环境:
Ubuntu16.04 AMD 64位
双核
python 2.7.12
虚拟环境:Virtualenv
虚拟环境路径如下:
(python2.7) appleyuchi@ubuntu:~/.virtualenvs$
下面这个安装的缺陷在于,暂时不涉及OpenCV,有需要的请自己配置.
下面开始操作:
-----------------------------------------------------------
root权限下:
apt-fast install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler --no-install-recommends libboost-all-dev libopenblas-dev liblapack-dev libatlas-base-dev libgflags-dev libgoogle-glog-dev liblmdb-dev
git的下载速度太恶心,caffe百度链接:
https://pan.baidu.com/s/1p6eEPtHxwJC_va1LgMQaRw
稍微注意下:
每次只能编译一种python版本,根据这个Makefile.config来改具体编译哪种python版本
(python2.7) appleyuchi@ubuntu:~$ pip install scikit-image protobuf numpy
cd /home/appleyuchi/深度学习/caffe/Makefile.config
make all
***************************************
#include <numpy/arrayobject.h>
改为
#include <arrayobject.h>
这里之所以要这么改的原因是,root的python安装目录文件结构与virtualenv的安装目录结构稍微有些不太一样
****************************************
不想自己配置的,直接把下面这份文件拷贝到自己的caffe下面即可.
make test(这个其实不检查也行,就是图个心里安慰,可以直接跳过)
(python2.7) appleyuchi@ubuntu:~/深度学习/caffe/python$ for req in $(cat requirements.txt); do pip install $req; done
make pycaffe -j81
make runtest
最后,/home/appleyuchi/深度学习/caffe/build/lib/
下面会生成libcaffe.so文件,为了能在python中import,
~/.bashrc中加入以下语句:
export PYTHONPATH=~/深度学习/caffe/python:$PYTHONPATH
关掉终端,新开一个,效果如下:
(python2.7) appleyuchi@ubuntu:~$ python
Python 2.7.12 (default, Dec 4 2017, 14:50:18)
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import caffe
>>>
***************************************一些与include相关的报错解决方案*******************************************************
如果是缺少arrayobject.h,那么
以下命令可以查看include的路径
`g++ -print-prog-name=cc1plus` -v
修改include默认路径的办法如下:
除了默认的/usr/include, /usr/local/include等include路径外，还可以通过设置环境变量来添加系统include的路径：
#.C
export C_INCLUDE_PATH=~/.virtualenvs/python2.7/lib/python2.7/site-packages/numpy/core/include/numpy:$C_INCLUDE_PATH
# CPP
export CPLUS_INCLUDE_PATH=~/.virtualenvs/python2.7/lib/python2.7/site-packages/numpy/core/include/numpy:$CPLUS_INCLUDE_PATH
同时修改(python2.7) appleyuchi@ubuntu:~/深度学习/caffe/python/caffe路径下面的_caffe.cpp文件中的
#include <numpy/arrayobject.h>
为
#include <arrayobject.h>
即可
**********************************************************************************************
完整的修改后的Makefile.config如下:
## 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.
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda
VIRTUALENV_HOME := ~/.virtualenvs/python2.7
PYTHON_INCLUDE := $(VIRTUALENV_HOME)/include \
$(VIRTUALENV_HOME)/include/site/python2.7 \
$(VIRTUALENV_HOME)/lib/python2.7/site-packages/numpy/core/include \
/usr/include/python2.7 \
~/.virtualenvs/python2.7/lib/python2.7/site-packages/numpy/core/include/numpy
# 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
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/include/hdf5/serial /usr/local/include
# LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
LIBRARY_DIRS := $(PYTHON_LIB) /usr/lib/x86_64-linux-gnu/hdf5/serial /usr/local/lib /usr/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 ?= @参考:
https://www.cnblogs.com/pprp/p/9121631.html