Python2.7与Python3.5共存Caffe

最新推荐文章于 2024-05-11 23:32:26 发布

原创

最新推荐文章于 2024-05-11 23:32:26 发布 · 1.3k 阅读

3 ·

CC 4.0 BY-SA版权

文章标签：

#Caffe

本文介绍了如何在Ubuntu16.04系统中同时配置Python2.7的NVCaffe和Python3.5的BVLC Caffe，包括修改Makefile.config，解决protobuf动态库冲突问题，以及确保每个Python版本能正确导入对应的Caffe库。详细步骤包括注释Python Include，静态编译protobuf等。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

先简单说一下我的环境：

我用的是Ubuntu16.04，电脑中有两个版本的Caffe，分别是NVCaffe-0.17-dev与BVLC Caffe 1.0版本。由于工作需要，两个版本的caffe都要用，因此想要尝试在Python2.7中使用NVCaffe，在Python3.5中使用BVLC Caffe。电脑装有CUDA9.0+CUDNNv7，显卡为GTX1080。经过一番折腾终于搞定，记录下来以备不时之需。

Python2.7下安装NVCaffe

NVCaffe的安装与正常安装一样，默认安装在Python2.7中，只需要按照自己的需要更改下Makefile.config即可，我的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).
# cuDNN version 6 or higher is required.
USE_CUDNN := 1

# NCCL acceleration switch (uncomment to build with NCCL)
# See https://github.com/NVIDIA/nccl
# USE_NCCL := 1

# Builds tests with 16 bit float support in addition to 32 and 64 bit.
#TEST_FP16 := 1

# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0

# 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.
CUDA_ARCH := 	\
		-gencode arch=compute_61,code=sm_61 \
		-gencode arch=compute_61,code=compute_61

# BLAS choice:
# atlas for ATLAS
# mkl for MKL
# open for OpenBlas - default, see https://github.com/xianyi/OpenBLAS
BLAS := open
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
#BLAS_INCLUDE := /opt/OpenBLAS/include/
#BLAS_LIB := /opt/OpenBLAS/lib/

# 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/local/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)/anaconda
# 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 no