以上就表明caffe2安装成功，只要没有问题就往下做。
测试时错误1：ImportError: No module named google.protobuf.internal
测试时错误2：ImportError: No module named past.builtins
可以通过安装下面常用的包解决：
conda install cython conda install protobuf==3.6.1 conda install future安装opencv-python错误，直接pip安装会安装最新的4.3，导致出错，手动安装opencv-python：
pip install opencv-python==3.2.0.6 编译源代码： cd detectron && make 直接编译成功 测试安装的detectron python ./detectron/tests/test_spatial_narrow_as_op.py 出现ok表明安装成功哦，错误：fataerror : python.h找不到 解决方案：
sudo apt-get install python-dev 再次运行 sudo make 验证make安装： python ./detectron/tests/test_spatial_narrow_as_op.py错误：找不到caffe2, $DENSEPOSE/detectron/utils/env.py 在这个文件中 AssertionError: Detectron ops lib not found; make sure that your Caffe2 version includes Detectron module
解决方案参考： https://zhuanlan.zhihu.com/p/104395486
找不到：libcaffe2_detectron_ops_gpu.so，先找到这个东西在哪里
1. 执行命令：sudo find / -name libcaffe2_detectron_ops_gpu.so 2. 找到自己torch的那个路径把：/MY/PATH/.conda/envs/dense2/lib/python2.7/site-packages/torch/ 加入到python的环境变中中，sys.path.append('/MY/PATH/.conda/envs/dense2/lib/python2.7/site-packages/torch/') 3. 然后在env.py文件中：修改 prefixes = [_CMAKE_INSTALL_PREFIX, sys.prefix, sys.exec_prefix] + sys.path + ['/MY/PATH/.conda/envs/dense2/lib/python2.7/site-packages/torch/'] 4. 保存 5. 运行：python ./detectron/tests/test_spatial_narrow_as_op.py 成功再次测试，测试成功！！！
make ops(最大bug出来了)为防止逐个踩坑，现直接掏出生化武器，直接修改cmakelist.txt，可以解决好几个小bug：
cmake_minimum_required(VERSION 2.8.12 FATAL_ERROR) set(Caffe2_DIR "/opt/conda/envs/densepose/lib/python2.7/site-packages/torch/share/cmake/Caffe2") include_directories("/opt/conda/envs/densepose/include") include_directories("/opt/caffe/external/mkl/mklml_lnx_2019.0.20180710/include/") add_library(libprotobuf STATIC IMPORTED) set(PROTOBUF_LIB "/opt/conda/envs/densepose/lib/libprotobuf.a") set_property(TARGET libprotobuf PROPERTY IMPORTED_LOCATION "${PROTOBUF_LIB}") # Find the Caffe2 package. # Caffe2 exports the required targets, so find_package should work for # the standard Caffe2 installation. If you encounter problems with finding # the Caffe2 package, make sure you have run `make install` when installing # Caffe2 (`make install` populates your share/cmake/Caffe2). find_package(Caffe2 REQUIRED) include_directories(${CAFFE2_INCLUDE_DIRS}) if (${CAFFE2_VERSION} VERSION_LESS 0.8.2) # Pre-0.8.2 caffe2 does not have proper interface libraries set up, so we # will rely on the old path. message(WARNING "You are using an older version of Caffe2 (version " ${CAFFE2_VERSION} "). Please consider moving to a newer version.") include(cmake/legacy/legacymake.cmake) return() endif() # Add compiler flags. set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -std=c11") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -O2 -fPIC -Wno-narrowing") # Print configuration summary. include(cmake/Summary.cmake) detectron_print_config_summary() # Collect custom ops sources. file(GLOB CUSTOM_OPS_CPU_SRCS ${CMAKE_CURRENT_SOURCE_DIR}/detectron/ops/*.cc) file(GLOB CUSTOM_OPS_GPU_SRCS ${CMAKE_CURRENT_SOURCE_DIR}/detectron/ops/*.cu) # Install custom CPU ops lib. add_library( caffe2_detectron_custom_ops SHARED ${CUSTOM_OPS_CPU_SRCS}) target_link_libraries(caffe2_detectron_custom_ops caffe2_library