` + """ + + def __enter__(self): + disable_progress_bar() + + def __exit__(self, exc_type, exc_val, exc_tb): + enable_progress_bar() + + class TimerManager: """ Lightweight utility to measure elapsed time. diff --git a/tests/async_inference/test_e2e.py b/tests/async_inference/test_e2e.py index ebaef2ef1a2..11941ce32e0 100644 --- a/tests/async_inference/test_e2e.py +++ b/tests/async_inference/test_e2e.py @@ -139,7 +139,6 @@ def _fake_send_policy_instructions(self, request, context): # noqa: N802 policy_type="test", pretrained_name_or_path="test", actions_per_chunk=20, - verify_robot_cameras=False, ) client = RobotClient(client_config) diff --git a/tests/async_inference/test_robot_client.py b/tests/async_inference/test_robot_client.py index dfdb8ce4200..5b138d91bc9 100644 --- a/tests/async_inference/test_robot_client.py +++ b/tests/async_inference/test_robot_client.py @@ -51,7 +51,6 @@ def robot_client(): policy_type="test", pretrained_name_or_path="test", actions_per_chunk=20, - verify_robot_cameras=False, ) client = RobotClient(test_config) diff --git a/tests/datasets/test_aggregate.py b/tests/datasets/test_aggregate.py index 4f316f80eb2..b710a3a4be2 100644 --- a/tests/datasets/test_aggregate.py +++ b/tests/datasets/test_aggregate.py @@ -181,6 +181,54 @@ def assert_dataset_iteration_works(aggr_ds): pass +def assert_video_timestamps_within_bounds(aggr_ds): + """Test that all video timestamps are within valid bounds for their respective video files. + + This catches bugs where timestamps point to frames beyond the actual video length, + which would cause "Invalid frame index" errors during data loading. + """ + try: + from torchcodec.decoders import VideoDecoder + except ImportError: + return + + for ep_idx in range(aggr_ds.num_episodes): + ep = aggr_ds.meta.episodes[ep_idx] + + for vid_key in aggr_ds.meta.video_keys: + from_ts = ep[f"videos/{vid_key}/from_timestamp"] + to_ts = ep[f"videos/{vid_key}/to_timestamp"] + video_path = aggr_ds.root / aggr_ds.meta.get_video_file_path(ep_idx, vid_key) + + if not video_path.exists(): + continue + + from_frame_idx = round(from_ts * aggr_ds.fps) + to_frame_idx = round(to_ts * aggr_ds.fps) + + try: + decoder = VideoDecoder(str(video_path)) + num_frames = len(decoder) + + # Verify timestamps don't exceed video bounds + assert from_frame_idx >= 0, ( + f"Episode {ep_idx}, {vid_key}: from_frame_idx ({from_frame_idx}) < 0" + ) + assert from_frame_idx < num_frames, ( + f"Episode {ep_idx}, {vid_key}: from_frame_idx ({from_frame_idx}) >= video frames ({num_frames})" + ) + assert to_frame_idx <= num_frames, ( + f"Episode {ep_idx}, {vid_key}: to_frame_idx ({to_frame_idx}) > video frames ({num_frames})" + ) + assert from_frame_idx < to_frame_idx, ( + f"Episode {ep_idx}, {vid_key}: from_frame_idx ({from_frame_idx}) >= to_frame_idx ({to_frame_idx})" + ) + except Exception as e: + raise AssertionError( + f"Failed to verify timestamps for episode {ep_idx}, {vid_key}: {e}" + ) from e + + def test_aggregate_datasets(tmp_path, lerobot_dataset_factory): """Test basic aggregation functionality with standard parameters.""" ds_0_num_frames = 400 @@ -227,6 +275,7 @@ def test_aggregate_datasets(tmp_path, lerobot_dataset_factory): assert_metadata_consistency(aggr_ds, ds_0, ds_1) assert_episode_indices_updated_correctly(aggr_ds, ds_0, ds_1) assert_video_frames_integrity(aggr_ds, ds_0, ds_1) + assert_video_timestamps_within_bounds(aggr_ds) assert_dataset_iteration_works(aggr_ds) @@ -277,6 +326,7 @@ def test_aggregate_with_low_threshold(tmp_path, lerobot_dataset_factory): assert_metadata_consistency(aggr_ds, ds_0, ds_1) assert_episode_indices_updated_correctly(aggr_ds, ds_0, ds_1) assert_video_frames_integrity(aggr_ds, ds_0, ds_1) + assert_video_timestamps_within_bounds(aggr_ds) assert_dataset_iteration_works(aggr_ds) # Check that multiple files were actually created due to small size limits @@ -290,3 +340,43 @@ def test_aggregate_with_low_threshold(tmp_path, lerobot_dataset_factory): if video_dir.exists(): video_files = list(video_dir.rglob("*.mp4")) assert len(video_files) > 1, "Small file size limits should create multiple video files" + + +def test_video_timestamps_regression(tmp_path, lerobot_dataset_factory): + """Regression test for video timestamp bug when merging datasets. + + This test specifically checks that video timestamps are correctly calculated + and accumulated when merging multiple datasets. + """ + datasets = [] + for i in range(3): + ds = lerobot_dataset_factory( + root=tmp_path / f"regression_{i}", + repo_id=f"{DUMMY_REPO_ID}_regression_{i}", + total_episodes=2, + total_frames=100, + ) + datasets.append(ds) + + aggregate_datasets( + repo_ids=[ds.repo_id for ds in datasets], + roots=[ds.root for ds in datasets], + aggr_repo_id=f"{DUMMY_REPO_ID}_regression_aggr", + aggr_root=tmp_path / "regression_aggr", + ) + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(tmp_path / "regression_aggr") + aggr_ds = LeRobotDataset(f"{DUMMY_REPO_ID}_regression_aggr", root=tmp_path / "regression_aggr") + + assert_video_timestamps_within_bounds(aggr_ds) + + for i in range(len(aggr_ds)): + item = aggr_ds[i] + for key in aggr_ds.meta.video_keys: + assert key in item, f"Video key {key} missing from item {i}" + assert item[key].shape[0] == 3, f"Expected 3 channels for video key {key}" diff --git a/tests/datasets/test_dataset_tools.py b/tests/datasets/test_dataset_tools.py new file mode 100644 index 00000000000..a9c04d6f244 --- /dev/null +++ b/tests/datasets/test_dataset_tools.py @@ -0,0 +1,895 @@ +#!/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. +"""Tests for dataset tools utilities.""" + +from unittest.mock import patch + +import numpy as np +import pytest +import torch + +from lerobot.datasets.dataset_tools import ( + add_feature, + delete_episodes, + merge_datasets, + remove_feature, + split_dataset, +) + + +@pytest.fixture +def sample_dataset(tmp_path, empty_lerobot_dataset_factory): + """Create a sample dataset for testing.""" + features = { + "action": {"dtype": "float32", "shape": (6,), "names": None}, + "observation.state": {"dtype": "float32", "shape": (4,), "names": None}, + "observation.images.top": {"dtype": "image", "shape": (224, 224, 3), "names": None}, + } + + dataset = empty_lerobot_dataset_factory( + root=tmp_path / "test_dataset", + features=features, + ) + + for ep_idx in range(5): + for _ in range(10): + frame = { + "action": np.random.randn(6).astype(np.float32), + "observation.state": np.random.randn(4).astype(np.float32), + "observation.images.top": np.random.randint(0, 255, size=(224, 224, 3), dtype=np.uint8), + "task": f"task_{ep_idx % 2}", + } + dataset.add_frame(frame) + dataset.save_episode() + + dataset.finalize() + return dataset + + +def test_delete_single_episode(sample_dataset, tmp_path): + """Test deleting a single episode.""" + output_dir = tmp_path / "filtered" + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(output_dir) + + new_dataset = delete_episodes( + sample_dataset, + episode_indices=[2], + output_dir=output_dir, + ) + + assert new_dataset.meta.total_episodes == 4 + assert new_dataset.meta.total_frames == 40 + + episode_indices = {int(idx.item()) for idx in new_dataset.hf_dataset["episode_index"]} + assert episode_indices == {0, 1, 2, 3} + + assert len(new_dataset) == 40 + + +def test_delete_multiple_episodes(sample_dataset, tmp_path): + """Test deleting multiple episodes.""" + output_dir = tmp_path / "filtered" + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(output_dir) + + new_dataset = delete_episodes( + sample_dataset, + episode_indices=[1, 3], + output_dir=output_dir, + ) + + assert new_dataset.meta.total_episodes == 3 + assert new_dataset.meta.total_frames == 30 + + episode_indices = {int(idx.item()) for idx in new_dataset.hf_dataset["episode_index"]} + assert episode_indices == {0, 1, 2} + + +def test_delete_invalid_episodes(sample_dataset, tmp_path): + """Test error handling for invalid episode indices.""" + with pytest.raises(ValueError, match="Invalid episode indices"): + delete_episodes( + sample_dataset, + episode_indices=[10, 20], + output_dir=tmp_path / "filtered", + ) + + +def test_delete_all_episodes(sample_dataset, tmp_path): + """Test error when trying to delete all episodes.""" + with pytest.raises(ValueError, match="Cannot delete all episodes"): + delete_episodes( + sample_dataset, + episode_indices=list(range(5)), + output_dir=tmp_path / "filtered", + ) + + +def test_delete_empty_list(sample_dataset, tmp_path): + """Test error when no episodes specified.""" + with pytest.raises(ValueError, match="No episodes to delete"): + delete_episodes( + sample_dataset, + episode_indices=[], + output_dir=tmp_path / "filtered", + ) + + +def test_split_by_episodes(sample_dataset, tmp_path): + """Test splitting dataset by specific episode indices.""" + splits = { + "train": [0, 1, 2], + "val": [3, 4], + } + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + + def mock_snapshot(repo_id, **kwargs): + if "train" in repo_id: + return str(tmp_path / f"{sample_dataset.repo_id}_train") + elif "val" in repo_id: + return str(tmp_path / f"{sample_dataset.repo_id}_val") + return str(kwargs.get("local_dir", tmp_path)) + + mock_snapshot_download.side_effect = mock_snapshot + + result = split_dataset( + sample_dataset, + splits=splits, + output_dir=tmp_path, + ) + + assert set(result.keys()) == {"train", "val"} + + assert result["train"].meta.total_episodes == 3 + assert result["train"].meta.total_frames == 30 + + assert result["val"].meta.total_episodes == 2 + assert result["val"].meta.total_frames == 20 + + train_episodes = {int(idx.item()) for idx in result["train"].hf_dataset["episode_index"]} + assert train_episodes == {0, 1, 2} + + val_episodes = {int(idx.item()) for idx in result["val"].hf_dataset["episode_index"]} + assert val_episodes == {0, 1} + + +def test_split_by_fractions(sample_dataset, tmp_path): + """Test splitting dataset by fractions.""" + splits = { + "train": 0.6, + "val": 0.4, + } + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + + def mock_snapshot(repo_id, **kwargs): + for split_name in splits: + if split_name in repo_id: + return str(tmp_path / f"{sample_dataset.repo_id}_{split_name}") + return str(kwargs.get("local_dir", tmp_path)) + + mock_snapshot_download.side_effect = mock_snapshot + + result = split_dataset( + sample_dataset, + splits=splits, + output_dir=tmp_path, + ) + + assert result["train"].meta.total_episodes == 3 + assert result["val"].meta.total_episodes == 2 + + +def test_split_overlapping_episodes(sample_dataset, tmp_path): + """Test error when episodes appear in multiple splits.""" + splits = { + "train": [0, 1, 2], + "val": [2, 3, 4], + } + + with pytest.raises(ValueError, match="Episodes cannot appear in multiple splits"): + split_dataset(sample_dataset, splits=splits, output_dir=tmp_path) + + +def test_split_invalid_fractions(sample_dataset, tmp_path): + """Test error when fractions sum to more than 1.""" + splits = { + "train": 0.7, + "val": 0.5, + } + + with pytest.raises(ValueError, match="Split fractions must sum to <= 1.0"): + split_dataset(sample_dataset, splits=splits, output_dir=tmp_path) + + +def test_split_empty(sample_dataset, tmp_path): + """Test error with empty splits.""" + with pytest.raises(ValueError, match="No splits provided"): + split_dataset(sample_dataset, splits={}, output_dir=tmp_path) + + +def test_merge_two_datasets(sample_dataset, tmp_path, empty_lerobot_dataset_factory): + """Test merging two datasets.""" + features = { + "action": {"dtype": "float32", "shape": (6,), "names": None}, + "observation.state": {"dtype": "float32", "shape": (4,), "names": None}, + "observation.images.top": {"dtype": "image", "shape": (224, 224, 3), "names": None}, + } + + dataset2 = empty_lerobot_dataset_factory( + root=tmp_path / "test_dataset2", + features=features, + ) + + for ep_idx in range(3): + for _ in range(10): + frame = { + "action": np.random.randn(6).astype(np.float32), + "observation.state": np.random.randn(4).astype(np.float32), + "observation.images.top": np.random.randint(0, 255, size=(224, 224, 3), dtype=np.uint8), + "task": f"task_{ep_idx % 2}", + } + dataset2.add_frame(frame) + dataset2.save_episode() + dataset2.finalize() + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(tmp_path / "merged_dataset") + + merged = merge_datasets( + [sample_dataset, dataset2], + output_repo_id="merged_dataset", + output_dir=tmp_path / "merged_dataset", + ) + + assert merged.meta.total_episodes == 8 # 5 + 3 + assert merged.meta.total_frames == 80 # 50 + 30 + + episode_indices = sorted({int(idx.item()) for idx in merged.hf_dataset["episode_index"]}) + assert episode_indices == list(range(8)) + + +def test_merge_empty_list(tmp_path): + """Test error when merging empty list.""" + with pytest.raises(ValueError, match="No datasets to merge"): + merge_datasets([], output_repo_id="merged", output_dir=tmp_path) + + +def test_add_feature_with_values(sample_dataset, tmp_path): + """Test adding a feature with pre-computed values.""" + num_frames = sample_dataset.meta.total_frames + reward_values = np.random.randn(num_frames, 1).astype(np.float32) + + feature_info = { + "dtype": "float32", + "shape": (1,), + "names": None, + } + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(tmp_path / "with_reward") + + new_dataset = add_feature( + sample_dataset, + feature_name="reward", + feature_values=reward_values, + feature_info=feature_info, + output_dir=tmp_path / "with_reward", + ) + + assert "reward" in new_dataset.meta.features + assert new_dataset.meta.features["reward"] == feature_info + + assert len(new_dataset) == num_frames + sample_item = new_dataset[0] + assert "reward" in sample_item + assert isinstance(sample_item["reward"], torch.Tensor) + + +def test_add_feature_with_callable(sample_dataset, tmp_path): + """Test adding a feature with a callable.""" + + def compute_reward(frame_dict, episode_idx, frame_idx): + return float(episode_idx * 10 + frame_idx) + + feature_info = { + "dtype": "float32", + "shape": (1,), + "names": None, + } + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(tmp_path / "with_reward") + + new_dataset = add_feature( + sample_dataset, + feature_name="reward", + feature_values=compute_reward, + feature_info=feature_info, + output_dir=tmp_path / "with_reward", + ) + + assert "reward" in new_dataset.meta.features + + items = [new_dataset[i] for i in range(10)] + first_episode_items = [item for item in items if item["episode_index"] == 0] + assert len(first_episode_items) == 10 + + first_frame = first_episode_items[0] + assert first_frame["frame_index"] == 0 + assert float(first_frame["reward"]) == 0.0 + + +def test_add_existing_feature(sample_dataset, tmp_path): + """Test error when adding an existing feature.""" + feature_info = {"dtype": "float32", "shape": (1,)} + + with pytest.raises(ValueError, match="Feature 'action' already exists"): + add_feature( + sample_dataset, + feature_name="action", + feature_values=np.zeros(50), + feature_info=feature_info, + output_dir=tmp_path / "modified", + ) + + +def test_add_feature_invalid_info(sample_dataset, tmp_path): + """Test error with invalid feature info.""" + with pytest.raises(ValueError, match="feature_info must contain keys"): + add_feature( + sample_dataset, + feature_name="reward", + feature_values=np.zeros(50), + feature_info={"dtype": "float32"}, + output_dir=tmp_path / "modified", + ) + + +def test_remove_single_feature(sample_dataset, tmp_path): + """Test