python调用caffe环境配置

背景是这样的，项目需要，必须将训练的模型通过C++进行调用，所以必须使用caffe或者mxnet，而caffe是用C++实现，所以有时候简单的加载一张图片然后再进行预测十分不方便

用caffe写prototxt比较容易，写solver也是很容易，但是如何根据传入的lmdb数据来predict每一个样本的类别，抑或如何得到样本预测为其他类的概率？这看起来是一个简单的问题，实际上，在pytorch中很容易实现，在caffe中可能需要修改c++代码，用起来不是很方便直观，所以能否通过python调用已经训练完的caffemodel以及deploy.prototxt来实现类别的预测？

这个时候需要在ubuntu上配置caffe，在ubuntu上配置caffe我主要参考了这篇博客，http://www.cnblogs.com/denny402/p/5088399.html

其实主要是有两部分，第一部分是修改Make.config文件，第二部分是解决so库找不到的问题

1.修改Makefile.config

关键点在于修改配置文件Make.config然后进行编译，我的Make.config文件如下，

## 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

# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1

# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# 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

# 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
# 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 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_50,code=compute_50

# 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/R2014a
# MATLAB_DIR := /Applications/MATLAB_R2012b.app

# 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 
#        /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)/anaconda
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

# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib

# 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

# 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

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 ?= @

主要是注意PYTHON_INCLUDE这一块怎么写，因为我系统中安装了anaconda2，所以我修改PYTHON_INCLUDE这一块为anaconda的路径

修改完成之后，进入caffe根目录，运行

sudo make pycaffe

，编译成功后，如果重复编译则会提示Nothing to be done for "pycaffe"

为了防止其他错误，还是编译一下test

sudo make test -j8
sudo make runtest -j8

2,解决so库找不到的问题

在编译的时候我倒是没有遇到什么问题，但是在进入到python环境中去的时候，我import caffe的时候倒是遇到了各种各样的问题，但是这种问题大致可以归结为一种类型，就是

error while loading shared libraries: libhdf5.so.10: cannot open shared object file: No such file or directory

就是找不到caffe想要的库文件，这个时候这个链接 （http://www.cnblogs.com/denny402/p/5088399.html给了一种解决的方法，原因大概是缺少动态链接库，这些库基本上我们之前都已经安装了，安装的路径是

/use/lib/x86_64-linux-gnu，ll libhdf*的话能够列出所有的libhdf相关的库文件，如下图

如上图所示，基本上系统里面有很多我们自己的库，只不过caffe依赖的版本与系统中的版本号不一致，这一点儿与caffe在包含cudnn库文件的时候类似，只不过caffe的cudnn貌似是在/usr/local/lib下

对已有的库创建软链接，能够解决找不到so库的问题，所以

cd /usr/lib/x86_64-linux-gnu
sudo ln -s libhdf5.so.7(我文件夹中的so库的版本号) libhdf5.so.10(caffe需要的版本号)
sudo ldconfig

可能还会遇到其他的有关羽so库找不到的问题，基本上都是按照这个套路来解决

然后import caffe就不会报错，保险起见，可以再编译运行一下test