network.py 文件源码

def anchor_target_layer(self, input, _feat_stride, anchor_scales, name):
        if isinstance(input[0], tuple):
            input[0] = input[0][0]

        with tf.variable_scope(name) as scope:
            # 'rpn_cls_score', 'gt_boxes', 'gt_ishard', 'dontcare_areas', 'im_info'
            rpn_labels,rpn_bbox_targets,rpn_bbox_inside_weights,rpn_bbox_outside_weights = \
                tf.py_func(anchor_target_layer_py,
                           [input[0],input[1],input[2],input[3],input[4], _feat_stride, anchor_scales],
                           [tf.float32,tf.float32,tf.float32,tf.float32])

            rpn_labels = tf.convert_to_tensor(tf.cast(rpn_labels,tf.int32), name = 'rpn_labels') # shape is (1 x H x W x A, 2)
            rpn_bbox_targets = tf.convert_to_tensor(rpn_bbox_targets, name = 'rpn_bbox_targets') # shape is (1 x H x W x A, 4)
            rpn_bbox_inside_weights = tf.convert_to_tensor(rpn_bbox_inside_weights , name = 'rpn_bbox_inside_weights') # shape is (1 x H x W x A, 4)
            rpn_bbox_outside_weights = tf.convert_to_tensor(rpn_bbox_outside_weights , name = 'rpn_bbox_outside_weights') # shape is (1 x H x W x A, 4)


            return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights