本文将介绍 YOLOv4 官方 Darknet 实现，如何于 Ubuntu 18.04 编译，及使用 Python 接口。
主要内容有：
- 准备基础环境： Nvidia Driver, CUDA, cuDNN, CMake, Python
- 编译应用环境： OpenCV, Darknet
- 用预训练模型进行推断：
darknet执行，或python
而 YOLOv4 的介绍或训练，可见前文《YOLOv4: Darknet 如何于 Docker 编译，及训练 COCO 子集》。
准备基础环境
Nvidia Driver
推荐使用 graphics drivers PPA 安装 Nvidia 驱动：
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update查看推荐的 Nvidia 显卡驱动：
ubuntu-drivers devices安装 Nvidia 驱动：
apt-cache search nvidia | grep ^nvidia-driver
sudo apt install nvidia-driver-450之后， sudo reboot 重启。运行 nvidia-smi 查看 Nvidia 驱动信息。
Nvidia CUDA Toolkit
获取地址：
- CUDA Toolkit Archive: https://developer.nvidia.com/...
建议选择 CUDA 10.2 ，为目前 PyTorch 可支持的最新版本。
下载安装：
wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
sudo sh cuda_10.2.89_440.33.01_linux.run注意：安装时，请手动取消驱动安装选项。
安装输出：
===========
= Summary =
===========
Driver: Not Selected
Toolkit: Installed in /usr/local/cuda-10.2/
Samples: Installed in /home/john/cuda-10.2/, but missing recommended libraries
Please make sure that
- PATH includes /usr/local/cuda-10.2/bin
- LD_LIBRARY_PATH includes /usr/local/cuda-10.2/lib64, or, add /usr/local/cuda-10.2/lib64 to /etc/ld.so.conf and run ldconfig as root
To uninstall the CUDA Toolkit, run cuda-uninstaller in /usr/local/cuda-10.2/bin
Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-10.2/doc/pdf for detailed information on setting up CUDA.
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 440.00 is required for CUDA 10.2 functionality to work.
To install the driver using this installer, run the following command, replacing with the name of this run file:
sudo .run --silent --driver
Logfile is /var/log/cuda-installer.log 添加环境变量：
$ vi ~/.bashrc
export CUDA_HOME=/usr/local/cuda
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH重启终端后，检查：
$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:24:38_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89Nvida cuDNN
获取地址：
- cuDNN Download: https://developer.nvidia.com/...
需选择 CUDA 10.2 对应的版本。
安装 deb 包：
sudo apt install ./libcudnn8_8.0.2.39-1+cuda10.2_amd64.deb
sudo apt install ./libcudnn8-dev_8.0.2.39-1+cuda10.2_amd64.deb
sudo apt install ./libcudnn8-doc_8.0.2.39-1+cuda10.2_amd64.deb查看 deb 包：
dpkg -c libcudnn8_8.0.2.39-1+cuda10.2_amd64.debCMake
下载安装：
curl -O -L https://github.com/Kitware/CMake/releases/download/v3.18.2/cmake-3.18.2-Linux-x86_64.sh
sh cmake-*.sh --prefix=$HOME/Applications/添加环境变量：
$ vi ~/.bashrc
export PATH=$HOME/Applications/cmake-3.18.2-Linux-x86_64/bin:$PATH说明： apt 源的 cmake 太旧， darknet 编译不过。
Python
获取地址：
- Anaconda: https://www.anaconda.com/dist...
Python 建议用 Anaconda 发行版。
安装命令：
# bash Anaconda3-2020.07-Linux-x86_64.sh
bash Anaconda3-2019.10-Linux-x86_64.sh编译应用环境
OpenCV 4.4.0
安装依赖：
apt install -y build-essential git libgtk-3-dev编译命令：
conda deactivate
# export CONDA_HOME="/home/john/anaconda3/envs/clenv"
export CONDA_HOME=`conda info -s | grep -Po "sys.prefix:s*K[/w]*"`
cd ~/Codes/
git clone -b 4.4.0 --depth 1 https://github.com/opencv/opencv.git
git clone -b 4.4.0 --depth 1 https://github.com/opencv/opencv_contrib.git
cd opencv/
mkdir _build && cd _build/
cmake -DCMAKE_BUILD_TYPE=Release
-DCMAKE_INSTALL_PREFIX=$HOME/opencv-cuda-4.4.0
-DOPENCV_EXTRA_MODULES_PATH=$HOME/Codes/opencv_contrib/modules
-DPYTHON_EXECUTABLE=$CONDA_HOME/bin/python3.7
-DPYTHON3_EXECUTABLE=$CONDA_HOME/bin/python3.7
-DPYTHON3_LIBRARY=$CONDA_HOME/lib/libpython3.7m.so
-DPYTHON3_INCLUDE_DIR=$CONDA_HOME/include/python3.7m
-DPYTHON3_NUMPY_INCLUDE_DIRS=$CONDA_HOME/lib/python3.7/site-packages/numpy/core/include
-DBUILD_opencv_python2=OFF
-DBUILD_opencv_python3=ON
-DWITH_CUDA=ON
-DBUILD_DOCS=OFF
-DBUILD_EXAMPLES=OFF
-DBUILD_TESTS=OFF
..
make -j$(nproc)
make install其中 Python 路径请对应自己安装的版本。
运行检查：
conda activate
export LD_LIBRARY_PATH=$HOME/opencv-cuda-4.4.0/lib:$LD_LIBRARY_PATH
export PYTHONPATH=$HOME/opencv-cuda-4.4.0/lib/python3.7/site-packages:$PYTHONPATH
python - <问题: libfontconfig.so.1
Traceback (most recent call last):
File "", line 1, in
File "/home/john/opencv-cuda-4.4.0/lib/python3.7/site-packages/cv2/__init__.py", line 96, in
bootstrap()
File "/home/john/opencv-cuda-4.4.0/lib/python3.7/site-packages/cv2/__init__.py", line 86, in bootstrap
import cv2
ImportError: /home/john/anaconda3/bin/../lib/libfontconfig.so.1: undefined symbol: FT_Done_MM_Var 解决办法：
cd $HOME/anaconda3/lib/
mv libfontconfig.so.1 libfontconfig.so.1.bak
ln -s /usr/lib/x86_64-linux-gnu/libfontconfig.so.1 libfontconfig.so.1问题: libpangoft2-1.0.so.0
ImportError: /home/john/anaconda3/bin/../lib/libpangoft2-1.0.so.0: undefined symbol: FcWeightToOpenTypeDouble解决办法:
cd $HOME/anaconda3/lib/
mv libpangoft2-1.0.so.0 libpangoft2-1.0.so.0.bak
ln -s /usr/lib/x86_64-linux-gnu/libpangoft2-1.0.so.0 libpangoft2-1.0.so.0Darknet
编译命令：
export OpenCV_DIR=$HOME/opencv-cuda-4.4.0/lib/cmake
cd ~/Codes/
git clone https://github.com/AlexeyAB/darknet.git
cd darknet/
./build.sh运行检查：
$ export LD_LIBRARY_PATH=$HOME/opencv-cuda-4.4.0/lib:$LD_LIBRARY_PATH
$ ./darknet v
CUDA-version: 10020 (10020), cuDNN: 8.0.2, CUDNN_HALF=1, GPU count: 1
CUDNN_HALF=1
OpenCV version: 4.4.0
Not an option: v用预训练模型进行推断
准备模型与数据
预训练模型 yolov4.weights ，下载地址 https://github.com/AlexeyAB/d... 。
可以准备 MS COCO 数据集，下载地址 http://cocodataset.org/#download 。或者自己找个图片。
darknet 执行
cd ~/Codes/darknet/
export LD_LIBRARY_PATH=$HOME/opencv-cuda-4.4.0/lib:$LD_LIBRARY_PATH
export MY_MODEL_DIR=~/Codes/devel/models/yolov4
export MY_COCO_DIR=~/Codes/devel/datasets/coco2017
./darknet detector test cfg/coco.data cfg/yolov4.cfg
$MY_MODEL_DIR/yolov4.weights
-thresh 0.25 -ext_output -show
$MY_COCO_DIR/test2017/000000000001.jpg推断结果：
python 执行
Darknet 于其根目录，提供有 Python 接口。如下执行：
cd ~/Codes/darknet/
export LD_LIBRARY_PATH=$HOME/opencv-cuda-4.4.0/lib:$LD_LIBRARY_PATH
export PYTHONPATH=$HOME/opencv-cuda-4.4.0/lib/python3.7/site-packages:$PYTHONPATH
python darknet_images.py -h
python darknet_images.py
--batch_size 1
--thresh 0.1
--ext_output
--config_file cfg/yolov4.cfg
--data_file cfg/coco.data
--weights $MY_MODEL_DIR/yolov4.weights
--input $MY_COCO_DIR/test2017/000000000001.jpg推断结果，如前一小节。
结语
Let's go coding ~
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