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Python factory.get_imdb方法代码示例

本文整理汇总了Python中lib.datasets.factory.get_imdb方法的典型用法代码示例。如果您正苦于以下问题:Python factory.get_imdb方法的具体用法?Python factory.get_imdb怎么用?Python factory.get_imdb使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在lib.datasets.factory的用法示例。


在下文中一共展示了factory.get_imdb方法的7个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: combined_roidb

# 需要导入模块: from lib.datasets import factory [as 别名]
# 或者: from lib.datasets.factory import get_imdb [as 别名]
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 
开发者ID:InnerPeace-Wu,项目名称:densecap-tensorflow,代码行数:21,代码来源:train_net.py

示例2: combined_roidb

# 需要导入模块: from lib.datasets import factory [as 别名]
# 或者: from lib.datasets.factory import get_imdb [as 别名]
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 
开发者ID:dBeker,项目名称:Faster-RCNN-TensorFlow-Python3,代码行数:25,代码来源:train.py

示例3: combined_roidb

# 需要导入模块: from lib.datasets import factory [as 别名]
# 或者: from lib.datasets.factory import get_imdb [as 别名]
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 
开发者ID:yingxingde,项目名称:FasterRCNN-pytorch,代码行数:25,代码来源:train.py

示例4: combined_roidb

# 需要导入模块: from lib.datasets import factory [as 别名]
# 或者: from lib.datasets.factory import get_imdb [as 别名]
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 
开发者ID:pf67,项目名称:GeetChinese_crack,代码行数:25,代码来源:train.py

示例5: from_dets

# 需要导入模块: from lib.datasets import factory [as 别名]
# 或者: from lib.datasets.factory import get_imdb [as 别名]
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) 
开发者ID:wanjinchang,项目名称:SSH-TensorFlow,代码行数:17,代码来源:reval.py

示例6: get_roidb_limit_ram

# 需要导入模块: from lib.datasets import factory [as 别名]
# 或者: from lib.datasets.factory import get_imdb [as 别名]
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 
开发者ID:InnerPeace-Wu,项目名称:densecap-tensorflow,代码行数:15,代码来源:train_net.py

示例7: __init__

# 需要导入模块: from lib.datasets import factory [as 别名]
# 或者: from lib.datasets.factory import get_imdb [as 别名]
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 
开发者ID:shijx12,项目名称:DeepSim,代码行数:15,代码来源:util.py


注:本文中的lib.datasets.factory.get_imdb方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。