pcb defect detetcion application

[ealt-edge.git] / example-apps / PDD / pcb-defect-detection / libs / networks / slim_nets / overfeat_test.py

diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat_test.py
new file mode 100755 (executable)
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+++ b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat_test.py
@@ -0,0 +1,145 @@
+# 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.slim_nets.overfeat."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import overfeat
+
+slim = tf.contrib.slim
+
+
+class OverFeatTest(tf.test.TestCase):
+
+  def testBuild(self):
+    batch_size = 5
+    height, width = 231, 231
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = overfeat.overfeat(inputs, num_classes)
+      self.assertEquals(logits.op.name, 'overfeat/fc8/squeezed')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+
+  def testFullyConvolutional(self):
+    batch_size = 1
+    height, width = 281, 281
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = overfeat.overfeat(inputs, num_classes, spatial_squeeze=False)
+      self.assertEquals(logits.op.name, 'overfeat/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 2, 2, num_classes])
+
+  def testEndPoints(self):
+    batch_size = 5
+    height, width = 231, 231
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      _, end_points = overfeat.overfeat(inputs, num_classes)
+      expected_names = ['overfeat/conv1',
+                        'overfeat/pool1',
+                        'overfeat/conv2',
+                        'overfeat/pool2',
+                        'overfeat/conv3',
+                        'overfeat/conv4',
+                        'overfeat/conv5',
+                        'overfeat/pool5',
+                        'overfeat/fc6',
+                        'overfeat/fc7',
+                        'overfeat/fc8'
+                       ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+
+  def testModelVariables(self):
+    batch_size = 5
+    height, width = 231, 231
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      overfeat.overfeat(inputs, num_classes)
+      expected_names = ['overfeat/conv1/weights',
+                        'overfeat/conv1/biases',
+                        'overfeat/conv2/weights',
+                        'overfeat/conv2/biases',
+                        'overfeat/conv3/weights',
+                        'overfeat/conv3/biases',
+                        'overfeat/conv4/weights',
+                        'overfeat/conv4/biases',
+                        'overfeat/conv5/weights',
+                        'overfeat/conv5/biases',
+                        'overfeat/fc6/weights',
+                        'overfeat/fc6/biases',
+                        'overfeat/fc7/weights',
+                        'overfeat/fc7/biases',
+                        'overfeat/fc8/weights',
+                        'overfeat/fc8/biases',
+                       ]
+      model_variables = [v.op.name for v in slim.get_model_variables()]
+      self.assertSetEqual(set(model_variables), set(expected_names))
+
+  def testEvaluation(self):
+    batch_size = 2
+    height, width = 231, 231
+    num_classes = 1000
+    with self.test_session():
+      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = overfeat.overfeat(eval_inputs, is_training=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      predictions = tf.argmax(logits, 1)
+      self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
+
+  def testTrainEvalWithReuse(self):
+    train_batch_size = 2
+    eval_batch_size = 1
+    train_height, train_width = 231, 231
+    eval_height, eval_width = 281, 281
+    num_classes = 1000
+    with self.test_session():
+      train_inputs = tf.random_uniform(
+          (train_batch_size, train_height, train_width, 3))
+      logits, _ = overfeat.overfeat(train_inputs)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [train_batch_size, num_classes])
+      tf.get_variable_scope().reuse_variables()
+      eval_inputs = tf.random_uniform(
+          (eval_batch_size, eval_height, eval_width, 3))
+      logits, _ = overfeat.overfeat(eval_inputs, is_training=False,
+                                    spatial_squeeze=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [eval_batch_size, 2, 2, num_classes])
+      logits = tf.reduce_mean(logits, [1, 2])
+      predictions = tf.argmax(logits, 1)
+      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
+
+  def testForward(self):
+    batch_size = 1
+    height, width = 231, 231
+    with self.test_session() as sess:
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = overfeat.overfeat(inputs)
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits)
+      self.assertTrue(output.any())
+
+if __name__ == '__main__':
+  tf.test.main()