Caffe

This version of Caffe is forked from weiliu89, and some extra files are added:

ReLU6

In order to support ReLU6, some files from chuanqi305 are added, details can be found in my blog.

Installation

--A Chinese version can be found in my blog.

GPU Environment

In order to install the GPU version of caffe, CUDA and cudnn support is in need. Here we use CUDA 8.0.61 + cudnn 5.1.1.

Creating a Virtual Environment

By creating a virtual environment to avoid the impact of packages installed during Caffe installation on the base environment, since Python 2.7 is used, create a caffe-specific virtual environment using the following commands:

conda create --name caffe-py27 python=2.7

Download caffe

Enter the virtual environment created in the previous step and download caffe:

cd ~/anaconda3/envs/caffe-py27/
git clone https://github.com/ShowLo/caffe.git

If SSD is to be used later, you need to use the following commands here to switch to the SSD branch:

git checkout ssd

At this point, you should be able to find a folder named ssd in the examples folder.

Activate the Environment, Install opencv

It's better to specify the installation of version 3.1.0 opencv from the beginning:

conda activate caffe-py27
conda install opencv=3.1.0

Make Makefile.config

Because the make instruction can only make Makefile.config file, and Makefile.config.example is an example of makefile given by caffe, first copy the content of Makefile.config.example to Makefile.config:

cd caffe
cp Makefile.config.example Makefile.config

Next we need to make the following changes to Makefile.config:

a.If using cudnn, the corresponding comment is removed:

# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1

b.Because the opencv version used is 3, the corresponding comments are removed:

# Uncomment if you're using OpenCV 3
OPENCV_VERSION := 3

c.Delete/comment out the corresponding line in CUDA_ARCH := according to the CUDA version

According to the CUDA version I used, two lines have been deleted here:

-gencode arch=compute_20,code=sm_20 \
-gencode arch=compute_20,code=sm_21 \

d.In order to use anaconda, you need to comment out the default Python 2.7, then remove the corresponding annotation about anaconda, and change ANACONDA_HOME to your Anaconda address.

# 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/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/chenjiarong/anaconda3/envs/caffe-py27
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		  $(ANACONDA_HOME)/include/python2.7 \
		  $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include

In the same time, you need to modify:

# We need to be able to find libpythonX.X.so or .dylib.
# PYTHON_LIB := /usr/lib
PYTHON_LIB := $(ANACONDA_HOME)/lib

e.To use Python to write layers, you can remove the corresponding comments:

# Uncomment to support layers written in Python (will link against Python libs)
WITH_PYTHON_LAYER := 1

f.If using Ubuntu, you need to modify it to tell caffe your hdf5's address:

# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial

g.If a `try using-rpath or-rpath-link' prompt appears in the error, you can add:

LINKFLAGS := -Wl,-rpath,$(ANACONDA_HOME)/lib

Modify Makefile

NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler-fPIC $(COMMON_FLAGS)
change to：
NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)

LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5
change to：
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial

Compile caffe

Now that we have finished the preliminary work, we begin compiling caffe:

make all -j8
make test -j8
make runtest -j8

If successful, you can finally see output such as the following:

[ RUN      ] ArgMaxLayerTest/0.TestCPUMaxValTopK
[       OK ] ArgMaxLayerTest/0.TestCPUMaxValTopK (1 ms)
[----------] 12 tests from ArgMaxLayerTest/0 (45 ms total)

[----------] Global test environment tear-down
[==========] 2101 tests from 277 test cases ran. (992402 ms total)
[  PASSED  ] 2101 tests.

Configuration Environment

Compile pycaffe

make pycaffe -j8

Add to environmental variables

echo export PYTHONPATH="/home/chenjiarong/anaconda3/envs/caffe-py27/caffe/python" >> ~/.bashrc

Save and exit

source ~/.bashrc

Test

Activate caffe environment

conda activate caffe-py27

Opeb python

python

Import caffe

>>>import caffe

If no error is reported, it means that the python interface of Caffe has been compiled correctly. Of course, it's usually not so lucky. More oftern there will be ImportError.

So using:

conda install cython scikit-image protobuf scikit-learn ipython pandas jupyter tqdm lxml pillow

to install all dependency libraries.

Finally, make a test:

python -c "import caffe;print caffe.__version__"

if output 1.0.0, congratulation!

ShowLo / caffe