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.inception_v4."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  from research.slim.nets import inception
  
  
  class InceptionTest(tf.test.TestCase):
  
      def testBuildLogits(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_v4(inputs, num_classes)
          auxlogits = end_points['AuxLogits']
          predictions = end_points['Predictions']
          self.assertTrue(auxlogits.op.name.startswith('InceptionV4/AuxLogits'))
          self.assertListEqual(auxlogits.get_shape().as_list(),
                               [batch_size, num_classes])
          self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
          self.assertListEqual(logits.get_shape().as_list(),
                               [batch_size, num_classes])
          self.assertTrue(predictions.op.name.startswith(
              'InceptionV4/Logits/Predictions'))
          self.assertListEqual(predictions.get_shape().as_list(),
                               [batch_size, num_classes])
  
      def testBuildPreLogitsNetwork(self):
          batch_size = 5
          height, width = 299, 299
          num_classes = None
          inputs = tf.random_uniform((batch_size, height, width, 3))
          net, end_points = inception.inception_v4(inputs, num_classes)
          self.assertTrue(net.op.name.startswith('InceptionV4/Logits/AvgPool'))
          self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1536])
          self.assertFalse('Logits' in end_points)
          self.assertFalse('Predictions' in end_points)
  
      def testBuildWithoutAuxLogits(self):
          batch_size = 5
          height, width = 299, 299
          num_classes = 1000
          inputs = tf.random_uniform((batch_size, height, width, 3))
          logits, endpoints = inception.inception_v4(inputs, num_classes,
                                                     create_aux_logits=False)
          self.assertFalse('AuxLogits' in endpoints)
          self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
          self.assertListEqual(logits.get_shape().as_list(),
                               [batch_size, num_classes])
  
      def testAllEndPointsShapes(self):
          batch_size = 5
          height, width = 299, 299
          num_classes = 1000
          inputs = tf.random_uniform((batch_size, height, width, 3))
          _, end_points = inception.inception_v4(inputs, num_classes)
          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],
                              'Mixed_3a': [batch_size, 73, 73, 160],
                              'Mixed_4a': [batch_size, 71, 71, 192],
                              'Mixed_5a': [batch_size, 35, 35, 384],
                              # 4 x Inception-A blocks
                              'Mixed_5b': [batch_size, 35, 35, 384],
                              'Mixed_5c': [batch_size, 35, 35, 384],
                              'Mixed_5d': [batch_size, 35, 35, 384],
                              'Mixed_5e': [batch_size, 35, 35, 384],
                              # Reduction-A block
                              'Mixed_6a': [batch_size, 17, 17, 1024],
                              # 7 x Inception-B blocks
                              'Mixed_6b': [batch_size, 17, 17, 1024],
                              'Mixed_6c': [batch_size, 17, 17, 1024],
                              'Mixed_6d': [batch_size, 17, 17, 1024],
                              'Mixed_6e': [batch_size, 17, 17, 1024],
                              'Mixed_6f': [batch_size, 17, 17, 1024],
                              'Mixed_6g': [batch_size, 17, 17, 1024],
                              'Mixed_6h': [batch_size, 17, 17, 1024],
                              # Reduction-A block
                              'Mixed_7a': [batch_size, 8, 8, 1536],
                              # 3 x Inception-C blocks
                              'Mixed_7b': [batch_size, 8, 8, 1536],
                              'Mixed_7c': [batch_size, 8, 8, 1536],
                              'Mixed_7d': [batch_size, 8, 8, 1536],
                              # Logits and predictions
                              'AuxLogits': [batch_size, num_classes],
                              'global_pool': [batch_size, 1, 1, 1536],
                              'PreLogitsFlatten': [batch_size, 1536],
                              'Logits': [batch_size, num_classes],
                              'Predictions': [batch_size, num_classes]}
          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 testBuildBaseNetwork(self):
          batch_size = 5
          height, width = 299, 299
          inputs = tf.random_uniform((batch_size, height, width, 3))
          net, end_points = inception.inception_v4_base(inputs)
          self.assertTrue(net.op.name.startswith(
              'InceptionV4/Mixed_7d'))
          self.assertListEqual(net.get_shape().as_list(), [batch_size, 8, 8, 1536])
          expected_endpoints = [
              'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
              'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
              'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
              'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
              'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
          self.assertItemsEqual(end_points.keys(), expected_endpoints)
          for name, op in end_points.items():
              self.assertTrue(op.name.startswith('InceptionV4/' + name))
  
