背景是這樣的，項目需要，必須將訓練的模型通過C++進行調用，所以必須使用caffe或者mxnet，而caffe是用C++實現，所以有時候簡單的加載一張圖片然后再進行預測十分不方便
用caffe寫prototxt比較容易，寫solver也是很容易，但是如何根據傳入的lmdb數據來predict每一個樣本的類別，抑或如何得到樣本預測為其他類的概率？這看起來是一個簡單的問題，實際上，在pytorch中很容易實現，在caffe中可能需要修改c++代碼，用起來不是很方便直觀，所以能否通過python調用已經訓練完的caffemodel以及deploy.prototxt來實現類別的預測？
這個時候需要在ubuntu上配置caffe，在ubuntu上配置caffe我主要參考了這篇博客，http://www.cnblogs.com/denny402/p/5088399.html
其實主要是有兩部分，第一部分是修改Make.config文件，第二部分是解決so庫找不到的問題
1.修改Makefile.config
關鍵點在於修改配置文件Make.config然后進行編譯，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這一塊怎么寫，因為我系統中安裝了anaconda2，所以我修改PYTHON_INCLUDE這一塊為anaconda的路徑
修改完成之后，進入caffe根目錄，運行
1 sudo make pycaffe
，編譯成功后，如果重復編譯則會提示Nothing to be done for "pycaffe"
為了防止其他錯誤，還是編譯一下test
1 sudo make test -j8 2 sudo make runtest -j8
2,解決so庫找不到的問題
在編譯的時候我倒是沒有遇到什么問題，但是在進入到python環境中去的時候，我import caffe的時候倒是遇到了各種各樣的問題，但是這種問題大致可以歸結為一種類型，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想要的庫文件，這個時候這個鏈接 （http://www.cnblogs.com/denny402/p/5088399.html給了一種解決的方法，原因大概是缺少動態鏈接庫，這些庫基本上我們之前都已經安裝了，安裝的路徑是
/use/lib/x86_64-linux-gnu，ll libhdf*的話能夠列出所有的libhdf相關的庫文件，如下圖
如上圖所示，基本上系統里面有很多我們自己的庫，只不過caffe依賴的版本與系統中的版本號不一致，這一點兒與caffe在包含cudnn庫文件的時候類似，只不過caffe的cudnn貌似是在/usr/local/lib下
對已有的庫創建軟鏈接，能夠解決找不到so庫的問題，所以
1 cd /usr/lib/x86_64-linux-gnu 2 sudo ln -s libhdf5.so.7(我文件夾中的so庫的版本號) libhdf5.so.10(caffe需要的版本號) 3 sudo ldconfig
可能還會遇到其他的有關羽so庫找不到的問題，基本上都是按照這個套路來解決
然后import caffe就不會報錯，保險起見，可以再編譯運行一下test