py-rfcn算法caffe配置，训练及应用到自己的数据集

下载程序，

git clone https://github.com/Orpine/py-R-FCN.git

打开py-R-FCN,下载caffe

git clone https://github.com/Microsoft/caffe.git

编译Cython模块

cd lib

make

结果如下图所示：

编译caffe和pycaffe

cd caffe

cp Makefile.config.example MAkefile.config

然后配置Makefile.config文件，可参考我的Makefile.config

## Refer to http://caffe.berkeleyvision.org/installation.html# Contributions simplifying and improving our build system are welcome!# cuDNN acceleration switch (uncomment to build with cuDNN). USE_CUDNN := 1   # CPU-only switch (uncomment to build without GPU support).# CPU_ONLY := 1# uncomment to disable IO dependencies and corresponding data layers# USE_OPENCV := 0# USE_LEVELDB := 0# USE_LMDB := 0# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)#You should not set this flag if you will be reading LMDBs with any#possibility of simultaneous read and write# ALLOW_LMDB_NOLOCK := 1# Uncomment if you're using OpenCV 3# OPENCV_VERSION := 3# To customize your choice of compiler, uncomment and set the following.# N.B. the default for Linux is g++ and the default for OSX is clang++# CUSTOM_CXX := g++# CUDA directory contains bin/ and lib/ directories that we need.CUDA_DIR := /usr/local/cuda# On Ubuntu 14.04, if cuda tools are installed via# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:# CUDA_DIR := /usr# CUDA architecture setting: going with all of them.# For CUDA < 6.0, comment the *_50 lines for compatibility.CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \-gencode arch=compute_35,code=sm_35 \-gencode arch=compute_50,code=sm_50 \-gencode arch=compute_50,code=compute_50 \                -gencode arch=compute_53,code=compute_53 \                -gencode arch=compute_61,code=compute_61 # BLAS choice:# atlas for ATLAS (default)# mkl for MKL# open for OpenBlasBLAS := open# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.# Leave commented to accept the defaults for your choice of BLAS# (which should work)!# BLAS_INCLUDE := /path/to/your/blas# BLAS_LIB := /path/to/your/blas# Homebrew puts openblas in a directory that is not on the standard search path# BLAS_INCLUDE := $(shell brew --prefix openblas)/include# BLAS_LIB := $(shell brew --prefix openblas)/lib# This is required only if you will compile the matlab interface.# MATLAB directory should contain the mex binary in /bin. MATLAB_DIR := /usr/local/MATLAB/R2013b# MATLAB_DIR := /Applications/MATLAB_R2012b.app# 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/lib64/python2.7/site-packages/numpy/core/include \/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)/anaconda# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \# $(ANACONDA_HOME)/include/python2.7 \# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \# Uncomment to use Python 3 (default is Python 2)# PYTHON_LIBRARIES := boost_python3 python3.5m# PYTHON_INCLUDE := /usr/include/python3.5m \#                 /usr/lib/python3.5/dist-packages/numpy/core/include# We need to be able to find libpythonX.X.so or .dylib.PYTHON_LIB := /usr/lib# PYTHON_LIB := $(ANACONDA_HOME)/lib# Homebrew installs numpy in a non standard path (keg only)# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include# PYTHON_LIB += $(shell brew --prefix numpy)/lib# Uncomment to support layers written in Python (will link against Python libs)WITH_PYTHON_LAYER := 1# Whatever else you find you need goes here.INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/includeLIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies INCLUDE_DIRS += /usr/local/hdf5/include LIBRARY_DIRS += /usr/local/hdf5/lib# 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# N.B. both build and distribute dirs are cleared on `make clean`BUILD_DIR := buildDISTRIBUTE_DIR := distribute# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171# DEBUG := 1# The ID of the GPU that 'make runtest' will use to run unit tests.TEST_GPUID := 0# enable pretty build (comment to see full commands)Q ?= @

make -j8

结果如下图所示：

make pycaffe

结果如下图所示：

下载预训练模型（https://1drv.ms/u/s!AoN7vygOjLIQqUWHpY67oaC7mopf），放到data数据集下，如图所示（第二个是我自己训练的模型）：

