python类imdb()的实例源码

def __init__(self, image_set, year, devkit_path=None):
        datasets.imdb.__init__(self, 'coco_' + year + '_' + image_set)
        # COCO specific config options
        self.config = {'top_k' : 2000,
                       'use_salt' : True,
                       'cleanup' : True,
                       'crowd_thresh' : 0.7,
                       'min_size' : 2}
        # name, paths
        self._year = year
        self._image_set = image_set
        self._data_path = self._get_default_path() if devkit_path is None \
                            else devkit_path
        # load COCO API, classes, class <-> id mappings
        self._COCO = COCO(self._get_ann_file())
        cats = self._COCO.loadCats(self._COCO.getCatIds())
        self._classes = tuple(['__background__'] + [c['name'] for c in cats])
        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
        self._class_to_coco_cat_id = dict(zip([c['name'] for c in cats],
                                              self._COCO.getCatIds()))
        self._image_index = self._load_image_set_index()
        # Default to roidb handler
        self._roidb_handler = self.gt_roidb

        self.competition_mode(False)

        self._data_name = image_set + year  # e.g., "val2014"

        if self._data_name == 'test-dev2015':
            self._data_name_path = 'test2015'
        else:
            self._data_name_path = self._data_name

def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Train RON network')
    parser.add_argument('--gpu', dest='gpu_id',
                        help='GPU device id to use [0]',
                        default=0, type=int)
    parser.add_argument('--solver', dest='solver',
                        help='solver prototxt',
                        default=None, type=str)
    parser.add_argument('--iters', dest='max_iters',
                        help='number of iterations to train',
                        default=300000, type=int)
    parser.add_argument('--weights', dest='model',
                        help='initialize with pretrained model weights',
                        default=None, type=str)
    parser.add_argument('--cfg', dest='cfg_file',
                        help='optional config file',
                        default=None, type=str)
    parser.add_argument('--imdb', dest='imdb_name',
                        help='dataset to train on',
                        default='voc_2007_trainval', type=str)
    parser.add_argument('--batchsize', dest='batchsize',
                        help='batch size used to train',
                        default=20, type=int)

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)

    args = parser.parse_args()
    return args

def region_proposal_roidb(self):
        """
        Return the database of regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_' + cfg.REGION_PROPOSAL + '_region_proposal_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if self._image_set != 'test':
            gt_roidb = self.gt_roidb()

            print 'Loading region proposal network boxes...'
            model = cfg.REGION_PROPOSAL
            rpn_roidb = self._load_rpn_roidb(gt_roidb, model)
            print 'Region proposal network boxes loaded'
            roidb = datasets.imdb.merge_roidbs(rpn_roidb, gt_roidb)
        else:
            print 'Loading region proposal network boxes...'
            model = cfg.REGION_PROPOSAL
            roidb = self._load_rpn_roidb(None, model)
            print 'Region proposal network boxes loaded'

        print '{} region proposals per image'.format(self._num_boxes_proposal / len(self.image_index))

        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote roidb to {}'.format(cache_file)

        return roidb

def selective_search_roidb(self):
        """
        Return the database of selective search regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_selective_search_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} ss roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if int(self._year) == 2007 or self._image_set != 'test':
            gt_roidb = self.gt_roidb()
            ss_roidb = self._load_selective_search_roidb(gt_roidb)
            roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb)
        else:
            roidb = self._load_selective_search_roidb(None)
        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote ss roidb to {}'.format(cache_file)

        return roidb

def region_proposal_roidb(self):
        """
        Return the database of regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_' + cfg.SUBCLS_NAME + '_' + cfg.REGION_PROPOSAL + '_region_proposal_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if self._image_set != 'test':
            gt_roidb = self.gt_roidb()

            print 'Loading region proposal network boxes...'
            model = cfg.REGION_PROPOSAL
            rpn_roidb = self._load_rpn_roidb(gt_roidb, model)
            print 'Region proposal network boxes loaded'
            roidb = datasets.imdb.merge_roidbs(rpn_roidb, gt_roidb)
        else:
            print 'Loading region proposal network boxes...'
            model = cfg.REGION_PROPOSAL
            roidb = self._load_rpn_roidb(None, model)
            print 'Region proposal network boxes loaded'

        print '{} region proposals per image'.format(self._num_boxes_proposal / len(self.image_index))

