一、拉取源码
下载 fast-rcnn
因下载解压后 caffe-fast-rcnn是空文件夹，故需要单独下
caffe-fast-rcnn-bcd9b4eadc7d8fbc433aeefd564e82ec63aaf69c.zip
unzip caffe-fast-rcnn-bcd9b4eadc7d8fbc433aeefd564e82ec63aaf69c.zip
cp ./caffe-fast-rcnn-bcd9b4eadc7d8fbc433aeefd564e82ec63aaf69c/ /home/cmwang/fast-rcnn-master/caffe-fast-rcnn/二、copy缺失文件夹
因编译中出现缺失layer的问题，故需要copy缺失文件夹layers（caffe-fast-rcnn/include/caffe/ 无 layers文件夹）
可直接从caffe-master中copy或者从
cp /home/cmwang/caffe-master/include/caffe/layers/ /home/cmwang/fast-rcnn-master/caffe-fast-rcnn/include/caffe/layers/或者
cp /home/cmwang/py-faster-rcnn/caffe-fast-rcnn/include/caffe/layers/ /home/cmwang/fast-rcnn-master/caffe-fast-rcnn/include/caffe/layers/三、修改Makefile文件
终端输入
cd /home/cmwang/fast-rcnn-master/caffe-fast-rcnn/
cp Makefile.config.example Makefile.config #备份Makefile
gedit Makefile.config使用Python层
将# WITH_PYTHON_LAYER := 1修改为 WITH_PYTHON_LAYER := 1
调用matlab
将#MATLAB_DIR := / usr/local/MATLAB/R2015b 中的#去掉。
使用cudnn加速
将# USE_CUDNN := 1修改为USE_CUDNN := 1
保留# CPU_ONLY := 1不变，使用GPU运行faster r-cnn
四、编译Cython模块
终端输入
cd ~/caffe-fast-rcnn/lib/
make五、编译caffe和pycaffe & matcaffe
终端输入
cd ~/caffe-fast-rcnn/caffe-fast-rcnn/
make -j8 && make pycaffe && make matcaffe六、下载模型
终端输入
cd ~/caffe-fast-rcnn/./data/scripts/fetch_faste_rcnn_models.sh 七、Demo测试
终端输入
cd ~/caffe-fast-rcnn/./tools/demo.py出现的问题1
caffe —找不到lhdf5_hl和lhdf5的错误
解决方法：
cd
gedit Makefile.config将
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib修改为
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这是因为ubuntu16.04的文件包含位置发生了变化，尤其是需要用到的hdf5的位置，所以需要更改这一路径
建立软连接
cd /usr/lib/x86_64-linux-gnu
sudo ln -s libhdf5_serial.so.8 libhdf5.so
sudo ln -s libhdf5_serial_hl.so.8.0.2 libhdf5_hl.so出现的问题2
When use fast R-CNN, got error like this:
I0310 08:26:43.672577 144950 net.cpp:340] Input 0 -> data
I0310 08:26:43.672601 144950 net.cpp:340] Input 1 -> rois
I0310 08:26:43.672621 144950 layer_factory.hpp:74] Creating layer conv1
I0310 08:26:43.672639 144950 net.cpp:84] Creating Layer conv1
I0310 08:26:43.672650 144950 net.cpp:380] conv1 <- data
I0310 08:26:43.672664 144950 net.cpp:338] conv1 -> conv1
I0310 08:26:43.672680 144950 net.cpp:113] Setting up conv1
Floating point exception(core dumped).
解决方案
gedit lib/fast_rcnn/train.py添加 filter_roidb 范围，示范如下
def filter_roidb(roidb):"""Remove roidb entries that have no usable RoIs."""def is_valid(entry):# Valid images have:# (1) At least one foreground RoI OR# (2) At least one background RoIoverlaps = entry['max_overlaps']# find boxes with sufficient overlapfg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &(overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]# image is only valid if such boxes existvalid = len(fg_inds) > 0 or len(bg_inds) > 0return validnum = len(roidb)filtered_roidb = [entry for entry in roidb if is_valid(entry)]num_after = len(filtered_roidb)print 'Filtered {} roidb entries: {} -> {}'.format(num - num_after,num, num_after)return filtered_roidbIt’s like something about box size. the solution is add filter_roidb function in lib/fast_rcnn/train.py file, like here.
Reference: https://github.com/rbgirshick/py-faster-rcnn/issues/159
其他的相关问题可参考
Faster R-CNN的安装及测试中常出现问题部分。
参考文献
caffe —找不到lhdf5_hl和lhdf5的错误
fast-rcnn github
fast-rcnn caffe-fast-rcnn
安装和运行Fast R-CNN的demo
caffe compilation troubleshooting
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