example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2_test.py

   1 # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
   2 #
   3 # Licensed under the Apache License, Version 2.0 (the "License");
   4 # you may not use this file except in compliance with the License.
   5 # You may obtain a copy of the License at
   6 #
   7 # http://www.apache.org/licenses/LICENSE-2.0
   8 #
   9 # Unless required by applicable law or agreed to in writing, software
  10 # distributed under the License is distributed on an "AS IS" BASIS,
  11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12 # See the License for the specific language governing permissions and
  13 # limitations under the License.
  14 # ==============================================================================
  15 """Tests for slim.inception_resnet_v2."""
  16 from __future__ import absolute_import
  17 from __future__ import division
  18 from __future__ import print_function
  19
  20 import tensorflow as tf
  21
  22 from nets import inception
  23
  24
  25 class InceptionTest(tf.test.TestCase):
  26
  27   def testBuildLogits(self):
  28     batch_size = 5
  29     height, width = 299, 299
  30     num_classes = 1000
  31     with self.test_session():
  32       inputs = tf.random_uniform((batch_size, height, width, 3))
  33       logits, endpoints = inception.inception_resnet_v2(inputs, num_classes)
  34       self.assertTrue('AuxLogits' in endpoints)
  35       auxlogits = endpoints['AuxLogits']
  36       self.assertTrue(
  37           auxlogits.op.name.startswith('InceptionResnetV2/AuxLogits'))
  38       self.assertListEqual(auxlogits.get_shape().as_list(),
  39                            [batch_size, num_classes])
  40       self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
  41       self.assertListEqual(logits.get_shape().as_list(),
  42                            [batch_size, num_classes])
  43
  44   def testBuildWithoutAuxLogits(self):
  45     batch_size = 5
  46     height, width = 299, 299
  47     num_classes = 1000
  48     with self.test_session():
  49       inputs = tf.random_uniform((batch_size, height, width, 3))
  50       logits, endpoints = inception.inception_resnet_v2(inputs, num_classes,
  51                                                         create_aux_logits=False)
  52       self.assertTrue('AuxLogits' not in endpoints)
  53       self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
  54       self.assertListEqual(logits.get_shape().as_list(),
  55                            [batch_size, num_classes])
  56
  57   def testBuildEndPoints(self):
  58     batch_size = 5
  59     height, width = 299, 299
  60     num_classes = 1000
  61     with self.test_session():
  62       inputs = tf.random_uniform((batch_size, height, width, 3))
  63       _, end_points = inception.inception_resnet_v2(inputs, num_classes)
  64       self.assertTrue('Logits' in end_points)
  65       logits = end_points['Logits']
  66       self.assertListEqual(logits.get_shape().as_list(),
  67                            [batch_size, num_classes])
  68       self.assertTrue('AuxLogits' in end_points)
  69       aux_logits = end_points['AuxLogits']
  70       self.assertListEqual(aux_logits.get_shape().as_list(),
  71                            [batch_size, num_classes])
  72       pre_pool = end_points['Conv2d_7b_1x1']
  73       self.assertListEqual(pre_pool.get_shape().as_list(),
  74                            [batch_size, 8, 8, 1536])
  75
  76   def testBuildBaseNetwork(self):
  77     batch_size = 5
  78     height, width = 299, 299
  79
  80     inputs = tf.random_uniform((batch_size, height, width, 3))
  81     net, end_points = inception.inception_resnet_v2_base(inputs)
  82     self.assertTrue(net.op.name.startswith('InceptionResnetV2/Conv2d_7b_1x1'))
  83     self.assertListEqual(net.get_shape().as_list(),
  84                          [batch_size, 8, 8, 1536])
  85     expected_endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
  86                           'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
  87                           'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_6a',
  88                           'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
  89     self.assertItemsEqual(end_points.keys(), expected_endpoints)
  90
  91   def testBuildOnlyUptoFinalEndpoint(self):
  92     batch_size = 5
  93     height, width = 299, 299
  94     endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
  95                  'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
  96                  'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_6a',
  97                  'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
  98     for index, endpoint in enumerate(endpoints):
