域自适应的代码环境再次配置笔记

有用的文章参考：
https://github.com/jwyang/faster-rcnn.pytorch

https://github.com/tiancity-NJU/da-faster-rcnn-PyTorch

https://github.com/divyam02/dafrcnn-pytorch

https://blog.youkuaiyun.com/xinyu_cheng/article/details/89321655

https://github.com/naoto0804/cross-domain-detection

https://github.com/VisionLearningGroup/DA_Detection

https://www.cnblogs.com/wzyuan/p/11105758.html

https://www.commonlounge.com/discussion/da0c2181dbd34c4aa7776f0a351408b8

https://zhuanlan.zhihu.com/p/57737230

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在服务器上新建立用户，然后安装anaconda

bash Anaconda3-5.2.0-Linux-x86_64.sh

conda create -n tch04 python=3.6
conda activate tch04
conda install pytorch=0.4.0 cuda90 -c pytorch
conda install torchvision=0.2.1
conda install cudatoolkit=9.0
conda update -n base -c defaults conda
conda update numpy
pip install numpy==1.17.0
pip install --upgrade numpy
conda install scipy=1.2.1

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查看驱动

nvidia-smi

安装cuda9.0

sudo sh cuda_9.0.176_384.81_linux.run --no-opengl-libs

Do you accept the previously read EULA?
 2 accept/decline/quit: accept
 3 Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81?
 4 (y)es/(n)o/(q)uit: n
 5 Install the CUDA 9.0 Toolkit?
 6 (y)es/(n)o/(q)uit: y
 7 Enter Toolkit Location
 8 [ default is /usr/local/cuda-9.0 ]:  
 9 Do you want to install a symbolic link at /usr/local/cuda?
10 (y)es/(n)o/(q)uit: y
11 Install the CUDA 9.0 Samples?
12 (y)es/(n)o/(q)uit: y
13 Enter CUDA Samples Location
14 [ default is /home/pertor ]:  
15 Installing the CUDA Toolkit in /usr/local/cuda-9.0 ...
16 Missing recommended library: libXmu.so

sudo vim ~/.bashrc

“i”
export PATH=/usr/local/cuda-9.0/bin:$PATH 
export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64:$LD_LIBRARY_PATH
“esc” “：wq：”

source ~/.bashrc

检查是否安装成功

cd /usr/l