151 lines
5.1 KiB
Python
151 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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import queue
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import threading
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from pathlib import Path
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import numpy as np
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import torch
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from PIL import Image
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DEFAULT_IMAGE_PATH = "{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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def write_image(image_array: np.ndarray, fpath: Path):
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try:
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image = Image.fromarray(image_array)
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image.save(fpath)
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except Exception as e:
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print(f"Error writing image {fpath}: {e}")
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def worker_thread_process(queue: queue.Queue):
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while True:
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item = queue.get()
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if item is None:
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queue.task_done()
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break
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image_array, fpath = item
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write_image(image_array, fpath)
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queue.task_done()
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def worker_process(queue: queue.Queue, num_threads: int):
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threads = []
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for _ in range(num_threads):
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t = threading.Thread(target=worker_thread_process, args=(queue,))
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t.daemon = True
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t.start()
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threads.append(t)
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for t in threads:
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t.join()
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class ImageWriter:
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"""
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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.write_dir = write_dir
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self.write_dir.mkdir(parents=True, exist_ok=True)
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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 = num_threads
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self.queue = None
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self.threads = []
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self.processes = []
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if self.num_processes == 0:
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# Use threading
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self.queue = queue.Queue()
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for _ in range(self.num_threads):
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t = threading.Thread(target=worker_thread_process, args=(self.queue,))
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t.daemon = True
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t.start()
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self.threads.append(t)
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else:
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# Use multiprocessing
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self.queue = multiprocessing.JoinableQueue()
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for _ in range(self.num_processes):
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p = multiprocessing.Process(target=worker_process, args=(self.queue, self.num_threads))
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p.daemon = True
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p.start()
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self.processes.append(p)
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def get_image_file_path(self, episode_index: int, image_key: str, frame_index: int) -> 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 self.write_dir / fpath
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def get_episode_dir(self, episode_index: int, image_key: str) -> Path:
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return self.get_image_file_path(
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episode_index=episode_index, image_key=image_key, frame_index=0
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).parent
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def save_image(self, image_array: torch.Tensor | np.ndarray, fpath: Path):
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if isinstance(image_array, torch.Tensor):
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image_array = image_array.numpy()
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self.queue.put((image_array, fpath))
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def wait_until_done(self):
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self.queue.join()
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def stop(self):
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if self.num_processes == 0:
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# For threading
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for _ in self.threads:
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self.queue.put(None)
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for t in self.threads:
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t.join()
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else:
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# For multiprocessing
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num_nones = self.num_processes * self.num_threads
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for _ in range(num_nones):
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self.queue.put(None)
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self.queue.close()
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self.queue.join_thread()
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for p in self.processes:
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p.join()
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