Add collections_test

inception/inception/slim/collections_test.py

+# 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 inception."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from inception.slim import slim
+
+
+def get_variables(scope=None):
+  return slim.variables.get_variables(scope)
+
+
+def get_variables_by_name(name):
+  return slim.variables.get_variables_by_name(name)
+
+
+class CollectionsTest(tf.test.TestCase):
+
+  def testVariables(self):
+    batch_size = 5
+    height, width = 299, 299
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      with slim.arg_scope([slim.ops.conv2d],
+                          batch_norm_params={'decay': 0.9997}):
+        slim.inception.inception_v3(inputs)
+      self.assertEqual(len(get_variables()), 388)
+      self.assertEqual(len(get_variables_by_name('weights')), 98)
+      self.assertEqual(len(get_variables_by_name('biases')), 2)
+      self.assertEqual(len(get_variables_by_name('beta')), 96)
+      self.assertEqual(len(get_variables_by_name('gamma')), 0)
+      self.assertEqual(len(get_variables_by_name('moving_mean')), 96)
+      self.assertEqual(len(get_variables_by_name('moving_variance')), 96)
+
+  def testVariablesWithoutBatchNorm(self):
+    batch_size = 5
+    height, width = 299, 299
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      with slim.arg_scope([slim.ops.conv2d],
+                          batch_norm_params=None):
+        slim.inception.inception_v3(inputs)
+      self.assertEqual(len(get_variables()), 196)
+      self.assertEqual(len(get_variables_by_name('weights')), 98)
+      self.assertEqual(len(get_variables_by_name('biases')), 98)
+      self.assertEqual(len(get_variables_by_name('beta')), 0)
+      self.assertEqual(len(get_variables_by_name('gamma')), 0)
+      self.assertEqual(len(get_variables_by_name('moving_mean')), 0)
+      self.assertEqual(len(get_variables_by_name('moving_variance')), 0)
+
+  def testVariablesByLayer(self):
+    batch_size = 5
+    height, width = 299, 299
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      with slim.arg_scope([slim.ops.conv2d],
+                          batch_norm_params={'decay': 0.9997}):
+        slim.inception.inception_v3(inputs)
+      self.assertEqual(len(get_variables()), 388)
+      self.assertEqual(len(get_variables('conv0')), 4)
+      self.assertEqual(len(get_variables('conv1')), 4)
+      self.assertEqual(len(get_variables('conv2')), 4)
+      self.assertEqual(len(get_variables('conv3')), 4)
+      self.assertEqual(len(get_variables('conv4')), 4)
+      self.assertEqual(len(get_variables('mixed_35x35x256a')), 28)
+      self.assertEqual(len(get_variables('mixed_35x35x288a')), 28)
+      self.assertEqual(len(get_variables('mixed_35x35x288b')), 28)
+      self.assertEqual(len(get_variables('mixed_17x17x768a')), 16)
+      self.assertEqual(len(get_variables('mixed_17x17x768b')), 40)
+      self.assertEqual(len(get_variables('mixed_17x17x768c')), 40)
+      self.assertEqual(len(get_variables('mixed_17x17x768d')), 40)
+      self.assertEqual(len(get_variables('mixed_17x17x768e')), 40)
+      self.assertEqual(len(get_variables('mixed_8x8x2048a')), 36)
+      self.assertEqual(len(get_variables('mixed_8x8x2048b')), 36)
+      self.assertEqual(len(get_variables('logits')), 2)
+      self.assertEqual(len(get_variables('aux_logits')), 10)
+
+  def testVariablesToRestore(self):
+    batch_size = 5
+    height, width = 299, 299
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      with slim.arg_scope([slim.ops.conv2d],
+                          batch_norm_params={'decay': 0.9997}):
+        slim.inception.inception_v3(inputs)
+      variables_to_restore = tf.get_collection(
+          slim.variables.VARIABLES_TO_RESTORE)
+      self.assertEqual(len(variables_to_restore), 388)
+      self.assertListEqual(variables_to_restore, get_variables())
+
+  def testVariablesToRestoreWithoutLogits(self):
+    batch_size = 5
+    height, width = 299, 299
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      with slim.arg_scope([slim.ops.conv2d],
+                          batch_norm_params={'decay': 0.9997}):
+        slim.inception.inception_v3(inputs, restore_logits=False)
+      variables_to_restore = tf.get_collection(
+          slim.variables.VARIABLES_TO_RESTORE)
+      self.assertEqual(len(variables_to_restore), 384)
+
+  def testRegularizationLosses(self):
+    batch_size = 5
+    height, width = 299, 299
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0.00004):
+        slim.inception.inception_v3(inputs)
+      losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
+      self.assertEqual(len(losses), len(get_variables_by_name('weights')))
+
+  def testTotalLossWithoutRegularization(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1001
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      dense_labels = tf.random_uniform((batch_size, num_classes))
+      with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0):
+        logits, end_points = slim.inception.inception_v3(
+            inputs,
+            num_classes=num_classes)
+        # Cross entropy loss for the main softmax prediction.
+        slim.losses.cross_entropy_loss(logits,
+                                       dense_labels,
+                                       label_smoothing=0.1,
+                                       weight=1.0)
+        # Cross entropy loss for the auxiliary softmax head.
+        slim.losses.cross_entropy_loss(end_points['aux_logits'],
+                                       dense_labels,
+                                       label_smoothing=0.1,
+                                       weight=0.4,
+                                       scope='aux_loss')
+      losses = tf.get_collection(slim.losses.LOSSES_COLLECTION)
+      self.assertEqual(len(losses), 2)
+
+  def testTotalLossWithRegularization(self):
+    batch_size = 5
+    height, width = 299, 299
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      dense_labels = tf.random_uniform((batch_size, num_classes))
+      with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0.00004):
+        logits, end_points = slim.inception.inception_v3(inputs, num_classes)
+        # Cross entropy loss for the main softmax prediction.
+        slim.losses.cross_entropy_loss(logits,
+                                       dense_labels,
+                                       label_smoothing=0.1,
+                                       weight=1.0)
+        # Cross entropy loss for the auxiliary softmax head.
+        slim.losses.cross_entropy_loss(end_points['aux_logits'],
+                                       dense_labels,
+                                       label_smoothing=0.1,
+                                       weight=0.4,
+                                       scope='aux_loss')
+      losses = tf.get_collection(slim.losses.LOSSES_COLLECTION)
+      self.assertEqual(len(losses), 2)
+      reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
+      self.assertEqual(len(reg_losses), 98)
+
+
+if __name__ == '__main__':
+  tf.test.main()