code: def main(args):
      tf_input = tf.placeholder("float32", [1, 448, 448, 3])
      init_dict = {
          "yolo_model_input": tf_input,
          "yolo_mode": args.mode,
          "yolo_disp_console": args.disp_console,
          "session": tf.Session(),
      }
  
      yolo_detector = YOLO_tiny_model_updated(init_dict)
      yolo_variables = yolo_detector.get_yolo_variables()
  
      yolo_detector.init_variables(yolo_variables)
      yolo_detector.load_weight(yolo_variables)
  
      output = yolo_detector.get_output_tensor()
  
      fetch_list = [
          output,
          yolo_detector.conv_1,
          yolo_detector.pool_2,
          yolo_detector.conv_3,
          yolo_detector.pool_4,
          yolo_detector.conv_5,
          yolo_detector.pool_6,
          yolo_detector.conv_7,
          yolo_detector.pool_8,
          yolo_detector.conv_9,
          yolo_detector.pool_10,
          yolo_detector.conv_11,
          yolo_detector.pool_12,
          yolo_detector.conv_13,
          yolo_detector.conv_14,
          yolo_detector.conv_15,
          yolo_detector.fc_16,
          yolo_detector.fc_17,
          yolo_detector.fc_19,
      ]
  
      for i in range(len(fetch_list)):
          print(fetch_list[i])
      pdb.set_trace()
  
      file_mean_stds = []
  
      filenames = os.listdir(args.input_folder_dir)
      with tqdm(total=len(filenames)) as pbar:
          for filename in filenames:
              activation_mean = []
              activation_stds = []
              pic_dir = os.path.join(args.input_folder_dir, filename)
              pic = cv2.imread(pic_dir)
              pic = old_div(cv2.resize(pic, (448, 448)), 255) * 2 - 1
              feed_batch = pic[np.newaxis, :]
              feed_dict = {tf_input: feed_batch}
  
              # 必须要获得model的会话才可以操纵她
              sess = yolo_detector.get_model_sess()
              results = sess.run(fetch_list, feed_dict=feed_dict)
              for result in results[1:19]:
                  activation_mean.append(result.mean())
                  activation_stds.append(result.std())
  
              file_mean_stds.append((activation_mean, activation_stds))
  
              # print(results[0])
              pbar.update(1)
  
      yolo_detector.terminate_model()
  
imports: from __future__ import division
  from __future__ import print_function
  from builtins import str
  from builtins import range
  from past.utils import old_div
  from builtins import object
  import tensorflow
  import cv2
  import numpy
  import os
  import sys
  import argparse
  import pdb
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: main(args) without return types