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 functools
  
  import tensorflow as tf
  
  from nets import inception
  from nets import mobilenet_v1
  from nets import resnet_v2
  
  slim = tf.contrib.slim
  
  networks_map = {
                  'inception_v1': inception.inception_v1,
                  'inception_v2': inception.inception_v2,
                  'inception_v3': inception.inception_v3,
                  'inception_v4': inception.inception_v4,
                  'inception_resnet_v2': inception.inception_resnet_v2,
                  'resnet_v2_50': resnet_v2.resnet_v2_50,
                  'resnet_v2_101': resnet_v2.resnet_v2_101,
                  'resnet_v2_152': resnet_v2.resnet_v2_152,
                  'resnet_v2_200': resnet_v2.resnet_v2_200,
                  'mobilenet_v1': mobilenet_v1.mobilenet_v1,
                  'mobilenet_v1_075': mobilenet_v1.mobilenet_v1_075,
                  'mobilenet_v1_050': mobilenet_v1.mobilenet_v1_050,
                  'mobilenet_v1_025': mobilenet_v1.mobilenet_v1_025,
                 }
  
  arg_scopes_map = {'inception_v1': inception.inception_v3_arg_scope,
                    'inception_v2': inception.inception_v3_arg_scope,
                    'inception_v3': inception.inception_v3_arg_scope,
                    'inception_v4': inception.inception_v4_arg_scope,
                    'inception_resnet_v2': inception.inception_resnet_v2_arg_scope,
                    'resnet_v2_50': resnet_v2.resnet_arg_scope,
                    'resnet_v2_101': resnet_v2.resnet_arg_scope,
                    'resnet_v2_152': resnet_v2.resnet_arg_scope,
                    'resnet_v2_200': resnet_v2.resnet_arg_scope,
                    'mobilenet_v1': mobilenet_v1.mobilenet_v1_arg_scope,
                    'mobilenet_v1_075': mobilenet_v1.mobilenet_v1_arg_scope,
                    'mobilenet_v1_050': mobilenet_v1.mobilenet_v1_arg_scope,
                    'mobilenet_v1_025': mobilenet_v1.mobilenet_v1_arg_scope,
                   }
  
  
  def get_network_fn(name, num_classes, weight_decay=0.0, is_training=False):
    """Returns a network_fn such as `logits, end_points = network_fn(images)`.
  
    Args:
      name: The name of the network.
      num_classes: The number of classes to use for classification.
      weight_decay: The l2 coefficient for the model weights.
      is_training: `True` if the model is being used for training and `False`
        otherwise.
  
    Returns:
      network_fn: A function that applies the model to a batch of images. It has
        the following signature:
          logits, end_points = network_fn(images)
    Raises:
      ValueError: If network `name` is not recognized.
    """
    if name not in networks_map:
      raise ValueError('Name of network unknown %s' % name)
    arg_scope = arg_scopes_map[name](weight_decay=weight_decay)
    func = networks_map[name]
    @functools.wraps(func)
    def network_fn(images):
      with slim.arg_scope(arg_scope):
        return func(images, num_classes, is_training=is_training)
    if hasattr(func, 'default_image_size'):
      network_fn.default_image_size = func.default_image_size
  
    return network_fn
  