DeepMatching is an algorithm that finds corresponding points in two images. Its GPU implementation was written for Fedora 21, which makes things a bit more difficult if you want to run it on an Ubuntu system. This document contains step-by-step instructions on how to get DeepMatching running on Ubuntu 16.10. I only tested it with Ubuntu 16.10, just let me know if it works with previous versions too.
To compile the GPU version you first need to compile the Caffe version that is included that comes with the DeepMatching files. Newer versions of Caffe won't work because Caffe changed the structure of its header files.
Before compiling Caffe we need to make sure all its dependencies are installed. From the installation guide for Ubuntu 16.04/15.10:
sudo apt-get install build-essential cmake git pkg-config
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install libatlas-base-dev
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
# (Python general)
sudo apt-get install python-pip
# (Python 2.7 development files)
sudo apt-get install python-dev
sudo apt-get install python-numpy python-scipy
# (or, Python 3.5 development files)
sudo apt-get install python3-dev
sudo apt-get install python3-numpy python3-scipyYou also need to install Nvidia CUDA. All you need to do is install the packages nvidia-cuda-dev and nvidia-cuda-toolkit
sudo apt-get install nvidia-cuda-dev nvidia-cuda-toolkitTo install all python dependencies, extract the caffe.zip folder, navigate to caffe\python and execute
cd caffe/python
for req in $(cat requirements.txt); do pip install $req; doneTo compile Caffe we will use CMake. Navigate back to the parent directory caffe, create a build folder ant then start cmake-gui
cd ..
mkdir build
cmake-guiIf you get the error that cmake-gui is not install, install it with
sudo apt-get install cmake-qt-guiEnter the path to the caffe directory as path to the source code and the path to caffe/build as directory for the binaries. Then hit Configure. If everything went right, the log will show Configuring done at the end. Then hit Generate. The log should now say Generating done. Now close the CMake window, navigate to the caffe/build directory and execute make all. To speed things up, use the -jX flag, where X specifies the number of jobs that are executed simultaneously.
cd build
make all -j8During compiling you might run into the issue that your gcc version is not supported.
#error -- unsupported GNU version! gcc versions later than 5 are not supported!
First check, if the packages gcc-5 and g++5 are installed on your system.
gcc-5 -v
g++-5 -vIf not, install them.
sudo apt-get install gcc-5 g++-5Now delete the build folder and restart at the cmake-gui step, but this time search for the variables CMAKE_CXX_COMPILER and CMAKE_C_COMPILER and set them to /usr/bin/g++-5 and /usr/bin/gcc-5 respectively.
When make all was successful, execute make install. This will create an install folder in your build directory. We will need this folder later to compile DeepMatching. You can save this folder and then delete the whole caffe directory, if you want to.
make install -j8If you want to test if your compiled caffe library runs correctly, you can call make runtest in the build directory.
make runtest -j8Now we can start compiling DeepMatching.
Before we can compile DeepMatching, we need to install the packages python-matplotlib and swig.
sudo apt-get install python-matplotlib swig
The Makefile that comes with DeepMatching isn't really compatible with Ubuntu, it contains many paths that don't work on Ubuntu. That's why I wrote my own Makefile.
# Path to caffe's install directory that was created by Cmake
CAFFEDIR=install
CAFFELIB=$(CAFFEDIR)/lib
# 1: Include the location of the libcaffe.so library in the _gpudm.so library. Usefull if the CAFFEDIR is not in a standard location
# for libraries because you don't need to set the location by setting the variable LD_LIBRARY_PATH before using _gpudm.so.
# A disadvantage is, that _gpudm.so won't work anymore if you move the CAFFEDIR folder.
INCLUDE_CAFFE_LOCATION = 1
OPTFLAGS=-g -O2
# Path to python header file
INCLUDES += -I/usr/include/python2.7/
# Path to caffe's header files
INCLUDES += -I$(CAFFEDIR)/include/
# Path to hdf5's header files
INCLUDES += -I/usr/include/hdf5/serial/
#include gpudm/Makefile.config
CUDA_ARCH := \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=sm_50 \
-gencode arch=compute_60,code=sm_60 \
-gencode arch=compute_61,code=sm_61
HEADERS := $(shell find . -maxdepth 1 -name '*.hpp')
EXTRA_LAYERS := $(shell find . -maxdepth 1 -name '*.hpp')
all: _gpudm.so
_gpudm.so: gpudm_wrap.o $(EXTRA_LAYERS:.hpp=.o) $(EXTRA_LAYERS:.hpp=.cuo)
ifeq ($(INCLUDE_CAFFE_LOCATION),1)
g++ $(OPTFLAGS) -fPIC -L$(CAFFELIB) $^ -shared -Xlinker -rpath $(CAFFELIB) -o $@ -lcaffe -lcusparse
else
g++ $(OPTFLAGS) -fPIC -L$(CAFFELIB) $^ -o $@ -lcaffe -lcusparse
endif
%.cuo: %.cu %.hpp
nvcc $(CUDA_ARCH) -Xcompiler -fPIC $(INCLUDES) $(OPTFLAGS) -c $< -o $@
gpudm_wrap.cxx: gpudm.swig $(HEADERS)
swig -cpperraswarn -python -c++ $(INCLUDES) gpudm.swig
gpudm_wrap.o: gpudm_wrap.cxx
g++ $(OPTFLAGS) -c gpudm_wrap.cxx -fPIC $(INCLUDES) -o gpudm_wrap.o
%.o: %.cpp %.hpp
g++ $(OPTFLAGS) -c $< -fPIC $(INCLUDES) -L$(CAFFELIB) -o $@
clean:
rm -f *.pyc *~ _gpudm.so gpudm_wrap.o $(EXTRA_LAYERS:.hpp=.o) $(EXTRA_LAYERS:.hpp=.cuo)
cleanswig: clean
rm -f gpudm.py gpudm_wrap.cxx gpudm_wrap.o
The only thing you need to do is set CAFFE_DIR to the location of the install folder that was created during the compiling of Caffe. To compile, simply run make all. To test if DeepMatching runs correctly, execute
python deep_matching_gpu.py liberty1.png liberty2.png -v -viz corres
For more information about DeepMatching, look into the included README.txt file.
which kind of caffe are you using, I used current version of caffe, I followed your instruction, but I was not able to compile the deepmatching gpu version, although compiling caffe works well.
I am using Ubuntu16.04 LTS, python2.7 system default version, and CUDA8.0. cudnn6.0