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 nets.inception_v1."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import tensorflow as tf
  
  from nets import inception
  
  slim = tf.contrib.slim
  
  
  class InceptionV3Test(tf.test.TestCase):
  
    def testBuildClassificationNetwork(self):
      batch_size = 5
      height, width = 299, 299
      num_classes = 1000
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, end_points = inception.inception_v3(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      self.assertTrue('Predictions' in end_points)
      self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
                           [batch_size, num_classes])
  
    def testBuildBaseNetwork(self):
      batch_size = 5
      height, width = 299, 299
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      final_endpoint, end_points = inception.inception_v3_base(inputs)
      self.assertTrue(final_endpoint.op.name.startswith(
          'InceptionV3/Mixed_7c'))
      self.assertListEqual(final_endpoint.get_shape().as_list(),
                           [batch_size, 8, 8, 2048])
      expected_endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
                            'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
                            'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
                            'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
                            'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c']
      self.assertItemsEqual(end_points.keys(), expected_endpoints)
  
    def testBuildOnlyUptoFinalEndpoint(self):
      batch_size = 5
      height, width = 299, 299
      endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
                   'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
                   'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
                   'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
                   'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c']
  
      for index, endpoint in enumerate(endpoints):
        with tf.Graph().as_default():
          inputs = tf.random_uniform((batch_size, height, width, 3))
          out_tensor, end_points = inception.inception_v3_base(
              inputs, final_endpoint=endpoint)
          self.assertTrue(out_tensor.op.name.startswith(
              'InceptionV3/' + endpoint))
          self.assertItemsEqual(endpoints[:index+1], end_points)
  
    def testBuildAndCheckAllEndPointsUptoMixed7c(self):
      batch_size = 5
      height, width = 299, 299
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      _, end_points = inception.inception_v3_base(
          inputs, final_endpoint='Mixed_7c')
      endpoints_shapes = {'Conv2d_1a_3x3': [batch_size, 149, 149, 32],
                          'Conv2d_2a_3x3': [batch_size, 147, 147, 32],
                          'Conv2d_2b_3x3': [batch_size, 147, 147, 64],
                          'MaxPool_3a_3x3': [batch_size, 73, 73, 64],
                          'Conv2d_3b_1x1': [batch_size, 73, 73, 80],
                          'Conv2d_4a_3x3': [batch_size, 71, 71, 192],
                          'MaxPool_5a_3x3': [batch_size, 35, 35, 192],
                          'Mixed_5b': [batch_size, 35, 35, 256],
                          'Mixed_5c': [batch_size, 35, 35, 288],
                          'Mixed_5d': [batch_size, 35, 35, 288],
                          'Mixed_6a': [batch_size, 17, 17, 768],
                          'Mixed_6b': [batch_size, 17, 17, 768],
                          'Mixed_6c': [batch_size, 17, 17, 768],
                          'Mixed_6d': [batch_size, 17, 17, 768],
                          'Mixed_6e': [batch_size, 17, 17, 768],
                          'Mixed_7a': [batch_size, 8, 8, 1280],
                          'Mixed_7b': [batch_size, 8, 8, 2048],
                          'Mixed_7c': [batch_size, 8, 8, 2048]}
      self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
      for endpoint_name in endpoints_shapes:
        expected_shape = endpoints_shapes[endpoint_name]
        self.assertTrue(endpoint_name in end_points)
        self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                             expected_shape)
  
    def testModelHasExpectedNumberOfParameters(self):
      batch_size = 5
      height, width = 299, 299
      inputs = tf.random_uniform((batch_size, height, width, 3))
      with slim.arg_scope(inception.inception_v3_arg_scope()):
        inception.inception_v3_base(inputs)
      total_params, _ = slim.model_analyzer.analyze_vars(
          slim.get_model_variables())
      self.assertAlmostEqual(21802784, total_params)
  
    def testBuildEndPoints(self):
      batch_size = 5
      height, width = 299, 299
      num_classes = 1000
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      _, end_points = inception.inception_v3(inputs, num_classes)
      self.assertTrue('Logits' in end_points)
      logits = end_points['Logits']
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      self.assertTrue('AuxLogits' in end_points)
      aux_logits = end_points['AuxLogits']
      self.assertListEqual(aux_logits.get_shape().as_list(),
                           [batch_size, num_classes])
      self.assertTrue('Mixed_7c' in end_points)
      pre_pool = end_points['Mixed_7c']
      self.assertListEqual(pre_pool.get_shape().as_list(),
                           [batch_size, 8, 8, 2048])
      self.assertTrue('PreLogits' in end_points)
      pre_logits = end_points['PreLogits']
      self.assertListEqual(pre_logits.get_shape().as_list(),
                           [batch_size, 1, 1, 2048])
  
    def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
      batch_size = 5
      height, width = 299, 299
      num_classes = 1000
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      _, end_points = inception.inception_v3(inputs, num_classes)
  
      endpoint_keys = [key for key in end_points.keys()
                       if key.startswith('Mixed') or key.startswith('Conv')]
  
