pcb defect detetcion application

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

diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/nets_factory_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/nets_factory_test.py
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+++ b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/nets_factory_test.py
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+# Copyright 2016 Google Inc. 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.inception."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import nets_factory
+
+slim = tf.contrib.slim
+
+
+class NetworksTest(tf.test.TestCase):
+
+  def testGetNetworkFn(self):
+    batch_size = 5
+    num_classes = 1000
+    for net in nets_factory.networks_map:
+      with self.test_session():
+        net_fn = nets_factory.get_network_fn(net, num_classes)
+        # Most networks use 224 as their default_image_size
+        image_size = getattr(net_fn, 'default_image_size', 224)
+        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
+        logits, end_points = net_fn(inputs)
+        self.assertTrue(isinstance(logits, tf.Tensor))
+        self.assertTrue(isinstance(end_points, dict))
+        self.assertEqual(logits.get_shape().as_list()[0], batch_size)
+        self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
+
+  def testGetNetworkFnArgScope(self):
+    batch_size = 5
+    num_classes = 10
+    net = 'cifarnet'
+    with self.test_session(use_gpu=True):
+      net_fn = nets_factory.get_network_fn(net, num_classes)
+      image_size = getattr(net_fn, 'default_image_size', 224)
+      with slim.arg_scope([slim.model_variable, slim.variable],
+                          device='/CPU:0'):
+        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
+        net_fn(inputs)
+      weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, 'CifarNet/conv1')[0]
+      self.assertDeviceEqual('/CPU:0', weights.device)
+
+if __name__ == '__main__':
+  tf.test.main()