  99       with tf.Graph().as_default():
 100         inputs = tf.random_uniform((batch_size, height, width, 3))
 101         out_tensor, end_points = inception.inception_resnet_v2_base(
 102             inputs, final_endpoint=endpoint)
 103         if endpoint != 'PreAuxLogits':
 104           self.assertTrue(out_tensor.op.name.startswith(
 105               'InceptionResnetV2/' + endpoint))
 106         self.assertItemsEqual(endpoints[:index+1], end_points)
 107
 108   def testBuildAndCheckAllEndPointsUptoPreAuxLogits(self):
 109     batch_size = 5
 110     height, width = 299, 299
 111
 112     inputs = tf.random_uniform((batch_size, height, width, 3))
 113     _, end_points = inception.inception_resnet_v2_base(
 114         inputs, final_endpoint='PreAuxLogits')
 115     endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32],
 116                         'Conv2d_2a_3x3': [5, 147, 147, 32],
 117                         'Conv2d_2b_3x3': [5, 147, 147, 64],
 118                         'MaxPool_3a_3x3': [5, 73, 73, 64],
 119                         'Conv2d_3b_1x1': [5, 73, 73, 80],
 120                         'Conv2d_4a_3x3': [5, 71, 71, 192],
 121                         'MaxPool_5a_3x3': [5, 35, 35, 192],
 122                         'Mixed_5b': [5, 35, 35, 320],
 123                         'Mixed_6a': [5, 17, 17, 1088],
 124                         'PreAuxLogits': [5, 17, 17, 1088]
 125                        }
 126
 127     self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
 128     for endpoint_name in endpoints_shapes:
 129       expected_shape = endpoints_shapes[endpoint_name]
 130       self.assertTrue(endpoint_name in end_points)
 131       self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
 132                            expected_shape)
 133
 134   def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithAlignedFeatureMaps(self):
 135     batch_size = 5
 136     height, width = 299, 299
 137
 138     inputs = tf.random_uniform((batch_size, height, width, 3))
 139     _, end_points = inception.inception_resnet_v2_base(
 140         inputs, final_endpoint='PreAuxLogits', align_feature_maps=True)
 141     endpoints_shapes = {'Conv2d_1a_3x3': [5, 150, 150, 32],
 142                         'Conv2d_2a_3x3': [5, 150, 150, 32],
 143                         'Conv2d_2b_3x3': [5, 150, 150, 64],
 144                         'MaxPool_3a_3x3': [5, 75, 75, 64],
 145                         'Conv2d_3b_1x1': [5, 75, 75, 80],
 146                         'Conv2d_4a_3x3': [5, 75, 75, 192],
 147                         'MaxPool_5a_3x3': [5, 38, 38, 192],
 148                         'Mixed_5b': [5, 38, 38, 320],
 149                         'Mixed_6a': [5, 19, 19, 1088],
 150                         'PreAuxLogits': [5, 19, 19, 1088]
 151                        }
 152
 153     self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
 154     for endpoint_name in endpoints_shapes:
 155       expected_shape = endpoints_shapes[endpoint_name]
 156       self.assertTrue(endpoint_name in end_points)
 157       self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
 158                            expected_shape)
 159
 160   def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithOutputStrideEight(self):
 161     batch_size = 5
 162     height, width = 299, 299
 163
 164     inputs = tf.random_uniform((batch_size, height, width, 3))
 165     _, end_points = inception.inception_resnet_v2_base(
 166         inputs, final_endpoint='PreAuxLogits', output_stride=8)
 167     endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32],
 168                         'Conv2d_2a_3x3': [5, 147, 147, 32],
 169                         'Conv2d_2b_3x3': [5, 147, 147, 64],
 170                         'MaxPool_3a_3x3': [5, 73, 73, 64],
 171                         'Conv2d_3b_1x1': [5, 73, 73, 80],
 172                         'Conv2d_4a_3x3': [5, 71, 71, 192],
 173                         'MaxPool_5a_3x3': [5, 35, 35, 192],
 174                         'Mixed_5b': [5, 35, 35, 320],
 175                         'Mixed_6a': [5, 33, 33, 1088],
 176                         'PreAuxLogits': [5, 33, 33, 1088]
 177                        }
 178
 179     self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
 180     for endpoint_name in endpoints_shapes:
 181       expected_shape = endpoints_shapes[endpoint_name]
 182       self.assertTrue(endpoint_name in end_points)
 183       self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
 184                            expected_shape)
 185
 186   def testVariablesSetDevice(self):
 187     batch_size = 5
 188     height, width = 299, 299
 189     num_classes = 1000
 190     with self.test_session():
 191       inputs = tf.random_uniform((batch_size, height, width, 3))
 192       # Force all Variables to reside on the device.