      def testBuildOnlyUpToFinalEndpoint(self):
          batch_size = 5
          height, width = 299, 299
          all_endpoints = [
              'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
              'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
              'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
              'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
              'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
          for index, endpoint in enumerate(all_endpoints):
              with tf.Graph().as_default():
                  inputs = tf.random_uniform((batch_size, height, width, 3))
                  out_tensor, end_points = inception.inception_v4_base(
                      inputs, final_endpoint=endpoint)
                  self.assertTrue(out_tensor.op.name.startswith(
                      'InceptionV4/' + endpoint))
                  self.assertItemsEqual(all_endpoints[:index + 1], end_points.keys())
  
      def testVariablesSetDevice(self):
          batch_size = 5
          height, width = 299, 299
          num_classes = 1000
          inputs = tf.random_uniform((batch_size, height, width, 3))
          # Force all Variables to reside on the device.
          with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
              inception.inception_v4(inputs, num_classes)
          with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
              inception.inception_v4(inputs, num_classes)
          for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'):
              self.assertDeviceEqual(v.device, '/cpu:0')
          for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'):
              self.assertDeviceEqual(v.device, '/gpu: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_v4(inputs, num_classes)
          self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
          self.assertListEqual(logits.get_shape().as_list(),
                               [batch_size, num_classes])
          pre_pool = end_points['Mixed_7d']
          self.assertListEqual(pre_pool.get_shape().as_list(),
                               [batch_size, 3, 3, 1536])
  
      def testGlobalPool(self):
          batch_size = 1
          height, width = 350, 400
          num_classes = 1000
          inputs = tf.random_uniform((batch_size, height, width, 3))
          logits, end_points = inception.inception_v4(inputs, num_classes)
          self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
          self.assertListEqual(logits.get_shape().as_list(),
                               [batch_size, num_classes])
          pre_pool = end_points['Mixed_7d']
          self.assertListEqual(pre_pool.get_shape().as_list(),
                               [batch_size, 9, 11, 1536])
  
      def testGlobalPoolUnknownImageShape(self):
          batch_size = 1
          height, width = 350, 400
          num_classes = 1000
          with self.test_session() as sess:
              inputs = tf.placeholder(tf.float32, (batch_size, None, None, 3))
              logits, end_points = inception.inception_v4(
                  inputs, num_classes, create_aux_logits=False)
              self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
              self.assertListEqual(logits.get_shape().as_list(),
                                   [batch_size, num_classes])
              pre_pool = end_points['Mixed_7d']
              images = tf.random_uniform((batch_size, height, width, 3))
              sess.run(tf.global_variables_initializer())
              logits_out, pre_pool_out = sess.run([logits, pre_pool],
                                                  {inputs: images.eval()})
              self.assertTupleEqual(logits_out.shape, (batch_size, num_classes))
              self.assertTupleEqual(pre_pool_out.shape, (batch_size, 9, 11, 1536))
  
      def testUnknownBatchSize(self):
          batch_size = 1
          height, width = 299, 299
          num_classes = 1000
          with self.test_session() as sess:
              inputs = tf.placeholder(tf.float32, (None, height, width, 3))
              logits, _ = inception.inception_v4(inputs, num_classes)
              self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
              self.assertListEqual(logits.get_shape().as_list(),
                                   [None, num_classes])
              images = tf.random_uniform((batch_size, height, width, 3))
              sess.run(tf.global_variables_initializer())
              output = sess.run(logits, {inputs: images.eval()})
              self.assertEqual(output.shape, (batch_size, num_classes))
  
      def testEvaluation(self):
          batch_size = 2
          height, width = 299, 299
          num_classes = 1000
          with self.test_session() as sess:
              eval_inputs = tf.random_uniform((batch_size, height, width, 3))
              logits, _ = inception.inception_v4(eval_inputs,
                                                 num_classes,
                                                 is_training=False)
              predictions = tf.argmax(logits, 1)
              sess.run(tf.global_variables_initializer())
              output = sess.run(predictions)
              self.assertEqual(output.shape, (batch_size,))
  
      def testTrainEvalWithReuse(self):
          train_batch_size = 5
          eval_batch_size = 2
          height, width = 150, 150
          num_classes = 1000
          with self.test_session() as sess:
              train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
              inception.inception_v4(train_inputs, num_classes)
              eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
              logits, _ = inception.inception_v4(eval_inputs,
                                                 num_classes,
                                                 is_training=False,
                                                 reuse=True)
              predictions = tf.argmax(logits, 1)
              sess.run(tf.global_variables_initializer())
              output = sess.run(predictions)
              self.assertEqual(output.shape, (eval_batch_size,))
  
  
  if __name__ == '__main__':
      tf.test.main()
  