libprotobuf) install(TARGETS caffe2_detectron_custom_ops DESTINATION lib) # Install custom GPU ops lib, if gpu is present. if (CAFFE2_USE_CUDA OR CAFFE2_FOUND_CUDA) # Additional -I prefix is required for CMake versions before commit (< 3.7): # https://github.com/Kitware/CMake/commit/7ded655f7ba82ea72a82d0555449f2df5ef38594 list(APPEND CUDA_INCLUDE_DIRS -I${CAFFE2_INCLUDE_DIRS}) CUDA_ADD_LIBRARY( caffe2_detectron_custom_ops_gpu SHARED ${CUSTOM_OPS_CPU_SRCS} ${CUSTOM_OPS_GPU_SRCS}) target_link_libraries(caffe2_detectron_custom_ops_gpu caffe2_gpu_library libprotobuf) install(TARGETS caffe2_detectron_custom_ops_gpu DESTINATION lib) endif()还剩下几个比较坚持的bug，下面一一进行解决。
错误1: make ops到了50%出错了，protobuf出问题
参考https://blog.csdn.net/FatMigo/article/details/88648107 分成四步完成
cp -r /home/xwt/.conda/pkgs/libprotobuf-3.6.1-hd408876_0/include/google /home/xwt/anaconda3/include cp -r /home/xwt/.conda/pkgs/libprotobuf-3.6.1-hd408876_0/lib/libprotobuf* /home/xwt/anaconda3/libprotobuf的问题：解决了
错误2：fatal error: mkl_cblas.h: No such file or directory
bug解决链接： http://linkinpark213.com/2018/11/18/densepose-minesweeping/#2-3-cmake-files-not-found-amp-Unknown-CMake-command-quot-caffe2-interface-library-quot
查找：
sudo find / -name mkl_cblas.h 然后找到mkl_cblas.h export CPATH=$CPATH:/opt/caffe/external/mkl/mklml_lnx_2019.0.20180710/include/也可直接安装源码（到官网安装）
(1)export CPATH=$CPATH:/opt/intel/compilers_and_libraries_2020.1.217/linux/mkl/include (2)在cmakelist.txt中添加 include_directories("/opt/intel/compilers_and_libraries_2020.1.217/linux/mkl/include")cblas.h的问题解决了
错误3：fatal error: caffe2/utils/math/broadcast.h: No such file or directory 解决思路就是去报错的路径中查看是否有相关的文件，发现报错的文件确实不存在，解决思路就是需要把相关的文件添加到路径当中–我再次把pytorch的源代码下载了下来，源代码里面有一个caffe2模块:
1. 下载pytorch源码，找到pytorch里面的caffe2里面的utils然后把里面的额math文件复制到（虚拟境境中的caffe2） /home/xwt/.conda/envs/densepose/lib/python2.7/site-packages/torch/include/caffe2/utils 2. 3. （1）cd /home/xwt/.conda/envs/densepose/lib/python2.7/site-packages/torch/include/caffe2/utils 4. （2）cp -r /home/xwt/下载/pytorch-master/caffe2/utils/math/ ./ 5. 成功， 6. 新bug出现：fatal error: caffe2/utils/threadpool/ThreadPool.h: 没有那个文件或目录 7. 和上面的解决思路一样的，也是去源码中复制， 8. （1）cd /home/xwt/.conda/envs/densepose/lib/python2.7/site-packages/torch/include/caffe2/utils 9. （2）cp -r /home/xwt/下载/pytorch-master/caffe2/utils/threadpool/ ./ 10. 最后运行 make ops。错误4：error This file was generated by an older version of protoc which is error incompatible with your Protocol Buffer headers.
这是protobuf版本与代码要求版本不一致导致的，确定代码所需要版本为3.6.1。使用conda安装指定版本的protobuf。
conda install protobuf==3.6.1再次make ops。
验证densepose： python ./detectron/tests/test_zero_even_op.py错误1：OSError: /root/cwt1/DensePose/build/libcaffe2_detectron_custom_ops_gpu.so: undefined symbol: _ZN6caffe219CPUOperatorRegistryB5cxx11Ev 解决方法：将gcc版本从降到4.9.x即可
sudo apt-get install gcc-4.9 g++-4.9 sudo cp /usr/bin/gcc-4.9 /usr/bin/gcc sudo cp /usr/bin/g++-4.9 /usr/bin/g++使用图片测试densepose:
python2 tools/infer_simple.py \ --cfg configs/DensePose_ResNet101_FPN_s1x-e2e.yaml \ --output-dir DensePoseData/infer_out/ \ --image-ext jpg \ --wts https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet101_FPN_s1x-e2e.pkl \ DensePoseData/demo_data/demo_im.jpg走到这里就成功了，一步一个坑，鼓励一下自己！
