利用anaconda來配置python環境
先到https://www.continuum.io/downloads 下載anaconda,
現在的版本有python2.7版本和python3.6版本,下載好對應版本、對應系統的anaconda,它實際上是一個sh腳本文件,大約500M左右。我下載的是linux版的python 2.7版本。
下載成功後,在終端執行(2.7版本):
# bash Anaconda2-2.4.1-Linux-x86_64.sh
在安裝的過程中,會問你安裝路徑,直接回車默認就可以了。有個地方問你是否將anaconda安裝路徑加入到環境變量(.bashrc)中,這個一定要輸入yes
安裝成功後,會有當前用戶根目錄下生成一個anaconda2的文件夾,裏面就是安裝好的內容。
輸入conda list 就可以查詢,你現在安裝了哪些庫,常用的numpy, scipy名列其中。如果你還有什麼包沒有安裝上,可以運行
conda install *** 來進行安裝,
如果某個包版本不是最新的,運行 conda update *** 就可以了。
首次conda list 出現找不到這個命令的錯誤:在終端輸入export PATH=~/anaconda2/bin:$PATH
編譯python接口
首先,將caffe根目錄下的python文件夾加入到環境變量
打開配置文件bashrc,在最後面加入
export PYTHONPATH=/home/xxx/caffe/python:$PYTHONPATH
sudo ldconfig因爲之前配置好的caffe,,不能再繼續執行sudo make與sudo make python等指令,,首先運行
sudo make clean
sudo make pycaffe clean
sudo make test clean
sudo make runtest clean
然後修改Makefile.config文件,下邊是我的
## 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 := 1
# 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 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_50,code=compute_50
# 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
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
# 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
# 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
# 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 ?= @
然後執行
sudo make
sudo make pycaffe
sudo make test
sudo make runtest
sudo make runtest 沒編譯過去,也不是必須的,先不管了。。
最終查看python接口是否編譯成功:
進入python環境,進行import操作
python
import caffe
如果沒有提示錯誤,則編譯成功
安裝jupyter
安裝了python還不行,還得安裝一下ipython,後者更加方便快捷,更有自動補全功能。而ipython
notebook是ipython的最好展現方式。最新的版本改名爲jupyter notebook,我們先來安裝一下。(如果安裝了anaconda, jupyter notebook就已經自動裝好,不需要再安裝)
sudo pip install jupyter 提示sudo:pip:command not find
去掉sudo 執行,,成功安裝!!
原因是:我們知道在執行Linux命令時,如果在其前面加上sudo,就表示以root權限執行。但是這其實是有一個前提的,就是隻有那些Linux內置系統命令纔可以用如此的形式來執行,而對於Shell內置命令或其他用戶自定義命令、別名等,是不能用sudo來使用root權限的。
$ jupyter notebook
就會在瀏覽器中打開notebook, 點擊右上角的New-python2, 就可以新建一個網頁一樣的文件,擴展名爲ipynb。在這個網頁上,我們就可以像在命令行下面一樣運行python代碼了。輸入代碼後,按shift+enter運行,更多的快捷鍵,可點擊上方的help-Keyboard shortcuts查看,或者先按esc退出編輯狀態,再按h鍵查看。
在jupyter裏輸入
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import caffe
caffe_root='/home/shan/caffe/'
import os,sys
os.chdir(caffe_root)
sys.path.insert(0,caffe_root+'python')
im = caffe.io.load_image('examples/images/cat.jpg')
print im.shape
plt.imshow(im)
plt.axis('off')
報錯:No module named google.protobuf.internal
解決:sudo chmod 777 -R anaconda2
conda install protobuf