cuda11估计可行（可以试试^_^），实在折腾没办法了（降低了cudnn版本），装了cuda10.1+cudnn7.6.0
安装caffe的主要目的是用来装openpose的环境，但是实际上单独安装caffe环境并不能直接用到openpose上，需要下载openpose自带的caffe版本。这篇仅用来参考，其实差别不大。
被这个caffe环境折腾了好几天，要崩溃了。。。
下面是正文。。。
依赖： Ubuntu20.04 cuda10.1 cudnn7.6.0 OpenCV4.5.1 Python 3.8+Numpy
1. 依赖
1. cuda10.1+cudnn7.6.0
卸载cuda11
cd /usr/local/cuda-11.0/bin/ sudo ./cuda-uninstaller # 选中所有cuda相关选项 sudo rm -rf /usr/local/cuda-11.0
删除cudnn
sudo rm -rf /usr/local/cuda/include/cudnn.h sudo rm -rf /usr/local/cuda/lib64/libcudnn*
安装cuda10.1
查看cudnn版本
$ cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
#define CUDNN_MAJOR 7
#define CUDNN_MINOR 6
#define CUDNN_PATCHLEVEL 0
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
#include "driver_types.h"
2. opencv4.5.1
3. python3.8+numpy
使用系统Ubuntu自带python3.8，如果配置anaconda就要更改下面安装教程对应位置的路径，仅使用caffe是没问题的
openpose部分：由于刚开始配置的是anaconda但是出了各种小问题可能是路径没配置好，免于各种路径麻烦还是用系统的吧。。。
(base) lhw@lhw:~/lib/caffe/build$ python Python 3.8.5 (default, Sep 4 2020, 07:30:14) [GCC 7.3.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information.
安装numpy，安装完numpy可以继续使用此安装命令查看numpy安装位置
(base) lhw@lhw:~/lib/caffe/build$ pip install numpy Requirement already satisfied: numpy in /home/lhw/anaconda3/lib/python3.8/site-packages (1.19.2) 如果使用系统环境，则在conda的base环境中再使用conda deactivate退出conda环境，然后再pip install numpy lhw@lhw:~/Gradute/collage/openpose$ pip install numpy Defaulting to user installation because normal site-packages is not writeable Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (1.20.2)
4. 其他依赖
我也不晓得全不全，由于折腾许久也不知道自己到底装了哪些依赖了，后面如果有错误的话先百度一下看看对应的依赖有没有安装完
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 sudo apt install libatlas-base-dev sudo apt install libgflags-dev libgoogle-glog-dev liblmdb-dev
2. 安装(系统自带python3.8)
1.下载源码
git clone https://github.com/BVLC/caffe.git cd caffe/ cp Makefile.config.example Makefile.config
2.修改Makefile.config
PYTHON_LIBRARIES := boost_python38 python3.8 # 这一行根据是可以在 /usr/lib/x86_64-linux-gnu 文件夹下找到libboost_python38.so # 这个是在之前安装依赖的时候生成的，如果没有就要安装相关的依赖，Ubuntu20.04自带python3.8
# 注意下面的中文部分
## 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 #取消注释 # 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 # 取消注释 # 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. # 删除20和21两行，cuda11可能要删除20--50行 CUDA_ARCH := -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 \ # 行前注释加# # /usr/lib/python2.7/dist-packages/numpy/core/include # 行前注释加# # Anaconda Python distribution is quite popular. Include path: # Verify anaconda location, sometimes it\'s in root. #ANACONDA_HOME := $(HOME)/anaconda3/envs/py2_caffe_source #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_python38 python3.8 # 取消注释并更改版本 # 取消注释并更改版本，系统自带python3.8 PYTHON_INCLUDE := /usr/include/python3.8 \ /usr/local/lib/python3.8/dist-packages/numpy/core/include # We need to be able to find libpythonX.X.so or .dylib. # 更改一下python lib路径 PYTHON_LIB :=/usr/lib/python3.8/config-3.8-x86_64-linux-gnu #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) # 取消注释，打开python接口 WITH_PYTHON_LAYER := 1 # Whatever else you find you need goes here. # 添加include路径，添加你的OpenCV路径 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial /usr/local/include/opencv4 #添加lib路径，最后那个是添加cudnn路径好像也没啥用。。 LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial /usr/local/cuda/lib64/libcudnn.so # 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 $LIBRARY_DIRS.) # USE_PKG_CONFIG := 1 # N.B. both build and distribute dirs are cleared on `make clean` BUILD_DIR := build DISTRIBUTE_DIR := distribute # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1 # The ID of the GPU that \'make runtest\' will use to run unit tests. TEST_GPUID := 0 # enable pretty build (comment to see full commands) Q ?= @
Makefile.config
