PVA Faster RCNN配置安装及使用

论文：PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
简介
PVANET是目前目标检测算法比较好的实现，也是Region Proposal系列方法的一个新实现，目前达到了real-time的单张图像40ms，且在VOC12上达到了精度83.8%，是region proposal目前精度最高的，也突破了region proposal系列方法RCNN、Fast RCNN、Faster RCNN的精度高不能实时的问题。目前回归类方法最新的SSD以及YOLO9000虽然速度很快，但是精度比其差了不少。所以PVANET还是值得实现的。
论文地址：https://www.arxiv.org/pdf/1608.08021v3.pdf
github项目:https://github.com/sanghoon/pva-faster-rcnn
实现环境
操作系统：Ubuntu 16.04LTS
显卡：NVIDIA GTX 1080TI
CUDA8.0
Cudnn V5.1
其它配置忽略（不重要）

Caffe General Dependencies安装

sudo apt-get update  

sudo apt-get upgrade 

sudo apt-get install -y build-essential cmake git pkg-config

sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler

sudo apt-get install -y libatlas-base-dev 

sudo apt-get install -y --no-install-recommends libboost-all-dev

sudo apt-get install -y libgflags-dev libgoogle-glog-dev liblmdb-dev

(Python general)

sudo apt-get install -y python-pip

\#(Python 2.7 development files)

sudo apt-get install -y python-dev

sudo apt-get install -y python-numpy python-scipy

OpenCV安装
此处根据git master branch安装的3.2.0-dev版本

\#Build tools:

sudo apt-get install build-essential

sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev

sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

cd ~
git clone https://github.com/opencv/opencv.git
cd opencv
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE \
  -D CMAKE_INSTALL_PREFIX=/usr/local \
  -D WITH_CUDA=ON \
  -D WITH_CUBLAS=1 \
  -D INSTALL_PYTHON_EXAMPLES=ON \
  -D BUILD_EXAMPLES=ON ..

make all -j16（此处根据你自己的计算机性能进行安装）
sudo make -j16 install

sudo ldconfig

检查安装

$ python

\>>>import cv2

\>>>cv2.__version__

输出：'3.2.0-dev'

有关CUDA 8.0+Cudnn5.1的安装请自行百度 pva-faster-rcnn的搭建 1.获取项目

git clone --recursive https://github.com/sanghoon/pva-faster-rcnn.git

2.编译建立Cython模块 安装python依赖

  
sudo pip install Cython  

sudo pip install easydict

cd pva-faster-rcnn/lib

此处需要修改lib下的setup.py第135行 GPU 计算能力查看地址'nvcc': ['-arch=sm_35', https://developer.nvidia.com/cuda-gpus  比如我的是1080TI，计算能力为6.1，则修改为

'nvcc': ['-arch=sm_61',

执行

make all -j16

进行编译 3.编译建立Caffe和pycaffe

cd pva-faster-rcnn/caffe-fast-rcnn

cp Makefile.config.example Makefile.config

gedit Makefile.config

修改Makefile.config文件: 需要修改的行如下

USE_CUDNN := 1

OPENCV_VERSION := 3

#CUDA directory contains bin/ and lib/ directories that we need.

CUDA_DIR := /usr/local/cuda

CUDA_DIR := /usr/local/cuda-8.0

# 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/hdf5/serial/

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/hdf5/serial/

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

#Uncomment to use `pkg-config` to specify OpenCV library paths.

# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)

USE_PKG_CONFIG := 1

Makefile修改

cd ~/pva-faster-rcnn/caffe-faster-rcnn

gedit Makefile

需要注意的是暂时不要采用最新的cudnn v6版本，我使用的时候发觉会出错，v5.1版本修改此配置。 原有

NVCCFLAGS += -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)

修改为

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

更新caffe-fast-rcnn的caffe部分，因为此项目caffe不是最新涉及到cudnn的计算会报错。

# Make sure to clone with --recursive
cd ~/pva-faster-rcnn/caffe-fast-rcnn
git remote add -f caffe https://github.com/BVLC/caffe.git

git merge -X theirs caffe/master

gedit include/caffe/layers/python_layer.hpp

\# Remove

self_.attr("phase") = static_cast(this->phase_);

cd ~/pva-faster-rcnn/caffe-faster-rcnn/python

for req in $(cat requirements.txt); do sudo -H pip install $req --upgrade; done

编译安装

mkdir build
cd build
cmake ..
make -j16 all
make -j16 pycaffe
make -j16 install 

Caffe路径设置

gedit ~/.bashrc
export CAFFE_ROOT=~/py-faster-rcnn/caffe-fast-rcnn
export PYTHONPATH=~/py-faster-rcnn/caffe-fast-rcnn/python:$PYTHONPATH
source ~/.bashrc

检查安装

$ python
>>> import caffe
>>> caffe.__version__

sudo -H pip install easydict
sudo apt-get install python-gi-cairo python-tk

4.下载预训练模型
如果有VPN网速好的话直接采用pva-faster-rcnn/models下的的shell脚本进行下载
此处采用百度网盘下载
链接：http://pan.baidu.com/s/1kVRRPDd 密码：1cdt
1、打开文件将test.model放入$pva-faster-rcnn/models/pvanet/full/这个目录下
2、将test(1).model重命名为test.model放入$pva-faster-rcnn/models/pvanet/comp/目录下
5.voc2007数据集下载
打开终端（任何目录）输入：

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar

解压（严格按照此顺序）：

tar xvf VOCtrainval_06-Nov-2007.tar
tar xvf VOCtest_06-Nov-2007.tar
tar xvf VOCdevkit_08-Jun-2007.tar

将生成的VOCdevkit文件夹更名为VOCdevkit2007移动至$pva-faster-rcnn/data/目录下面
6.测试
需要注意的是缺少test.pt，请将pvanet_obsolete下的对应文件复制过来即可
终端输入：

cd $pva-faster-rcnn

1、full/test.model测试：

./tools/test_net.py --gpu 0 --def models/pvanet/full/test.pt --net models/pvanet/full/test.model --cfg models/pvanet/cfgs/submit_0716.yml

2、Comp/test.model测试：

./tools/test_net.py --gpu 0 --def models/pvanet/comp/test.pt --net models/pvanet/comp/test.model --cfg models/pvanet/cfgs/submit_0716.yml

此测试会得到系列类别的AP值
3.模型的可视化检测demo
此处需要重新编写demo.py文件
可在我github下fork的分支获取该文件demo.py文件
https://github.com/YgRen/pva-faster-rcnn

cd pva-faster-rcnn

执行

./tools/demo.py --gpu 0 --def models/pvanet/comp/test.pt --net models/pvanet/comp/test.model

生成可视化结果

记录：关于自定义数据集的训练和调参后期会进行发布，关于VOC07和12数据集的训练，直接参照github项目地址操作即可，最近会更新论文精读。