text: # Copyright 2017 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."""
  
  
  
  
  import functools
  
  import tensorflow as tf
  
  from nets import alexnet
  from nets import cifarnet
  from nets import inception
  from nets import lenet
  from nets import mobilenet_v1
  from nets import overfeat
  from nets import resnet_v1
  from nets import resnet_v2
  from nets import vgg
  from nets.mobilenet import mobilenet_v2
  from nets.nasnet import nasnet
  from nets.nasnet import pnasnet
  
  slim = tf.contrib.slim
  
  networks_map = {'alexnet_v2': alexnet.alexnet_v2,
                  'cifarnet': cifarnet.cifarnet,
                  'overfeat': overfeat.overfeat,
                  'vgg_a': vgg.vgg_a,
                  'vgg_16': vgg.vgg_16,
                  'vgg_19': vgg.vgg_19,
                  '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,
                  'lenet': lenet.lenet,
                  'resnet_v1_50': resnet_v1.resnet_v1_50,
                  'resnet_v1_101': resnet_v1.resnet_v1_101,
                  'resnet_v1_152': resnet_v1.resnet_v1_152,
                  'resnet_v1_200': resnet_v1.resnet_v1_200,
                  '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,
                  'mobilenet_v2': mobilenet_v2.mobilenet,
                  'nasnet_cifar': nasnet.build_nasnet_cifar,
                  'nasnet_mobile': nasnet.build_nasnet_mobile,
                  'nasnet_large': nasnet.build_nasnet_large,
                  'pnasnet_large': pnasnet.build_pnasnet_large,
                 }
  
  arg_scopes_map = {'alexnet_v2': alexnet.alexnet_v2_arg_scope,
                    'cifarnet': cifarnet.cifarnet_arg_scope,
                    'overfeat': overfeat.overfeat_arg_scope,
                    'vgg_a': vgg.vgg_arg_scope,
                    'vgg_16': vgg.vgg_arg_scope,
                    'vgg_19': vgg.vgg_arg_scope,
                    '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,
                    'lenet': lenet.lenet_arg_scope,
                    'resnet_v1_50': resnet_v1.resnet_arg_scope,
                    'resnet_v1_101': resnet_v1.resnet_arg_scope,
                    'resnet_v1_152': resnet_v1.resnet_arg_scope,
                    'resnet_v1_200': resnet_v1.resnet_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,
                    'mobilenet_v2': mobilenet_v2.training_scope,
                    'nasnet_cifar': nasnet.nasnet_cifar_arg_scope,
                    'nasnet_mobile': nasnet.nasnet_mobile_arg_scope,
                    'nasnet_large': nasnet.nasnet_large_arg_scope,
                    'pnasnet_large': pnasnet.pnasnet_large_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. If 0 or None,
        the logits layer is omitted and its input features are returned instead.
      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:
            net, end_points = network_fn(images)
        The `images` input is a tensor of shape [batch_size, height, width, 3]
        with height = width = network_fn.default_image_size. (The permissibility
        and treatment of other sizes depends on the network_fn.)
        The returned `end_points` are a dictionary of intermediate activations.
        The returned `net` is the topmost layer, depending on `num_classes`:
        If `num_classes` was a non-zero integer, `net` is a logits tensor
        of shape [batch_size, num_classes].
        If `num_classes` was 0 or `None`, `net` is a tensor with the input
        to the logits layer of shape [batch_size, 1, 1, num_features] or
        [batch_size, num_features]. Dropout has not been applied to this
        (even if the network's original classification does); it remains for
        the caller to do this or not.
  
    Raises:
      ValueError: If network `name` is not recognized.
    """
    if name not in networks_map:
      raise ValueError('Name of network unknown %s' % name)
    func = networks_map[name]
    @functools.wraps(func)
    def network_fn(images, **kwargs):
      arg_scope = arg_scopes_map[name](weight_decay=weight_decay)
      with slim.arg_scope(arg_scope):
        return func(images, num_classes, is_training=is_training, **kwargs)
    if hasattr(func, 'default_image_size'):
      network_fn.default_image_size = func.default_image_size
  
    return network_fn
  