Installing DensePose

Installing DensePose is not an easy thing except building it from the provided Dockerfile. Here, I provided an installation guide based on the official DensePose Installation and an installation guide provided by @linkinpark213.

Requirements:

• NVIDIA GPU, Linux, Python2 (It is not recommended to use Python3)
• Caffe2, various standard Python packages, and the COCO API; Instructions for installing these dependencies are found below
• cmake 3.8.2. (Do not use the latest version.)
• GCC-4.9.2 (Because libcaffe2.so installed from conda was pre-compiled using exactly this version of gcc)
• protobuf-3.5.0 (this version is exactly specified in the header of caffe2.pb.h of caffe2 installed from conda)

Notes:

• Detectron operators currently do not have CPU implementation; a GPU system is required.
• My system configurations:
  • System: CentOS 7.0
  • GPU: NVIDIA Tesla P100
  • CUDA 9.0 with cuDNN 7.4.2
  • Python 2.7.15 environment created by conda create -n py2 python=2.7, set CONDA_ENV_PATH=/path/to/my/conda/envs/py2

Installing required python packages (dependencies)

• PyTorch dependencies: conda install numpy pyyaml mkl mkl-include setuptools cffi typing
• COCOAPI dependencies: conda install cython matplotlib
• Caffe2 dependencies: conda install pydot future networkx
• DensePose dependencies: conda install opencv mock scipy h5py memory_profiler
• SMPL model dependencies: pip install chumpy

Installing Caffe2 (from PyTorch)

Caffe2 has been merged and integrated into PyTorch, which makes its easier to be installed by the following command:

conda install pytorch torchvision cudatoolkit=9.0 -c pytorch

By this command, the latest stable version of PyTorch (currently, PyTorch-1.0.1) and Caffe2 will be installed under the conda site-packages directory, set TORCH_PATH=$CONDA_ENV_PATH/lib/python2.7/site-packages/torch.

Please note that the command above is for CUDA 9.0 on Linux system, you can select your preferences and run the install command from PyTorch - Get Started.

Adjust your PYTHONPATH environment variable to include this location: $TORCH_PATH/lib (referred to the issue: Detectron ops lib not found). If this doesn't work when setting up python modules of densepose, try creating a symbol link to this path in the root directory of your densepose: ln -s $TORCH_PATH/lib /path/to/your/densepose/lib

Please ensure that your Caffe2 installation was successful before proceeding by running the following commands and checking their output as directed in the comments.

# To check if Caffe2 build was successful
$ python2 -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure"

# To check if Caffe2 GPU build was successful
# This must print a number > 0 in order to use Detectron
$ python2 -c 'from caffe2.python import workspace; print(workspace.NumCudaDevices())'

To avoid this issue in case multiple CUDA libraries have been installed in your system and the symbol link /usr/local/cuda doesn't link to the specific version of CUDA that you're using, it is recommended to edit $TORCH_PATH/share/cmake/Caffe2/Caffe2Target.cmake in which replace /usr/local/cuda/lib64/libculibos.a with /usr/local/cuda-x.x/lib64/libculibos.a, otherwise it may trigger a linking error when installing the custom DensePose operator.

Other Dependencies

• Install the COCO API:

  # COCOAPI=/path/to/clone/cocoapi
  $ git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
  $ cd $COCOAPI/PythonAPI
  # Install into global site-packages
  $ make install

  Note that instructions like # COCOAPI=/path/to/install/cocoapi indicate that you should pick a path where you'd like to have the software cloned and then set an environment variable (COCOAPI in this case) accordingly.

• Install GCC-4.9.2

  Before installing GCC, you need to make sure which exactly version of GCC was used to build caffe2 in the precompiled conda package.

  So far, running command strings -a $TORCH_PATH/lib/libcaffe2.so | grep "GCC: (" will give the output: GCC: (GNU) 4.9.2 20150212 (Red Hat 4.9.2-6)

  $ mkdir /path/to/gcc-4.9.2/build && cd /path/to/gcc-4.9.2/build
  $ ../configure --prefix=/path/to/build --enable-checking=release --enable-languages=c,c++ --disable-multilib
  $ make
  $ make install
  $ cd bin/
  $ ln -s gcc cc  # create a symbol link 'cc' for 'gcc'
  $ cp -r include/c++ $CONDA_ENV_PATH/include

  After installing gcc-4.9.2 successfully, you need to adjust your $PATH and $LD_LIBRARY_PATH environment variables for build/bin and build/lib64.

