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
1 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
1 sudo make test -j8 2 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
1 cd /usr/lib/x86_64-linux-gnu 2 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) 3 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