data.py 文件源码

def parse_example_proto(example_serialized):
  # Dense features in Example proto.
  feature_map = {
      'image/encoded': tf.FixedLenFeature([], dtype=tf.string,
                                          default_value=''),
      'image/filename': tf.FixedLenFeature([], dtype=tf.string,
                                          default_value=''),

      'image/class/label': tf.FixedLenFeature([1], dtype=tf.int64,
                                              default_value=-1),
      'image/class/text': tf.FixedLenFeature([], dtype=tf.string,
                                             default_value=''),
      'image/height': tf.FixedLenFeature([1], dtype=tf.int64,
                                         default_value=-1),
      'image/width': tf.FixedLenFeature([1], dtype=tf.int64,
                                         default_value=-1),

  }

  features = tf.parse_single_example(example_serialized, feature_map)
  label = tf.cast(features['image/class/label'], dtype=tf.int32)
  return features['image/encoded'], label, features['image/filename']