使用ImageNet在faster-rcnn上训练自己的分类网络

具体代码见https://github.com/zhiyishou/py-faster-rcnn

这是我对cup, glasses训练的识别

faster-rcnn在fast-rcnn的基础上加了rpn来将整个训练都置于GPU内，以用来提高效率，这里我们将使用ImageNet的数据集来在faster-rcnn上来训练自己的分类器。从ImageNet上可下载到很多类别的Image与bounding box annotation来进行训练（每一个类别下的annotation都少于等于image的个数，所以我们从annotation来建立索引）。

在lib/dataset/factory.py中提供了coco与voc的数据集获取方法，而我们要做的就是在这里加上我们自己的ImageNet获取方法，我们先来建立ImageNet数据获取主文件。coco与pascal_voc的获取都是继承于父类imdb，所以我们可根据pascal_voc的获取方法来做模板修改完成我们的ImageNet类。

创建ImageNet类

由于在faster-rcnn里使用rpn来代替了selective_search，所以我们可以在使用时直接略过有关selective_search的方法，根据pascal_voc类做模板，我们需要留下的方法有：

__init__ //初始化image_path_at //根据数据集列表的index来取图片绝对地址image_path_from_index //配合上面_load_image_set_index //获取数据集列表_gt_roidb //获取ground-truth数据rpn_roidb //获取region proposal数据_load_rpn_roidb //根据gt_roidb生成rpn_roidb数据并合成_load_psacal_annotation //加载annotation文件并对bounding box进行数据整理

__init__:

def __init__(self, image_set):        imdb.__init__(self, 'imagenet')        self._image_set = image_set        self._data_path = os.path.join(cfg.DATA_DIR, "imagenet")        #类别与对应的wnid，可以修改成自己要训练的类别        self._class_wnids = {            'cup': 'n03147509',            'glasses': 'n04272054'        }        #类别，修改类别时同时要修改这里        self._classes = ('__background__', self._class_wnids['cup'], self._class_wnids['glasses'])        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))        #bounding box annotation 文件的目录        self._xml_path = os.path.join(self._data_path, "Annotations")        self._image_ext = '.JPEG'        #我们使用xml文件名来做数据集的索引        # the xml file name and each one corresponding to image file name        self._image_index = self._load_xml_filenames()        self._salt = str(uuid.uuid4())        self._comp_id = 'comp4'        self.config = {'cleanup'     : True,                       'use_salt'    : True,                       'use_diff'    : False,                       'matlab_eval' : False,                       'rpn_file'    : None,                       'min_size'    : 2}        assert os.path.exists(self._data_path), \                'Path does not exist: {}'.format(self._data_path)

image_path_at

def image_path_at(self, i):        #使用index来从xml_filenames取到filename，生成绝对路径        return self.image_path_from_image_filename(self._image_index[i])

image_path_from_image_filename(类似pascal_voc中的image_path_from_index)

def image_path_from_image_filename(self, image_filename):        image_path = os.path.join(self._data_path, 'Images',                                  image_filename + self._image_ext)        assert os.path.exists(image_path), \                'Path does not exist: {}'.format(image_path)        return image_path

_load_xml_filenames(类似pascal_voc中的_load_image_set_index)

def _load_xml_filenames(self):        #从Annotations文件夹中拿取到bounding box annotation文件名        #用来做数据集的索引        xml_folder_path = os.path.join(self._data_path, "Annotations")        assert os.path.exists(xml_folder_path), \            'Path does not exist: {}'.format(xml_folder_path)        for dirpath, dirnames, filenames in os.walk(xml_folder_path):                xml_filenames = [xml_filename.split(".")[0] for xml_filename in filenames]        return xml_filenames

gt_roidb

def gt_roidb(self):        #Ground-Truth 数据缓存        cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')        if os.path.exists(cache_file):            with open(cache_file, 'rb') as fid:                roidb = cPickle.load(fid)            print '{} gt roidb loaded from {}'.format(self.name, cache_file)            return roidb        #从xml中获取Ground-Truth数据        gt_roidb = [self._load_imagenet_annotation(xml_filename)                    for xml_filename in self._image_index]        with open(cache_file, 'wb') as fid:            cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)        print 'wrote gt roidb to {}'.format(cache_file)        return gt_roidb

rpn_roidb

def rpn_roidb(self):        #根据gt_roidb生成rpn_roidb，并进行合并                   gt_roidb = self.gt_roidb()        rpn_roidb = self._load_rpn_roidb(gt_roidb)        roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)        return roidb

_load_rpn_roidb

def _load_rpn_roidb(self, gt_roidb):        filename = self.config['rpn_file']        print 'loading {}'.format(filename)        assert os.path.exists(filename), \               'rpn data not found at: {}'.format(filename)        with open(filename, 'rb') as f:            box_list = cPickle.load(f)        return self.create_roidb_from_box_list(box_list, gt_roidb)

