ubuntu16.04编译caffe

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安装ubuntu16.04镜像

安装cuda8.0

** 先安装驱动()
** 安装runfile
** 取消勾选安装驱动

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export PATH=/usr/local/cuda-8.0/bin:$PATH  
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH
source ~/.bashrc

安装cudnn5.1

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sudo cp cuda/include/cudnn.h /usr/local/cuda/include/  
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/  
sudo chmod a+r /usr/local/cuda/include/cudnn.h  
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*

安装BLAS

sudo apt-get install libatlas-base-dev

安装opencv2.4.10

安装前：
修改graphcuts.cpp文件（搜索即可，不修改会编译报错）
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)

改成

#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) || (CUDART_VERSION >= 8000)
编译:
cmake -D CMAKE_BUILD_TYPE=release -D CMAKE_INSTALL_PREFIX=/usr/local -D CUDA_GENERATION=Kepler .. (2个点)

终端输入：
pkg-config –modversion opencv出现对应版本号即安装成功。

安装Caffe python3依赖包

sudo apt-get install python3-pip(安装pip3)(pip3会提示版本过低 不要升级)
进入caffe的python目录,执行

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for req in $(cat requirements.txt); do pip3 install $req; done

sudo vim ~/.bashrc

在文件末尾添加（添加caffe的python路径）：

export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH（需要修改为自己的路径，如）

export PYTHONPATH=/home/stan/work/caffe/python:$PYTHONPATH
运行source ~/.bashrc

链接python3.5

进入/usr/lib/x86_64-linux-gnu/ 下，查看有没有libboost_python-py35.so，如果有链接一下就可以了

sudo ln -s libboost_python-py35.so libboost_python3.so

编译需要依赖

sudo apt-get install libatlas-base-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
打开 caffe 所在目录，找到Makefile.config.example文件，将其改名为Makefile.config,使用如下的配置

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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 := 0
# USE_LEVELDB := 0
# USE_LMDB := 0
# This code is taken from https://github.com/sh1r0/caffe-android-lib
# USE_HDF5 := 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++
#CUSTOM_CXX := clang++ -std=c++11

# 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/local/cuda

# 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 := 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/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 \
                 /home/stan/.local/lib/python3.5/site-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/lib /usr/local/include /usr/include/hdf5/serial /home/stan/work/opencv-2.4.10/release/bin

LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial /home/stan/work/opencv-2.4.10/release/bin



# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
#INCLUDE_DIRS += ~/local_install/include
#LIBRARY_DIRS += ~/local_install/bin

# 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
#LIBRARIES += glog gflags protobuf leveldb snappy \
       lmdb boost_system m  hdf5_serial_hl hdf5_serial 
#       opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs opencv_imagecodecs
# 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 ?= @

make -j8 (根据cpu核心 -j16 或者-j4)

make test

make runtest

make pycaffe

Caffe测试

下载地址 http://dl.caffe.berkeleyvision.org/

下载bvlc_reference_caffenet.caffemodel到models/bvlc_reference_caffenet/ 文件夹下面
下载caffe_ilsvrc12.tar.gz解压到data/ilsvrc12/ 文件夹

python/classify.py修改

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# Classify.
    start = time.time()
    predictions = classifier.predict(inputs, not args.center_only)
    print("Done in %.2f s." % (time.time() - start))
    imagenet_labels_filename = '../data/ilsvrc12/synset_words.txt'
    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\t')
    top_k = predictions.flatten().argsort()[-1:-6:-1]
    for i in np.arange(top_k.size):
        print(top_k[i], labels[top_k[i]])

执行

sudo python3 classify.py ../examples/images/cat.jpg result.npy