运行演示脚本：

./tools/demo_rfcn.py

结果如下图所示：

下载训练，测试，验证数据集：

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

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar

解压到VOCdevkit文件夹中：

tar xvf VOCtrainval_06-Nov-2007.tar

tar xvf VOCtest_06-Nov-2007.tar

tar xvf VOCdevkit_08-Jun-2007.tar

tar xvf VOCtrainval_11-May-2012.tar

VOCdevkit文件夹的结构如下图所示：

由于py-faster-rcnn不支持多个训练集，我们创造一个新的文件夹叫做VOC0712，把VOC2007和VOC2012里的JPEGImage和Annonation融合到一个单独的文件夹JPEGImage和Annonation里，用下面的程序生成新的ImageSets文件夹：

%writetxt.mfile = dir('F:\VOC0712\Annotations\*.xml');len = length(file);num_trainval=sort(randperm(len, floor(9*len/10)));%trainval集占所有数据的9/10，可以根据需要设置num_train=sort(num_trainval(randperm(length(num_trainval), floor(5*length(num_trainval)/6))));%train集占trainval集的5/6，可以根据需要设置num_val=setdiff(num_trainval,num_train);%trainval集剩下的作为val集num_test=setdiff(1:len,num_trainval);%所有数据中剩下的作为test集path = 'F:\VOC0712\ImageSets\Main\';fid=fopen(strcat(path, 'trainval.txt'),'a+');for i=1:length(num_trainval)    s = sprintf('%s',file(num_trainval(i)).name);    fprintf(fid,[s(1:length(s)-4) '\n']);endfclose(fid);fid=fopen(strcat(path, 'train.txt'),'a+');for i=1:length(num_train)    s = sprintf('%s',file(num_train(i)).name);    fprintf(fid,[s(1:length(s)-4) '\n']);endfclose(fid);fid=fopen(strcat(path, 'val.txt'),'a+');for i=1:length(num_val)    s = sprintf('%s',file(num_val(i)).name);    fprintf(fid,[s(1:length(s)-4) '\n']);endfclose(fid);fid=fopen(strcat(path, 'test.txt'),'a+');for i=1:length(num_test)    s = sprintf('%s',file(num_test(i)).name);if ~isempty(strfind(s,'plain'))fprintf(fid,[s(1:length(s)-4) '\n']);endendfclose(fid);

为VOCdevkit创造新的超链接：VOCdevkit0712，如下图所示

下载在ImageNet上预训练好的模型，放到./data/imagenet_models里，如下图所示：

下面开始用VOC0712训练：

experiments/scripts/rfcn_end2end.sh 使用联合近似训练

experiments/scripts/rfcn_end2end_ohem.sh 使用联合近似训练+OHEM

experiments/scripts/rfcn_alt_opt_5stage_ohem.sh 使用分布训练+OHEM

./experiments/scripts/rfcn_end2end[_ohem].sh [GPU_ID] [NET] [DATASET] [--set ...]

下面开始用py-rfcn来训练自己的数据集:(我的数据集是标准pascal voc数据集，名字叫做VOC5000)

首先修改网络模型：

1.修改/py-R-FCN/models/pascal_voc/ResNet-50/rfcn_end2end/class-aware/train_ohem.prototxt

name: "ResNet-50"layer {  name: 'input-data'  type: 'Python'  top: 'data'  top: 'im_info'  top: 'gt_boxes'  python_param {    module: 'roi_data_layer.layer'    layer: 'RoIDataLayer'    param_str: "'num_classes': 2" #改为你的数据集的类别数+1  }}layer {  name: 'roi-data'  type: 'Python'  bottom: 'rpn_rois'  bottom: 'gt_boxes'  top: 'rois'  top: 'labels'  top: 'bbox_targets'  top: 'bbox_inside_weights'  top: 'bbox_outside_weights'  python_param {    module: 'rpn.proposal_target_layer'    layer: 'ProposalTargetLayer'    param_str: "'num_classes': 2"#改为你的数据集的类别数+1  }}layer {    bottom: "conv_new_1"    top: "rfcn_cls"    name: "rfcn_cls"    type: "Convolution"    convolution_param {        num_output: 98 #2*(7^2) cls_num*(score_maps_size^2)(类别数+1)*49        kernel_size: 1        pad: 0        weight_filler {            type: "gaussian"            std: 0.01        }        bias_filler {            type: "constant"            value: 0        }    }    param {        lr_mult: 1.0    }    param {        lr_mult: 2.0    }}layer {    bottom: "conv_new_1"    top: "rfcn_bbox"    name: "rfcn_bbox"    type: "Convolution"    convolution_param {        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)（类别数+1）*49*4        kernel_size: 1        pad: 0        weight_filler {            type: "gaussian"            std: 0.01        }        bias_filler {            type: "constant"            value: 0        }    }    param {        lr_mult: 1.0    }    param {        lr_mult: 2.0    }}layer {    bottom: "rfcn_cls"    bottom: "rois"    top: "psroipooled_cls_rois"    name: "psroipooled_cls_rois"    type: "PSROIPooling"    psroi_pooling_param {        spatial_scale: 0.0625        output_dim: 2  #类别数+1        group_size: 7    }}layer {    bottom: "rfcn_bbox"    bottom: "rois"    top: "psroipooled_loc_rois"    name: "psroipooled_loc_rois"    type: "PSROIPooling"    psroi_pooling_param {        spatial_scale: 0.0625        output_dim: 8#类别数*4        group_size: 7    }}

