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

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：

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, V10.2.89

Nvida cuDNN

获取地址：

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.deb

CMake

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：

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 - <

import cv2

print(cv2.__version__)

EOF

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.0

Darknet

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

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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