MacOS 10.14 Mojave 安装caffe记录

MacOS 10.14 Mojave 安装caffe记录

做毕设需要用到caffe，尝试在自己电脑上安装，过程中发现安装caffe还是有不少坑的，特此记录。主要参考https://zhuanlan.zhihu.com/p/46930024

安装依赖

首先需要更新xcode(暂时是10.12)。

假设你已经在mac上安装了homebrew，根据caffe的官方指南，我们需要安装一些依赖：

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brew install -vd snappy leveldb gflags glog szip lmdb
brew install openblas

跟官方指南不一样的是homebrew已经取消了science这个tap，所以使用如下的命令来安装hdf5 和 opencv：

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brew install hdf5 opencv

这一步，有可能需要更新gcc，其中提示需要CLT：The bottle needs the Xcode CLT to be installed.
首先确认xcode中clt已经安装：

然后运行brew config,查看brew中是否有clt的配置。如果显示CLT: N/A，那么运行xcode-select —install。设置好CLT后，继续安装依赖。

因为新版本的boost, boost-python和旧版本有兼容性问题[1], 我们使用homebrew 所提供的boost@1.59版本 来代替最新版本的boost:

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brew install boost@1.59 boost-python@1.59
brew link boost@1.59 --force
brew link boost-python@1.59 --force

这一步应该没什么问题

至于protobuf，实测在3.6.1版本上也不能正确编译，而homebrew带其他版本太旧，这里我们从source编译protobuf的3.5.1版本：

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cd ~/Downloads 
wget https://github.com/protocolbuffers/protobuf/archive/v3.5.1.zip
unzip protobuf-3.5.1.zip
cd protobuf-3.5.1
./autogen.sh
./configure
make
make install

这里，我按照它的做法安装protobuf，然而在import caffe时会出现ImportError: No module named google.protobuf.internal最后还是选择使用anaconda管理:conda install protobuf用的是3.6.1版本，并没有出现错误。

之前的编译没用opencv，没法计算图片均值。于是重新编译了一次。事实证明protobuf3.6.1果然没法编译。

设置python版本

依赖安装完了，现在我们对所需要用到的python版本进行设置，因为据说caffe在python3的表现并不是很好很容易出bug，这里我们使用的是anaconda的虚拟环境来构建一个python2.7的版本，并且安装相关依赖：

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conda create --name py27 python=2.7
source activate py27
pip install numpy scipy scikit-image

这一步，因为我用的已经是anaconda2，应该可以略去。保险起见还是创建了这样一个环境。重新编译证明，这步的确可略去</span>

配置caffe

现在开始装caffe。

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

其次需要在Makefile.config里做一些调整：

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1. 取消CPU_ONLY前面的注释
2. 取消USE_OPENCV前面的注释
3. 取消USE_HDF5前面的注释，并且改成1
4. 取消OPENCV_VERSION前面的注释
5. 注释掉CUDA_DIR那一行
6. 将BLAS改成open
7. 设置blas的路径，取消这两行的注释
# 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
8.设置python的路径，因为我使用的是anaconda3，
所以我创建了一个名叫py27的python=2.7的子环境，然后设置如下：

# Uncomment to support layers written in Python (will link against Python libs)
WITH_PYTHON_LAYER := 1

ANACONDA_HOME := $(HOME)/anaconda3/envs/py27
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

这一步，注意修改ANACONDA_HOME为自己的anaconda路径。如果使用opencv，使用brew安装后修改INCLUDE_DIRS以及LIBRARY_DIRS
下面贴一个我的配置：

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## 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 := 1
# USE_LEVELDB := 0
# USE_LMDB := 0
# This code is taken from https://github.com/sh1r0/caffe-android-lib
USE_HDF5 := 1

# 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.
# For CUDA >= 9.0, comment the *_20 and *_21 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 OpenBlas
BLAS := open
# 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/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)/anaconda2
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 /usr/local/Cellar/opencv@2/2.4.13.7_2/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/local/Cellar/opencv@2/2.4.13.7_2/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 := 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 ?= @

编译

一切就绪，就可以编译了：

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cd ~/caffe
make all
make test
make runtest
make pycaffe
source activate py27
python -c "import caffe"

这一步，有可能以下问题：
1.import caffe Segmentation fault: 11原因是caffe用的python和系统默认的不符，建议使用ananconda的python，然后在caffe的Makefile.config中设置python为anaconda的python。具体使用哪一个python可以使用which python查看。

可以看到在两个环境下默认的python是不同的。
2.ImportError: No module named google.protobuf.internal在py27环境下，使用anaconda安装protobuf。如果是手动编译的protobuf，需要进到protobuf目录下python文件夹，运行python setup.py install
3.ImportError: No module named caffepython的搜索路径中没有caffe。在~/.bash_profile中添加export PYTHONPATH=~/caffe/python:$PYTHONPATH

成功

如果import caffe没有问题，那么caffe就安装成功了。

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tag:

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很惭愧<br><br>只做了一点微小的工作