        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote roidb to {}'.format(cache_file)

        return roidb

def selective_search_roidb(self):
        """
        Return the database of selective search regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_selective_search_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} ss roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if int(self._year) == 2007 or self._image_set != 'test':
            gt_roidb = self.gt_roidb()
            ss_roidb = self._load_selective_search_roidb(gt_roidb)
            roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb)
        else:
            roidb = self._load_selective_search_roidb(None)
        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote ss roidb to {}'.format(cache_file)

        return roidb

def region_proposal_roidb(self):
        """
        Return the database of regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_' + cfg.SUBCLS_NAME + '_' + cfg.REGION_PROPOSAL + '_region_proposal_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if self._image_set != 'testing':
            gt_roidb = self.gt_roidb()

            print 'Loading region proposal network boxes...'
            if self._image_set == 'trainval':
                model = cfg.REGION_PROPOSAL + '_trainval/'
            else:
                model = cfg.REGION_PROPOSAL + '_train/'
            rpn_roidb = self._load_rpn_roidb(gt_roidb, model)
            print 'Region proposal network boxes loaded'
            roidb = datasets.imdb.merge_roidbs(rpn_roidb, gt_roidb)
        else:
            print 'Loading region proposal network boxes...'
            model = cfg.REGION_PROPOSAL + '_trainval/'
            roidb = self._load_rpn_roidb(None, model)
            print 'Region proposal network boxes loaded'

        print '{} region proposals per image'.format(self._num_boxes_proposal / len(self.image_index))

        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote roidb to {}'.format(cache_file)

        return roidb

def __init__(self, image_set, year, devkit_path=None):
        datasets.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 = ('aeroplane', 'bicycle', 'bird', 'boat',
                         'bottle', 'bus', 'car', 'cat', 'chair',
                         'cow', 'diningtable', 'dog', 'horse',
                         'motorbike', 'person', 'pottedplant',
                         'sheep', 'sofa', 'train', 'tvmonitor')
        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
        self._image_ext = '.jpg'
        self._image_index = self._load_image_set_index()
        # Default to roidb handler
        self._roidb_handler = self.selective_search_roidb
        self._salt = str(uuid.uuid4())
        self._comp_id = 'comp4'

        # PASCAL specific config options
        self.config = {'cleanup'  : True,
                       'use_salt' : True,
                       'use_diff' : False,
                       'matlab_eval' : False,
                       'top_k'    : 2000}

        assert os.path.exists(self._devkit_path), \
                'VOCdevkit path does not exist: {}'.format(self._devkit_path)
        assert os.path.exists(self._data_path), \
                'Path does not exist: {}'.format(self._data_path)

def selective_search_roidb(self):
        """
        Return the database of selective search regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_selective_search_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} ss roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if int(self._year) == 2007 or self._image_set != 'test':
            gt_roidb = self.gt_roidb()
            # ss_roidb = self._load_selective_search_roidb(gt_roidb)
            # roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb)
            roidb = self._load_selective_search_roidb(gt_roidb)
        else:
            roidb = self._load_selective_search_roidb(None)
        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote ss roidb to {}'.format(cache_file)

        return roidb

def region_proposal_roidb(self):
        """
        Return the database of regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_' + cfg.REGION_PROPOSAL + '_region_proposal_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if self._image_set != 'test':
            gt_roidb = self.gt_roidb()

            print 'Loading region proposal network boxes...'
            model = cfg.REGION_PROPOSAL
            rpn_roidb = self._load_rpn_roidb(gt_roidb, model)
            print 'Region proposal network boxes loaded'
            roidb = datasets.imdb.merge_roidbs(rpn_roidb, gt_roidb)
        else:
            print 'Loading region proposal network boxes...'
            model = cfg.REGION_PROPOSAL
            roidb = self._load_rpn_roidb(None, model)
            print 'Region proposal network boxes loaded'

        print '{} region proposals per image'.format(self._num_boxes_proposal / len(self.image_index))

