text: # Copyright 2016 Google Inc. 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.
  # ==============================================================================
  
  """Tests for slim.inception."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  from alpharotate.libs.models.backbones.slim_nets import nets_factory
  
  slim = tf.contrib.slim
  
  
  class NetworksTest(tf.test.TestCase):
  
    def testGetNetworkFn(self):
      batch_size = 5
      num_classes = 1000
      for net in nets_factory.networks_map:
        with self.test_session():
          net_fn = nets_factory.get_network_fn(net, num_classes)
          # Most networks use 224 as their default_image_size
          image_size = getattr(net_fn, 'default_image_size', 224)
          inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
          logits, end_points = net_fn(inputs)
          self.assertTrue(isinstance(logits, tf.Tensor))
          self.assertTrue(isinstance(end_points, dict))
          self.assertEqual(logits.get_shape().as_list()[0], batch_size)
          self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
  
    def testGetNetworkFnArgScope(self):
      batch_size = 5
      num_classes = 10
      net = 'cifarnet'
      with self.test_session(use_gpu=True):
        net_fn = nets_factory.get_network_fn(net, num_classes)
        image_size = getattr(net_fn, 'default_image_size', 224)
        with slim.arg_scope([slim.model_variable, slim.variable],
                            device='/CPU:0'):
          inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
          net_fn(inputs)
        weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, 'CifarNet/conv1')[0]
        self.assertDeviceEqual('/CPU:0', weights.device)
  
  if __name__ == '__main__':
    tf.test.main()
  