removing a single feature.""" + feature_info = {"dtype": "float32", "shape": (1,), "names": None} + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.side_effect = lambda repo_id, **kwargs: str(kwargs.get("local_dir", tmp_path)) + + dataset_with_reward = add_feature( + sample_dataset, + feature_name="reward", + feature_values=np.random.randn(50, 1).astype(np.float32), + feature_info=feature_info, + output_dir=tmp_path / "with_reward", + ) + + dataset_without_reward = remove_feature( + dataset_with_reward, + feature_names="reward", + output_dir=tmp_path / "without_reward", + ) + + assert "reward" not in dataset_without_reward.meta.features + + sample_item = dataset_without_reward[0] + assert "reward" not in sample_item + + +def test_remove_multiple_features(sample_dataset, tmp_path): + """Test removing multiple features at once.""" + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.side_effect = lambda repo_id, **kwargs: str(kwargs.get("local_dir", tmp_path)) + + dataset = sample_dataset + for feature_name in ["reward", "success"]: + feature_info = {"dtype": "float32", "shape": (1,), "names": None} + dataset = add_feature( + dataset, + feature_name=feature_name, + feature_values=np.random.randn(dataset.meta.total_frames, 1).astype(np.float32), + feature_info=feature_info, + output_dir=tmp_path / f"with_{feature_name}", + ) + + dataset_clean = remove_feature( + dataset, + feature_names=["reward", "success"], + output_dir=tmp_path / "clean", + ) + + assert "reward" not in dataset_clean.meta.features + assert "success" not in dataset_clean.meta.features + + +def test_remove_nonexistent_feature(sample_dataset, tmp_path): + """Test error when removing non-existent feature.""" + with pytest.raises(ValueError, match="Feature 'nonexistent' not found"): + remove_feature( + sample_dataset, + feature_names="nonexistent", + output_dir=tmp_path / "modified", + ) + + +def test_remove_required_feature(sample_dataset, tmp_path): + """Test error when trying to remove required features.""" + with pytest.raises(ValueError, match="Cannot remove required features"): + remove_feature( + sample_dataset, + feature_names="timestamp", + output_dir=tmp_path / "modified", + ) + + +def test_remove_camera_feature(sample_dataset, tmp_path): + """Test removing a camera feature.""" + camera_keys = sample_dataset.meta.camera_keys + if not camera_keys: + pytest.skip("No camera keys in dataset") + + camera_to_remove = camera_keys[0] + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(tmp_path / "without_camera") + + dataset_without_camera = remove_feature( + sample_dataset, + feature_names=camera_to_remove, + output_dir=tmp_path / "without_camera", + ) + + assert camera_to_remove not in dataset_without_camera.meta.features + assert camera_to_remove not in dataset_without_camera.meta.camera_keys + + sample_item = dataset_without_camera[0] + assert camera_to_remove not in sample_item + + +def test_complex_workflow_integration(sample_dataset, tmp_path): + """Test a complex workflow combining multiple operations.""" + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.side_effect = lambda repo_id, **kwargs: str(kwargs.get("local_dir", tmp_path)) + + dataset = add_feature( + sample_dataset, + feature_name="reward", + feature_values=np.random.randn(50, 1).astype(np.float32), + feature_info={"dtype": "float32", "shape": (1,), "names": None}, + output_dir=tmp_path / "step1", + ) + + dataset = delete_episodes( + dataset, + episode_indices=[2], + output_dir=tmp_path / "step2", + ) + + splits = split_dataset( + dataset, + splits={"train": 0.75, "val": 0.25}, + output_dir=tmp_path / "step3", + ) + + merged = merge_datasets( + list(splits.values()), + output_repo_id="final_dataset", + output_dir=tmp_path / "step4", + ) + + assert merged.meta.total_episodes == 4 + assert merged.meta.total_frames == 40 + assert "reward" in merged.meta.features + + assert len(merged) == 40 + sample_item = merged[0] + assert "reward" in sample_item + + +def test_delete_episodes_preserves_stats(sample_dataset, tmp_path): + """Test that deleting episodes preserves statistics correctly.""" + output_dir = tmp_path / "filtered" + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(output_dir) + + new_dataset = delete_episodes( + sample_dataset, + episode_indices=[2], + output_dir=output_dir, + ) + + assert new_dataset.meta.stats is not None + for feature in ["action", "observation.state"]: + assert feature in new_dataset.meta.stats + assert "mean" in new_dataset.meta.stats[feature] + assert "std" in new_dataset.meta.stats[feature] + + +def test_delete_episodes_preserves_tasks(sample_dataset, tmp_path): + """Test that tasks are preserved correctly after deletion.""" + output_dir = tmp_path / "filtered" + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(output_dir) + + new_dataset = delete_episodes( + sample_dataset, + episode_indices=[0], + output_dir=output_dir, + ) + + assert new_dataset.meta.tasks is not None + assert len(new_dataset.meta.tasks) == 2 + + tasks_in_dataset = {str(item["task"]) for item in new_dataset} + assert len(tasks_in_dataset) > 0 + + +def test_split_three_ways(sample_dataset, tmp_path): + """Test