import tensorflow as tf


                img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch, img_h_batch, img_w_batch = \
                    self.reader.next_batch(dataset_name=cfgs.DATASET_NAME,
                                           batch_size=cfgs.BATCH_SIZE * num_gpu,
                                           shortside_len=shortside_len,
                                           is_training=True)

            # data processing
            inputs_list = []
            for i in range(num_gpu):
                img = tf.expand_dims(img_batch[i], axis=0)
                pretrain_zoo = PretrainModelZoo()
                if self.cfgs.NET_NAME in pretrain_zoo.pth_zoo or self.cfgs.NET_NAME in pretrain_zoo.mxnet_zoo:
                    img = img / tf.constant([cfgs.PIXEL_STD])

                gtboxes_and_label_r = tf.py_func(backward_convert,
                                                 inp=[gtboxes_and_label_batch[i]],
                                                 Tout=tf.float32)
                gtboxes_and_label_r = tf.reshape(gtboxes_and_label_r, [-1, 6])