 193       with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
 194         inception.inception_resnet_v2(inputs, num_classes)
 195       with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
 196         inception.inception_resnet_v2(inputs, num_classes)
 197       for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'):
 198         self.assertDeviceEqual(v.device, '/cpu:0')
 199       for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'):
 200         self.assertDeviceEqual(v.device, '/gpu:0')
 201
 202   def testHalfSizeImages(self):
 203     batch_size = 5
 204     height, width = 150, 150
 205     num_classes = 1000
 206     with self.test_session():
 207       inputs = tf.random_uniform((batch_size, height, width, 3))
 208       logits, end_points = inception.inception_resnet_v2(inputs, num_classes)
 209       self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
 210       self.assertListEqual(logits.get_shape().as_list(),
 211                            [batch_size, num_classes])
 212       pre_pool = end_points['Conv2d_7b_1x1']
 213       self.assertListEqual(pre_pool.get_shape().as_list(),
 214                            [batch_size, 3, 3, 1536])
 215
 216   def testUnknownBatchSize(self):
 217     batch_size = 1
 218     height, width = 299, 299
 219     num_classes = 1000
 220     with self.test_session() as sess:
 221       inputs = tf.placeholder(tf.float32, (None, height, width, 3))
 222       logits, _ = inception.inception_resnet_v2(inputs, num_classes)
 223       self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
 224       self.assertListEqual(logits.get_shape().as_list(),
 225                            [None, num_classes])
 226       images = tf.random_uniform((batch_size, height, width, 3))
 227       sess.run(tf.global_variables_initializer())
 228       output = sess.run(logits, {inputs: images.eval()})
 229       self.assertEquals(output.shape, (batch_size, num_classes))
 230
 231   def testEvaluation(self):
 232     batch_size = 2
 233     height, width = 299, 299
 234     num_classes = 1000
 235     with self.test_session() as sess:
 236       eval_inputs = tf.random_uniform((batch_size, height, width, 3))
 237       logits, _ = inception.inception_resnet_v2(eval_inputs,
 238                                                 num_classes,
 239                                                 is_training=False)
 240       predictions = tf.argmax(logits, 1)
 241       sess.run(tf.global_variables_initializer())
 242       output = sess.run(predictions)
 243       self.assertEquals(output.shape, (batch_size,))
 244
 245   def testTrainEvalWithReuse(self):
 246     train_batch_size = 5
 247     eval_batch_size = 2
 248     height, width = 150, 150
 249     num_classes = 1000
 250     with self.test_session() as sess:
 251       train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
 252       inception.inception_resnet_v2(train_inputs, num_classes)
 253       eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
 254       logits, _ = inception.inception_resnet_v2(eval_inputs,
 255                                                 num_classes,
 256                                                 is_training=False,
 257                                                 reuse=True)
 258       predictions = tf.argmax(logits, 1)
 259       sess.run(tf.global_variables_initializer())
 260       output = sess.run(predictions)
 261       self.assertEquals(output.shape, (eval_batch_size,))
 262
 263
 264 if __name__ == '__main__':
 265   tf.test.main()