//  [python]
def from_snapshot(self, sess):
        if self.Restore_flag == -1:
            ckpt = tf.train.get_checkpoint_state(self.output_dir)
            # saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path + '.meta')
            saver = tf.train.Saver()
            saver.restore(sess, ckpt.model_checkpoint_path)
            print('Restoring model snapshots from {:s}'.format(ckpt.model_checkpoint_path))
        if self.Restore_flag == 0:
            saver_t = [var for var in tf.model_variables() if 'conv1' in var.name and 'conv1_sp' not in var.name]
            saver_t += [var for var in tf.model_variables() if 'conv2' in var.name and 'conv2_sp' not in var.name]
            saver_t += [var for var in tf.model_variables() if 'conv3' in var.name]
            saver_t += [var for var in tf.model_variables() if 'conv4' in var.name]
            saver_t += [var for var in tf.model_variables() if 'conv5' in var.name]
            saver_t += [var for var in tf.model_variables() if 'shortcut' in var.name]

            sess.run(tf.global_variables_initializer())
            # for var in tf.trainable_variables():
            #     print(var.name, var.eval().mean())

            print('Restoring model snapshots from {:s}'.format(self.pretrained_model))

            self.saver_restore = tf.train.Saver(saver_t)
            self.saver_restore.restore(sess, self.pretrained_model)

            # for var in tf.trainable_variables():
            #     print(var.name, var.eval().mean())

        if self.Restore_flag == 5 or self.Restore_flag == 6 or self.Restore_flag == 7:

            sess.run(tf.global_variables_initializer())
            for var in tf.trainable_variables():
                print(var.name, var.eval().mean())

            print('Restoring model snapshots from {:s}'.format(self.pretrained_model))
            saver_t = {}
            if self.net.model_name.__contains__('res101'):
                model_backbone = 'resnet_v1_101'
            else:
                model_backbone = 'resnet_v1_50'

            # Add block0
            for ele in tf.model_variables():
                if model_backbone + '/conv1/weights' in ele.name or model_backbone + '/conv1/BatchNorm/beta' in ele.name or model_backbone + '/conv1/BatchNorm/gamma' in ele.name or model_backbone + '/conv1/BatchNorm/moving_mean' in ele.name or model_backbone + '/conv1/BatchNorm/moving_variance' in ele.name:
                    saver_t[ele.name[:-2]] = ele
            # Add block1
            for ele in tf.model_variables():
                if 'block1' in ele.name:
                    saver_t[ele.name[:-2]] = ele

            # Add block2
            for ele in tf.model_variables():
                if 'block2' in ele.name:
                    saver_t[ele.name[:-2]] = ele

            # Add block3
            for ele in tf.model_variables():
                if 'block3' in ele.name:
                    saver_t[ele.name[:-2]] = ele

            # Add block4
            for ele in tf.model_variables():
                if 'block4' in ele.name:
                    saver_t[ele.name[:-2]] = ele

            self.saver_restore = tf.train.Saver(saver_t)
            self.saver_restore.restore(sess, self.pretrained_model)

            if self.Restore_flag >= 5:

                saver_t = {}
                # Add block5
                for ele in tf.model_variables():
                    if 'block4' in ele.name:
                        saver_t[ele.name[:-2]] = \
                            [var for var in tf.model_variables() if
                             ele.name[:-2].replace('block4', 'block5') in var.name][
                                0]

                self.saver_restore = tf.train.Saver(saver_t)
                self.saver_restore.restore(sess, self.pretrained_model)

            if self.Restore_flag >= 6:
                saver_t = {}
                # Add block6
                for ele in tf.model_variables():
                    if 'block4' in ele.name:
                        saver_t[ele.name[:-2]] = \
                            [var for var in tf.model_variables() if
                             ele.name[:-2].replace('block4', 'block6') in var.name][
                                0]

                self.saver_restore = tf.train.Saver(saver_t)
                self.saver_restore.restore(sess, self.pretrained_model)

            if self.Restore_flag >= 7:

                saver_t = {}
                # Add block7
                for ele in tf.model_variables():
                    if 'block4' in ele.name:
                        saver_t[ele.name[:-2]] = \
                            [var for var in tf.model_variables() if
                             ele.name[:-2].replace('block4', 'block7') in var.name][
                                0]

                self.saver_restore = tf.train.Saver(saver_t)
                self.saver_restore.restore(sess, self.pretrained_model)