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.
  # ==============================================================================
  """Tests for slim.nets.alexnet."""
  
  
  
  
  import tensorflow as tf
  
  from nets import alexnet
  
  slim = tf.contrib.slim
  
  
  class AlexnetV2Test(tf.test.TestCase):
  
    def testBuild(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
      with self.test_session():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        logits, _ = alexnet.alexnet_v2(inputs, num_classes)
        self.assertEqual(logits.op.name, 'alexnet_v2/fc8/squeezed')
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
  
    def testFullyConvolutional(self):
      batch_size = 1
      height, width = 300, 400
      num_classes = 1000
      with self.test_session():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        logits, _ = alexnet.alexnet_v2(inputs, num_classes, spatial_squeeze=False)
        self.assertEqual(logits.op.name, 'alexnet_v2/fc8/BiasAdd')
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, 4, 7, num_classes])
  
    def testGlobalPool(self):
      batch_size = 1
      height, width = 256, 256
      num_classes = 1000
      with self.test_session():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        logits, _ = alexnet.alexnet_v2(inputs, num_classes, spatial_squeeze=False,
                                       global_pool=True)
        self.assertEqual(logits.op.name, 'alexnet_v2/fc8/BiasAdd')
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, 1, 1, num_classes])
  
    def testEndPoints(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
      with self.test_session():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        _, end_points = alexnet.alexnet_v2(inputs, num_classes)
        expected_names = ['alexnet_v2/conv1',
                          'alexnet_v2/pool1',
                          'alexnet_v2/conv2',
                          'alexnet_v2/pool2',
                          'alexnet_v2/conv3',
                          'alexnet_v2/conv4',
                          'alexnet_v2/conv5',
                          'alexnet_v2/pool5',
                          'alexnet_v2/fc6',
                          'alexnet_v2/fc7',
                          'alexnet_v2/fc8'
                         ]
        self.assertSetEqual(set(end_points.keys()), set(expected_names))
  
    def testNoClasses(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = None
      with self.test_session():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        net, end_points = alexnet.alexnet_v2(inputs, num_classes)
        expected_names = ['alexnet_v2/conv1',
                          'alexnet_v2/pool1',
                          'alexnet_v2/conv2',
                          'alexnet_v2/pool2',
                          'alexnet_v2/conv3',
                          'alexnet_v2/conv4',
                          'alexnet_v2/conv5',
                          'alexnet_v2/pool5',
                          'alexnet_v2/fc6',
                          'alexnet_v2/fc7'
                         ]
        self.assertSetEqual(set(end_points.keys()), set(expected_names))
        self.assertTrue(net.op.name.startswith('alexnet_v2/fc7'))
        self.assertListEqual(net.get_shape().as_list(),
                             [batch_size, 1, 1, 4096])
  
    def testModelVariables(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
      with self.test_session():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        alexnet.alexnet_v2(inputs, num_classes)
        expected_names = ['alexnet_v2/conv1/weights',
                          'alexnet_v2/conv1/biases',
                          'alexnet_v2/conv2/weights',
                          'alexnet_v2/conv2/biases',
                          'alexnet_v2/conv3/weights',
                          'alexnet_v2/conv3/biases',
                          'alexnet_v2/conv4/weights',
                          'alexnet_v2/conv4/biases',
                          'alexnet_v2/conv5/weights',
                          'alexnet_v2/conv5/biases',
                          'alexnet_v2/fc6/weights',
                          'alexnet_v2/fc6/biases',
                          'alexnet_v2/fc7/weights',
                          'alexnet_v2/fc7/biases',
                          'alexnet_v2/fc8/weights',
                          'alexnet_v2/fc8/biases',
                         ]
        model_variables = [v.op.name for v in slim.get_model_variables()]
        self.assertSetEqual(set(model_variables), set(expected_names))
  
    def testEvaluation(self):
      batch_size = 2
      height, width = 224, 224
      num_classes = 1000
      with self.test_session():
        eval_inputs = tf.random_uniform((batch_size, height, width, 3))
        logits, _ = alexnet.alexnet_v2(eval_inputs, is_training=False)
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
        predictions = tf.argmax(logits, 1)
        self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
  
    def testTrainEvalWithReuse(self):
      train_batch_size = 2
      eval_batch_size = 1
      train_height, train_width = 224, 224
      eval_height, eval_width = 300, 400
      num_classes = 1000
      with self.test_session():
        train_inputs = tf.random_uniform(
            (train_batch_size, train_height, train_width, 3))
        logits, _ = alexnet.alexnet_v2(train_inputs)
        self.assertListEqual(logits.get_shape().as_list(),
                             [train_batch_size, num_classes])
        tf.get_variable_scope().reuse_variables()
        eval_inputs = tf.random_uniform(
            (eval_batch_size, eval_height, eval_width, 3))
        logits, _ = alexnet.alexnet_v2(eval_inputs, is_training=False,
                                       spatial_squeeze=False)
        self.assertListEqual(logits.get_shape().as_list(),
                             [eval_batch_size, 4, 7, num_classes])
        logits = tf.reduce_mean(logits, [1, 2])
        predictions = tf.argmax(logits, 1)
        self.assertEqual(predictions.get_shape().as_list(), [eval_batch_size])
  
    def testForward(self):
      batch_size = 1
      height, width = 224, 224
      with self.test_session() as sess:
        inputs = tf.random_uniform((batch_size, height, width, 3))
        logits, _ = alexnet.alexnet_v2(inputs)
        sess.run(tf.global_variables_initializer())
        output = sess.run(logits)
        self.assertTrue(output.any())
  
  if __name__ == '__main__':
    tf.test.main()
  