利用py-faster-rcnn训练目标检测模型

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参考：https://www.cnblogs.com/dudumiaomiao/p/6556111.html

http://m.blog.csdn.net/hongbin_xu/article/details/77278329

http://www.cnblogs.com/dudumiaomiao/p/6556111.html

http://bealin.github.io/2016/10/23/Caffe%E5%AD%A6%E4%B9%A0%E7%B3%BB%E5%88%97%E2%80%94%E2%80%946%E4%BD%BF%E7%94%A8Faster-RCNN%E8%BF%9B%E8%A1%8C%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B/

https://www.zhihu.com/question/57091642/answer/165134753

http://blog.csdn.net/qq_14975217/article/details/51523907

http://www.cnblogs.com/louyihang-loves-baiyan/p/4903231.html

http://blog.csdn.net/qq_36219202/article/details/72896203

1. 首先下载pascal voc2007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

2. 解压

tar -xvf VOCdevkit_08-Jun-2007.tar

tar -xvf VOCtest_06-Nov-2007.tar

3.将VOCdevkit文件夹放到py-faster-rcnn/data中

4. 为VOCdevkit创建软连接

ln -s VOCdevkit VOCdevkit2007

5. 准备训练模型的数据

JPEGImages：存放图像数据，jpg/jpeg格式；图片名称6位数，例如000001；图片长宽比0.462-6.828，瘦长的图片剔除；

Annotations ：存放目标位置及框大小的xml 文件

制作xml文件的工具https://github.com/tzutalin/labelImg

ImageSets/Main ：存放train.txt, trainval.txt, val.txt, test.txt文件，

train.txt : 训练数据集，trainval的70%

trainval.txt : 训练验证集，整个数据量的70%

val.txt :验证集，trainval的30%

test.txt :测试数据集，整个数据量的30%

生成txt文件的sh代码段：

#/usr/bin/env shDATATRAIN=VOC2007/DATATEST=VOC2007/MY=VOC2007echo "create train.txt"rm -f $MY/trainval.txtfor i in 0 dofind $DATATRAIN/JPEGImages -name $i*.jpg | cut -d '/' -f4 | cut -d '.' -f1 | sed "s/$//">>$MY/trainval.txtdoneecho "all done"

生成的txt，如图

后面没有写类别号，（1代表正样本，-1代表负样本。如果是0，应该代表较难分辨出来的样本！）

（https://www.cnblogs.com/dudumiaomiao/p/6556111.html中指出：

VOC的数据集可以做很多的CV任务，比如Object detection, Semantic segementation, Edge detection等，所以Imageset 下有几个子文件夹(Layout, Main, Segementation)，修改下Main下的文件 (train.txt, trainval.txt, val.txt, test.txt)，里面写上你想要进行任务的图片的编号

将上述你的数据集放在py-faster-rcnn/data/VOCdevkit2007/VOC2007下面，替换原始VOC2007/

JPEGIMages,Imagesets,Annotations）

6. 修改相应代码（使用faster_rcnn_alt_opt.sh训练方法及ZF小型网络，若使用faster_rcnn_end2end.sh训练方法，需要修改faster_rcnn_end2end文件中的是三个文件）

a： py-faster-rcnn/models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_fast_rcnn_train.pt

name: "ZF"layer {  name: 'data'  type: 'Python'  top: 'data'  top: 'rois'  top: 'labels'  top: 'bbox_targets'  top: 'bbox_inside_weights'  top: 'bbox_outside_weights'  python_param {    module: 'roi_data_layer.layer'    layer: 'RoIDataLayer'    param_str: "'num_classes': 21"          //修改训练类别，类别数+1,1表示背景  }}

layer {  name: "cls_score"  type: "InnerProduct"  bottom: "fc7"  top: "cls_score"  param { lr_mult: 1.0 }  param { lr_mult: 2.0 }  inner_product_param {    num_output: 2      //修改训练类别，类别数+1,1表示背景    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0    }  }}

layer {  name: "bbox_pred"  type: "InnerProduct"  bottom: "fc7"  top: "bbox_pred"  param { lr_mult: 1.0 }  param { lr_mult: 2.0 }  inner_product_param {    num_output: 84    //（训练的类别数+1）*4    weight_filler {      type: "gaussian"      std: 0.001    }    bias_filler {      type: "constant"      value: 0    }  }}

b：修改py-faster-rcnn/models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_rpn_train.pt

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': 21"   //按训练类别数改，类别数+1  }}

c：修改py-faster-rcnn/models/pascal_voc/ZF/faster_rcnn_alt_opt/stage2_fast_rcnn_train.pt

修改层及方式与stage1_fast_rcnn_train.pt相同

d：修改py-faster-rcnn/models/pascal_voc/ZF/faster_rcnn_alt_opt/stage2_rpn_train.pt

修改层及方式与stage1_rpn_train.pt相同

e：修改py-faster-rcnn/models/pascal_voc/ZF/faster_rcnn_alt_opt/faster_rcnn_test.pt

layer {  name: "cls_score"  type: "InnerProduct"  bottom: "fc7"  top: "cls_score"  inner_product_param {    num_output: 21 //修改位类别数+1  }}layer {  name: "bbox_pred"  type: "InnerProduct"  bottom: "fc7"  top: "bbox_pred"  inner_product_param {    num_output: 84    //修改位（类别数+1）*4  }}

f：修改py-faster-rcnn/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                         '自己的类别', '自己的类别'')    //修改，写上自己的类别

 def _load_pascal_annotation(self, index):                    //若自己的XML与原VOC2007中的格式不同，根据自己的格式修改此段        """        Load image and bounding boxes info from XML file in the PASCAL VOC        format.        """        filename = os.path.join(self._data_path, 'Annotations', index + '.xml')        tree = ET.parse(filename)        objs = tree.findall('object')        if not self.config['use_diff']:            # Exclude the samples labeled as difficult            non_diff_objs = [                  obj for obj in objs if int(obj.find('difficult').text) == 0]               //例如我的需要修改为non_diff_objs = [obj for obj in objs if obj.find('name').text == 'bocode']            # if len(non_diff_objs) != len(objs):            #     print 'Removed {} difficult objects'.format(            #         len(objs) - len(non_diff_objs))            objs = non_diff_objs        num_objs = len(objs)

