本文共 6607 字，大约阅读时间需要 22 分钟。

本人编译如下版本caffe后，记录下需要注意（修改）Makefile.config的选项

## 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).# 是否使用cudnn，默认不使用# USE_CUDNN := 1# CPU-only switch (uncomment to build without GPU support).# 是否仅支持cup，注释后会编译gpu部分，因人而异，我自己学习支持cpu就好CPU_ONLY := 1# uncomment to disable IO dependencies and corresponding data layers# 选择支持库，可以先按默认都不选，包括下面对opencv，leveldb，lmdb库的选项都可以注释# 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++# 可选g++版本，如果编译报错因g++库问题，可以尝试换个g++版本，比如曾经报错然后试过g++-5.x，g++-4.x等CUSTOM_CXX := g++# CUDA directory contains bin/ and lib/ directories that we need.# cuda安装命令，其实使用NVADIA gpu才会用到cuda,但是可以使用sudo apt-get install nvidia-cuda-toolkit# 安装后，which cuda，添加路径编译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 through *_61 lines for compatibility.# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.# For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility.# 这个根据cuda版本不同选择注释，看上面注释，我是cuda7.5，所以注释*_60等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库，安装atlas就使用这个吧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# 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.# 这个是python库路径，可通过ubuntu的python库在 /usr/lib/x86-xxxx(忘了全拼，自己按tab补全吧)/python2.7/里面PYTHON_LIBRARIES := boost_python-py27 python2.7   # Python头文件路径，根据实际情况填写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是一个封装的环境，里面有编译caffe所需的库，如果通过anaconda安装caffe时候打开配置，并# 注释上面python2.7的配置，修改成anaconda的环境路径，这里我不需要# 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)# python3的路径配置，我是2，所以不需要，根据实际情况配置# 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.# 按默认的，如果使用anaconda，则换成下面的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)# 如果想要caffe支持python接口，则这个打开，我会编译make && make pycaffe，所以需要，如果不编译，则# 使用C++接口WITH_PYTHON_LAYER := 1# Whatever else you find you need goes here.# 库的配置，这里添加hdf的库，我环境会编译报错：can not find hdf5.hINCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/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# 库的配置，默认# 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)# gpu相关，跟我没关系# 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.)# opencv支持，暂时不需要# USE_PKG_CONFIG := 1# N.B. both build and distribute dirs are cleared on `make clean`BUILD_DIR := buildDISTRIBUTE_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 ?= @

另外补充intel分支的Makefile.config增加项说明：

# 这两项是intel对使用mkl2017和mkldnn的优化，选择成功后（可以看下/src/caffe/net.cpp中部分代码），# caffe.Net解析网络时会对Scale层进行融合，认为是Drop层，计算时忽略。# USE_MKL2017_AS_DEFAULT_ENGINE := 1# or put this at the top your train_val.protoxt or solver.prototxt file:# engine: "MKL2017"# or use this option with caffe tool:# -engine "MKL2017"USE_MKLDNN_AS_DEFAULT_ENGINE := 1# Put this at the top your train_val.protoxt or solver.prototxt file:# engine: "MKLDNN"# or use this option with caffe tool:# -engine "MKLDNN"# caffe.Net解析网络时会对Bn层进行融合，认为是Drop层，计算时忽略，下面也是相关算法优化# Use remove batch norm optimization to boost inferenceDISABLE_BN_FOLDING := 0# Use Conv + Relu fusion to boost inferenceDISABLE_CONV_RELU_FUSION:= 0# Use Bn + ReLU fusion to boost inferenceDISABLE_BN_RELU_FUSION := 0# Use Conv + Eltwise + Relu layer fusion to boost inference.DISABLE_CONV_SUM_FUSION := 0# Use sparse to boost inference.DISABLE_SPARSE := 0