caffe fast-rcnn GPU編譯流程

caffe fast-rcnn GPU編譯流程(含python layer)

2015/12/21 | 標籤： 無

嗨, 大家好今天要來分享的是 fast-rcnn GPU編譯流程

1. 前期準備工作, 安裝相依程式
#下列為C++ 快速安裝方式, 若想要安裝最新版本請至各官網下載自行編譯
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 libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

#下列為python 快速安裝方式
sudo apt-get -y install python-pip
cd ./caffe-fast-rcnn/python
for req in $(cat requirements.txt); do sudo pip install$req; done
#install easydict
wget https://pypi.python.org/packages/source/e/easydict/easydict-1.6.zip
unzip easydict-1.6.zip
cd easydict-1.6
python setup.py install

#下列為安裝CUDA 流程
#先到官網下載安裝檔
https://developer.nvidia.com/cuda-downloads
#Ctrl + Alt + F1
sudo lightdm stop
sudo dpkg -i cuda-repo-ubuntu1404_7.5-18_amd64.deb
sudo apt-get update
sudo apt-get install cuda
#安裝驅動期間必須要重新開機才能繼續

#安裝cuDNN
tar -zxf cudnn-7.0-linux-x64-v3.0-prod.tgz
cd cuda
sudo cp lib64/* /usr/local/cuda/lib64/
sudo cp include/cudnn.h /usr/local/cuda/include/

#build caffe
cp Makefile.config.example Makefile.config
vi Makefile.config

#1.Uncomment the line: USE_CUDNN := 1 and WITH_PYTHON_LAYER := 1
#2.Make sure the CUDA_DIR correctly points to our CUDA installation.

make pycaffe -j8
make all -j8
make test -j8

cd ./caffe-fast-rcnn/lib
rm ./nms/*.so
make

#恭喜完成