image.py 文件源码

def imagenet_preprocess_example(example, mode, resize_size=None):
  """Preprocessing used for Imagenet and similar problems."""
  if resize_size is None:
    resize_size = [299, 299]

  def preprocess(img):
    img = tf.image.resize_images(img, [360, 360])
    img = common_layers.image_augmentation(
        tf.to_float(img) / 255., crop_size=resize_size)
    return tf.to_int64(img * 255.)

  def resize(img):
    return tf.to_int64(tf.image.resize_images(img, resize_size))

  inputs = tf.cast(example["inputs"], tf.int64)
  if mode == tf.estimator.ModeKeys.TRAIN:
    example["inputs"] = tf.cond(  # Preprocess 90% of the time.
        tf.less(tf.random_uniform([]), 0.9),
        lambda img=inputs: preprocess(img),
        lambda img=inputs: resize(img))
  else:
    example["inputs"] = resize(inputs)
  return example