example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4_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_v4."""
  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     inputs = tf.random_uniform((batch_size, height, width, 3))
  32     logits, end_points = inception.inception_v4(inputs, num_classes)
  33     auxlogits = end_points['AuxLogits']
  34     predictions = end_points['Predictions']
  35     self.assertTrue(auxlogits.op.name.startswith('InceptionV4/AuxLogits'))
  36     self.assertListEqual(auxlogits.get_shape().as_list(),
  37                          [batch_size, num_classes])
  38     self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
  39     self.assertListEqual(logits.get_shape().as_list(),
  40                          [batch_size, num_classes])
  41     self.assertTrue(predictions.op.name.startswith(
  42         'InceptionV4/Logits/Predictions'))
  43     self.assertListEqual(predictions.get_shape().as_list(),
  44                          [batch_size, num_classes])
  45
  46   def testBuildWithoutAuxLogits(self):
  47     batch_size = 5
  48     height, width = 299, 299
  49     num_classes = 1000
  50     inputs = tf.random_uniform((batch_size, height, width, 3))
  51     logits, endpoints = inception.inception_v4(inputs, num_classes,
  52                                                create_aux_logits=False)
  53     self.assertFalse('AuxLogits' in endpoints)
  54     self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
  55     self.assertListEqual(logits.get_shape().as_list(),
  56                          [batch_size, num_classes])
  57
  58   def testAllEndPointsShapes(self):
  59     batch_size = 5
  60     height, width = 299, 299
  61     num_classes = 1000
  62     inputs = tf.random_uniform((batch_size, height, width, 3))
  63     _, end_points = inception.inception_v4(inputs, num_classes)
  64     endpoints_shapes = {'Conv2d_1a_3x3': [batch_size, 149, 149, 32],
  65                         'Conv2d_2a_3x3': [batch_size, 147, 147, 32],
  66                         'Conv2d_2b_3x3': [batch_size, 147, 147, 64],
  67                         'Mixed_3a': [batch_size, 73, 73, 160],
  68                         'Mixed_4a': [batch_size, 71, 71, 192],
  69                         'Mixed_5a': [batch_size, 35, 35, 384],
  70                         # 4 x Inception-A blocks
  71                         'Mixed_5b': [batch_size, 35, 35, 384],
  72                         'Mixed_5c': [batch_size, 35, 35, 384],
  73                         'Mixed_5d': [batch_size, 35, 35, 384],
  74                         'Mixed_5e': [batch_size, 35, 35, 384],
  75                         # Reduction-A block
  76                         'Mixed_6a': [batch_size, 17, 17, 1024],
  77                         # 7 x Inception-B blocks
  78                         'Mixed_6b': [batch_size, 17, 17, 1024],
  79                         'Mixed_6c': [batch_size, 17, 17, 1024],
  80                         'Mixed_6d': [batch_size, 17, 17, 1024],
  81                         'Mixed_6e': [batch_size, 17, 17, 1024],
  82                         'Mixed_6f': [batch_size, 17, 17, 1024],
  83                         'Mixed_6g': [batch_size, 17, 17, 1024],
  84                         'Mixed_6h': [batch_size, 17, 17, 1024],
  85                         # Reduction-A block
  86                         'Mixed_7a': [batch_size, 8, 8, 1536],
  87                         # 3 x Inception-C blocks
  88                         'Mixed_7b': [batch_size, 8, 8, 1536],
  89                         'Mixed_7c': [batch_size, 8, 8, 1536],
  90                         'Mixed_7d': [batch_size, 8, 8, 1536],
  91                         # Logits and predictions
  92                         'AuxLogits': [batch_size, num_classes],
  93                         'PreLogitsFlatten': [batch_size, 1536],
  94                         'Logits': [batch_size, num_classes],
  95                         'Predictions': [batch_size, num_classes]}
  96     self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
  97     for endpoint_name in endpoints_shapes:
  98       expected_shape = endpoints_shapes[endpoint_name]
  99       self.assertTrue(endpoint_name in end_points)
 100       self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
 101                            expected_shape)
 102
 103   def testBuildBaseNetwork(self):
 104     batch_size = 5
 105     height, width = 299, 299
 106     inputs = tf.random_uniform((batch_size, height, width, 3))
 107     net, end_points = inception.inception_v4_base(inputs)
 108     self.assertTrue(net.op.name.startswith(
 109         'InceptionV4/Mixed_7d'))
 110     self.assertListEqual(net.get_shape().as_list(), [batch_size, 8, 8, 1536])
 111     expected_endpoints = [
 112         'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
 113         'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
 114         'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
