Ensure ExponentialMovingAverage returns correct average_name when used in scope

Fixes #2740
Change: 126251978

tensorflow/python/training/moving_averages.py

@@ -339,7 +339,10 @@ class ExponentialMovingAverage(object):
       by the `ExponentialMovingAverage class` to hold the moving average of
       `var`.
     """
-    return var.op.name + "/" + self._name
+    if var in self._averages:
+      return self._averages[var].op.name
+    return ops.get_default_graph().unique_name(
+        var.op.name + "/" + self._name, mark_as_used=False)
 
   def variables_to_restore(self, moving_avg_variables=None):
     """Returns a map of names to `Variables` to restore.

tensorflow/python/training/moving_averages_test.py

@@ -208,6 +208,37 @@ class ExponentialMovingAverageTest(tf.test.TestCase):
       self.assertEqual(ema.average_name(v1), ema.average(v1).op.name)
       self.assertEqual(ema.average_name(tensor2), ema.average(tensor2).op.name)
 
+  def testAverageVariablesNamesRespectScope(self):
+    # See discussion on #2740.
+    with self.test_session():
+      with tf.variable_scope("scope1"):
+        v0 = tf.Variable(10.0, name="v0")
+        v1 = tf.Variable(30.0, name="v1")
+        # Add a non-trainable variable.
+        v2 = tf.Variable(20.0, name="v2", trainable=False)
+        tensor2 = v0 + v1
+      with tf.variable_scope("scope2"):
+        ema = tf.train.ExponentialMovingAverage(0.25, name="foo_avg")
+        self.assertEqual("scope2/scope1/v0/foo_avg", ema.average_name(v0))
+        self.assertEqual("scope2/scope1/v1/foo_avg", ema.average_name(v1))
+        self.assertEqual("scope2/scope1/add/foo_avg", ema.average_name(tensor2))
+        ema.apply([v0, v1, tensor2])
+        vars_to_restore = ema.variables_to_restore()
+        # vars_to_restore should contain the following:
+        # {scope2/scope1/v0/foo_avg : v0,
+        #  scope2/scope1/v1/foo_avg : v1,
+        #  scope2/scope1/add/foo_avg : add/foo_avg
+        #  scope1/v2 : v2}
+        self.assertEqual(sorted(vars_to_restore.keys()),
+                         sorted([ema.average_name(v0),
+                                 ema.average_name(v1),
+                                 ema.average_name(tensor2),
+                                 v2.op.name]))
+        self.assertEqual(ema.average_name(v0), ema.average(v0).op.name)
+        self.assertEqual(ema.average_name(v1), ema.average(v1).op.name)
+        self.assertEqual(ema.average_name(tensor2),
+                         ema.average(tensor2).op.name)
+
   def testSubsetAverageVariablesNames(self):
     with self.test_session():
       v0 = tf.Variable(10.0, name="v0")

tensorflow/python/training/slot_creator_test.py

@@ -73,5 +73,13 @@ class SlotCreatorTest(tf.test.TestCase):
       self.assertEqual(slot.dtype.base_dtype, tf.float32)
       self.assertAllEqual(slot.eval(), [0.0, 0.0])
 
+  def testCreateSlotFromVariableRespectsScope(self):
+    # See discussion on #2740.
+    with self.test_session():
+      with tf.variable_scope("scope"):
+        v = tf.Variable([1.0, 2.5], name="var")
+        slot = slot_creator.create_slot(v, v.initialized_value(), name="slot")
+        self.assertEqual(slot.op.name, "scope/scope/var/slot")
+
 if __name__ == "__main__":
   tf.test.main()