Allows for importing of graphs with no variable nodes.

Change: 117365836

tensorflow/python/training/saver.py

@@ -404,8 +404,9 @@ class BaseSaverBuilder(object):
           if slice_name is None:
             slice_name = variable._save_slice_info.full_name
           elif slice_name != variable._save_slice_info.full_name:
-            raise ValueError("Slices must all be from the same tensor: %s != %s"
-                             % (slice_name, variable._save_slice_info.full_name))
+            raise ValueError(
+                "Slices must all be from the same tensor: %s != %s"
+                % (slice_name, variable._save_slice_info.full_name))
           self._AddVarToSave(vars_to_save, seen_variables,
                              variable, variable._save_slice_info.spec, name)
         # pylint: enable=protected-access
@@ -1292,7 +1293,10 @@ def _import_meta_graph_def(meta_graph_def):
     meta_graph_def: `MetaGraphDef` protocol buffer.
 
   Returns:
-    A saver constructed rom `saver_def` in `meta_graph_def`.
+    A saver constructed from `saver_def` in `meta_graph_def` or None.
+
+    A None value is returned if no variables exist in the `meta_graph_def`
+    (i.e., no variables to restore).
   """
   # Gathers the list of nodes we are interested in.
   importer.import_graph_def(meta_graph_def.graph_def, name="")
@@ -1331,7 +1335,14 @@ def _import_meta_graph_def(meta_graph_def):
   if meta_graph_def.HasField("saver_def"):
     return Saver(saver_def=meta_graph_def.saver_def)
   else:
-    return Saver()
+    if variables.all_variables():
+      # Return the default saver instance for all graph variables.
+      return Saver()
+    else:
+      # If not graph variables exist, then a Saver cannot be constructed.
+      logging.info("Saver not created because there are no variables in the"
+                   " graph to restore")
+      return None
 
 
 def import_meta_graph(meta_graph_or_file):
@@ -1390,7 +1401,10 @@ def import_meta_graph(meta_graph_or_file):
       the path) containing a `MetaGraphDef`.
 
   Returns:
-    A saver constructed rom `saver_def` in `MetaGraphDef`.
+    A saver constructed rom `saver_def` in `MetaGraphDef` or None.
+
+    A None value is returned if no variables exist in the `MetaGraphDef`
+    (i.e., there are no variables to restore).
   """
   if isinstance(meta_graph_or_file, meta_graph_pb2.MetaGraphDef):
     return _import_meta_graph_def(meta_graph_or_file)

tensorflow/python/training/saver_test.py

@@ -36,6 +36,7 @@ from tensorflow.core.protobuf import meta_graph_pb2
 from tensorflow.core.protobuf import queue_runner_pb2
 from tensorflow.python.framework import function
 from tensorflow.python.platform import gfile
+from tensorflow.python.training import saver as saver_module
 
 
 def _TestDir(test_name):
@@ -760,6 +761,58 @@ class CheckpointStateTest(tf.test.TestCase):
 
 class MetaGraphTest(tf.test.TestCase):
 
+  def testNoVariables(self):
+    test_dir = _TestDir("no_variables")
+    filename = os.path.join(test_dir, "metafile")
+
+    input_feed_value = -10  # Arbitrary input value for feed_dict.
+
+    orig_graph = tf.Graph()
+    with self.test_session(graph=orig_graph) as sess:
+      # Create a minimal graph with zero variables.
+      input_tensor = tf.placeholder(tf.float32, shape=[], name="input")
+      offset = tf.constant(42, dtype=tf.float32, name="offset")
+      output_tensor = tf.add(input_tensor, offset, name="add_offset")
+
+      # Add input and output tensors to graph collections.
+      tf.add_to_collection("input_tensor", input_tensor)
+      tf.add_to_collection("output_tensor", output_tensor)
+
+      output_value = sess.run(output_tensor, {input_tensor: input_feed_value})
+      self.assertEqual(output_value, 32)
+
+      # Generates MetaGraphDef.
+      #
+      # Note that this is calling the saver *module-level* export_meta_graph and
+      # not the Saver.export_meta_graph instance-level method.
+      meta_graph_def = saver_module.export_meta_graph(
+          filename=filename,
+          graph_def=tf.get_default_graph().as_graph_def(),
+          collection_list=["input_tensor", "output_tensor"],
+          saver_def=None,
+      )
+
+    # Create a clean graph and import the MetaGraphDef nodes.
+    new_graph = tf.Graph()
+    with self.test_session(graph=new_graph) as sess:
+      # Import the previously export meta graph.
+      saver_instance = saver_module.import_meta_graph(filename)
+      # The saver instance should be None since there are no graph variables
+      # to be restored in this case.
+      self.assertIsNone(saver_instance)
+
+      # Re-exports the current graph state for comparison to the original.
+      new_meta_graph_def = saver_module.export_meta_graph(filename + "_new")
+      self.assertProtoEquals(meta_graph_def, new_meta_graph_def)
+
+      # Ensures that we can still get a reference to our graph collections.
+      new_input_tensor = tf.get_collection("input_tensor")[0]
+      new_output_tensor = tf.get_collection("output_tensor")[0]
+      # Verifies that the new graph computes the same result as the original.
+      new_output_value = sess.run(
+          new_output_tensor, {new_input_tensor: input_feed_value})
+      self.assertEqual(new_output_value, output_value)
+
   def testAddCollectionDef(self):
     test_dir = _TestDir("good_collection")
     filename = os.path.join(test_dir, "metafile")
@@ -911,6 +964,7 @@ class MetaGraphTest(tf.test.TestCase):
     with self.test_session(graph=tf.Graph()):
       # Imports the binary format graph.
       saver = tf.train.import_meta_graph(filename)
+      self.assertIsNotNone(saver)
       # Exports the graph as text format.
       saver.export_meta_graph(filename, as_text=True)
     with self.test_session(graph=tf.Graph()):
@@ -947,6 +1001,7 @@ class MetaGraphTest(tf.test.TestCase):
     with tf.Graph().as_default():
       # Restores from MetaGraphDef.
       new_saver = tf.train.import_meta_graph(filename)
+      self.assertIsNotNone(new_saver)
       # Generates a new MetaGraphDef.
       new_meta_graph_def = new_saver.export_meta_graph()
       # It should be the same as the original.

tensorflow/python/training/supervisor_test.py

@@ -257,6 +257,7 @@ class SupervisorTest(tf.test.TestCase):
     # Create a new Graph and Supervisor and recover.
     with tf.Graph().as_default():
       new_saver = tf.train.import_meta_graph(".".join([filename, "meta"]))
+      self.assertIsNotNone(new_saver)
       sv2 = tf.train.Supervisor(logdir=logdir, saver=new_saver)
       sess = sv2.prepare_or_wait_for_session("")
       self.assertEquals(1, sess.run("v0:0"))