 115         'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
 116         'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
 117     self.assertItemsEqual(end_points.keys(), expected_endpoints)
 118     for name, op in end_points.iteritems():
 119       self.assertTrue(op.name.startswith('InceptionV4/' + name))
 120
 121   def testBuildOnlyUpToFinalEndpoint(self):
 122     batch_size = 5
 123     height, width = 299, 299
 124     all_endpoints = [
 125         'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
 126         'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
 127         'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
 128         'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
 129         'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
 130     for index, endpoint in enumerate(all_endpoints):
 131       with tf.Graph().as_default():
 132         inputs = tf.random_uniform((batch_size, height, width, 3))
 133         out_tensor, end_points = inception.inception_v4_base(
 134             inputs, final_endpoint=endpoint)
 135         self.assertTrue(out_tensor.op.name.startswith(
 136             'InceptionV4/' + endpoint))
 137         self.assertItemsEqual(all_endpoints[:index+1], end_points)
 138
 139   def testVariablesSetDevice(self):
 140     batch_size = 5
 141     height, width = 299, 299
 142     num_classes = 1000
 143     inputs = tf.random_uniform((batch_size, height, width, 3))
 144     # Force all Variables to reside on the device.
 145     with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
 146       inception.inception_v4(inputs, num_classes)
 147     with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
 148       inception.inception_v4(inputs, num_classes)
 149     for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'):
 150       self.assertDeviceEqual(v.device, '/cpu:0')
 151     for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'):
 152       self.assertDeviceEqual(v.device, '/gpu:0')
 153
 154   def testHalfSizeImages(self):
 155     batch_size = 5
 156     height, width = 150, 150
 157     num_classes = 1000
 158     inputs = tf.random_uniform((batch_size, height, width, 3))
 159     logits, end_points = inception.inception_v4(inputs, num_classes)
 160     self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
 161     self.assertListEqual(logits.get_shape().as_list(),
 162                          [batch_size, num_classes])
 163     pre_pool = end_points['Mixed_7d']
 164     self.assertListEqual(pre_pool.get_shape().as_list(),
 165                          [batch_size, 3, 3, 1536])
 166
 167   def testUnknownBatchSize(self):
 168     batch_size = 1
 169     height, width = 299, 299
 170     num_classes = 1000
 171     with self.test_session() as sess:
 172       inputs = tf.placeholder(tf.float32, (None, height, width, 3))
 173       logits, _ = inception.inception_v4(inputs, num_classes)
 174       self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
 175       self.assertListEqual(logits.get_shape().as_list(),
 176                            [None, num_classes])
 177       images = tf.random_uniform((batch_size, height, width, 3))
 178       sess.run(tf.global_variables_initializer())
 179       output = sess.run(logits, {inputs: images.eval()})
 180       self.assertEquals(output.shape, (batch_size, num_classes))
 181
 182   def testEvaluation(self):
 183     batch_size = 2
 184     height, width = 299, 299
 185     num_classes = 1000
 186     with self.test_session() as sess:
 187       eval_inputs = tf.random_uniform((batch_size, height, width, 3))
 188       logits, _ = inception.inception_v4(eval_inputs,
 189                                          num_classes,
 190                                          is_training=False)
 191       predictions = tf.argmax(logits, 1)
 192       sess.run(tf.global_variables_initializer())
 193       output = sess.run(predictions)
 194       self.assertEquals(output.shape, (batch_size,))
 195
 196   def testTrainEvalWithReuse(self):
 197     train_batch_size = 5
 198     eval_batch_size = 2
 199     height, width = 150, 150
 200     num_classes = 1000
 201     with self.test_session() as sess:
 202       train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
 203       inception.inception_v4(train_inputs, num_classes)
 204       eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
 205       logits, _ = inception.inception_v4(eval_inputs,
 206                                          num_classes,
 207                                          is_training=False,
 208                                          reuse=True)
 209       predictions = tf.argmax(logits, 1)
 210       sess.run(tf.global_variables_initializer())
 211       output = sess.run(predictions)
 212       self.assertEquals(output.shape, (eval_batch_size,))
 213
 214
 215 if __name__ == '__main__':
 216   tf.test.main()