def _init_inception()

in fid.py [0:0]

• 36 lines of code
• 10 McCabe index (conditional complexity)

def _init_inception():
  global pool3
  if not os.path.exists(MODEL_DIR):
    os.makedirs(MODEL_DIR)
  filename = DATA_URL.split('/')[-1]
  filepath = os.path.join(MODEL_DIR, filename)
  if not os.path.exists(filepath):
    def _progress(count, block_size, total_size):
      sys.stdout.write('\r>> Downloading %s %.1f%%' % (
          filename, float(count * block_size) / float(total_size) * 100.0))
      sys.stdout.flush()
    filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
    print()
    statinfo = os.stat(filepath)
    print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
  tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR)
  with tf.gfile.FastGFile(os.path.join(
      MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    _ = tf.import_graph_def(graph_def, name='')
  # Works with an arbitrary minibatch size.
  with tf.Session() as sess:
    pool3 = sess.graph.get_tensor_by_name('pool_3:0')
    ops = pool3.graph.get_operations()
    for op_idx, op in enumerate(ops):
        for o in op.outputs:
            shape = o.get_shape()
            if shape._dims != []:
              shape = [s.value for s in shape]
              new_shape = []
              for j, s in enumerate(shape):
                if s == 1 and j == 0:
                  new_shape.append(None)
                else:
                  new_shape.append(s)
              o.__dict__['_shape_val'] = tf.TensorShape(new_shape)