Question

当我尝试在图像列表中循环运行Google的Inception模型时，我会在大约100张左右的图像后面出现此问题。它似乎耗尽了内存。我在CPU上运行。还有其他人遇到过这个问题吗？

Traceback (most recent call last):
  File "clean_dataset.py", line 33, in <module>
    description, score = inception.run_inference_on_image(f.read())
  File "/Volumes/EXPANSION/research/dcgan-transfer/data/classify_image.py", line 178, in run_inference_on_image
    node_lookup = NodeLookup()
  File "/Volumes/EXPANSION/research/dcgan-transfer/data/classify_image.py", line 83, in __init__
    self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
  File "/Volumes/EXPANSION/research/dcgan-transfer/data/classify_image.py", line 112, in load
    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/lib/io/file_io.py", line 110, in readlines
    self._prereadline_check()
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/lib/io/file_io.py", line 72, in _prereadline_check
    compat.as_bytes(self.__name), 1024 * 512, status)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/contextlib.py", line 24, in __exit__
    self.gen.next()
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/framework/errors.py", line 463, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors.ResourceExhaustedError: /tmp/imagenet/imagenet_2012_challenge_label_map_proto.pbtxt


real    6m32.403s
user    7m8.210s
sys     1m36.114s

https://github.com/tensorflow/models/tree/master/inception

Answer 1

问题是你不能简单地在你自己的代码中导入原始的'classify_image.py'（https://github.com/tensorflow/tensorflow/blob/master/tensorflow/models/image/imagenet/classify_image.py），特别是当你把它放入一个巨大的循环中以批量模式分类成千上万的图像时。

在这里查看原始代码：

with tf.Session() as sess:
# Some useful tensors:
# 'softmax:0': A tensor containing the normalized prediction across
#   1000 labels.
# 'pool_3:0': A tensor containing the next-to-last layer containing 2048
#   float description of the image.
# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
#   encoding of the image.
# Runs the softmax tensor by feeding the image_data as input to the graph.
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
predictions = sess.run(softmax_tensor,
                       {'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)

# Creates node ID --> English string lookup.
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]
  print('%s (score = %.5f)' % (human_string, score))

从上面你可以看到，对于每个分类任务，它会生成一个新的Class'NodeLookup'实例，它从文件加载到下面：

label_lookup = “imagenet_2012_challenge_label_map_proto.pbtxt”
uid_lookup_path = “imagenet_synset_to_human_label_map.txt”

所以实例会非常庞大，然后在你的代码循环中它将生成这个类的数百个实例，这会导致'tensorflow.python.framework.errors.ResourceExhaustedError'。

我建议采用的方法是编写一个新脚本并从'classify_image.py'修改这些类和函数，并避免为每个循环实例化NodeLookup类，只需将其实例化一次并使用它在循环。像这样：

with tf.Session() as sess:
        softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
        print 'Making classifications:'

        # Creates node ID --> English string lookup.
        node_lookup = NodeLookup(label_lookup_path=self.Model_Save_Path + self.label_lookup,
                                 uid_lookup_path=self.Model_Save_Path + self.uid_lookup_path)

        current_counter = 1
        for (tensor_image, image) in self.tensor_files:
            print 'On ' + str(current_counter)

            predictions = sess.run(softmax_tensor, {'DecodeJpeg/contents:0': tensor_image})
            predictions = np.squeeze(predictions)

            top_k = predictions.argsort()[-int(self.filter_level):][::-1]

             for node_id in top_k:
                 human_string = node_lookup.id_to_string(node_id)
                 score = predictions[node_id]

Google Inception tensorflow.python.framework.errors.ResourceExhaustedError

1 个答案: