【深度学习环境搭建】ubuntu16.04+python2.7+opencv3.1+caffe0.15

整理一下深度学习搭建环境遇到的坑，这次实验使用Fast-rcnn（caffe环境） Faster-rcnn（caffe环境）yolo(在C++环境，不需要搭建)算法，实验是识别汽车前后门板上的50多个小焊点。

感谢曾师兄、裴师兄、老莫为了实验的环境搭建所作出的贡献

关于实验平台

1. 实验使用阿里云平台（没钱买服务器，按小时扣费那种），注意重要文件存在系统盘，数据盘每次登陆清空;

2. 选择服务器类别，配置按自己的算法要求来，GPU选择了12G，显卡是Nvida M40，镜像选择ubuntu16.04，可视化桌面需要自己安装。不建议选择ubuntu16.04+nvida的镜像，因为它不会帮你禁用原来的显卡，会出现循环登录或屏幕显示有较大问题；

3. 有钱还是自己配置好的显卡和GPU吧，实验室的话可以申请买服务器，阿里云真是个坑，平台使用一个月左右花了2000，对个人用户十分不友好，有时还会出现资源被占用的情况。如果使用服务器建议可现在淘宝购买优惠券，能省个几百块钱。深度学习应用图像处理对显卡要求比较高，大家根据自己的实际情况，尽量选择较好的，后面的训练才不会报溢出，而且训练时间比较短，我的Fast-rcnn用VGG16网络训练一次7个小时，时间成本非常大；

4. 说一下GPU，网上有不少CPU训练的教程，实际CPU不能用来训练，甚至测试都不可以，只能用来跑demo，所以还是老老实实用GPU吧。

ubuntu16.04+CUDA8.0+cudnn5.0/6.0

Ubuntu建议用16.04，用最新的18.06经常会出现找不到包的情况，而且会出现各种奇奇怪怪的问题。还要注意CUDA CUDNN安装版本需对应。CUDA9.0对应CUDNN7.0，CUDA8.0对应CUDNN5.0、CUDNN6.0，两个都装了，需要的时候更换软连接就好。
CUDA8.0的安装参考 https://blog.csdn.net/jonms/article/details/79318566 ，这一部分是老莫安装的所以在此不详写，以下是安装cudnn步骤。

1. 在官网 https://developer.nvidia.com/cudnn 下载cudnn安装包
2. 在include的文件夹复制头文件

   sudo cp cudnn.h /usr/local/cuda/include/ 
   

3. 在lib64文件夹复制动态链接库

   sudo cp lib* /usr/local/cuda/lib64/  
   

4. 删除原有动态文件

   cd /usr/local/cuda/lib64/
   sudo rm -rf libcudnn.so libcudnn.so.5  
   

5. 生成新的软衔接

   sudo ln -s libcudnn.so.6.0.21 libcudnn.so.6 
   sudo ln -s libcudnn.so.6 libcudnn.so   
   

6. 安装成功

python2.7+opencv3.1

caffe基于opencv环境的，我不确定仅仅安装python-opencv是否有用，建议大家还是老实安装opencv比较好。这里说一下不建议用anaconda安装opencv。anaconda会隐藏一些系统路径，导致一些包你安装在系统里，一些装在anaconda上，出现找不到这种模块的问题，最后安装不成功。用C编译是最稳妥并且之后使用不太容易出错。
安装教程参考 https://www.pyimagesearch.com/2016/10/24/ubuntu-16-04-how-to-install-opencv/， 以下归纳所有步骤，并对一些关键点做出备注。

步骤1：在Ubuntu 16.04上安装OpenCV依赖项

1. 更新软件包

   sudo apt-get update
   sudo apt-get upgrade
   

2. 安装开发人员工具

   sudo apt-get install build-essential cmake pkg-config
   

3. 安装图像调用I/O库

   sudo apt-get install libjpeg8-dev libtiff5-dev libjasper-dev libpng12-dev
   

4. 安装视频流访问包

   sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
   sudo apt-get install libxvidcore-dev libx264-dev
   

5. 安装GUI模块依赖库

   sudo apt-get install libgtk-3-dev
   

6. 安装优化opencv内部功能的库

   sudo apt-get install libatlas-base-dev gfortran
   

7. 安装opencv支持库

   sudo apt-get install python2.7-dev
   

步骤2：下载opencv源代码

1. 下载opencv源代码并解压

   cd ~
   wget -O opencv.zip https://github.com/Itseez/opencv/archive/3.1.0.zip
   unzip opencv.zip
   

