multiple_grid_anchor_generator.py 文件源码

def __init__(self,
               box_specs_list,
               base_anchor_size=None,
               clip_window=None):
    """Constructs a MultipleGridAnchorGenerator.

    To construct anchors, at multiple grid resolutions, one must provide a
    list of feature_map_shape_list (e.g., [(8, 8), (4, 4)]), and for each grid
    size, a corresponding list of (scale, aspect ratio) box specifications.

    For example:
    box_specs_list = [[(.1, 1.0), (.1, 2.0)],  # for 8x8 grid
                      [(.2, 1.0), (.3, 1.0), (.2, 2.0)]]  # for 4x4 grid

    To support the fully convolutional setting, we pass grid sizes in at
    generation time, while scale and aspect ratios are fixed at construction
    time.

    Args:
      box_specs_list: list of list of (scale, aspect ratio) pairs with the
        outside list having the same number of entries as feature_map_shape_list
        (which is passed in at generation time).
      base_anchor_size: base anchor size as [height, width]
                        (length-2 float tensor, default=[256, 256]).
      clip_window: a tensor of shape [4] specifying a window to which all
        anchors should be clipped. If clip_window is None, then no clipping
        is performed.

    Raises:
      ValueError: if box_specs_list is not a list of list of pairs
      ValueError: if clip_window is not either None or a tensor of shape [4]
    """
    if isinstance(box_specs_list, list) and all(
        [isinstance(list_item, list) for list_item in box_specs_list]):
      self._box_specs = box_specs_list
    else:
      raise ValueError('box_specs_list is expected to be a '
                       'list of lists of pairs')
    if base_anchor_size is None:
      base_anchor_size = tf.constant([256, 256], dtype=tf.float32)
    self._base_anchor_size = base_anchor_size
    if clip_window is not None and clip_window.get_shape().as_list() != [4]:
      raise ValueError('clip_window must either be None or a shape [4] tensor')
    self._clip_window = clip_window
    self._scales = []
    self._aspect_ratios = []
    for box_spec in self._box_specs:
      if not all([isinstance(entry, tuple) and len(entry) == 2
                  for entry in box_spec]):
        raise ValueError('box_specs_list is expected to be a '
                         'list of lists of pairs')
      scales, aspect_ratios = zip(*box_spec)
      self._scales.append(scales)
      self._aspect_ratios.append(aspect_ratios)