      _, end_points_with_multiplier = inception.inception_v3(
          inputs, num_classes, scope='depth_multiplied_net',
          depth_multiplier=0.5)
  
      for key in endpoint_keys:
        original_depth = end_points[key].get_shape().as_list()[3]
        new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
        self.assertEqual(0.5 * original_depth, new_depth)
  
    def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
      batch_size = 5
      height, width = 299, 299
      num_classes = 1000
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      _, end_points = inception.inception_v3(inputs, num_classes)
  
      endpoint_keys = [key for key in end_points.keys()
                       if key.startswith('Mixed') or key.startswith('Conv')]
  
      _, end_points_with_multiplier = inception.inception_v3(
          inputs, num_classes, scope='depth_multiplied_net',
          depth_multiplier=2.0)
  
      for key in endpoint_keys:
        original_depth = end_points[key].get_shape().as_list()[3]
        new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
        self.assertEqual(2.0 * original_depth, new_depth)
  
    def testRaiseValueErrorWithInvalidDepthMultiplier(self):
      batch_size = 5
      height, width = 299, 299
      num_classes = 1000
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      with self.assertRaises(ValueError):
        _ = inception.inception_v3(inputs, num_classes, depth_multiplier=-0.1)
      with self.assertRaises(ValueError):
        _ = inception.inception_v3(inputs, num_classes, depth_multiplier=0.0)
  
    def testHalfSizeImages(self):
      batch_size = 5
      height, width = 150, 150
      num_classes = 1000
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, end_points = inception.inception_v3(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_7c']
      self.assertListEqual(pre_pool.get_shape().as_list(),
                           [batch_size, 3, 3, 2048])
  
    def testUnknownImageShape(self):
      tf.reset_default_graph()
      batch_size = 2
      height, width = 299, 299
      num_classes = 1000
      input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
      with self.test_session() as sess:
        inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
        logits, end_points = inception.inception_v3(inputs, num_classes)
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
        pre_pool = end_points['Mixed_7c']
        feed_dict = {inputs: input_np}
        tf.global_variables_initializer().run()
        pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
        self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
  
    def testUnknowBatchSize(self):
      batch_size = 1
      height, width = 299, 299
      num_classes = 1000
  
      inputs = tf.placeholder(tf.float32, (None, height, width, 3))
      logits, _ = inception.inception_v3(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [None, num_classes])
      images = tf.random_uniform((batch_size, height, width, 3))
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        output = sess.run(logits, {inputs: images.eval()})
        self.assertEquals(output.shape, (batch_size, num_classes))
  
    def testEvaluation(self):
      batch_size = 2
      height, width = 299, 299
      num_classes = 1000
  
      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, _ = inception.inception_v3(eval_inputs, num_classes,
                                         is_training=False)
      predictions = tf.argmax(logits, 1)
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        output = sess.run(predictions)
        self.assertEquals(output.shape, (batch_size,))
  
    def testTrainEvalWithReuse(self):
      train_batch_size = 5
      eval_batch_size = 2
      height, width = 150, 150
      num_classes = 1000
  
      train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
      inception.inception_v3(train_inputs, num_classes)
      eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
      logits, _ = inception.inception_v3(eval_inputs, num_classes,
                                         is_training=False, reuse=True)
      predictions = tf.argmax(logits, 1)
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        output = sess.run(predictions)
        self.assertEquals(output.shape, (eval_batch_size,))
  
    def testLogitsNotSqueezed(self):
      num_classes = 25
      images = tf.random_uniform([1, 299, 299, 3])
      logits, _ = inception.inception_v3(images,
                                         num_classes=num_classes,
                                         spatial_squeeze=False)
  
      with self.test_session() as sess:
        tf.global_variables_initializer().run()
        logits_out = sess.run(logits)
        self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
  
  
  if __name__ == '__main__':
    tf.test.main()
  