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:

            input_0a = input[0][:,0:5]  # rpn_rois as original
            input_0b = input[0][:,5:6]  # orientation that corresponds to the rpn_rois
            #print(input[3])
            rois,labels,bbox_targets,bbox_inside_weights,bbox_outside_weights, gt_orientations_new, orientations_pred_new, \
            orientations_targets_new = tf.py_func(proposal_target_layer_py,[input_0a,input[1],classes, input[2], input_0b],
                                              [tf.float32,tf.float32,tf.float32,tf.float32,tf.float32, tf.float32, tf.float32, tf.float32])

            rois_wo = tf.reshape(rois,[-1,6] , name = 'rois')   # does the name 'rois' matter?
            rois = rois_wo[:, 0:5]
            labels = tf.convert_to_tensor(tf.cast(labels,tf.int32), name = 'labels')
            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')
            gt_orientations_new = tf.reshape(gt_orientations_new, [-1,1], name = 'gt_orientations_new') # these are the rotation angles for the predicted boxes
            orientations_pred_new = tf.reshape(orientations_pred_new, [-1,1], name = 'orientations_pred_new')
            orientations_targets_new = tf.reshape(orientations_targets_new, [-1, 1], name='orientations_targets_new')


            return rois_wo, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights, gt_orientations_new, orientations_pred_new, orientations_targets_new