Change contrib estimator to save relative paths in checkpoint.

Change: 155016674

tensorflow/contrib/learn/python/learn/estimators/estimator.py

@@ -966,7 +966,8 @@ class BaseEstimator(
             saver.Saver(
                 sharded=True,
                 max_to_keep=self._config.keep_checkpoint_max,
-                defer_build=True))
+                defer_build=True,
+                save_relative_paths=True))
 
       chief_hooks = []
       if (self._config.save_checkpoints_secs or

tensorflow/contrib/learn/python/learn/estimators/estimator_test.py

@@ -28,6 +28,8 @@ import numpy as np
 import six
 from six.moves import xrange  # pylint: disable=redefined-builtin
 
+from google.protobuf import text_format
+
 from tensorflow.contrib import learn
 from tensorflow.contrib import lookup
 from tensorflow.contrib.framework.python.ops import variables
@@ -50,6 +52,7 @@ from tensorflow.python.client import session as session_lib
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
+from tensorflow.python.lib.io import file_io
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
@@ -61,6 +64,7 @@ from tensorflow.python.platform import test
 from tensorflow.python.saved_model import loader
 from tensorflow.python.saved_model import tag_constants
 from tensorflow.python.training import basic_session_run_hooks
+from tensorflow.python.training import checkpoint_state_pb2
 from tensorflow.python.training import input as input_lib
 from tensorflow.python.training import monitored_session
 from tensorflow.python.training import saver as saver_lib
@@ -674,6 +678,38 @@ class EstimatorTest(test.TestCase):
         metrics={'MSE': metric_ops.streaming_mean_squared_error})
     self.assertLess(scores3['MSE'], scores['MSE'])
 
+  def test_checkpoint_contains_relative_paths(self):
+    tmpdir = tempfile.mkdtemp()
+    est = estimator.Estimator(
+        model_dir=tmpdir,
+        model_fn=linear_model_fn_with_model_fn_ops)
+    est.fit(input_fn=boston_input_fn, steps=5)
+
+    checkpoint_file_content = file_io.read_file_to_string(
+        os.path.join(tmpdir, 'checkpoint'))
+    ckpt = checkpoint_state_pb2.CheckpointState()
+    text_format.Merge(checkpoint_file_content, ckpt)
+    self.assertEqual(ckpt.model_checkpoint_path, 'model.ckpt-5')
+    self.assertAllEqual(
+        ['model.ckpt-1', 'model.ckpt-5'], ckpt.all_model_checkpoint_paths)
+
+  def test_train_save_copy_reload(self):
+    tmpdir = tempfile.mkdtemp()
+    model_dir1 = os.path.join(tmpdir, 'model_dir1')
+    est1 = estimator.Estimator(
+        model_dir=model_dir1,
+        model_fn=linear_model_fn_with_model_fn_ops)
+    est1.fit(input_fn=boston_input_fn, steps=5)
+
+    model_dir2 = os.path.join(tmpdir, 'model_dir2')
+    os.renames(model_dir1, model_dir2)
+    est2 = estimator.Estimator(
+        model_dir=model_dir2,
+        model_fn=linear_model_fn_with_model_fn_ops)
+    self.assertEqual(5, est2.get_variable_value('global_step'))
+    est2.fit(input_fn=boston_input_fn, steps=5)
+    self.assertEqual(10, est2.get_variable_value('global_step'))
+
   def testEstimatorParams(self):
     boston = base.load_boston()
     est = estimator.SKCompat(