example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v1_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_nets.inception_v1."""
  16
  17 from __future__ import absolute_import
  18 from __future__ import division
  19 from __future__ import print_function
  20
  21 import numpy as np
  22 import tensorflow as tf
  23
  24 from nets import inception
  25
  26 slim = tf.contrib.slim
  27
  28
  29 class InceptionV1Test(tf.test.TestCase):
  30
  31   def testBuildClassificationNetwork(self):
  32     batch_size = 5
  33     height, width = 224, 224
  34     num_classes = 1000
  35
  36     inputs = tf.random_uniform((batch_size, height, width, 3))
  37     logits, end_points = inception.inception_v1(inputs, num_classes)
  38     self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
  39     self.assertListEqual(logits.get_shape().as_list(),
  40                          [batch_size, num_classes])
  41     self.assertTrue('Predictions' in end_points)
  42     self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
  43                          [batch_size, num_classes])
  44
  45   def testBuildBaseNetwork(self):
  46     batch_size = 5
  47     height, width = 224, 224
  48
  49     inputs = tf.random_uniform((batch_size, height, width, 3))
  50     mixed_6c, end_points = inception.inception_v1_base(inputs)
  51     self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c'))
  52     self.assertListEqual(mixed_6c.get_shape().as_list(),
  53                          [batch_size, 7, 7, 1024])
  54     expected_endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
  55                           'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b',
  56                           'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c',
  57                           'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2',
  58                           'Mixed_5b', 'Mixed_5c']
  59     self.assertItemsEqual(end_points.keys(), expected_endpoints)
  60
  61   def testBuildOnlyUptoFinalEndpoint(self):
  62     batch_size = 5
  63     height, width = 224, 224
  64     endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
  65                  'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
  66                  'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
  67                  'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
  68                  'Mixed_5c']
  69     for index, endpoint in enumerate(endpoints):
  70       with tf.Graph().as_default():
  71         inputs = tf.random_uniform((batch_size, height, width, 3))
  72         out_tensor, end_points = inception.inception_v1_base(
  73             inputs, final_endpoint=endpoint)
  74         self.assertTrue(out_tensor.op.name.startswith(
  75             'InceptionV1/' + endpoint))
  76         self.assertItemsEqual(endpoints[:index+1], end_points)
  77
  78   def testBuildAndCheckAllEndPointsUptoMixed5c(self):
  79     batch_size = 5
  80     height, width = 224, 224
  81
  82     inputs = tf.random_uniform((batch_size, height, width, 3))
  83     _, end_points = inception.inception_v1_base(inputs,
  84                                                 final_endpoint='Mixed_5c')
  85     endpoints_shapes = {'Conv2d_1a_7x7': [5, 112, 112, 64],
  86                         'MaxPool_2a_3x3': [5, 56, 56, 64],
  87                         'Conv2d_2b_1x1': [5, 56, 56, 64],
  88                         'Conv2d_2c_3x3': [5, 56, 56, 192],
  89                         'MaxPool_3a_3x3': [5, 28, 28, 192],
  90                         'Mixed_3b': [5, 28, 28, 256],
  91                         'Mixed_3c': [5, 28, 28, 480],
  92                         'MaxPool_4a_3x3': [5, 14, 14, 480],
  93                         'Mixed_4b': [5, 14, 14, 512],
  94                         'Mixed_4c': [5, 14, 14, 512],
  95                         'Mixed_4d': [5, 14, 14, 512],
  96                         'Mixed_4e': [5, 14, 14, 528],
  97                         'Mixed_4f': [5, 14, 14, 832],
  98                         'MaxPool_5a_2x2': [5, 7, 7, 832],
  99                         'Mixed_5b': [5, 7, 7, 832],
 100                         'Mixed_5c': [5, 7, 7, 1024]}
 101
 102     self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
 103     for endpoint_name in endpoints_shapes:
 104       expected_shape = endpoints_shapes[endpoint_name]
 105       self.assertTrue(endpoint_name in end_points)