        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote roidb to {}'.format(cache_file)

        return roidb

def selective_search_roidb(self):
        """
        Return the database of selective search regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_selective_search_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} ss roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if int(self._year) == 2007 or self._image_set != 'test':
            gt_roidb = self.gt_roidb()
            ss_roidb = self._load_selective_search_roidb(gt_roidb)
            roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb)
        else:
            roidb = self._load_selective_search_roidb(None)
        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote ss roidb to {}'.format(cache_file)

        return roidb

def region_proposal_roidb(self):
        """
        Return the database of regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_' + cfg.SUBCLS_NAME + '_' + cfg.REGION_PROPOSAL + '_region_proposal_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if self._image_set != 'test':
            gt_roidb = self.gt_roidb()

            print 'Loading region proposal network boxes...'
            model = cfg.REGION_PROPOSAL
            rpn_roidb = self._load_rpn_roidb(gt_roidb, model)
            print 'Region proposal network boxes loaded'
            roidb = datasets.imdb.merge_roidbs(rpn_roidb, gt_roidb)
        else:
            print 'Loading region proposal network boxes...'
            model = cfg.REGION_PROPOSAL
            roidb = self._load_rpn_roidb(None, model)
            print 'Region proposal network boxes loaded'

        print '{} region proposals per image'.format(self._num_boxes_proposal / len(self.image_index))

        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote roidb to {}'.format(cache_file)

        return roidb

def selective_search_roidb(self):
        """
        Return the database of selective search regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_selective_search_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} ss roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if int(self._year) == 2007 or self._image_set != 'test':
            gt_roidb = self.gt_roidb()
            ss_roidb = self._load_selective_search_roidb(gt_roidb)
            roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb)
        else:
            roidb = self._load_selective_search_roidb(None)
        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote ss roidb to {}'.format(cache_file)

        return roidb

def region_proposal_roidb(self):
        """
        Return the database of regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_' + cfg.SUBCLS_NAME + '_' + cfg.REGION_PROPOSAL + '_region_proposal_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if self._image_set != 'testing':
            gt_roidb = self.gt_roidb()

            print 'Loading region proposal network boxes...'
            if self._image_set == 'trainval':
                model = cfg.REGION_PROPOSAL + '_trainval/'
            else:
                model = cfg.REGION_PROPOSAL + '_train/'
            rpn_roidb = self._load_rpn_roidb(gt_roidb, model)
            print 'Region proposal network boxes loaded'
            roidb = datasets.imdb.merge_roidbs(rpn_roidb, gt_roidb)
        else:
            print 'Loading region proposal network boxes...'
            model = cfg.REGION_PROPOSAL + '_trainval/'
            roidb = self._load_rpn_roidb(None, model)
            print 'Region proposal network boxes loaded'

        print '{} region proposals per image'.format(self._num_boxes_proposal / len(self.image_index))

        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote roidb to {}'.format(cache_file)

        return roidb

def __init__(self, image_set, year, devkit_path=None):
        datasets.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', 'bicycle', 'bird', 'boat',
                         'bottle', 'bus', 'car', 'cat', 'chair',
                         'cow', 'diningtable', 'dog', 'horse',
                         'motorbike', 'person', 'pottedplant',
                         'sheep', 'sofa', 'train', 'tvmonitor')
        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
        self._image_ext = '.jpg'
        self._image_index = self._load_image_set_index()
        # Default to roidb handler
        self._roidb_handler = self.selective_search_roidb

        # PASCAL specific config options
        self.config = {'cleanup'  : True,
                       'use_salt' : True,
                       'top_k'    : 2000,
                       'use_diff' : False,
                       'rpn_file' : None}

        assert os.path.exists(self._devkit_path), \
                'VOCdevkit path does not exist: {}'.format(self._devkit_path)
        assert os.path.exists(self._data_path), \
                'Path does not exist: {}'.format(self._data_path)

def selective_search_roidb(self):
        """
        Return the database of selective search regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_selective_search_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} ss roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if int(self._year) == 2007 or self._image_set != 'test':
            gt_roidb = self.gt_roidb()
            ss_roidb = self._load_selective_search_roidb(gt_roidb)
            roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb)
        else:
            roidb = self._load_selective_search_roidb(None)
        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote ss roidb to {}'.format(cache_file)

        return roidb

def rpn_roidb(self):
        if int(self._year) == 2007 or self._image_set != 'test':
            gt_roidb = self.gt_roidb()
            rpn_roidb = self._load_rpn_roidb(gt_roidb)
            roidb = datasets.imdb.merge_roidbs(gt_roidb, rpn_roidb)
        else:
            roidb = self._load_rpn_roidb(None)

        return roidb

def __init__(self, image_set, year, devkit_path=None):
        print(image_set)
        datasets.imdb.__init__(self, 'tattc_' + year)
        self._year = year
        self._image_set = image_set
        self._devkit_path = self._get_default_path() if devkit_path is None \
                            else devkit_path
        print(self._devkit_path)
        self._data_path = os.path.join(self._devkit_path, self._year)
        print(self._data_path)
        self._classes = ('__background__', # always index 0, total 2
                         'tattoo')
        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
        print(self._class_to_ind)
        self._image_ext = '.jpg'
        self._image_index = self._load_image_set_index()
        self._index_to_fname = self._load_fname_index()
        # Default to roidb handler
        self._roidb_handler = self.selective_search_roidb

        # PASCAL specific config options
        self.config = {'cleanup'  : True,
                       'use_salt' : True,
                       'top_k'    : 2000,
                       'use_diff' : False,
                       'rpn_file' : None}

        assert os.path.exists(self._devkit_path), \
                'devkit path does not exist: {}'.format(self._devkit_path)
        assert os.path.exists(self._data_path), \
                'Path does not exist: {}'.format(self._data_path)

def selective_search_roidb(self):
        """
        Return the database of selective search regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_selective_search_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} ss roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if int(self._year) == 2007 or self._image_set != 'test':
            gt_roidb = self.gt_roidb()
            ss_roidb = self._load_selective_search_roidb(gt_roidb)
            roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb)
        else:
            roidb = self._load_selective_search_roidb(None)
        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote ss roidb to {}'.format(cache_file)

        return roidb

def __init__(self, image_set, year, devkit_path=None):
        print('[tattic_voc init]')
        print(image_set)

        datasets.imdb.__init__(self, 'tattc_voc_' + year)

        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)
        self._classes = ('__background__', # always index 0, total 22
                         'aeroplane', 'bicycle', 'bird', 'boat',
                         'bottle', 'bus', 'car', 'cat', 'chair',
                         'cow', 'diningtable', 'dog', 'horse',
                         'motorbike', 'person', 'pottedplant',
                         'sheep', 'sofa', 'train', 'tvmonitor',
                         'tattoo')
        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
        self._image_ext = '.jpg'
        self._image_index = self._load_image_set_index()
        # Default to roidb handler
        self._roidb_handler = self.selective_search_roidb

        # TATTC specific config options
        self.config = {'cleanup'  : True,
                       'use_salt' : True,
                       'top_k'    : 2000,
                       'use_diff' : False,
                       'rpn_file' : None}

        assert os.path.exists(self._devkit_path), \
                'VOCdevkit path does not exist: {}'.format(self._devkit_path)
        assert os.path.exists(self._data_path), \
                'Path does not exist: {}'.format(self._data_path)