network.py 文件源码

def proposal_target_layer(self, input, classes, name):
        if isinstance(input[0], tuple):
            input[0] = input[0][0]
        with tf.variable_scope(name) as scope:
            # (Yuliang)
            rois,labels,eye,smile,bbox_targets,bbox_inside_weights,bbox_outside_weights = tf.py_func(proposal_target_layer_py,[input[0],input[1],classes],[tf.float32,tf.float32,tf.float32,tf.float32,tf.float32,tf.float32,tf.float32])

            rois = tf.reshape(rois,[-1,5] , name = 'rois') 
            labels = tf.convert_to_tensor(tf.cast(labels,tf.int32), name = 'labels')
            # (Yuliang)
            eye = tf.convert_to_tensor(tf.cast(eye,tf.int32), name = 'eye')
            smile = tf.convert_to_tensor(tf.cast(smile,tf.int32), name = 'smile')

            bbox_targets = tf.convert_to_tensor(bbox_targets, name = 'bbox_targets')
            bbox_inside_weights = tf.convert_to_tensor(bbox_inside_weights, name = 'bbox_inside_weights')
            bbox_outside_weights = tf.convert_to_tensor(bbox_outside_weights, name = 'bbox_outside_weights')

            # (Yuliang)
            return rois, labels, eye, smile, bbox_targets, bbox_inside_weights, bbox_outside_weights
            # return rois, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights

    # (Yuliang)
    # Since we have 2 more outputs in proposal_target_layer, we now want to
    # exclude them so that we can feed into the roi_pooling layer