3.修改Makefile、CMakeLists.txt、cmake/Dependencies.cmake
# Makefile LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial PYTHON_LIBRARIES ?= boost_python38 python3.8 # CmakeLists.txt 将python_version 改成"3" set(python_version "3" CACHE STRING "Specify which Python version to use") # cmake/Dependencies.cmake 将python部分中的两行都修改成下面格式与libboost_python38.so相匹配 find_package(Boost 1.46 COMPONENTS "python${boost_py_version}")
4. cmake指令
由于不知为啥老是出现找不到cudnn错误，所以改成cmake编译
mkdir build && cd build
cmake -DCUDNN_LIBRARY=/usr/local/cuda/lib64/libcudnn.so \
-DPYTHON_LIBRARY=/usr/lib/python3.8/config-3.8-x86_64-linux-gnu/libpython3.8.so \
-DPYTHON_INCLUDE_DIRS=/usr/include/python3.8 ..
# 完整的是下面部分，但是两个目录好像用不到，执行上面的就够了
cmake -DCUDNN_INCLUDE_DIR=/usr/local/cuda/include \
-DCUDNN_LIBRARY=/usr/local/cuda/lib64/libcudnn.so \ -DPYTHON_LIBRARY=/usr/lib/python3.8/config-3.8-x86_64-linux-gnu/libpython3.8.so \-DPYTHON_INCLUDE_DIRS=/usr/include/python3.8 \ -DPYTHON_DEFAULT_EXECUTABLE=/usr/bin/python3 ..
下面这是反馈信息
(base) lhw@lhw:~/lib/caffe/build$ cmake -DCUDNN_LIBRARY=/usr/local/cuda/lib64/libcudnn.so \ > -DPYTHON_LIBRARY=/usr/lib/x86_64-linux-gnu/libpython3.8.so \ > -DPYTHON_INCLUDE_DIRS=/usr/include/python3.8 .. CMake Deprecation Warning at CMakeLists.txt:1 (cmake_minimum_required): Compatibility with CMake < 2.8.12 will be removed from a future version of CMake. Update the VERSION argument <min> value or use a ...<max> suffix to tell CMake that the project does not need compatibility with older versions. -- The C compiler identification is GNU 7.5.0 -- The CXX compiler identification is GNU 7.5.0 -- Detecting C compiler ABI info -- Detecting C compiler ABI info - done -- Check for working C compiler: /usr/bin/cc - skipped -- Detecting C compile features -- Detecting C compile features - done -- Detecting CXX compiler ABI info -- Detecting CXX compiler ABI info - done -- Check for working CXX compiler: /usr/bin/c++ - skipped -- Detecting CXX compile features -- Detecting CXX compile features - done CMake Warning (dev) at cmake/Misc.cmake:32 (set): implicitly converting \'BOOLEAN\' to \'STRING\' type. Call Stack (most recent call first): CMakeLists.txt:25 (include) This warning is for project developers. Use -Wno-dev to suppress it. -- Found Boost: /usr/lib/x86_64-linux-gnu/cmake/Boost-1.71.0/BoostConfig.cmake (found suitable version "1.71.0", minimum required is "1.54") found components: system thread filesystem -- Found Threads: TRUE -- Found GFlags: /usr/include -- Found gflags (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libgflags.so) -- Found Glog: /usr/include -- Found glog (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libglog.so) -- Found Protobuf: /usr/lib/x86_64-linux-gnu/libprotobuf.so;-lpthread (found version "3.6.1") -- Found PROTOBUF Compiler: /usr/bin/protoc /home/lhw/anaconda3/bin/h5cc: 1: eval: x86_64-conda_cos6-linux-gnu-cc: not found -- HDF5 C compiler wrapper is unable to compile a minimal HDF5 program. /home/lhw/anaconda3/bin/h5c++: 1: eval: x86_64-conda_cos6-linux-gnu-c++: not found -- HDF5 CXX compiler wrapper is unable to compile a minimal HDF5 program. -- Found HDF5: /usr/lib/x86_64-linux-gnu/hdf5/serial/libhdf5_cpp.so;/usr/lib/x86_64-linux-gnu/hdf5/serial/libhdf5.so (found version "1.10.4") found components: HL /home/lhw/anaconda3/bin/h5cc: 1: eval: x86_64-conda_cos6-linux-gnu-cc: not found -- HDF5 C compiler wrapper is unable to compile a minimal HDF5 program. /home/lhw/anaconda3/bin/h5c++: 1: eval: x86_64-conda_cos6-linux-gnu-c++: not found -- HDF5 CXX compiler wrapper is unable to compile a minimal HDF5 program. -- Found LMDB: /usr/include -- Found lmdb (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/liblmdb.so) -- Found LevelDB: /usr/include -- Found LevelDB (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libleveldb.so) -- Found Snappy: /usr/include -- Found Snappy (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libsnappy.so) -- CUDA detected: 10.1 -- Found cuDNN: ver. 7.6.0 found (include: /usr/local/cuda-10.1/include, library: /usr/local/cuda/lib64/libcudnn.so) -- Added CUDA NVCC flags for: sm_75 -- OpenCV found (/usr/local/lib/cmake/opencv4) -- Found Atlas: /usr/include/x86_64-linux-gnu -- Found Atlas (include: /usr/include/x86_64-linux-gnu library: /usr/lib/x86_64-linux-gnu/libatlas.so lapack: /usr/lib/x86_64-linux-gnu/liblapack.so -- Found PythonInterp: /home/lhw/anaconda3/bin/python3 (found suitable version "3.8.5", minimum required is "3.0") -- Found PythonLibs: /usr/lib/x86_64-linux-gnu/libpython3.8.so (found suitable version "3.8.5", minimum required is "3.0") -- Found NumPy: /home/lhw/anaconda3/lib/python3.8/site-packages/numpy/core/include (found suitable version "1.19.2", minimum required is "1.7.1") -- NumPy ver. 1.19.2 found (include: /home/lhw/anaconda3/lib/python3.8/site-packages/numpy/core/include) -- Could NOT find Boost: missing: python385 (found /usr/lib/x86_64-linux-gnu/cmake/Boost-1.71.0/BoostConfig.cmake (found suitable version "1.71.0", minimum required is "1.46")) -- Found Boost: /usr/lib/x86_64-linux-gnu/cmake/Boost-1.71.0/BoostConfig.cmake (found suitable version "1.71.0", minimum required is "1.46") found components: python38 -- Could NOT find Boost: missing: python3 (found /usr/lib/x86_64-linux-gnu/cmake/Boost-1.71.0/BoostConfig.cmake (found suitable version "1.71.0", minimum required is "1.46")) -- Found Boost: /usr/lib/x86_64-linux-gnu/cmake/Boost-1.71.0/BoostConfig.cmake (found suitable version "1.71.0", minimum required is "1.46") found components: python -- Found Doxygen: /usr/bin/doxygen (found version "1.8.17") found components: doxygen dot -- Detected Doxygen OUTPUT_DIRECTORY: ./doxygen/ -- Found Git: /usr/bin/git (found version "2.25.1") -- -- ******************* Caffe Configuration Summary ******************* -- General: -- Version : 1.0.0 -- Git : 1.0-136-g9b891540-dirty -- System : Linux -- C++ compiler : /usr/bin/c++ -- Release CXX flags : -O3 -DNDEBUG -fPIC -Wall -Wno-sign-compare -Wno-uninitialized -- Debug CXX flags : -g -fPIC -Wall -Wno-sign-compare -Wno-uninitialized -- Build type : Release -- -- BUILD_SHARED_LIBS : ON -- BUILD_python : ON -- BUILD_matlab : OFF -- BUILD_docs : ON -- CPU_ONLY : OFF -- USE_OPENCV : ON -- USE_LEVELDB : ON -- USE_LMDB : ON -- USE_NCCL : OFF -- ALLOW_LMDB_NOLOCK : OFF -- USE_HDF5 : ON -- -- Dependencies: -- BLAS : Yes (Atlas) -- Boost : Yes (ver. 1.71) -- glog : Yes -- gflags : Yes -- protobuf : Yes (ver. 3.6.1) -- lmdb : Yes (ver. 0.9.24) -- LevelDB : Yes (ver. 1.22) -- Snappy : Yes (ver. 1.1.8) -- OpenCV : Yes (ver. 4.5.1) -- CUDA : Yes (ver. 10.1) -- -- NVIDIA CUDA: -- Target GPU(s) : Auto -- GPU arch(s) : sm_75 -- cuDNN : Yes (ver. 7.6.0) -- -- Python: -- Interpreter : /home/lhw/anaconda3/bin/python3 (ver. 3.8.5) -- Libraries : /usr/lib/x86_64-linux-gnu/libpython3.8.so (ver 3.8.5) -- NumPy : /home/lhw/anaconda3/lib/python3.8/site-packages/numpy/core/include (ver 1.19.2) -- -- Documentaion: -- Doxygen : /usr/bin/doxygen (1.8.17) -- config_file : /home/lhw/lib/caffe/.Doxyfile -- -- Install: -- Install path : /home/lhw/lib/caffe/build/install -- -- Configuring done -- Generating done -- Build files have been written to: /home/lhw/lib/caffe/build
cmake
5. make all -j8
出现错误
error: ‘CV_LOAD_IMAGE_COLOR’ was not declared in this scope
cd .. sed -i \'s/CV_LOAD_IMAGE_COLOR/cv::IMREAD_COLOR/g\' src/caffe/layers/window_data_layer.cpp sed -i \'s/CV_LOAD_IMAGE_COLOR/cv::IMREAD_COLOR/g\' src/caffe/util/io.cpp sed -i \'s/CV_LOAD_IMAGE_GRAYSCALE/cv::ImreadModes::IMREAD_GRAYSCALE/g\' src/caffe/util/io.cpp #下面部分忽略。。。上面三行就可以 sed -i \'s/CV_LOAD_IMAGE_COLOR/cv::IMREAD_COLOR/g\' src/caffe/test/test_io.cpp sed -i \'s/CV_LOAD_IMAGE_GRAYSCALE/cv::ImreadModes::IMREAD_GRAYSCALE/g\' src/caffe/test/test_io.cpp
回到目录继续编译
cd build
make clean
make all -j8
编译结果：
Scanning dependencies of target pycaffe [ 95%] Built target upgrade_net_proto_binary [ 97%] Building CXX object python/CMakeFiles/pycaffe.dir/caffe/_caffe.cpp.o [ 97%] Linking CXX executable extract_features [ 98%] Linking CXX executable upgrade_net_proto_text [ 98%] Linking CXX executable mnist/convert_mnist_data [ 98%] Built target convert_mnist_data [ 98%] Built target upgrade_net_proto_text [ 98%] Built target extract_features [100%] Linking CXX executable cifar10/convert_cifar_data [100%] Built target convert_cifar_data [100%] Linking CXX executable caffe [100%] Built target caffe.bin [100%] Linking CXX executable cpp_classification/classification [100%] Built target classification [100%] Linking CXX shared library ../lib/_caffe.so Creating symlink /home/lhw/lib/caffe/python/caffe/_caffe.so -> /home/lhw/lib/caffe/build/lib/_caffe.so [100%] Built target pycaffe
make all -j8
6. 将caffe添加到环境变量
$ gedit ~/.bashrc # 将你的caffe路径下的python路径放到bashrc里面，保存后退出 export PYTHONPATH=$PYTHONPATH:/home/lhw/lib/caffe/python $ source ~/.bashrc
7.生成caffe.pb.h
这个是openpose需要做的步骤，不知道其他用途要不要,,,,咱也没用过
不然openpose会生成错误：caffe/include/caffe/blob.hpp:9:10: fatal error: caffe/proto/caffe.pb.h: 没有那个文件或目录
# 在caffe根目录 protoc src/caffe/proto/caffe.proto --cpp_out=. mkdir include/caffe/proto mv src/caffe/proto/caffe.pb.h include/caffe/proto
8. python-caffe
没报错表明安装成功
(base) lhw@lhw:~/lib/caffe/build$ python Python 3.8.5 (default, Sep 4 2020, 07:30:14) [GCC 7.3.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> import caffe >>>
参考：
http://www.ai111.vip/thread-1062-1-1.html https://qengineering.eu/install-caffe-on-ubuntu-20.04-with-opencv-4.4.html https://blog.csdn.net/weixin_34208185/article/details/93522778 https://blog.csdn.net/qq_35468937/article/details/81514198
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