ssd_meta_arch.py 文件源码

def preprocess(self, inputs):
    """Feature-extractor specific preprocessing.

    See base class.

    Args:
      inputs: a [batch, height_in, width_in, channels] float tensor representing
        a batch of images with values between 0 and 255.0.

    Returns:
      preprocessed_inputs: a [batch, height_out, width_out, channels] float
        tensor representing a batch of images.
    Raises:
      ValueError: if inputs tensor does not have type tf.float32
    """
    if inputs.dtype is not tf.float32:
      raise ValueError('`preprocess` expects a tf.float32 tensor')
    with tf.name_scope('Preprocessor'):
      # TODO: revisit whether to always use batch size as  the number of
      # parallel iterations vs allow for dynamic batching.
      resized_inputs = tf.map_fn(self._image_resizer_fn,
                                 elems=inputs,
                                 dtype=tf.float32)
      return self._feature_extractor.preprocess(resized_inputs)