#!/bin/bash

#安装时要注意有些库可能安装失败以及安装caffe有和protobuf相关错误时可能需要重新对protobuf进行make install

cd /home/zw/softwares #需要事先下载对应版本的cuda

sudo dpkg -i cuda-repo-ubuntu1604---local-ga2_8.0.61-1_amd64.deb

sudo apt-get update

sudo apt-get install cuda

cd /home/zw/git_home/ #我存放git项目的目录

git clone https://github.com/google/protobuf.git

sudo apt-get install autoconf automake libtool curl make g++ unzip

cd protobuf

./autogen.sh

./configure --prefix=/usr

make -j8

make check -j8

sudo make install -j8

sudo ldconfig # refresh shared library cache.

cd /home/zw/git_home/

git clone https://github.com/BVLC/caffe.git

cd caffe

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler

sudo apt-get install --no-install-recommends libboost-all-dev

sudo apt-get install libatlas-base-dev

sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

cp Makefile.config.example Makefile.config #config中如果启用anaconda目录改成anaconda2(安装时默认名称),否则sudo make pycaffe无法编译成功。不过建议不需要启用anaconda目录，因为没这个必要，后续只要在PYTHONPATH路径中加入caffe和安装protobuf即可。另外，如果事先安装了opencv3.0需要在Makefile.cinfig中修改对应选项

read -rsp $'更改你的Makefile.config, 完成后Press any key to continue...\n' -n1 key

make all -j8

make test -j8

make runtest

make pycaffe -j8

cd /home/zw/git_home/protobuf/python

~/anaconda2/bin/python setup.py install #安装对应版本的protobuf，这里要特别注意，如果使用conda安装最新版本的protobuf，可能出现不兼容问题的，因为上面的caffe是用这个版本的protobuf编译的，切记！这里是我自己尝试出来的，花了不少时间

#echo "export PYTHONPATH=~/git_home/protobuf/python:$PYTHONPATH" >> ~/.bashrc #如果你用的时zsh，那么应该导入到～/.zshrc

echo "export PYTHONPATH=~/git_home/caffe/python:$PYTHONPATH" >> ~/.bashrc

echo "export PATH=~/git_home/caffe/build/tools:$PATH" >> ~/.bashrc

## 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 := 

# CPU-only switch (uncomment to build without GPU support).

# CPU_ONLY := 

# uncomment to disable IO dependencies and corresponding data layers

# USE_OPENCV :=

# USE_LEVELDB :=

# USE_LMDB := 

# 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 := 

# Uncomment if you're using OpenCV 3

OPENCV_VERSION := 3 #事先安装了使用了opencv3，这里要启用

# 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 #使用了cuda，这里要启用

# 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/include/python2. \

        /usr/lib/python2./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. \

#         $(ANACONDA_HOME)/lib/python2./site-packages/numpy/core/include

# Uncomment to use Python  (default is Python )

# PYTHON_LIBRARIES := boost_python3 python3.5m

# PYTHON_INCLUDE := /usr/include/python3.5m \

#                 /usr/lib/python3./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 := 

# Whatever else you find you need goes here.

#INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include

#LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial

LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial

# 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 := 

# 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 := 

# 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 := 

# The ID of the GPU that 'make runtest' will use to run unit tests.

TEST_GPUID := 

# enable pretty build (comment to see full commands)

Q ?= @