 106       self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
 107                            expected_shape)
 108
 109   def testModelHasExpectedNumberOfParameters(self):
 110     batch_size = 5
 111     height, width = 224, 224
 112     inputs = tf.random_uniform((batch_size, height, width, 3))
 113     with slim.arg_scope(inception.inception_v1_arg_scope()):
 114       inception.inception_v1_base(inputs)
 115     total_params, _ = slim.model_analyzer.analyze_vars(
 116         slim.get_model_variables())
 117     self.assertAlmostEqual(5607184, total_params)
 118
 119   def testHalfSizeImages(self):
 120     batch_size = 5
 121     height, width = 112, 112
 122
 123     inputs = tf.random_uniform((batch_size, height, width, 3))
 124     mixed_5c, _ = inception.inception_v1_base(inputs)
 125     self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
 126     self.assertListEqual(mixed_5c.get_shape().as_list(),
 127                          [batch_size, 4, 4, 1024])
 128
 129   def testUnknownImageShape(self):
 130     tf.reset_default_graph()
 131     batch_size = 2
 132     height, width = 224, 224
 133     num_classes = 1000
 134     input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
 135     with self.test_session() as sess:
 136       inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
 137       logits, end_points = inception.inception_v1(inputs, num_classes)
 138       self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
 139       self.assertListEqual(logits.get_shape().as_list(),
 140                            [batch_size, num_classes])
 141       pre_pool = end_points['Mixed_5c']
 142       feed_dict = {inputs: input_np}
 143       tf.global_variables_initializer().run()
 144       pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
 145       self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
 146
 147   def testUnknowBatchSize(self):
 148     batch_size = 1
 149     height, width = 224, 224
 150     num_classes = 1000
 151
 152     inputs = tf.placeholder(tf.float32, (None, height, width, 3))
 153     logits, _ = inception.inception_v1(inputs, num_classes)
 154     self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
 155     self.assertListEqual(logits.get_shape().as_list(),
 156                          [None, num_classes])
 157     images = tf.random_uniform((batch_size, height, width, 3))
 158
 159     with self.test_session() as sess:
 160       sess.run(tf.global_variables_initializer())
 161       output = sess.run(logits, {inputs: images.eval()})
 162       self.assertEquals(output.shape, (batch_size, num_classes))
 163
 164   def testEvaluation(self):
 165     batch_size = 2
 166     height, width = 224, 224
 167     num_classes = 1000
 168
 169     eval_inputs = tf.random_uniform((batch_size, height, width, 3))
 170     logits, _ = inception.inception_v1(eval_inputs, num_classes,
 171                                        is_training=False)
 172     predictions = tf.argmax(logits, 1)
 173
 174     with self.test_session() as sess:
 175       sess.run(tf.global_variables_initializer())
 176       output = sess.run(predictions)
 177       self.assertEquals(output.shape, (batch_size,))
 178
 179   def testTrainEvalWithReuse(self):
 180     train_batch_size = 5
 181     eval_batch_size = 2
 182     height, width = 224, 224
 183     num_classes = 1000
 184
 185     train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
 186     inception.inception_v1(train_inputs, num_classes)
 187     eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
 188     logits, _ = inception.inception_v1(eval_inputs, num_classes, reuse=True)
 189     predictions = tf.argmax(logits, 1)
 190
 191     with self.test_session() as sess:
 192       sess.run(tf.global_variables_initializer())
 193       output = sess.run(predictions)
 194       self.assertEquals(output.shape, (eval_batch_size,))
 195
 196   def testLogitsNotSqueezed(self):
 197     num_classes = 25
 198     images = tf.random_uniform([1, 224, 224, 3])
 199     logits, _ = inception.inception_v1(images,
 200                                        num_classes=num_classes,
 201                                        spatial_squeeze=False)
 202
 203     with self.test_session() as sess:
 204       tf.global_variables_initializer().run()
 205       logits_out = sess.run(logits)
 206       self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
 207
 208
 209 if __name__ == '__main__':
 210   tf.test.main()