數據可視化環境Python接口的配置

2019-11-06 09:11:22

字體：大中小

來源：轉載

供稿：網友

caffe程序是由C++語言寫的，本身是不帶數據可視化功能的。只能借助其他的庫或者接口，如Python，MATLAB或者opencv。本文則選擇Python接口。

Python環境不能單獨配置，必須要先編譯好caffe，才能編譯Python環境。

Python環境配置說簡單，實際操作起來卻非常的復雜。此處強烈建議大家用anaconda這個集成工具進行安裝。

一、下載工具Anaconda

下載anaconda之前，先用以下命令，查看一下安裝的Python版本

#Python -V

然后，到https://www.continuum.io/downloads選擇和自己Python版本匹配的anaconda。

二、安裝

下載成功后，在終端執行（2.7版本）：

#bash Anaconda2-4.3.0-linux-x86_64.sh

或者3.6版本：

#bash Anaconda3-4.3.0-Linux-x86_64.sh

安裝過程中，會問你的安裝路徑，直接默認就可以。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yes.

安裝完成后，當前用戶根目錄，即/home/xxx/下會有個anaconda2的文件夾，里面就是安裝好的內容。

輸入conda list 就可以查詢安裝了哪些內容。

三、編譯接口

首先，將caffe根目錄下的Python文件夾加入到環境變量中

#sudo vi ~/.bashrc

在最后面加入

export PYTHONPATH=/home/xxx/caffe/python:$PYTHONPATH

保存退出，更新配置文件

#sudo ldconfig

然后修改編譯配置文件Makefile.config。我的配置是：

## 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# 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 OpenBlasBLAS := 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)/anaconda2PYTHON_INCLUDE := $(ANACONDA_HOME)/include / $(ANACONDA_HOME)/include/python2.7 / $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include /# We need to be able to find libpythonX.X.so or .dylib.# PYTHON_LIB := /usr/libPYTHON_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/includeLIBRARY_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 := 1BUILD_DIR := buildDISTRIBUTE_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 ?= @

修改外編譯配置文件后，最后進行編譯：

#sudo make pycaffe

編譯成功后，不能重復編譯，否則會出現nothing to be done for 'pycaffe' 的錯誤。

如果需要重復編譯，則先運行以下命令，清除之前的編譯結果

#sudo make clean

然后繼續編譯，直到編譯成功。

編譯成功后這樣顯示