splitting dataset into three splits.""" + splits = { + "train": 0.6, + "val": 0.2, + "test": 0.2, + } + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + + def mock_snapshot(repo_id, **kwargs): + for split_name in splits: + if split_name in repo_id: + return str(tmp_path / f"{sample_dataset.repo_id}_{split_name}") + return str(kwargs.get("local_dir", tmp_path)) + + mock_snapshot_download.side_effect = mock_snapshot + + result = split_dataset( + sample_dataset, + splits=splits, + output_dir=tmp_path, + ) + + assert set(result.keys()) == {"train", "val", "test"} + assert result["train"].meta.total_episodes == 3 + assert result["val"].meta.total_episodes == 1 + assert result["test"].meta.total_episodes == 1 + + total_frames = sum(ds.meta.total_frames for ds in result.values()) + assert total_frames == sample_dataset.meta.total_frames + + +def test_split_preserves_stats(sample_dataset, tmp_path): + """Test that statistics are preserved when splitting.""" + splits = {"train": [0, 1, 2], "val": [3, 4]} + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + + def mock_snapshot(repo_id, **kwargs): + for split_name in splits: + if split_name in repo_id: + return str(tmp_path / f"{sample_dataset.repo_id}_{split_name}") + return str(kwargs.get("local_dir", tmp_path)) + + mock_snapshot_download.side_effect = mock_snapshot + + result = split_dataset( + sample_dataset, + splits=splits, + output_dir=tmp_path, + ) + + for split_ds in result.values(): + assert split_ds.meta.stats is not None + for feature in ["action", "observation.state"]: + assert feature in split_ds.meta.stats + assert "mean" in split_ds.meta.stats[feature] + assert "std" in split_ds.meta.stats[feature] + + +def test_merge_three_datasets(sample_dataset, tmp_path, empty_lerobot_dataset_factory): + """Test merging three datasets.""" + features = { + "action": {"dtype": "float32", "shape": (6,), "names": None}, + "observation.state": {"dtype": "float32", "shape": (4,), "names": None}, + "observation.images.top": {"dtype": "image", "shape": (224, 224, 3), "names": None}, + } + + datasets = [sample_dataset] + + for i in range(2): + dataset = empty_lerobot_dataset_factory( + root=tmp_path / f"test_dataset{i + 2}", + features=features, + ) + + for ep_idx in range(2): + for _ in range(10): + frame = { + "action": np.random.randn(6).astype(np.float32), + "observation.state": np.random.randn(4).astype(np.float32), + "observation.images.top": np.random.randint(0, 255, size=(224, 224, 3), dtype=np.uint8), + "task": f"task_{ep_idx}", + } + dataset.add_frame(frame) + dataset.save_episode() + dataset.finalize() + + datasets.append(dataset) + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(tmp_path / "merged_dataset") + + merged = merge_datasets( + datasets, + output_repo_id="merged_dataset", + output_dir=tmp_path / "merged_dataset", + ) + + assert merged.meta.total_episodes == 9 + assert merged.meta.total_frames == 90 + + +def test_merge_preserves_stats(sample_dataset, tmp_path, empty_lerobot_dataset_factory): + """Test that statistics are computed for merged datasets.""" + features = { + "action": {"dtype": "float32", "shape": (6,), "names": None}, + "observation.state": {"dtype": "float32", "shape": (4,), "names": None}, + "observation.images.top": {"dtype": "image", "shape": (224, 224, 3), "names": None}, + } + + dataset2 = empty_lerobot_dataset_factory( + root=tmp_path / "test_dataset2", + features=features, + ) + + for ep_idx in range(3): + for _ in range(10): + frame = { + "action": np.random.randn(6).astype(np.float32), + "observation.state": np.random.randn(4).astype(np.float32), + "observation.images.top": np.random.randint(0, 255, size=(224, 224, 3), dtype=np.uint8), + "task": f"task_{ep_idx % 2}", + } + dataset2.add_frame(frame) + dataset2.save_episode() + dataset2.finalize() + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(tmp_path / "merged_dataset") + + merged = merge_datasets( + [sample_dataset, dataset2], + output_repo_id="merged_dataset", + output_dir=tmp_path / "merged_dataset", + ) + + assert merged.meta.stats is not None + for feature in ["action", "observation.state"]: + assert feature in merged.meta.stats + assert "mean" in merged.meta.stats[feature] + assert "std" in merged.meta.stats[feature] + + +def test_add_feature_preserves_existing_stats(sample_dataset, tmp_path): + """Test that adding a feature preserves existing stats.""" + num_frames = sample_dataset.meta.total_frames + reward_values = np.random.randn(num_frames, 1).astype(np.float32) + + feature_info = { + "dtype": "float32", + "shape": (1,), + "names": None, + } + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(tmp_path / "with_reward") + + new_dataset = add_feature( + sample_dataset, + feature_name="reward", + feature_values=reward_values, + feature_info=feature_info, + output_dir=tmp_path / "with_reward", + ) + + assert new_dataset.meta.stats is not None + for feature in ["action", "observation.state"]: + assert feature in new_dataset.meta.stats + assert "mean" in new_dataset.meta.stats[feature] + assert "std" in new_dataset.meta.stats[feature] + + +def test_remove_feature_updates_stats(sample_dataset, tmp_path): + """Test