How to install caffe in macOS 10.12.5

本文主要用于记录在MacBookPro笔记本电脑中安装Caffe(CPU-Only)框架。

安装过程

• 安装brew

$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

• 安装依赖

$ brew install git openblas python$ brew install --fresh -vd snappy leveldb gflags glog szip hdf5 lmdb homebrew/science/opencv$ brew install --fresh -vd --with-python  protobuf$ brew install --fresh -vd boost boost-python

• 下载配置Caffe

$ git clone https://github.com/BVLC/caffe.git  $ cd caffe  $ cp Makefile.config.example Makefile.config

修改后的 Makefile.config

## 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 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 OpenBlasBLAS := 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.PYTHON_INCLUDE := /usr/local/Cellar/python/2.7.13/Frameworks/Python.framework/Versions/2.7/include/python2.7  \        /usr/local/lib/python2.7/site-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/local/Cellar/python/2.7.13/Frameworks/Python.framework/Versions/2.7/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 /usr/local/Cellar/lmdb/0.9.21/includeLIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/local/Cellar/lmdb/0.9.21/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# 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 := 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 ?= @

• 编译Caffe

$ make all -j$ make test$ make runtest$ make distribute

• 编译pycaffe

$ cd caffe/python$ for req in $(cat requirements.txt); do pip install $req -i https://pypi.douban.com/simple; done$ cd caffe$ make pycaffe$ cd caffe/python$ pwd/Users/tianzhaixing/Github/caffe/python # 替换tianzhaixing为你自己的用户名$ vi ~/.bash_profile

在最后一行添加以下代码,并保存。

export PYTHONPATH=/Users/tianzhaixing/Github/caffe/python:$PYTHONPATH  # 替换tianzhaixing为你自己的用户名

然后，让修改立即生效$ source ~/.bash_profile

$ pythonPython 2.7.13 (default, Dec 18 2016, 07:03:39)[GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.42.1)] on darwinType "help", "copyright", "credits" or "license" for more information.>>> import caffe>>> caffe.__version__'1.0.0'>>>

测试MNIST

$ cd caffe$ ./data/mnist/get_mnist.sh        #下载MNIST数据库并解压缩$ ./examples/mnist/create_mnist.sh #将其转换成Lmdb数据库格式$ vi examples/mnist/lenet_solver.prototxt # 设置solver_mode: CPU$ ./examples/mnist/train_lenet.sh  # 训练网络

测试结果：

I0714 17:04:26.067178 2759803840 solver.cpp:397]     Test net output #0: accuracy = 0.991I0714 17:04:26.067211 2759803840 solver.cpp:397]     Test net output #1: loss = 0.0290302 (* 1 = 0.0290302 loss)I0714 17:04:26.067217 2759803840 solver.cpp:315] Optimization Done.I0714 17:04:26.067222 2759803840 caffe.cpp:259] Optimization Done.

问题

• Not found libhdf5.100.dylib

$ cd cd /usr/local/opt/hdf5/lib   $ cp libhdf5.101.dylib libhdf5.100.dylib # 或者用软连接

• python/caffe/_caffe.cpp:10:10: fatal error: ‘numpy/arrayobject.h’ file not found

$ pythonPython 2.7.13 (default, Dec 18 2016, 07:03:39)[GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.42.1)] on darwinType "help", "copyright", "credits" or "license" for more information.>>> import numpy as np>>> np.get_include()'/usr/local/lib/python2.7/site-packages/numpy/core/include'>>>

修改将Caffe中Makefile.config对应PYTHON_INCLUDE部分。

参考

1. MAC OS X10.10下Caffe无脑安装（CPU ONLY)
2. Mac下配置Caffe的Python接口
3. caffe