network_resnet.py 文件源码

def _anchor_target_layer(self, rpn_cls_score, name):
    with tf.variable_scope(name) as scope:
      rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = tf.py_func(
        anchor_target_layer,
        [rpn_cls_score, self._gt_boxes, self._im_info, self._feat_stride, self._anchors, self._num_anchors],
        [tf.float32, tf.float32, tf.float32, tf.float32],
        name="anchor_target")

      rpn_labels.set_shape([1, 1, None, None])
      rpn_bbox_targets.set_shape([1, None, None, self._num_anchors * 4])
      rpn_bbox_inside_weights.set_shape([1, None, None, self._num_anchors * 4])
      rpn_bbox_outside_weights.set_shape([1, None, None, self._num_anchors * 4])

      rpn_labels = tf.to_int32(rpn_labels, name="to_int32")
      self._anchor_targets['rpn_labels'] = rpn_labels
      self._anchor_targets['rpn_bbox_targets'] = rpn_bbox_targets
      self._anchor_targets['rpn_bbox_inside_weights'] = rpn_bbox_inside_weights
      self._anchor_targets['rpn_bbox_outside_weights'] = rpn_bbox_outside_weights

      self._score_summaries.update(self._anchor_targets)

    return rpn_labels