that removing a feature removes it from stats.""" + feature_info = {"dtype": "float32", "shape": (1,), "names": None} + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.side_effect = lambda repo_id, **kwargs: str(kwargs.get("local_dir", tmp_path)) + + dataset_with_reward = add_feature( + sample_dataset, + feature_name="reward", + feature_values=np.random.randn(50, 1).astype(np.float32), + feature_info=feature_info, + output_dir=tmp_path / "with_reward", + ) + + dataset_without_reward = remove_feature( + dataset_with_reward, + feature_names="reward", + output_dir=tmp_path / "without_reward", + ) + + if dataset_without_reward.meta.stats: + assert "reward" not in dataset_without_reward.meta.stats + + +def test_delete_consecutive_episodes(sample_dataset, tmp_path): + """Test deleting consecutive episodes.""" + output_dir = tmp_path / "filtered" + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(output_dir) + + new_dataset = delete_episodes( + sample_dataset, + episode_indices=[1, 2, 3], + output_dir=output_dir, + ) + + assert new_dataset.meta.total_episodes == 2 + assert new_dataset.meta.total_frames == 20 + + episode_indices = sorted({int(idx.item()) for idx in new_dataset.hf_dataset["episode_index"]}) + assert episode_indices == [0, 1] + + +def test_delete_first_and_last_episodes(sample_dataset, tmp_path): + """Test deleting first and last episodes.""" + output_dir = tmp_path / "filtered" + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(output_dir) + + new_dataset = delete_episodes( + sample_dataset, + episode_indices=[0, 4], + output_dir=output_dir, + ) + + assert new_dataset.meta.total_episodes == 3 + assert new_dataset.meta.total_frames == 30 + + episode_indices = sorted({int(idx.item()) for idx in new_dataset.hf_dataset["episode_index"]}) + assert episode_indices == [0, 1, 2] + + +def test_split_all_episodes_assigned(sample_dataset, tmp_path): + """Test that all episodes can be explicitly assigned to splits.""" + splits = { + "split1": [0, 1], + "split2": [2, 3], + "split3": [4], + } + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + + def mock_snapshot(repo_id, **kwargs): + for split_name in splits: + if split_name in repo_id: + return str(tmp_path / f"{sample_dataset.repo_id}_{split_name}") + return str(kwargs.get("local_dir", tmp_path)) + + mock_snapshot_download.side_effect = mock_snapshot + + result = split_dataset( + sample_dataset, + splits=splits, + output_dir=tmp_path, + ) + + total_episodes = sum(ds.meta.total_episodes for ds in result.values()) + assert total_episodes == sample_dataset.meta.total_episodes diff --git a/tests/datasets/test_datasets.py b/tests/datasets/test_datasets.py index 2bc3bea43be..e174c578968 100644 --- a/tests/datasets/test_datasets.py +++ b/tests/datasets/test_datasets.py @@ -806,6 +806,8 @@ def test_episode_index_distribution(tmp_path, empty_lerobot_dataset_factory): dataset.add_frame({"state": torch.randn(2), "task": f"task_{episode_idx}"}) dataset.save_episode() + dataset.finalize() + # Load the dataset and check episode indices loaded_dataset = LeRobotDataset(dataset.repo_id, root=dataset.root) @@ -855,6 +857,8 @@ def test_multi_episode_metadata_consistency(tmp_path, empty_lerobot_dataset_fact dataset.add_frame({"state": torch.randn(3), ACTION: torch.randn(2), "task": tasks[episode_idx]}) dataset.save_episode() + dataset.finalize() + # Load and validate episode metadata loaded_dataset = LeRobotDataset(dataset.repo_id, root=dataset.root) @@ -893,6 +897,8 @@ def test_data_consistency_across_episodes(tmp_path, empty_lerobot_dataset_factor dataset.add_frame({"state": torch.randn(1), "task": "consistency_test"}) dataset.save_episode() + dataset.finalize() + loaded_dataset = LeRobotDataset(dataset.repo_id, root=dataset.root) # Check data consistency - no gaps or overlaps @@ -944,6 +950,8 @@ def test_statistics_metadata_validation(tmp_path, empty_lerobot_dataset_factory) dataset.add_frame({"state": state_data, ACTION: action_data, "task": "stats_test"}) dataset.save_episode() + dataset.finalize() + loaded_dataset = LeRobotDataset(dataset.repo_id, root=dataset.root) # Check that statistics exist for all features @@ -989,6 +997,8 @@ def test_episode_boundary_integrity(tmp_path, empty_lerobot_dataset_factory): dataset.add_frame({"state": torch.tensor([float(frame_idx)]), "task": f"episode_{episode_idx}"}) dataset.save_episode() + dataset.finalize() + loaded_dataset = LeRobotDataset(dataset.repo_id, root=dataset.root) # Test episode boundaries @@ -1031,6 +1041,8 @@ def test_task_indexing_and_validation(tmp_path, empty_lerobot_dataset_factory): dataset.add_frame({"state": torch.randn(1), "task": task}) dataset.save_episode() + dataset.finalize() + loaded_dataset = LeRobotDataset(dataset.repo_id, root=dataset.root) # Check that all unique tasks are in the tasks metadata @@ -1056,3 +1068,134 @@ def test_task_indexing_and_validation(tmp_path, empty_lerobot_dataset_factory): # Check total number of tasks assert loaded_dataset.meta.total_tasks == len(unique_tasks) + + +def test_dataset_resume_recording(tmp_path, empty_lerobot_dataset_factory): + """Test that resuming dataset recording