readers.py 文件源码

def prepare_serialized_examples(self, serialized_examples, width=32, height=32, channels=3):
    # set the mapping from the fields to data types in the proto
    feature_map = {
           'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
           'image/filename': tf.FixedLenFeature((), tf.string, default_value=''),
           'image/class/label': tf.FixedLenFeature([], tf.int64)
    }
    features = tf.parse_example(serialized_examples, features=feature_map)


    def decode_and_resize(image_str_tensor):
      """Decodes jpeg string, resizes it and returns a uint8 tensor."""

      image = tf.image.decode_jpeg(image_str_tensor, channels=channels)

      # Note resize expects a batch_size, but tf_map supresses that index,
      # thus we have to expand then squeeze.  Resize returns float32 in the
      # range [0, uint8_max]
      image = tf.expand_dims(image, 0)
      image = tf.image.resize_bilinear(
          image, [height, width], align_corners=False)
      image = tf.squeeze(image, squeeze_dims=[0])
      image = tf.cast(image, dtype=tf.uint8)
      return image

    images_str_tensor = features["image/encoded"]
    images = tf.map_fn(
        decode_and_resize, images_str_tensor, back_prop=False, dtype=tf.uint8)
    images = tf.image.convert_image_dtype(images, dtype=tf.float32)
    images = tf.subtract(images, 0.5)
    images = tf.multiply(images, 2.0)

    def dense_to_one_hot(label_batch, num_classes):
      one_hot = tf.map_fn(lambda x : tf.cast(slim.one_hot_encoding(x, num_classes), tf.int32), label_batch)
      one_hot = tf.reshape(one_hot, [-1, num_classes])
      return one_hot

    labels = tf.cast(features['image/class/label'], tf.int32)
    labels = tf.reshape(labels, [-1, 1])


    image_ids = features['image/filename']

    return image_ids, images, labels