From 10ea197604b6760652773d2525f850bfd3238061 Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng@google.com>
Date: Tue, 12 Jul 2016 20:13:45 -0800
Subject: Partitioned vars: test for "save in N slices, restore in M slices"
 scenario.

Where N != M.

Previously, the Python tests were missing, so this functionality may not have been as
widely exposed to users as we'd like.
Change: 127276419
---
 tensorflow/python/training/saver_test.py | 67 ++++++++++++++++++++++++++++++++
 1 file changed, 67 insertions(+)

diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py
index 3f7150539d..3934f74feb 100644
--- a/tensorflow/python/training/saver_test.py
+++ b/tensorflow/python/training/saver_test.py
@@ -376,6 +376,73 @@ class SaveRestoreShardedTest(tf.test.TestCase):
       sd = save.as_saver_def()
       self.assertTrue(sd.sharded)
 
+  def testPartitionedVariables(self):
+    var_full_shape = [10, 3]
+    # Allows save/restore mechanism to work w/ different slicings.
+    var_name = "my_var"
+    saved_path = os.path.join(_TestDir("partitioned_variables"), "ckpt")
+
+    def _Save(slices):
+      with self.test_session(graph=tf.Graph()) as sess:
+        # Calls .eval() to return the ndarray that makes up the full variable.
+        rnd = tf.random_uniform(var_full_shape).eval()
+
+        if slices:
+          vs = tf.create_partitioned_variables(var_full_shape,
+                                               slices,
+                                               rnd,
+                                               name=var_name)
+        else:
+          vs = [tf.Variable(rnd, name=var_name)]
+
+        tf.initialize_all_variables().run()
+        saver = tf.train.Saver(vs)
+        actual_path = saver.save(sess, saved_path)
+        self.assertEqual(saved_path, actual_path)
+
+        return rnd
+
+    def _Restore(slices):
+      with self.test_session(graph=tf.Graph()) as sess:
+        if slices:
+          new_vs = tf.create_partitioned_variables(
+              var_full_shape,
+              slices,
+              tf.zeros(var_full_shape),  # != original contents.
+              name=var_name)
+        else:
+          new_vs = [tf.Variable(
+              tf.zeros(shape=var_full_shape),  # != original contents.
+              name=var_name)]
+
+        tf.initialize_all_variables().run()
+        saver = tf.train.Saver(new_vs)
+        saver.restore(sess, saved_path)
+
+        if slices and slices[0] != 1:
+          return tf.concat(0, new_vs).eval()
+        elif slices and slices[1] != 1:
+          return tf.concat(1, new_vs).eval()
+        else:  # Non-sliced.
+          return new_vs[0].eval()
+
+    # Saves 10 horizontal parts of a partitioned variable.
+    # Restores into a full variable, non-sliced.
+    saved_full = _Save(slices=[10, 1])
+    restored_full = _Restore(slices=None)
+    self.assertAllEqual(saved_full, restored_full)
+
+    # Restores into a different number/orientation of slices.
+    restored_full = _Restore(slices=[2, 1])  # 2 horizon parts.
+    self.assertAllEqual(saved_full, restored_full)
+    restored_full = _Restore(slices=[1, 3])  # 3 vertical parts.
+    self.assertAllEqual(saved_full, restored_full)
+
+    # Now, saves a full variable and restores in slices.
+    saved_full = _Save(slices=None)
+    restored_full = _Restore(slices=[1, 3])
+    self.assertAllEqual(saved_full, restored_full)
+
 
 class MaxToKeepTest(tf.test.TestCase):
 
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