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_labels,rpn_bbox_targets,rpn_bbox_inside_weights,rpn_bbox_outside_weights, rpn_orientation_targets = 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,tf.float32])

            rpn_labels = tf.convert_to_tensor(tf.cast(rpn_labels,tf.int32), name = 'rpn_labels')
            rpn_bbox_targets = tf.convert_to_tensor(rpn_bbox_targets, name = 'rpn_bbox_targets')
            rpn_bbox_inside_weights = tf.convert_to_tensor(rpn_bbox_inside_weights , name = 'rpn_bbox_inside_weights')
            rpn_bbox_outside_weights = tf.convert_to_tensor(rpn_bbox_outside_weights , name = 'rpn_bbox_outside_weights')
            rpn_orientation_targets = tf.convert_to_tensor(rpn_orientation_targets, name='rpn_orientations')


            return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights, rpn_orientation_targets