當前位置: 首頁>>代碼示例>>Python>>正文


Python factory.get_imdb方法代碼示例

本文整理匯總了Python中lib.datasets.factory.get_imdb方法的典型用法代碼示例。如果您正苦於以下問題:Python factory.get_imdb方法的具體用法?Python factory.get_imdb怎麽用?Python factory.get_imdb使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在lib.datasets.factory的用法示例。


在下文中一共展示了factory.get_imdb方法的7個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: combined_roidb

# 需要導入模塊: from lib.datasets import factory [as 別名]
# 或者: from lib.datasets.factory import get_imdb [as 別名]
def combined_roidb(imdb_names):
    # for now: imdb_names='vg_1.2_train'
    def get_roidb(imdb_name):
        imdb = get_imdb(imdb_name)
        print('Loaded dataset `{:s}` for training'.format(imdb.name))
        imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
        print('Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD))
        roidb = get_training_roidb(imdb)
        return roidb

    roidbs = [get_roidb(s) for s in imdb_names.split('+')]
    roidb = roidbs[0]
    if len(roidbs) > 1:
        for r in roidbs[1:]:
            roidb.extend(r)
        imdb = lib.datasets.imdb.imdb(imdb_names)
    else:
        imdb = get_imdb(imdb_names)
    return imdb, roidb 
開發者ID:InnerPeace-Wu,項目名稱:densecap-tensorflow,代碼行數:21,代碼來源:train_net.py

示例2: combined_roidb

# 需要導入模塊: from lib.datasets import factory [as 別名]
# 或者: from lib.datasets.factory import get_imdb [as 別名]
def combined_roidb(imdb_names):
    """
    Combine multiple roidbs
    """

    def get_roidb(imdb_name):
        imdb = get_imdb(imdb_name)
        print('Loaded dataset `{:s}` for training'.format(imdb.name))
        imdb.set_proposal_method("gt")
        print('Set proposal method: {:s}'.format("gt"))
        roidb = get_training_roidb(imdb)
        return roidb

    roidbs = [get_roidb(s) for s in imdb_names.split('+')]
    roidb = roidbs[0]
    if len(roidbs) > 1:
        for r in roidbs[1:]:
            roidb.extend(r)
        tmp = get_imdb(imdb_names.split('+')[1])
        imdb = imdb2(imdb_names, tmp.classes)
    else:
        imdb = get_imdb(imdb_names)
    return imdb, roidb 
開發者ID:dBeker,項目名稱:Faster-RCNN-TensorFlow-Python3,代碼行數:25,代碼來源:train.py

示例3: combined_roidb

# 需要導入模塊: from lib.datasets import factory [as 別名]
# 或者: from lib.datasets.factory import get_imdb [as 別名]
def combined_roidb(imdb_names):
  """
  Combine multiple roidbs
  """

  def get_roidb(imdb_name):
    imdb = get_imdb(imdb_name)
    print('Loaded dataset `{:s}` for training'.format(imdb.name))
    imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
    # print('Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD))
    roidb = get_training_roidb(imdb)
    return roidb

  roidbs = [get_roidb(s) for s in imdb_names.split('+')]
  roidb = roidbs[0]
  if len(roidbs) > 1:
    for r in roidbs[1:]:
      roidb.extend(r)
    tmp = get_imdb(imdb_names.split('+')[1])
    imdb = lib.datasets.imdb.imdb(imdb_names, tmp.classes)
  else:
    imdb = get_imdb(imdb_names)
  return imdb, roidb 
開發者ID:yingxingde,項目名稱:FasterRCNN-pytorch,代碼行數:25,代碼來源:train.py

示例4: combined_roidb

# 需要導入模塊: from lib.datasets import factory [as 別名]
# 或者: from lib.datasets.factory import get_imdb [as 別名]
def combined_roidb(imdb_names):#"voc_2007_trainval"
    """
    Combine multiple roidbs
    """

    def get_roidb(imdb_name):
        imdb = get_imdb(imdb_name)
        print('Loaded dataset `{:s}` for training'.format(imdb.name))
        imdb.set_proposal_method("gt")
        print('Set proposal method: {:s}'.format("gt"))
        roidb = get_training_roidb(imdb)
        return roidb

    roidbs = [get_roidb(s) for s in imdb_names.split('+')]
    roidb = roidbs[0]
    if len(roidbs) > 1:
        for r in roidbs[1:]:
            roidb.extend(r)
        tmp = get_imdb(imdb_names.split('+')[1])
        imdb = imdb2(imdb_names, tmp.classes)
    else:
        imdb = get_imdb(imdb_names)
    return imdb, roidb 
開發者ID:pf67,項目名稱:GeetChinese_crack,代碼行數:25,代碼來源:train.py

示例5: from_dets

# 需要導入模塊: from lib.datasets import factory [as 別名]
# 或者: from lib.datasets.factory import get_imdb [as 別名]
def from_dets(imdb_name, output_dir, args):
  imdb = get_imdb(imdb_name)
  imdb.competition_mode(args.comp_mode)
  imdb.config['matlab_eval'] = args.matlab_eval
  with open(os.path.join(output_dir, 'detections.pkl'), 'rb') as f:
    dets = pickle.load(f)

  if args.apply_nms:
    print('Applying NMS to all detections')
    nms_dets = apply_nms(dets, cfg.TEST.NMS)
  else:
    nms_dets = dets

  print('Evaluating detections')
  imdb.evaluate_detections(nms_dets, output_dir) 
開發者ID:wanjinchang,項目名稱:SSH-TensorFlow,代碼行數:17,代碼來源:reval.py

示例6: get_roidb_limit_ram

# 需要導入模塊: from lib.datasets import factory [as 別名]
# 或者: from lib.datasets.factory import get_imdb [as 別名]
def get_roidb_limit_ram(imdb_name):
    """
    Note: we need to run get_training_roidb sort of funcs later
    for now, it only supports single roidb.
    """

    imdb = get_imdb(imdb_name)
    roidb = imdb.roidb

    assert isinstance(roidb, six.string_types), \
        "for limit ram vision, roidb should be a path."

    return imdb, roidb 
開發者ID:InnerPeace-Wu,項目名稱:densecap-tensorflow,代碼行數:15,代碼來源:train_net.py

示例7: __init__

# 需要導入模塊: from lib.datasets import factory [as 別名]
# 或者: from lib.datasets.factory import get_imdb [as 別名]
def __init__(self, imdb_name, resize=True):
        imdb = get_imdb(imdb_name)
        # Ignore the background class!!! So ['gt_classes'] must minus 1.
        self.classes = [ np.zeros(imdb.num_classes - 1) for i in range(imdb.num_images) ]
        for i, anno in enumerate(imdb.gt_roidb()):
            np.put(self.classes[i], map(lambda x: x-1, anno['gt_classes']), 1)
            # np.put(self.classes[i], random.choice(map(lambda x: x-1, anno['gt_classes'])), 1)
        self.images = [ imdb.image_path_at(i) for i in range(imdb.num_images) ]
        assert len(self.classes) == len(self.images)

        self._perm = np.random.permutation(np.arange(len(self.images)))
        self._cur = 0
        self.resize = resize 
開發者ID:shijx12,項目名稱:DeepSim,代碼行數:15,代碼來源:util.py


注:本文中的lib.datasets.factory.get_imdb方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台,相關代碼片段篩選自各路編程大神貢獻的開源項目,源碼版權歸原作者所有,傳播和使用請參考對應項目的License;未經允許,請勿轉載。