• Install protobuf

  Before installing protobuf, you need to make sure which exactly version of protobuf specified by your installed caffe2. You can find this information in the header of $TORCH_PATH/lib/include/caffe2/proto/caffe2.pb.h:

  // Generated by the protocol buffer compiler.  DO NOT EDIT!
  // source: caffe2/proto/caffe2.proto
  
  #ifndef PROTOBUF_caffe2_2fproto_2fcaffe2_2eproto__INCLUDED
  #define PROTOBUF_caffe2_2fproto_2fcaffe2_2eproto__INCLUDED
  
  #include <string>
  
  #include <google/protobuf/stubs/common.h>
  
  #if GOOGLE_PROTOBUF_VERSION < 3005000
  #error This file was generated by a newer version of protoc which is
  #error incompatible with your Protocol Buffer headers.  Please update
  #error your headers.
  #endif
  #if 3005000 < GOOGLE_PROTOBUF_MIN_PROTOC_VERSION
  #error This file was generated by an older version of protoc which is
  #error incompatible with your Protocol Buffer headers.  Please
  #error regenerate this file with a newer version of protoc.
  #endif

  Now, let's first install protobuf-3.5.0 C/C++ API:

  $ mkdir /path/to/protobuf-3.5.0/build && cd /path/to/protobuf-3.5.0/build
  $ ../configure --prefix=/path/to/build CXXFLAGS="-fPIC"
  $ make
  $ make install

  After installing protobuf C/C++ API, you need to adjust the $PATH and $LD_LIBRARY_PATH for build/bin and build/lib, as aforementioned in the installation of GCC. Or you can put these built files into your conda environment which will only take effect within the environment (which I did in this way):

  $ cd /path/to/protobuf-3.5.0/build
  $ cp bin/protoc $CONDA_ENV_PATH/bin
  $ cp -r include/google $CONDA_ENV_PATH/include
  $ cp lib/libproto* $CONDA_ENV_PATH/lib

  Install Python API of protobuf (before that, ensure that you're in the correct python environment)

  $ cd /path/to/protobuf-3.5.0/python
  $ python2 setup.py build
  $ python2 setup.py install

Densepose

Clone the Densepose repository:

# DENSEPOSE=/path/to/clone/densepose
$ git clone https://github.com/facebookresearch/densepose $DENSEPOSE

Set up Python modules:

$ cd $DENSEPOSE
$ python2 setup.py develop

Check that Detectron tests pass (e.g. for SpatialNarrowAsOp test):

$ python2 $DENSEPOSE/detectron/tests/test_spatial_narrow_as_op.py

Build the custom operators library (this part took me too too much time):

1. Edit $DENSEPOSE/CMakeLists.txt (Thanks to @hyousamk's solution), you download CMakeLists.txt from this repository into your $DENSEPOSE, then replace corresponding paths specified in this file with yours:

   diff --git a/CMakeLists.txt b/CMakeLists.txt
   index 488ea86..b59d9bb 100644
   --- a/CMakeLists.txt
   +++ b/CMakeLists.txt
   @@ -1,11 +1,24 @@
   cmake_minimum_required(VERSION 2.8.12 FATAL_ERROR)
   
   +# set caffe2 cmake path manually
   +set(Caffe2_DIR "/path/to/conda/envs/py2/lib/python2.7/site-packages/torch/share/cmake/Caffe2")
   +# set cuDNN path
   +set(CUDNN_INCLUDE_DIR "/path/to/your/cudnn/include")
   +set(CUDNN_LIBRARY "/path/to/your/libcudnn/libcudnn.so")
   +include_directories("/path/to/conda/envs/py2/include")
   +# add static protobuf library
   +add_library(libprotobuf STATIC IMPORTED)
   +set(PROTOBUF_LIB "/path/to/conda/envs/py2/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
   @@ -34,19 +47,19 @@ add_library(
       caffe2_detectron_custom_ops SHARED
       ${CUSTOM_OPS_CPU_SRCS})
   
   -target_link_libraries(caffe2_detectron_custom_ops caffe2_library)
   +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${CAFFE2_INCLUDE_DIRS})
   +  # list(APPEND CUDA_INCLUDE_DIRS -I${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)
   +  target_link_libraries(caffe2_detectron_custom_ops_gpu caffe2_gpu_library libprotobuf)
   install(TARGETS caffe2_detectron_custom_ops_gpu DESTINATION lib)
   endif()

2. Download pytorch source code, then copy the folder $PYTORCH/caffe2/utils/threadpool into $TORCH_PATH/lib/include/caffe2/utils/ (referred to this issue).

3. Compile the custom operator:

   $ cd $DENSEPOSE/build
   $ cmake ..
   $ make

4. Check that the custom operator tests pass:

   $ python2 $DENSEPOSE/detectron/tests/test_zero_even_op.py

   If you installed the custom operator successfully without any error, you will see the following results in your console:

   $ python detectron/tests/test_zero_even_op.py
   [E init_intrinsics_check.cc:43] CPU feature avx is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU.
   [E init_intrinsics_check.cc:43] CPU feature avx2 is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU.
   [E init_intrinsics_check.cc:43] CPU feature fma is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU.
   ............
   ----------------------------------------------------------------------
   Ran 12 tests in 3.155s
   
   OK

Congratulations! Now you've installed DensePose successfully! 🎉

Happy Hunting DensePose!

Acknowledgements

• DensePose Github
• [MineSweeping] The Long Struggle of DensePose Installation, a very helpful post about the DensePose installation provided by @linkinpark213.
• Special thanks to @tete1030 for his great help in debugging the installation of custom operator with gcc compiler.