（此步骤可以省去） e：修改py-faster-rcnn/lib/datasets/voc_eval.py

根据xml修改相应语句

我的xml如图：

相应的修改位：

def parse_rec(filename):    """ Parse a PASCAL VOC xml file """    tree = ET.parse(filename)    objects = []    for obj in tree.findall('object'):        obj_struct = {}        obj_struct['name'] = obj.find('name').text       # obj_struct['pose'] = obj.find('pose').text        //注释掉了       #  obj_struct['truncated'] = int(obj.find('truncated').text)   //注释掉了       # obj_struct['difficult'] = int(obj.find('difficult').text)     //注释掉了        bbox = obj.find('bndbox')        obj_struct['bbox'] = [int(bbox.find('xmin').text),                              int(bbox.find('ymin').text),                              int(bbox.find('xmax').text),                              int(bbox.find('ymax').text)]        objects.append(obj_struct)

  for imagename in imagenames:        R = [obj for obj in recs[imagename] if obj['name'] == classname]        bbox = np.array([x['bbox'] for x in R])       # difficult = np.array([x['difficult'] for x in R]).astype(np.bool)    //注释掉了        difficult = 0;                               //增加了此句        det = [False] * len(R)        #npos = npos + sum(~difficult)          class_recs[imagename] = {'bbox': bbox,                                 'difficult': difficult,                                 'det': det}

        if ovmax > ovthresh:            #if not R['difficult'][jmax]:    //注释掉了                #if not R['det'][jmax]:     //注释掉了                   # tp[d] = 1.                //注释掉了                   # R['det'][jmax] = 1       //注释掉了               # else:         //注释掉了                  #  fp[d] = 1.              //注释掉了            fp[d] = 1.               //增加了此句        else:            fp[d] = 1.

不太清楚整个代码的意思。

g：修改py-faster-rcnn/lib/datasets/imdb.py

修改如下：

def append_flipped_images(self):        num_images = self.num_images        widths = self._get_widths()        for i in xrange(num_images):            boxes = self.roidb[i]['boxes'].copy()            oldx1 = boxes[:, 0].copy()            oldx2 = boxes[:, 2].copy()            boxes[:, 0] = widths[i] - oldx2 - 1            boxes[:, 2] = widths[i] - oldx1 - 1            for b in range(len(boxes)):   //添加此      if boxes[b][2]< boxes[b][0]:    //添加此          boxes[b][0] = 0    ///添加此            assert (boxes[:, 2] >= boxes[:, 0]).all()            entry = {'boxes' : boxes,                     'gt_overlaps' : self.roidb[i]['gt_overlaps'],                     'gt_classes' : self.roidb[i]['gt_classes'],                     'flipped' : True}            self.roidb.append(entry)        self._image_index = self._image_index * 2

7. 配置参数及迭代次数在solve及train_faster_rcnn_alt_opt.py中修改

修改 py-faster-rcnn/tools/train_faster_rcnn_alt_opt.py

                           # Solver for each training stage
   solvers = [[net_name, n, 'stage1_rpn_solver60k80k.pt'],
                 [net_name, n, 'stage1_fast_rcnn_solver30k40k.pt'],
                 [net_name, n, 'stage2_rpn_solver60k80k.pt'],
                 [net_name, n, 'stage2_fast_rcnn_solver30k40k.pt']]
      solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers]
   # Iterations for each training stage
             max_iters = [80000, 40000, 80000, 40000]    //按需要修改次数

分别对应rpn1，fast1，rpn2，fast2；

若max_iters修改后，'stage1_rpn_solver60k80k.pt，stage1_fast_rcnn_solver30k40k.pt，stage2_rpn_solver60k80k.pt，stage2_fast_rcnn_solver30k40k.pt'中的stepsize进行相应修改。 stepsize小于上面修改的数值，stepsize的意义是经过stepsize次的迭代后降低一次学习率（非必要修改），以及faster_rcnn_alt_opt.sh中（根据实际情况修改）

若使用faster_rcnn_end2end，修改迭代次数在faster_rcnn_end2end.sh（根据实际情况修改）及solver中修改

8. 开始训练

开始训练时，若存在py-faster-rcnn/output文件夹，注意保存里面的模型（未进行过训练无次文件夹）

cd py-faster-rcnn

sudo ./experiments/scripts/faster_rcnn_alt_opt.sh 0 ZF pascal_voc

9. 训练结果

训练结果保存在py-faster-rcnn/output中，名字一般为ZF_faster_rcnn_final.caffemodel

训练日志保存在experiments/logs

10. 问题

a: 训练结束后出现

原因：删掉上一次训练遗留的cache，主要是需要修改py-faster-rcnn/lib/datasets/voc_eval.py中的语句，根据xml修改

11. 测试模型

http://blog.csdn.net/zhy8623080/article/details/73188580

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