preserves previously recorded episodes. + + This test validates the critical resume functionality by: + 1. Recording initial episodes and finalizing + 2. Reopening the dataset + 3. Recording additional episodes + 4. Verifying all data (old + new) is intact + + This specifically tests the bug fix where parquet files were being overwritten + instead of appended to during resume. + """ + features = { + "observation.state": {"dtype": "float32", "shape": (2,), "names": ["x", "y"]}, + "action": {"dtype": "float32", "shape": (2,), "names": ["x", "y"]}, + } + + dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features, use_videos=False) + + initial_episodes = 2 + frames_per_episode = 3 + + for ep_idx in range(initial_episodes): + for frame_idx in range(frames_per_episode): + dataset.add_frame( + { + "observation.state": torch.tensor([float(ep_idx), float(frame_idx)]), + "action": torch.tensor([0.5, 0.5]), + "task": f"task_{ep_idx}", + } + ) + dataset.save_episode() + + assert dataset.meta.total_episodes == initial_episodes + assert dataset.meta.total_frames == initial_episodes * frames_per_episode + + dataset.finalize() + initial_root = dataset.root + initial_repo_id = dataset.repo_id + del dataset + + dataset_verify = LeRobotDataset(initial_repo_id, root=initial_root, revision="v3.0") + assert dataset_verify.meta.total_episodes == initial_episodes + assert dataset_verify.meta.total_frames == initial_episodes * frames_per_episode + assert len(dataset_verify.hf_dataset) == initial_episodes * frames_per_episode + + for idx in range(len(dataset_verify.hf_dataset)): + item = dataset_verify[idx] + expected_ep = idx // frames_per_episode + expected_frame = idx % frames_per_episode + assert item["episode_index"].item() == expected_ep + assert item["frame_index"].item() == expected_frame + assert item["index"].item() == idx + assert item["observation.state"][0].item() == float(expected_ep) + assert item["observation.state"][1].item() == float(expected_frame) + + del dataset_verify + + # Phase 3: Resume recording - add more episodes + dataset_resumed = LeRobotDataset(initial_repo_id, root=initial_root, revision="v3.0") + + assert dataset_resumed.meta.total_episodes == initial_episodes + assert dataset_resumed.meta.total_frames == initial_episodes * frames_per_episode + assert dataset_resumed.latest_episode is None # Not recording yet + assert dataset_resumed.writer is None + assert dataset_resumed.meta.writer is None + + additional_episodes = 2 + for ep_idx in range(initial_episodes, initial_episodes + additional_episodes): + for frame_idx in range(frames_per_episode): + dataset_resumed.add_frame( + { + "observation.state": torch.tensor([float(ep_idx), float(frame_idx)]), + "action": torch.tensor([0.5, 0.5]), + "task": f"task_{ep_idx}", + } + ) + dataset_resumed.save_episode() + + total_episodes = initial_episodes + additional_episodes + total_frames = total_episodes * frames_per_episode + assert dataset_resumed.meta.total_episodes == total_episodes + assert dataset_resumed.meta.total_frames == total_frames + + dataset_resumed.finalize() + del dataset_resumed + + dataset_final = LeRobotDataset(initial_repo_id, root=initial_root, revision="v3.0") + + assert dataset_final.meta.total_episodes == total_episodes + assert dataset_final.meta.total_frames == total_frames + assert len(dataset_final.hf_dataset) == total_frames + + for idx in range(total_frames): + item = dataset_final[idx] + expected_ep = idx // frames_per_episode + expected_frame = idx % frames_per_episode + + assert item["episode_index"].item() == expected_ep, ( + f"Frame {idx}: wrong episode_index. Expected {expected_ep}, got {item['episode_index'].item()}" + ) + assert item["frame_index"].item() == expected_frame, ( + f"Frame {idx}: wrong frame_index. Expected {expected_frame}, got {item['frame_index'].item()}" + ) + assert item["index"].item() == idx, ( + f"Frame {idx}: wrong index. Expected {idx}, got {item['index'].item()}" + ) + + # Verify data integrity + assert item["observation.state"][0].item() == float(expected_ep), ( + f"Frame {idx}: wrong observation.state[0]. Expected {float(expected_ep)}, " + f"got {item['observation.state'][0].item()}" + ) + assert item["observation.state"][1].item() == float(expected_frame), ( + f"Frame {idx}: wrong observation.state[1]. Expected {float(expected_frame)}, " + f"got {item['observation.state'][1].item()}" + ) + + assert len(dataset_final.meta.episodes) == total_episodes + for ep_idx in range(total_episodes): + ep_metadata = dataset_final.meta.episodes[ep_idx] + assert ep_metadata["episode_index"] == ep_idx + assert ep_metadata["length"] == frames_per_episode + assert ep_metadata["tasks"] == [f"task_{ep_idx}"] + + expected_from = ep_idx * frames_per_episode + expected_to = (ep_idx + 1) * frames_per_episode + assert ep_metadata["dataset_from_index"] == expected_from + assert ep_metadata["dataset_to_index"] == expected_to diff --git a/tests/plugins/reachy2_sdk.py b/tests/plugins/reachy2_sdk.py index f56b59efbe4..457fcf0f9ba 100644 --- a/tests/plugins/reachy2_sdk.py +++ b/tests/plugins/reachy2_sdk.py @@ -1,3 +1,19 @@ +#!