2. 下载opencv_contrib存储库

   wget -O opencv_contrib.zip https://github.com/Itseez/opencv_contrib/archive/3.1.0.zip
   unzip opencv_contrib.zip
   

步骤3：设置python环境-python2.7

1. 安装pip——python包管理器

   cd ~
   wget https://bootstrap.pypa.io/get-pip.py
   sudo python get-pip.py
   

2. 安装Numpy

   pip install numpy
   

   注意在Fast r-cnn中使用numpy==1.10,这里安装高版本的需要在训练Fast r-cnn时更改其python文件的部分代码

3. 使用cmake安装opencv
   结合 https://blog.kickview.com/building-a-digits-dev-machine-on-ubuntu-16-04/ 根据自己运行的需求更改编译内容

   cd ~/opencv-3.1.0/
   mkdir build
   cd build
   cmake -D CMAKE_BUILD_TYPE=RELEASE\
   -D CMAKE_INSTALL_PREFIX=/usr/local \
   -D WITH_CUDA=ON \
   -D ENABLE_FAST_MATH=1 \
   -D CUDA_FAST_MATH=1 \
   -D WITH_CUBLAS=1\
   -D INSTALL_PYTHON_EXAMPLES=ON \
   -D OPENCV_EXTRA_MODULES_PATH= ~ /opencv_contrib-3.1.0/modules \
   -D BUILD_opencv_dnn=OFF \
   -D BUILD_EXAMPLES=ON \
   -D PYTHON_EXECUTABLE= ~/.virtualenvs/cv/bin/python ..
   

   注意opencv_dnn=OFF，安装caffe包括dnn，如重复在opencv会出现问题, WITH_CUDA=ON使用CUDA8.0，PYTHON_EXECUTABLE使用系统里的python。运行后检查图示。
   
   检查是否支持cuda OpenCL 等，检查python2的路径是否正确（图示在虚拟环境里，根据自己的安装目录检查）等。

4. 编译

   make -j8
   

5. 安装

   sudo make install
   sudo ldconfig
   

步骤4：完成安装，进行测试

1. 成功出现cv2版本号3.1.0即为正确安装

   cd ~
   workon cv
   python
   import cv2
   cv2.__version__
   

caffe安装

参考 https://blog.csdn.net/ZWX2445205419/article/details/72673121 及 https://blog.kickview.com/building-a-digits-dev-machine-on-ubuntu-16-04/ 关于caffe安装这部分

• 首先安装以下依赖库

  sudo apt-get update
  sudo apt-get upgrade
  sudo apt-get install -y build-essential cmake git pkg-config
  sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler
  sudo apt-get install -y libatlas-base-dev 
  sudo apt-get install -y --no-install-recommends libboost-all-dev
  sudo apt-get install -y libgflags-dev libgoogle-glog-dev liblmdb-dev
  (Python general)
  sudo apt-get install -y python-pip
  (Python 2.7 development files)
  sudo apt-get install -y python-dev
  sudo apt-get install -y python-numpy python-scipy
  (OpenCV 2.4)
  sudo apt-get install -y libopencv-dev
  

• 下载caffe源文件

  git clone https://github.com/BVLC/caffe
  

• 给执行文件改名

  cp Makefile.config.example Makefile.config
  

• 配置Makefile.config
  依据个人安装路径情况进行配置

  ## Refer to http://caffe.berkeleyvision.org/installation.html
  # Contributions simplifying and improving our build system are welcome!
  
  # cuDNN acceleration switch (uncomment to build with cuDNN).
  USE_CUDNN := 1 # 使用CUDNN
  
  # CPU-only switch (uncomment to build without GPU support).
  # CPU_ONLY := 1 #使用GPU不更改
  
  # uncomment to disable IO dependencies and corresponding data layers
  # USE_OPENCV := 0  #使用opencv不更改
  # USE_LEVELDB := 0
  # USE_LMDB := 0
  
  # uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
  #   You should not set this flag if you will be reading LMDBs with any
  #   possibility of simultaneous read and write
  # ALLOW_LMDB_NOLOCK := 1
  
  # Uncomment if you're using OpenCV 3
  OPENCV_VERSION := 3 #安装的OpenCV版本为3.1.0
  
  # To customize your choice of compiler, uncomment and set the following.
  # N.B. the default for Linux is g++ and the default for OSX is clang++
  # CUSTOM_CXX := g++
  
  # CUDA directory contains bin/ and lib/ directories that we need.
  CUDA_DIR := /usr/local/cuda-8.0 # 更改为安装的cuda路径
  # On Ubuntu 14.04, if cuda tools are installed via
  # "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
  # CUDA_DIR := /usr
  
