YoloV5在容器中挂起

Question

我在把docker、python（3.11）和YOLOV5结合起来时遇到了麻烦。

我把问题简化成了下面这个脚本，在Windows 10下调试时一切正常，但在docker中运行时就卡住了（下面是输出内容）。在调试器中运行时，它会打印finished并等待ctrl+c，这很正常。如果我去掉最后的无限循环，它就会正常运行，先打印sleepy time，然后打印finished。只有在有循环的情况下，它在加载模型的过程中会卡住。这对我来说是个问题，因为这个程序应该是一个更大系统的一部分，它会等待新文件出现，并在文件出现时运行模型（使用watchdog.observers库）。

更复杂的是，python并不是我的第一语言，而且这个模型也不是我自己创建的。

from time import sleep

import torch
import os
import pathlib


def start_watching():
    print("test no watchdog, yes loop")    

    try:        
        if os.name == 'nt':
            pathlib.PosixPath = pathlib.WindowsPath
        #hardcoded paths for testing only
        model = torch.hub.load('./yolov5-master', 'custom', source='local', path='./best.pt', force_reload=True) 
        print("got model")
        results = model('./240206-154354_wheelset_149_22241687_Image_02.jpg')        
        labels, cord = results.xyxyn[0][:, -1], results.xyxyn[0][:, :-1]
        print(labels,cord)
    except Exception as ex:
        print(ex)
    
    print("sleepy time") 
    sleep(100)
    print("Finished")
    
    waitForFiles = True
    try:
        while waitForFiles:
            sleep(10)
    except KeyboardInterrupt:
        waitForFiles = False
        print("going down")


start_watching()

但是，当我在容器中运行这个时，我得到的输出是：

test no watchdog, yes loop
YOLOv5  2024-3-5 Python-3.11.6 torch-2.2.1+cpu CPU

Fusing layers...
Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs
Adding AutoShape...

它永远不会（无论我等多久）超过Adding AutoShape，这是在加载模型的过程中输出的。

我的dockerfile是：

FROM yolobase
#Copy the code in
COPY . .
RUN echo "min not working"

ENTRYPOINT python3 test.py

而上面提到的yolobase 镜像是根据用于构建模型的yolo版本附带的docker文件构建的（我被告知这很重要）。

# YOLOv5  by Ultralytics, AGPL-3.0 license
# Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments

# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
FROM ubuntu:23.10

# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/

# Install linux packages
# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
RUN apt update \
    && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
# RUN alias python=python3

# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error
RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED

# Install pip packages
COPY requirements.txt .
RUN python3 -m pip install --upgrade pip wheel
RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \
    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' \
    # tensorflow tensorflowjs \
    --extra-index-url https://download.pytorch.org/whl/cpu

# Create working directory
RUN mkdir -p /usr/src/app
WORKDIR /usr/src/app

# Copy contents
COPY . /usr/src/app


# Usage Examples -------------------------------------------------------------------------------------------------------

# Build and Push
# t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t

# Pull and Run
# t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

不过，如果我使用FROM ultralytics/yolov5:latest-cpu，我也会遇到同样的问题。

第二个dockerfile（即yolobase）使用的依赖项是：

# YOLOv5 requirements

# Usage: pip install -r requirements.txt

# Base ------------------------------------------------------------------------
gitpython>=3.1.30
matplotlib>=3.3
numpy>=1.23.5
opencv-python>=4.1.1
Pillow>=9.4.0
psutil  # system resources
PyYAML>=5.3.1
requests>=2.23.0
scipy>=1.4.1
thop>=0.1.1  # FLOPs computation
torch>=1.8.0  # see https://pytorch.org/get-started/locally (recommended)
torchvision>=0.9.0
tqdm>=4.64.0
ultralytics>=8.0.232
# protobuf<=3.20.1  # https://github.com/ultralytics/yolov5/issues/8012

# Logging ---------------------------------------------------------------------
# tensorboard>=2.4.1
# clearml>=1.2.0
# comet

# Plotting --------------------------------------------------------------------
pandas>=1.1.4
seaborn>=0.11.0

# Export ----------------------------------------------------------------------
# coremltools>=6.0  # CoreML export
# onnx>=1.10.0  # ONNX export
# onnx-simplifier>=0.4.1  # ONNX simplifier
# nvidia-pyindex  # TensorRT export
# nvidia-tensorrt  # TensorRT export
# scikit-learn<=1.1.2  # CoreML quantization
# tensorflow>=2.4.0,<=2.13.1  # TF exports (-cpu, -aarch64, -macos)
# tensorflowjs>=3.9.0  # TF.js export
# openvino-dev>=2023.0  # OpenVINO export

# Deploy ----------------------------------------------------------------------
setuptools>=65.5.1 # Snyk vulnerability fix
# tritonclient[all]~=2.24.0

# Extras ----------------------------------------------------------------------
# ipython  # interactive notebook
# mss  # screenshots
# albumentations>=1.0.3
# pycocotools>=2.0.6  # COCO mAP

注意，评论是从github上下载的，我没有注释掉任何内容，也没有添加任何内容。

debugging docker machine learning containerization yolo dependency management model loading infinite loop

YoloV5在容器中挂起

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