/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. + import sys import types from unittest.mock import MagicMock diff --git a/tests/policies/pi0_pi05/test_pi0.py b/tests/policies/pi0_pi05/test_pi0.py index 65f64e6bc08..b580310eb46 100644 --- a/tests/policies/pi0_pi05/test_pi0.py +++ b/tests/policies/pi0_pi05/test_pi0.py @@ -1,5 +1,19 @@ #!/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. + """Test script to verify PI0 policy integration with LeRobot, only meant to be run locally!""" import os diff --git a/tests/policies/pi0_pi05/test_pi05.py b/tests/policies/pi0_pi05/test_pi05.py index 72828a02f79..964539446a5 100644 --- a/tests/policies/pi0_pi05/test_pi05.py +++ b/tests/policies/pi0_pi05/test_pi05.py @@ -1,5 +1,19 @@ #!/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. + """Test script to verify PI0.5 (pi05) support in PI0 policy, only meant to be run locally!""" import os diff --git a/tests/policies/pi0_pi05/test_pi05_original_vs_lerobot.py b/tests/policies/pi0_pi05/test_pi05_original_vs_lerobot.py index 7bea8948620..0d5244e1c36 100644 --- a/tests/policies/pi0_pi05/test_pi05_original_vs_lerobot.py +++ b/tests/policies/pi0_pi05/test_pi05_original_vs_lerobot.py @@ -1,3 +1,19 @@ +#!/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. + """Test script to verify PI0OpenPI policy integration with LeRobot vs the original implementation, only meant to be run locally!""" import os diff --git a/tests/policies/pi0_pi05/test_pi0_original_vs_lerobot.py b/tests/policies/pi0_pi05/test_pi0_original_vs_lerobot.py index d91f716f19f..41db2dceb2e 100644 --- a/tests/policies/pi0_pi05/test_pi0_original_vs_lerobot.py +++ b/tests/policies/pi0_pi05/test_pi0_original_vs_lerobot.py @@ -1,3 +1,19 @@ +#!/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. + """Test script to verify PI0 policy integration with LeRobot vs the original implementation, only meant to be run locally!""" import os diff --git a/tests/processor/test_batch_conversion.py b/tests/processor/test_batch_conversion.py index 88b87312856..477381618e6 100644 --- a/tests/processor/test_batch_conversion.py +++ b/tests/processor/test_batch_conversion.py @@ -1,3 +1,19 @@ +#!/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. + import torch from lerobot.processor import DataProcessorPipeline, TransitionKey diff --git a/tests/processor/test_converters.py b/tests/processor/test_converters.py index bc58f7a61aa..47a6eea1823 100644 --- a/tests/processor/test_converters.py +++ b/tests/processor/test_converters.py @@ -1,3 +1,19 @@ +#!/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. + import numpy as np import pytest import torch diff --git a/tests/processor/test_tokenizer_processor.py b/tests/processor/test_tokenizer_processor.py index b81710db1ae..d6f87f56796 100644 --- a/tests/processor/test_tokenizer_processor.py +++ b/tests/processor/test_tokenizer_processor.py @@ -1,3 +1,19 @@ +#!/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. + """ Tests for the TokenizerProcessorStep class. """ diff --git a/tests/utils/test_io_utils.py b/tests/utils/test_io_utils.py index 9768a5ef9d9..0beea639d6a 100644 --- a/tests/utils/test_io_utils.py +++ b/tests/utils/test_io_utils.py @@ -1,4 +1,6 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +#!/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. @@ -11,6 +13,7 @@ # 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. + import json from pathlib import Path from typing import Any diff --git a/tests/utils/test_logging_utils.py b/tests/utils/test_logging_utils.py index 927fdc14dbc..560ba570155 100644 --- a/tests/utils/test_logging_utils.py +++ b/tests/utils/test_logging_utils.py @@ -1,4 +1,6 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +#!/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. @@ -11,6 +13,7 @@ # 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. + import pytest from lerobot.utils.logging_utils import AverageMeter, MetricsTracker diff --git a/tests/utils/test_random_utils.py b/tests/utils/test_random_utils.py index 5865361d0b5..e3a5d420f2c 100644 --- a/tests/utils/test_random_utils.py +++ b/tests/utils/test_random_utils.py @@ -1,4 +1,6 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +#!/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. @@ -11,6 +13,7 @@ # 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. + import random import numpy as np diff --git a/tests/utils/test_train_utils.py b/tests/utils/test_train_utils.py index 0eeaf907cc4..892503e9772 100644 --- a/tests/utils/test_train_utils.py +++ b/tests/utils/test_train_utils.py @@ -1,4 +1,6 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +#!/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. @@ -11,6 +13,7 @@ # 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. + from pathlib import Path from unittest.mock import Mock, patch diff --git a/tests/utils/test_visualization_utils.py b/tests/utils/test_visualization_utils.py index 65a97c6a305..08a82757080 100644 --- a/tests/utils/test_visualization_utils.py +++ b/tests/utils/test_visualization_utils.py @@ -1,3 +1,19 @@ +#!/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. + import importlib import sys from types import SimpleNamespace