new_contents: #!/usr/bin/env python
  
  from __future__ import absolute_import
  from __future__ import division
  
  import _init_paths
  from model.config import cfg
  
  from datasets.factory import get_imdb
  from model.test import test_net
  
  import os
  os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
  os.environ["CUDA_VISIBLE_DEVICES"]="3"
  os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
  
  import tensorflow as tf
  import numpy as np
  import collections
  
  from nets.vgg16 import vgg16
  from tensorflow.python import pywrap_tensorflow
  
  num_classes = 21
  old_filter_num = (64,64,128,128,256,256,256,512,512,512,512,512,512,512)
  new_filter_num = (64,64,128,128,256,256,256,512,512,512,128,512,512,512)
  names = ('weights','biases')
  # Defined path for saving pruned weights to npy files
  weights_name = 'pruned_conv11_momentum.npy'
  folder_path = '../output/pruning/'
  weights_path = os.path.join(folder_path,weights_name)
  
  if __name__=='__main__':
      '''
      load the new weigts to a new graph,
      test the pruned network
      '''
      cfg.TEST.HAS_RPN = True  # Use RPN for proposals
      # set config
      tfconfig = tf.ConfigProto(allow_soft_placement=True)
      tfconfig.gpu_options.allow_growth=True
      with tf.Graph().as_default() as g:
          with tf.Session(config=tfconfig,graph=g).as_default() as sess:
              #load the new graph
              net = vgg16(batch_size=1)
              net.create_architecture(sess,'TEST',num_classes,tag='default',
                                      anchor_scales = [8,16,32],
                                      filter_num = new_filter_num)
  
              # load the new weights from npy file
              weights_dic = np.load(weights_path).item()
  
              for name_scope in weights_dic:
                  with tf.variable_scope(name_scope,reuse = True):
                      for name in weights_dic[name_scope]:
                          var = tf.get_variable(name)
                          sess.run(var.assign(weights_dic[name_scope][name]['value']))
                          print 'assign pretrain model to {}/{}'.format(name_scope,name)
  
              # test the new model
              imdb = get_imdb('voc_2007_test')
              filename = 'demo_pruning'
              test_net(sess, net, imdb, filename, max_per_image=100)
  
subject: test using pruned network