  # CUDA architecture setting: going with all of them.
  # For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility.
  # For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
  CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
     -gencode arch=compute_20,code=sm_21 \
     -gencode arch=compute_30,code=sm_30 \
     -gencode arch=compute_35,code=sm_35 \
     -gencode arch=compute_50,code=sm_50 \
     -gencode arch=compute_52,code=sm_52 \
     -gencode arch=compute_60,code=sm_60 \
     -gencode arch=compute_61,code=sm_61 \
     -gencode arch=compute_61,code=compute_61
  
  # BLAS choice:
  # atlas for ATLAS (default)
  # mkl for MKL
  # open for OpenBlas
  BLAS := atlas
  # Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
  # Leave commented to accept the defaults for your choice of BLAS
  # (which should work)!
  # BLAS_INCLUDE := /path/to/your/blas
  # BLAS_LIB := /path/to/your/blas
  
  # Homebrew puts openblas in a directory that is not on the standard search path
  # BLAS_INCLUDE := $(shell brew --prefix openblas)/include
  # BLAS_LIB := $(shell brew --prefix openblas)/lib
  
  # This is required only if you will compile the matlab interface.
  # MATLAB directory should contain the mex binary in /bin.
  # MATLAB_DIR := /usr/local
  # MATLAB_DIR := /Applications/MATLAB_R2012b.app
  MATLAB_DIR := /usr/local/MATLAB/R2014a # matlab的安装路径，没有则注释掉
  
  # NOTE: this is required only if you will compile the python interface.
  # We need to be able to find Python.h and numpy/arrayobject.h.
  PYTHON_INCLUDE := /usr/include/python2.7 \ # 使用系统自带Python库和安装好的numpy库
     /usr/lib/python2.7/dist-packages/numpy/core/include
  # Anaconda Python distribution is quite popular. Include path:
  # Verify anaconda location, sometimes it's in root.
  # ANACONDA_HOME := $(HOME)/anaconda2 
  # PYTHON_INCLUDE := $(ANACONDA_HOME)/include \ 
     $(ANACONDA_HOME)/include/python2.7 \
     $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
  
  # Uncomment to use Python 3 (default is Python 2)
  # PYTHON_LIBRARIES := boost_python3 python3.5m
  # PYTHON_INCLUDE := /usr/include/python3.5m \
  #                 /usr/lib/python3.5/dist-packages/numpy/core/include
  
  # We need to be able to find libpythonX.X.so or .dylib.
  PYTHON_LIB := /usr/lib 
  # PYTHON_LIB := $(ANACONDA_HOME)/lib 
  
  # Homebrew installs numpy in a non standard path (keg only)
  # PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
  # PYTHON_LIB += $(shell brew --prefix numpy)/lib
  
  # Uncomment to support layers written in Python (will link against Python libs)
  WITH_PYTHON_LAYER := 1 #使用python支持，去掉注释
  
  # Whatever else you find you need goes here.
  INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial /usr/lib/x86_64-linux-gnu/hdf5/serial #注意添加路径是否正确
  LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial 
  # If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
  # INCLUDE_DIRS += $(shell brew --prefix)/include
  # LIBRARY_DIRS += $(shell brew --prefix)/lib
  
  # NCCL acceleration switch (uncomment to build with NCCL)
  # https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
  # USE_NCCL := 1
  
  # Uncomment to use `pkg-config` to specify OpenCV library paths.
  # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
  # USE_PKG_CONFIG := 1
  
  # N.B. both build and distribute dirs are cleared on `make clean`
  BUILD_DIR := build
  DISTRIBUTE_DIR := distribute
  
  # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
  # DEBUG := 1
  
  # The ID of the GPU that 'make runtest' will use to run unit tests.
  TEST_GPUID := 0
  
  # enable pretty build (comment to see full commands)
  Q ?= @
  

• 准备caffe支持python所需的库

  cd ~/caffe/python
  for req in $(cat requirements.txt); do pip install $req; done
  

• 开始编译

  cd ~/caffe 
  mkdir build
  cd build
  cmake ..
  make all -j8 
  make install
  make test
  make runtest -j8
  

  与opencv相似，检查支持的库及路径是否正确。包括检测到的CUDA版本，cudnn版本，opencv版本，numpy版本，各种支持的版本，python配置路径
• 配置caffe的python接口

  make pycaffe
  

  安装过程与caffe类似，不赘述
• 安装完成，在python路径中添加caffe路径

  export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH
  

• 测试

  Import caffe