tensorflow进程被杀死

Question

我正在尝试生成某些图像的池3层张量值。我试过这个代码。我工作3到4张图片很好但是当图像数量增加时，过程会在一段时间后被杀死。

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import re
import sys
import tarfile
from glob import glob
from scipy import spatial

import numpy as np
from six.moves import urllib
import tensorflow as tf

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string(
    'model_dir', '/tmp/imagenet',
    """Path to classify_image_graph_def.pb, """
    """imagenet_synset_to_human_label_map.txt, and """
    """imagenet_2012_challenge_label_map_proto.pbtxt.""")
tf.app.flags.DEFINE_string('image_file', '',
                           """Absolute path to image file.""")
tf.app.flags.DEFINE_integer('num_top_predictions', 5,
                            """Display this many predictions.""")


DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'

class NodeLookup(object):

  def __init__(self,label_lookup_path=None,uid_lookup_path=None):
    if not label_lookup_path:
      label_lookup_path = os.path.join(
          FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
    if not uid_lookup_path:
      uid_lookup_path = os.path.join(
          FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')
    self.node_lookup = self.load(label_lookup_path, uid_lookup_path)

  def load(self, label_lookup_path, uid_lookup_path):

    if not tf.gfile.Exists(uid_lookup_path):
      tf.logging.fatal('File does not exist %s', uid_lookup_path)
    if not tf.gfile.Exists(label_lookup_path):
      tf.logging.fatal('File does not exist %s', label_lookup_path)

    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
    uid_to_human = {}
    p = re.compile(r'[n\d]*[ \S,]*')
    for line in proto_as_ascii_lines:
      parsed_items = p.findall(line)
      uid = parsed_items[0]
      human_string = parsed_items[2]
      uid_to_human[uid] = human_string

    node_id_to_uid = {}
    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
    for line in proto_as_ascii:
      if line.startswith('  target_class:'):
        target_class = int(line.split(': ')[1])
      if line.startswith('  target_class_string:'):
        target_class_string = line.split(': ')[1]
        node_id_to_uid[target_class] = target_class_string[1:-2]

    node_id_to_name = {}
    for key, val in node_id_to_uid.items():
      if val not in uid_to_human:
        tf.logging.fatal('Failed to locate: %s', val)
      name = uid_to_human[val]
      node_id_to_name[key] = name

    return node_id_to_name

  def id_to_string(self, node_id):
    if node_id not in self.node_lookup:
      return ''
    return self.node_lookup[node_id]


def create_graph():

  with tf.gfile.FastGFile(os.path.join(
      FLAGS.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='')


def run_inference_on_image(image):

  if not tf.gfile.Exists(image):
    tf.logging.fatal('File does not exist %s', image)
  image_data = tf.gfile.FastGFile(image, 'rb').read()

  # Creates graph from saved GraphDef.
  create_graph()

  with tf.Session() as sess:
    softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
    feature_tensor = sess.graph.get_tensor_by_name('pool_3:0')
    predictions = sess.run(softmax_tensor,
                           {'DecodeJpeg/contents:0': image_data})
    predictions = np.squeeze(predictions)
    feature_set = sess.run(feature_tensor,
                        {'DecodeJpeg/contents:0': image_data}) #ADDED
    feature_set = np.squeeze(feature_set) #ADDED
    return feature_set

    node_lookup = NodeLookup()

    top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
    for node_id in top_k:
      human_string = node_lookup.id_to_string(node_id)
      score = predictions[node_id]

def maybe_download_and_extract():
  dest_directory = FLAGS.model_dir
  if not os.path.exists(dest_directory):
    os.makedirs(dest_directory)
  filename = DATA_URL.split('/')[-1]
  filepath = os.path.join(dest_directory, 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(dest_directory)

def main(_):
  maybe_download_and_extract()
  files = sorted(glob('*.jpg'))
  features = []
  result = []
  images = []
  i =0
  for image in files:

    images.append(image)
    run_inference_on_image(image)
    features += [run_inference_on_image(image)]
    new_result = 1 - spatial.distance.cosine(features[i], features[0])
    result.append(new_result)
    i +=1
  print (result)
  print (images)

if __name__ == '__main__':
  tf.app.run()

它在杀死进程之前显示错误消息

W tensorflow/core/framework/op_util.cc:332] op BatchNormWithGloabalNormalization is deprecated. It will cease to work in Graphdef version 9. Use tf.nn.batch_normalization().