text: # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Contains a factory for building various models."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  from preprocessing import cifarnet_preprocessing
  from preprocessing import inception_preprocessing
  from preprocessing import lenet_preprocessing
  from preprocessing import vgg_preprocessing
  
  slim = tf.contrib.slim
  
  
  def get_preprocessing(name, is_training=False):
    """Returns preprocessing_fn(image, height, width, **kwargs).
  
    Args:
      name: The name of the preprocessing function.
      is_training: `True` if the model is being used for training and `False`
        otherwise.
  
    Returns:
      preprocessing_fn: A function that preprocessing a single image (pre-batch).
        It has the following signature:
          image = preprocessing_fn(image, output_height, output_width, ...).
  
    Raises:
      ValueError: If Preprocessing `name` is not recognized.
    """
    preprocessing_fn_map = {
        'cifarnet': cifarnet_preprocessing,
        'inception': inception_preprocessing,
        'inception_v1': inception_preprocessing,
        'inception_v2': inception_preprocessing,
        'inception_v3': inception_preprocessing,
        'inception_v4': inception_preprocessing,
        'inception_resnet_v2': inception_preprocessing,
        'lenet': lenet_preprocessing,
        'mobilenet_v1': inception_preprocessing,
        'nasnet_mobile': inception_preprocessing,
        'nasnet_large': inception_preprocessing,
        'resnet_v1_50': vgg_preprocessing,
        'resnet_v1_101': vgg_preprocessing,
        'resnet_v1_152': vgg_preprocessing,
        'resnet_v1_200': vgg_preprocessing,
        'resnet_v2_50': vgg_preprocessing,
        'resnet_v2_101': vgg_preprocessing,
        'resnet_v2_152': vgg_preprocessing,
        'resnet_v2_200': vgg_preprocessing,
        'vgg': vgg_preprocessing,
        'vgg_a': vgg_preprocessing,
        'vgg_16': vgg_preprocessing,
        'vgg_19': vgg_preprocessing,
    }
  
    if name not in preprocessing_fn_map:
      raise ValueError('Preprocessing name [%s] was not recognized' % name)
  
    def preprocessing_fn(image, output_height, output_width, **kwargs):
      return preprocessing_fn_map[name].preprocess_image(
          image, output_height, output_width, is_training=is_training, **kwargs)
  
    return preprocessing_fn
  