本文主要用於記錄在MacBookPro膝上型電腦中安裝Caffe(CPU-Only)框架。

安裝過程

• 安裝brew

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

• 安裝依賴

$ 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

• 下載配置Caffe

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

修改後的 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 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_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/include
LIBRARY_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 := 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 ?= @

• 編譯Caffe

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

• 編譯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 # 替換tianzhaixing為你自己的使用者名稱
$ vi ~/.bash_profile

在最後一行新增以下程式碼,並儲存。

export PYTHONPATH=/Users/tianzhaixing/Github/caffe/python:$PYTHONPATH  # 替換tianzhaixing為你自己的使用者名稱

然後，讓修改立即生效$ source ~/.bash_profile

$ python
Python 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 darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import caffe
>>> caffe.__version__
'1.0.0'
>>>

測試MNIST

$ cd caffe
$ ./data/mnist/get_mnist.sh        #下載MNIST資料庫並解壓縮
$ ./examples/mnist/create_mnist.sh #將其轉換成Lmdb資料庫格式
$ vi examples/mnist/lenet_solver.prototxt # 設定solver_mode: CPU
$ ./examples/mnist/train_lenet.sh  # 訓練網路

測試結果：

I0714 17:04:26.067178 2759803840 solver.cpp:397]     Test net output #0: accuracy = 0.991
I0714 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.

問題

• Not found libhdf5.100.dylib

$ cd cd /usr/local/opt/hdf5/lib   
$ cp libhdf5.101.dylib libhdf5.100.dylib # 或者用軟連線

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

$ python
Python 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 darwin
Type "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'
>>>

修改將Caffe中Makefile.config對應PYTHON_INCLUDE部分。

參考