forked from tangger/lerobot
131 lines
5.1 KiB
Python
131 lines
5.1 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import multiprocessing
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from concurrent.futures import ThreadPoolExecutor, wait
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from pathlib import Path
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import torch
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import tqdm
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from PIL import Image
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DEFAULT_IMAGE_PATH = "images/{image_key}/episode_{episode_index:06d}/frame_{frame_index:06d}.png"
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def safe_stop_image_writer(func):
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def wrapper(*args, **kwargs):
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try:
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return func(*args, **kwargs)
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except Exception as e:
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dataset = kwargs.get("dataset", None)
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image_writer = getattr(dataset, "image_writer", None) if dataset else None
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if image_writer is not None:
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print("Waiting for image writer to terminate...")
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image_writer.stop()
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raise e
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return wrapper
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class ImageWriter:
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"""This class abstract away the initialisation of processes or/and threads to
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save images on disk asynchrounously, which is critical to control a robot and record data
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at a high frame rate.
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When `num_processes=0`, it creates a threads pool of size `num_threads`.
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When `num_processes>0`, it creates processes pool of size `num_processes`, where each subprocess starts
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their own threads pool of size `num_threads`.
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The optimal number of processes and threads depends on your computer capabilities.
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We advise to use 4 threads per camera with 0 processes. If the fps is not stable, try to increase or lower
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the number of threads. If it is still not stable, try to use 1 subprocess, or more.
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"""
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def __init__(self, write_dir: Path, num_processes: int = 0, num_threads: int = 1):
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self.dir = write_dir
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self.image_path = DEFAULT_IMAGE_PATH
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self.num_processes = num_processes
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self.num_threads = self.num_threads_per_process = num_threads
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if self.num_processes <= 0:
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self.type = "threads"
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self.threads = ThreadPoolExecutor(max_workers=self.num_threads)
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self.futures = []
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else:
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self.type = "processes"
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self.num_threads_per_process = self.num_threads
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self.image_queue = multiprocessing.Queue()
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self.processes: list[multiprocessing.Process] = []
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for _ in range(num_processes):
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process = multiprocessing.Process(target=self._loop_to_save_images_in_threads)
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process.start()
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self.processes.append(process)
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def _loop_to_save_images_in_threads(self) -> None:
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with ThreadPoolExecutor(max_workers=self.num_threads) as executor:
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futures = []
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while True:
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frame_data = self.image_queue.get()
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if frame_data is None:
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break
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image, file_path = frame_data
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futures.append(executor.submit(self._save_image, image, file_path))
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with tqdm.tqdm(total=len(futures), desc="Writing images") as progress_bar:
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wait(futures)
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progress_bar.update(len(futures))
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def async_save_image(self, image: torch.Tensor, file_path: Path) -> None:
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"""Save an image asynchronously using threads or processes."""
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if self.type == "threads":
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self.futures.append(self.threads.submit(self._save_image, image, file_path))
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else:
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self.image_queue.put((image, file_path))
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def _save_image(self, image: torch.Tensor, file_path: Path) -> None:
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img = Image.fromarray(image.numpy())
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img.save(str(file_path), quality=100)
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def get_image_file_path(
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self, episode_index: int, image_key: str, frame_index: int, return_str: bool = True
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) -> str | Path:
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fpath = self.image_path.format(
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image_key=image_key, episode_index=episode_index, frame_index=frame_index
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)
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return str(self.dir / fpath) if return_str else self.dir / fpath
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def stop(self, timeout=20) -> None:
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"""Stop the image writer, waiting for all processes or threads to finish."""
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if self.type == "threads":
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with tqdm.tqdm(total=len(self.futures), desc="Writing images") as progress_bar:
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wait(self.futures, timeout=timeout)
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progress_bar.update(len(self.futures))
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else:
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self._stop_processes(self.processes, self.image_queue, timeout)
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def _stop_processes(self, timeout) -> None:
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for _ in self.processes:
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self.image_queue.put(None)
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for process in self.processes:
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process.join(timeout=timeout)
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if process.is_alive():
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process.terminate()
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self.image_queue.close()
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self.image_queue.join_thread()
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