Ubuntu16.04 anaconda2 安装caffe（CPU版）辛酸史

程序员文章站 2022-04-23 11:20:45

...

一. 前言

以前不用caffe，不知其“傲娇”，近期因需要不得不安装，着实被“坑”了一把。。。（当然主要原因还在于我目前水平太菜。。。）为了下次在新环境下少走些弯路，少查点针对性不强的资料，现在抓紧把对我有帮助的文章整理如下，表示十分感谢这些文章的作者！若能对阅读此文的您解决相应问题有所帮助，着实荣幸之至。

二. 借鉴文章如下，表示感谢！

1. Ubuntu 16.04下Anaconda编译安装Caffe

2. Ubuntu16.04下安装Caffe

3. ubuntu16.04下编译caffe出现.build_release/lib/libcaffe.so: undefined reference to google ::protobuf…的问题

4. Ubuntu16.04重装protobuf2.6.1版本

5. from google.protobuf.internal import enum_type_wrapper ImportError: No module named google.protobuf

三. 我的安装经历（主要借鉴文章1）

1. 已有环境

（1）Anoconda 2
（2）cuda （9.0）
（3）cudnn （7.0.5）
（4）安装好TensorFlow、Pytorch，每个都单独有一个虚拟的Anoconda环境
（5）目标新环境（python=2.7）已安装opencv3.4.3 安装步骤

2. 安装依赖库

进入官网http://caffe.berkeleyvision.org/installation.html，可以看到编译caffe需要的依赖库：
Ubuntu16.04 anaconda2 安装caffe（CPU版）辛酸史
首先进入自己的caffe虚拟环境source activate xxxxx,
由于CUDA已经安装好了，直接跳过，安装各种依赖库：

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 libgflags-dev libgoogle-glog-dev liblmdb-dev
sudo apt-get install libatlas-dev
sudo apt-get install liblapack-dev
sudo apt-get install libatlas-base-dev

3. 配置caffe的Makefile.config文件

# 具体配置
## 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++

# 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 := 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)/anaconda2/envs/xxxxx
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 := $(ANACONDA_HOME)/lib
LINKFLAGS := -Wl,-rpath,$(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/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 := 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 ?= @

4. 编译（在下载好的caffe路径下进行）

[sudo] make all -j6

[sudo] make test -j6

make runtest -j6

sudo make pycaffe

python
import caffe

5. 出现过的问题

（1）常见问题可见文章1，2
（2）如果出现文章3提到的问题“.build_release/lib/libcaffe.so: undefined reference to google ::protobuf…”，采用文章3的方法可是需要很慎重的！！！我就是采用了，然后感觉闯祸了…删的东西太多，不好～其实应该有解决的余地的。无奈之下，继续搜资料，文章4帮助了我，感谢感谢！
（3）import caffe时，可能会出现文章5提到的问题“from google.protobuf.internal import enum_type_wrapper ImportError: No module named google.protobuf”，对于Python=2.7来讲：pip install protobuf（亲测有效）；对Python=3.5，可能就得sudo pip install protobuf了（没试过，因为我的是python2.7）。
（4）如遇到其他小问题，首先要做的是，不慌～肯定可以解决的，锻炼自己能力和魄力的方法就是先看终端的出错信息，自己猜一下怎么办，尝试一下。不行的话，再以理性的角度去搜资料，毕竟问题一样，问题背景不一定一样，别人的解决办法不一定真的适合自己的情况～