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_v2."""
  
  from __future__ import absolute_import, division, print_function
  
  import numpy as np
  import tensorflow as tf
  from nets import inception
  
  slim = tf.contrib.slim
  
  
  class InceptionV2Test(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_v2(inputs, num_classes)
          self.assertTrue(logits.op.name.startswith(
              'InceptionV2/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_v2(inputs, num_classes)
          self.assertTrue(net.op.name.startswith('InceptionV2/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_5c, end_points = inception.inception_v2_base(inputs)
          self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c'))
          self.assertListEqual(mixed_5c.get_shape().as_list(),
                               [batch_size, 7, 7, 1024])
          expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b',
                                'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a',
                                'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7',
                                'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3',
                                'MaxPool_3a_3x3']
          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',
                       'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
                       'Mixed_5a', '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_v2_base(
                      inputs, final_endpoint=endpoint)
                  self.assertTrue(out_tensor.op.name.startswith(
                      'InceptionV2/' + 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_v2_base(inputs,
                                                      final_endpoint='Mixed_5c')
          endpoints_shapes = {'Mixed_3b': [batch_size, 28, 28, 256],
                              'Mixed_3c': [batch_size, 28, 28, 320],
                              'Mixed_4a': [batch_size, 14, 14, 576],
                              'Mixed_4b': [batch_size, 14, 14, 576],
                              'Mixed_4c': [batch_size, 14, 14, 576],
                              'Mixed_4d': [batch_size, 14, 14, 576],
                              'Mixed_4e': [batch_size, 14, 14, 576],
                              'Mixed_5a': [batch_size, 7, 7, 1024],
                              'Mixed_5b': [batch_size, 7, 7, 1024],
                              'Mixed_5c': [batch_size, 7, 7, 1024],
                              'Conv2d_1a_7x7': [batch_size, 112, 112, 64],
                              'MaxPool_2a_3x3': [batch_size, 56, 56, 64],
                              'Conv2d_2b_1x1': [batch_size, 56, 56, 64],
                              'Conv2d_2c_3x3': [batch_size, 56, 56, 192],
                              'MaxPool_3a_3x3': [batch_size, 28, 28, 192]}
          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_v2_arg_scope()):
              inception.inception_v2_base(inputs)
          total_params, _ = slim.model_analyzer.analyze_vars(
              slim.get_model_variables())
          self.assertAlmostEqual(10173112, total_params)
  
      def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
          batch_size = 5
          height, width = 224, 224
          num_classes = 1000
  
          inputs = tf.random_uniform((batch_size, height, width, 3))
          _, end_points = inception.inception_v2(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_v2(
              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 = 224, 224
          num_classes = 1000
  
          inputs = tf.random_uniform((batch_size, height, width, 3))
          _, end_points = inception.inception_v2(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_v2(
              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 = 224, 224
          num_classes = 1000
  
          inputs = tf.random_uniform((batch_size, height, width, 3))
          with self.assertRaises(ValueError):
              _ = inception.inception_v2(inputs, num_classes, depth_multiplier=-0.1)
          with self.assertRaises(ValueError):
              _ = inception.inception_v2(inputs, num_classes, depth_multiplier=0.0)
  
      def testBuildEndPointsWithUseSeparableConvolutionFalse(self):
          batch_size = 5
          height, width = 224, 224
  
          inputs = tf.random_uniform((batch_size, height, width, 3))
          _, end_points = inception.inception_v2_base(inputs)
  
          endpoint_keys = [
              key for key in end_points.keys()
              if key.startswith('Mixed') or key.startswith('Conv')
          ]
  
          _, end_points_with_replacement = inception.inception_v2_base(
              inputs, use_separable_conv=False)
  
          # The endpoint shapes must be equal to the original shape even when the
          # separable convolution is replaced with a normal convolution.
          for key in endpoint_keys:
              original_shape = end_points[key].get_shape().as_list()
              self.assertTrue(key in end_points_with_replacement)
              new_shape = end_points_with_replacement[key].get_shape().as_list()
              self.assertListEqual(original_shape, new_shape)
  
      def testBuildEndPointsNCHWDataFormat(self):
          batch_size = 5
          height, width = 224, 224
  
          inputs = tf.random_uniform((batch_size, height, width, 3))
          _, end_points = inception.inception_v2_base(inputs)
  
          endpoint_keys = [
              key for key in end_points.keys()
              if key.startswith('Mixed') or key.startswith('Conv')
          ]
  
          inputs_in_nchw = tf.random_uniform((batch_size, 3, height, width))
          _, end_points_with_replacement = inception.inception_v2_base(
              inputs_in_nchw, use_separable_conv=False, data_format='NCHW')
  
          # With the 'NCHW' data format, all endpoint activations have a transposed
          # shape from the original shape with the 'NHWC' layout.
          for key in endpoint_keys:
              transposed_original_shape = tf.transpose(
                  end_points[key], [0, 3, 1, 2]).get_shape().as_list()
              self.assertTrue(key in end_points_with_replacement)
              new_shape = end_points_with_replacement[key].get_shape().as_list()
              self.assertListEqual(transposed_original_shape, new_shape)
  
      def testBuildErrorsForDataFormats(self):
          batch_size = 5
          height, width = 224, 224
  
          inputs = tf.random_uniform((batch_size, height, width, 3))
  
          # 'NCWH' data format is not supported.
          with self.assertRaises(ValueError):
              _ = inception.inception_v2_base(inputs, data_format='NCWH')
  
          # 'NCHW' data format is not supported for separable convolution.
          with self.assertRaises(ValueError):
              _ = inception.inception_v2_base(inputs, data_format='NCHW')
  
      def testHalfSizeImages(self):
          batch_size = 5
          height, width = 112, 112
          num_classes = 1000
  
          inputs = tf.random_uniform((batch_size, height, width, 3))
          logits, end_points = inception.inception_v2(inputs, num_classes)
          self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
          self.assertListEqual(logits.get_shape().as_list(),
                               [batch_size, num_classes])
          pre_pool = end_points['Mixed_5c']
          self.assertListEqual(pre_pool.get_shape().as_list(),
                               [batch_size, 4, 4, 1024])
  
      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_v2(inputs, num_classes)
              self.assertTrue(logits.op.name.startswith('InceptionV2/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_v2(inputs, num_classes,
                                                          global_pool=True)
              self.assertTrue(logits.op.name.startswith('InceptionV2/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_v2(inputs, num_classes)
          self.assertTrue(logits.op.name.startswith('InceptionV2/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_v2(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_v2(train_inputs, num_classes)
          eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
          logits, _ = inception.inception_v2(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_v2(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()
  