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| #!/usr/bin/env python | |
| # Copyright 2024 The HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import inspect | |
| from concurrent.futures import ThreadPoolExecutor | |
| from pathlib import Path | |
| from typing import Dict | |
| import datasets | |
| import numpy | |
| import PIL | |
| import torch | |
| from lerobot.common.datasets.video_utils import encode_video_frames | |
| def concatenate_episodes(ep_dicts): | |
| data_dict = {} | |
| keys = ep_dicts[0].keys() | |
| for key in keys: | |
| if torch.is_tensor(ep_dicts[0][key][0]): | |
| data_dict[key] = torch.cat([ep_dict[key] for ep_dict in ep_dicts]) | |
| else: | |
| if key not in data_dict: | |
| data_dict[key] = [] | |
| for ep_dict in ep_dicts: | |
| for x in ep_dict[key]: | |
| data_dict[key].append(x) | |
| total_frames = data_dict["frame_index"].shape[0] | |
| data_dict["index"] = torch.arange(0, total_frames, 1) | |
| return data_dict | |
| def save_images_concurrently(imgs_array: numpy.array, out_dir: Path, max_workers: int = 4): | |
| out_dir = Path(out_dir) | |
| out_dir.mkdir(parents=True, exist_ok=True) | |
| def save_image(img_array, i, out_dir): | |
| img = PIL.Image.fromarray(img_array) | |
| img.save(str(out_dir / f"frame_{i:06d}.png"), quality=100) | |
| num_images = len(imgs_array) | |
| with ThreadPoolExecutor(max_workers=max_workers) as executor: | |
| [executor.submit(save_image, imgs_array[i], i, out_dir) for i in range(num_images)] | |
| def get_default_encoding() -> dict: | |
| """Returns the default ffmpeg encoding parameters used by `encode_video_frames`.""" | |
| signature = inspect.signature(encode_video_frames) | |
| return { | |
| k: v.default | |
| for k, v in signature.parameters.items() | |
| if v.default is not inspect.Parameter.empty and k in ["vcodec", "pix_fmt", "g", "crf"] | |
| } | |
| def check_repo_id(repo_id: str) -> None: | |
| if len(repo_id.split("/")) != 2: | |
| raise ValueError( | |
| f"""`repo_id` is expected to contain a community or user id `/` the name of the dataset | |
| (e.g. 'lerobot/pusht'), but contains '{repo_id}'.""" | |
| ) | |
| # TODO(aliberts): remove | |
| def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> Dict[str, torch.Tensor]: | |
| """ | |
| Calculate episode data index for the provided HuggingFace Dataset. Relies on episode_index column of hf_dataset. | |
| Parameters: | |
| - hf_dataset (datasets.Dataset): A HuggingFace dataset containing the episode index. | |
| Returns: | |
| - episode_data_index: A dictionary containing the data index for each episode. The dictionary has two keys: | |
| - "from": A tensor containing the starting index of each episode. | |
| - "to": A tensor containing the ending index of each episode. | |
| """ | |
| episode_data_index = {"from": [], "to": []} | |
| current_episode = None | |
| """ | |
| The episode_index is a list of integers, each representing the episode index of the corresponding example. | |
| For instance, the following is a valid episode_index: | |
| [0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2] | |
| Below, we iterate through the episode_index and populate the episode_data_index dictionary with the starting and | |
| ending index of each episode. For the episode_index above, the episode_data_index dictionary will look like this: | |
| { | |
| "from": [0, 3, 7], | |
| "to": [3, 7, 12] | |
| } | |
| """ | |
| if len(hf_dataset) == 0: | |
| episode_data_index = { | |
| "from": torch.tensor([]), | |
| "to": torch.tensor([]), | |
| } | |
| return episode_data_index | |
| for idx, episode_idx in enumerate(hf_dataset["episode_index"]): | |
| if episode_idx != current_episode: | |
| # We encountered a new episode, so we append its starting location to the "from" list | |
| episode_data_index["from"].append(idx) | |
| # If this is not the first episode, we append the ending location of the previous episode to the "to" list | |
| if current_episode is not None: | |
| episode_data_index["to"].append(idx) | |
| # Let's keep track of the current episode index | |
| current_episode = episode_idx | |
| else: | |
| # We are still in the same episode, so there is nothing for us to do here | |
| pass | |
| # We have reached the end of the dataset, so we append the ending location of the last episode to the "to" list | |
| episode_data_index["to"].append(idx + 1) | |
| for k in ["from", "to"]: | |
| episode_data_index[k] = torch.tensor(episode_data_index[k]) | |
| return episode_data_index | |