2.修改/py-R-FCN/models/pascal_voc/ResNet-50/rfcn_end2end/class-aware/test.prototxt

layer {    bottom: "conv_new_1"    top: "rfcn_cls"    name: "rfcn_cls"    type: "Convolution"    convolution_param {        num_output: 98 #21*(7^2) cls_num*(score_maps_size^2)(类别数+1)*2        kernel_size: 1        pad: 0        weight_filler {            type: "gaussian"            std: 0.01        }        bias_filler {            type: "constant"            value: 0        }    }    param {        lr_mult: 1.0    }    param {        lr_mult: 2.0    }}layer {    bottom: "conv_new_1"    top: "rfcn_bbox"    name: "rfcn_bbox"    type: "Convolution"    convolution_param {        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)（类别数+1）*49*4        kernel_size: 1        pad: 0        weight_filler {            type: "gaussian"            std: 0.01        }        bias_filler {            type: "constant"            value: 0        }    }    param {        lr_mult: 1.0    }    param {        lr_mult: 2.0    }}layer {    bottom: "rfcn_cls"    bottom: "rois"    top: "psroipooled_cls_rois"    name: "psroipooled_cls_rois"    type: "PSROIPooling"    psroi_pooling_param {        spatial_scale: 0.0625        output_dim: 2 #（类别数+1）        group_size: 7    }}layer {    bottom: "rfcn_bbox"    bottom: "rois"    top: "psroipooled_loc_rois"    name: "psroipooled_loc_rois"    type: "PSROIPooling"    psroi_pooling_param {        spatial_scale: 0.0625        output_dim: 8 #（类别数+1）*4        group_size: 7    }}layer {    name: "cls_prob_reshape"    type: "Reshape"    bottom: "cls_prob_pre"    top: "cls_prob"    reshape_param {        shape {            dim: -1            dim: 2 #（类别数+1）        }    }}layer {    name: "bbox_pred_reshape"    type: "Reshape"    bottom: "bbox_pred_pre"    top: "bbox_pred"    reshape_param {        shape {            dim: -1            dim: 8 #（类别数+1）*4        }    }}

3.修改/py-R-FCN/lib/datasets/pascal_voc.py

class pascal_voc(imdb):    def __init__(self, image_set, year, devkit_path=None):        imdb.__init__(self, 'voc_' + year + '_' + image_set)        self._year = year        self._image_set = image_set        self._devkit_path = self._get_default_path() if devkit_path is None \                            else devkit_path        self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year)        self._classes = ('__background__', # always index 0                         'aeroplane')        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))        self._image_ext = '.jpg'修改self._classes为你的类别加背景。

4./py-R-FCN/lib/datasets/factory.py修改

for year in ['2007', '2012','2001','2002','2006','5000']:    for split in ['train', 'val', 'trainval', 'test']:        name = 'voc_{}_{}'.format(year, split)        __sets[name] = (lambda split=split, year=year: pascal_voc(split, year))我的数据集叫：VOC5000，所以把5000加到年份当中。

5/py-R-FCN/experiments/scripts/rfcn_end2end_ohem.sh修改

case $DATASET in  pascal_voc)    TRAIN_IMDB="voc_5000_trainval"    TEST_IMDB="voc_5000_test"    PT_DIR="pascal_voc"    ITERS=4000    ;;把训练数据集和测试数据集改为你的数据集，迭代次数改为4000。

开始训练：./experiments/scripts/rfcn_end2end_ohem.sh 0 ResNet-50 pascal_voc

迭代4000次，取得了81.2%的精度。