Linux编译CPU版本的caffe
caffe框架的编译过程是比较复杂,本人在编译过程中也遇到过比较多的坑,这里把我的经验记录下来,希望大家可以少走点弯路,如果大家觉得此教程有用,记得订阅点赞加分享哦.
1. 下载caffe
通过git克隆下载原版的caffe代码,命令如下
git clone https://github.com/BVLC/caffe.git
2. 相关环境的配置
安装必要的依赖,
注意caffe只支持python2,所以必须要在Python2.7环境下面编译,否则不能运行
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 libopenblas-dev liblapack-dev libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler libatlas-base-dev
sudo apt-get install python-dev python-pip gfortran
按照opencv,我的opencv编译的命令如下:
RUN cd /root && unzip opencv-3.4.5_contirbute_cache.zip && cd opencv-3.4.5_contirbute_cache && mkdir build && cd build && cmake -D CMAKE_BUILD_TYPE=Release \
-D BUILD_SHARED_LIBS=ON \
-D CMAKE_CXX_FLAGS=-fPIC \
-D CMAKE_C_FLAGS=-fPIC \
-D CMAKE_EXE_LINKER_FLAGS=-lpthread -lrt -ldl \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D OPENCV_EXTRA_MODULES_PATH=../opencv_contrib-3.4.5/modules \
-D OPENCV_ENABLE_NONFREE=True \
-D PYTHON3_EXECUTABLE=/usr/bin/python3.7 \
-D PYTHON3_INCLUDE_DIR=/usr/include/python3.7 \
-D PYTHON3_LIBRARY=/usr/lib/python3.7/config-3.7m-aarch64-linux-gnu/libpython3.7.so \
-D PYTHON3_NUMPY_INCLUDE_DIRS=/usr/local/lib/python3.7/dist-packages/numpy/core/include \
-D PYTHON2_EXECUTABLE=/usr/bin/python2.7 \
-D PYTHON2_INCLUDE_DIR=/usr/include/python2.7 \
-D PYTHON2_LIBRARY=/usr/lib/python2.7/config-aarch64-linux-gnu/libpython2.7.so \
-D PYTHON2_NUMPY_INCLUDE_DIRS=/usr/local/lib/python2.7/dist-packages/numpy/core/include \
-D BUILD_TESTS=OFF \
-D BUILD_PERF_TESTS=OFF \
-D BUILD_opencv_python3=ON \
.. && make -j128 && make install
3. 编译CPU版本的caffe
执行下面命令
cd caffe
cp Makefile.config.example Makefile.config
需要注意的是,这里的Makefile.config有内容需要修改,具体如下
第8行,取消CPU_ONLY的注释,并设置为1
CPU_ONLY := 1
第23行 OPENCV_VERSION
这里是使用3.4.3版本的opencv,所以需要如下设置
OPENCV_VERSION := 3
第39行,CUDA_ARCH
由于此处使用的是CPU版本,CUDA_ARCH部分可以全部注释
#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
第71行PYTHON_INCLUDE
这里需要填上正确的python地址
PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/local/lib/python2.7/dist-packages/numpy/core/include
第97行 INCLUDE_DIRS LIBRARY_DIRS
这里需要填上正确的libhdf5-dev,的引入文件地址,如果没有安装,执行下面的命令进行安装:
sudo apt-get install libhdf5-dev
我是使用apt安装的,所以include的地址为:
/usr/include/hdf5/serial
so库文件地址为:
/usr/lib/aarch64-linux-gnu/hdf5/serial
同理,配置opencv的include地址:
/usr/include/opencv
so库文件地址为:
/usr/lib/aarch64-linux-gnu
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial /usr/include/opencv
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/aarch64-linux-gnu/hdf5/serial /usr/lib/aarch64-linux-gnu
修改完成以后的Makefile.config文件如下
## 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 := 1
# 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/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 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 /usr/include/opencv
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/aarch64-linux-gnu/hdf5/serial /usr/lib/aarch64-linux-gnu
# 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 $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @
4. 编译
make –j6
make pycaffe (编译python接口，如果直接使用C++学习，可跳过)
cd /root/caffe/python
export PYTHONPATH=$PWD:$PYTHONPATH （将caffe导入到环境变量）
试一下在python import看看是否成功
import caffe
5. 常见问题
ImportError: No module named skimage.io
apt-get install python-skimage
ImportError: No module named google.protobuf.internal
apt-get install python-protobuf
其他常见的问题是环境包的版本不对导致的,特别注意scipy,numpy,sckit-image的版本不兼容,这里直接附上我已经成功编译的环境.
auto-tune @ file:///root/selfgz270731700/fwkacllib/lib64/auto_tune-0.1.0-py3-none-any.whl
backports.functools-lru-cache==1.4
cloudpickle==1.3.0
cycler==0.10.0
Cython==0.29.24
decorator==4.1.2
hccl @ file:///root/selfgz270731700/fwkacllib/lib64/hccl-0.1.0-py3-none-any.whl
matplotlib==2.1.1
networkx==1.11
nose==1.3.7
numpy==1.16.6
olefile==0.45.1
Pillow==5.1.0
protobuf==3.0.0
pyparsing==2.2.0
python-dateutil==2.6.1
pytz==2018.3
PyWavelets==0.5.1
PyYAML==3.12
schedule-search @ file:///root/selfgz270731700/fwkacllib/lib64/schedule_search-0.1.0-py3-none-any.whl
scikit-image==0.9.3
scipy==0.13.2
six==1.11.0
subprocess32==3.2.7
te @ file:///root/selfgz270731700/fwkacllib/lib64/te-0.4.0-py3-none-any.whl