_load_imagenet_annotation(类似于pascal_voc中的_load_pascal_annotation)

def _load_imagenet_annotation(self, xml_filename):        #从annotation的xml文件中拿取bounding box数据        filepath = os.path.join(self._data_path, 'Annotations', xml_filename + '.xml')        #这里使用了ap，是我写的一个annotation parser，在后面贴出代码        #它会返回这个xml文件的wnid, 图像文件名，以及里面包含的注解物体        wnid, image_name, objects = ap.parse(filepath)        num_objs = len(objects)        boxes = np.zeros((num_objs, 4), dtype=np.uint16)        gt_classes = np.zeros((num_objs), dtype=np.int32)        overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)        seg_areas = np.zeros((num_objs), dtype=np.float32)        # Load object bounding boxes into a data frame.        for ix, obj in enumerate(objects):            box = obj["box"]            x1 = box['xmin']            y1 = box['ymin']            x2 = box['xmax']            y2 = box['ymax']            # 如果这个bounding box并不是我们想要学习的类别，那则跳过            # go next if the wnid not exist in declared classes            try:                cls = self._class_to_ind[obj["wnid"]]            except KeyError:                print "wnid %s isn't show in given"%obj["wnid"]                continue            boxes[ix, :] = [x1, y1, x2, y2]            gt_classes[ix] = cls            overlaps[ix, cls] = 1.0            seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)        overlaps = scipy.sparse.csr_matrix(overlaps)        return {'boxes' : boxes,                'gt_classes': gt_classes,                'gt_overlaps' : overlaps,                'flipped' : False,                'seg_areas' : seg_areas}

annotation_parser.py文件

import osimport xml.dom.minidomdef getText(node):    return node.firstChild.nodeValuedef getWnid(node):    return getText(node.getElementsByTagName("name")[0])def getImageName(node):    return getText(node.getElementsByTagName("filename")[0])def getObjects(node):    objects = []    for obj in node.getElementsByTagName("object"):        objects.append({            "wnid": getText(obj.getElementsByTagName("name")[0]),            "box":{                "xmin": int(getText(obj.getElementsByTagName("xmin")[0])),                "ymin": int(getText(obj.getElementsByTagName("ymin")[0])),                "xmax": int(getText(obj.getElementsByTagName("xmax")[0])),                "ymax": int(getText(obj.getElementsByTagName("ymax")[0])),            }        })    return objectsdef parse(filepath):    dom = xml.dom.minidom.parse(filepath)    root = dom.documentElement    image_name = getImageName(root)    wnid = getWnid(root)    objects = getObjects(root)        return wnid, image_name, objects

则对数据结构的要求是：

|---data  |---imagenet    |---Annotations       |---n03147509          |---n03147509_*.xml          |---...       |---n04272054          |---n04272054_*.xml          |---...    |---Images       |---n03147508_*.JPEG       |---...       |---n04272054_*.JPEG       |---...

同时我在github上也提供了draw方法，可以用来将bounding box画于Image文件上，用来甄别该annotation的正确性

训练

这样，我们的ImageNet类则是生成好了，下面我们则可以训练我们的数据，但是在开始之前，还有一件事情，那就是修改prototxt中的与类别数目有关的值，我将models/pascal_voc拷贝到了models/imagenet进行修改，比如我想要训练ZF，如果使用的是train_faster_rcnn_alt_opt.py，则需要修改models/imagenet/ZF/faster_rcnn_alt_opt/下的所有pt文件里的内容，用如下的法则去替换：

//num为类别的个数input-data->num_classes = numclass_score->num_output = numbbox_pred->num_output   = num*4

我这里使用train_faster_rcnn_alt_opt.py进行的训练，这样的话则需要把添加的models/imagenet作为可选项

//pt_type 则是添加的选择项，默认使用psacal_voc的models./tools/train_faster_rcnn_alt_opt.py --gpu 0 \--net_name ZF \--weights data/imagenet_models/ZF.v2.caffemodel[optional] \--imdb imagenet \--cfg experiments/cfgs/faster_rcnn_alt_opt.yml \--pt_type imagenet

识别

这里我们则需要使用刚训练出来的模型进行识别

#就像demo.py一样，但是使用训练的models，我创建了tools/classify.py来单独识别prototxt = os.path.join(cfg.ROOT_DIR, 'models/imagenet', NETS[args.demo_net][0], 'faster_rcnn_alt_opt', 'faster_rcnn_test.pt')caffemodel = os.path.join(cfg.ROOT_DIR, 'output/faster_rcnn_alt_opt/imagenet/'+ NETS[args.demo_net][0] +'_faster_rcnn_final.caffemodel')

同样，在识别前我们要对识别方法里的Classes进行修改，修改成你自己训练的类别后

执行

./tools/classify.py --net zf

则可对data/demo下的图片文件使用训练的zf网络进行识别

Have fun