code: # 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 InceptionV1Test(tf.test.TestCase):
  
    def testBuildClassificationNetwork(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = 1000
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, end_points = inception.inception_v1(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith(
          'InceptionV1/Logits/SpatialSqueeze'))
      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 testBuildPreLogitsNetwork(self):
      batch_size = 5
      height, width = 224, 224
      num_classes = None
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      net, end_points = inception.inception_v1(inputs, num_classes)
      self.assertTrue(net.op.name.startswith('InceptionV1/Logits/AvgPool'))
      self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1024])
      self.assertFalse('Logits' in end_points)
      self.assertFalse('Predictions' in end_points)
  
    def testBuildBaseNetwork(self):
      batch_size = 5
      height, width = 224, 224
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      mixed_6c, end_points = inception.inception_v1_base(inputs)
      self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c'))
      self.assertListEqual(mixed_6c.get_shape().as_list(),
                           [batch_size, 7, 7, 1024])
      expected_endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                            'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b',
                            'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c',
                            'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2',
                            'Mixed_5b', 'Mixed_5c']
      self.assertItemsEqual(end_points.keys(), expected_endpoints)
  
    def testBuildOnlyUptoFinalEndpoint(self):
      batch_size = 5
      height, width = 224, 224
      endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                   'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                   'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                   'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                   'Mixed_5c']
      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_v1_base(
              inputs, final_endpoint=endpoint)
          self.assertTrue(out_tensor.op.name.startswith(
              'InceptionV1/' + endpoint))
          self.assertItemsEqual(endpoints[:index+1], end_points.keys())
  
    def testBuildAndCheckAllEndPointsUptoMixed5c(self):
      batch_size = 5
      height, width = 224, 224
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      _, end_points = inception.inception_v1_base(inputs,
                                                  final_endpoint='Mixed_5c')
      endpoints_shapes = {
          'Conv2d_1a_7x7': [5, 112, 112, 64],
          'MaxPool_2a_3x3': [5, 56, 56, 64],
          'Conv2d_2b_1x1': [5, 56, 56, 64],
          'Conv2d_2c_3x3': [5, 56, 56, 192],
          'MaxPool_3a_3x3': [5, 28, 28, 192],
          'Mixed_3b': [5, 28, 28, 256],
          'Mixed_3c': [5, 28, 28, 480],
          'MaxPool_4a_3x3': [5, 14, 14, 480],
          'Mixed_4b': [5, 14, 14, 512],
          'Mixed_4c': [5, 14, 14, 512],
          'Mixed_4d': [5, 14, 14, 512],
          'Mixed_4e': [5, 14, 14, 528],
          'Mixed_4f': [5, 14, 14, 832],
          'MaxPool_5a_2x2': [5, 7, 7, 832],
          'Mixed_5b': [5, 7, 7, 832],
          'Mixed_5c': [5, 7, 7, 1024]
      }
  
      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 = 224, 224
      inputs = tf.random_uniform((batch_size, height, width, 3))
      with slim.arg_scope(inception.inception_v1_arg_scope()):
        inception.inception_v1_base(inputs)
      total_params, _ = slim.model_analyzer.analyze_vars(
          slim.get_model_variables())
      self.assertAlmostEqual(5607184, total_params)
  
    def testHalfSizeImages(self):
      batch_size = 5
      height, width = 112, 112
  
      inputs = tf.random_uniform((batch_size, height, width, 3))
      mixed_5c, _ = inception.inception_v1_base(inputs)
      self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
      self.assertListEqual(mixed_5c.get_shape().as_list(),
                           [batch_size, 4, 4, 1024])
  
    def testBuildBaseNetworkWithoutRootBlock(self):
      batch_size = 5
      height, width = 28, 28
      channels = 192
  
      inputs = tf.random_uniform((batch_size, height, width, channels))
      _, end_points = inception.inception_v1_base(
          inputs, include_root_block=False)
      endpoints_shapes = {
          'Mixed_3b': [5, 28, 28, 256],
          'Mixed_3c': [5, 28, 28, 480],
          'MaxPool_4a_3x3': [5, 14, 14, 480],
          'Mixed_4b': [5, 14, 14, 512],
          'Mixed_4c': [5, 14, 14, 512],
          'Mixed_4d': [5, 14, 14, 512],
          'Mixed_4e': [5, 14, 14, 528],
          'Mixed_4f': [5, 14, 14, 832],
          'MaxPool_5a_2x2': [5, 7, 7, 832],
          'Mixed_5b': [5, 7, 7, 832],
          'Mixed_5c': [5, 7, 7, 1024]
      }
  
      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 testUnknownImageShape(self):
      tf.reset_default_graph()
      batch_size = 2
      height, width = 224, 224
      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_v1(inputs, num_classes)
        self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
        pre_pool = end_points['Mixed_5c']
        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, 7, 7, 1024])
  
    def testGlobalPoolUnknownImageShape(self):
      tf.reset_default_graph()
      batch_size = 1
      height, width = 250, 300
      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_v1(inputs, num_classes,
                                                    global_pool=True)
        self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
        pre_pool = end_points['Mixed_5c']
        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, 10, 1024])
  
    def testUnknowBatchSize(self):
      batch_size = 1
      height, width = 224, 224
      num_classes = 1000
  
      inputs = tf.placeholder(tf.float32, (None, height, width, 3))
      logits, _ = inception.inception_v1(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionV1/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 = 224, 224
      num_classes = 1000
  
      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, _ = inception.inception_v1(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 = 224, 224
      num_classes = 1000
  
      train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
      inception.inception_v1(train_inputs, num_classes)
      eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
      logits, _ = inception.inception_v1(eval_inputs, num_classes, 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, 224, 224, 3])
      logits, _ = inception.inception_v1(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])
  
    def testNoBatchNormScaleByDefault(self):
      height, width = 224, 224
      num_classes = 1000
      inputs = tf.placeholder(tf.float32, (1, height, width, 3))
      with slim.arg_scope(inception.inception_v1_arg_scope()):
        inception.inception_v1(inputs, num_classes, is_training=False)
  
      self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])
  
    def testBatchNormScale(self):
      height, width = 224, 224
      num_classes = 1000
      inputs = tf.placeholder(tf.float32, (1, height, width, 3))
      with slim.arg_scope(
          inception.inception_v1_arg_scope(batch_norm_scale=True)):
        inception.inception_v1(inputs, num_classes, is_training=False)
  
      gamma_names = set(
          v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
      self.assertGreater(len(gamma_names), 0)
      for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
        self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)
  
  
  if __name__ == '__main__':
    tf.test.main()
  
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