Split checkpoint management utility functions out of saver.py

Pure refactor, in preparation for adding a higher level checkpoint management utility. This utility will also need to work with the Checkpoint proto, and globbing it on to saver.py seems dirty.

PiperOrigin-RevId: 207179646

7 files changed, 31 insertions, 22 deletions

diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
index 7a026a15e4..c1de42782e 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
@@ -72,6 +72,7 @@ from tensorflow.python.saved_model import builder as saved_model_builder
 from tensorflow.python.saved_model import tag_constants
 from tensorflow.python.summary import summary as core_summary
 from tensorflow.python.training import basic_session_run_hooks
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import device_setter
 from tensorflow.python.training import monitored_session
 from tensorflow.python.training import saver
@@ -891,7 +892,7 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable,
 
     # Check that model has been trained (if nothing has been set explicitly).
     if not checkpoint_path:
-      latest_path = saver.latest_checkpoint(self._model_dir)
+      latest_path = checkpoint_management.latest_checkpoint(self._model_dir)
       if not latest_path:
         raise NotFittedError(
             "Couldn't find trained model at %s." % self._model_dir)
@@ -956,7 +957,7 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable,
                    as_iterable=True,
                    iterate_batches=False):
     # Check that model has been trained.
-    checkpoint_path = saver.latest_checkpoint(self._model_dir)
+    checkpoint_path = checkpoint_management.latest_checkpoint(self._model_dir)
     if not checkpoint_path:
       raise NotFittedError(
           "Couldn't find trained model at %s." % self._model_dir)
@@ -1364,7 +1365,7 @@ class Estimator(BaseEstimator):
 
     if not checkpoint_path:
       # Locate the latest checkpoint
-      checkpoint_path = saver.latest_checkpoint(self._model_dir)
+      checkpoint_path = checkpoint_management.latest_checkpoint(self._model_dir)
     if not checkpoint_path:
       raise NotFittedError(
           "Couldn't find trained model at %s." % self._model_dir)
diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index f8a3709ee5..08e907a608 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -41,7 +41,7 @@ from tensorflow.python.estimator import estimator as core_estimator
 from tensorflow.python.framework import ops
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import basic_session_run_hooks
-from tensorflow.python.training import saver
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import server_lib
 from tensorflow.python.util import compat
 from tensorflow.python.util import function_utils
@@ -95,7 +95,7 @@ class _EvalAndExportListener(basic_session_run_hooks.CheckpointSaverListener):
     # Load and cache the path of the most recent checkpoint to avoid duplicate
     # searches on GCS.
     logging.info("Checking for checkpoint in %s", self._model_dir)
-    latest_path = saver.latest_checkpoint(self._model_dir)
+    latest_path = checkpoint_management.latest_checkpoint(self._model_dir)
 
     if not latest_path:
       logging.warning("Skipping evaluation and export since model has not been "
@@ -516,7 +516,8 @@ class Experiment(object):
       start = time.time()
 
       error_msg = None
-      latest_path = saver.latest_checkpoint(self._estimator.model_dir)
+      latest_path = checkpoint_management.latest_checkpoint(
+          self._estimator.model_dir)
       if not latest_path:
         error_msg = ("Estimator is not fitted yet. "
                      "Will start an evaluation when a checkpoint is ready.")
@@ -778,7 +779,8 @@ class Experiment(object):
           saving_listeners=self._saving_listeners)
 
       logging.info("Evaluating model now.")
-      latest_checkpoint = saver.latest_checkpoint(self._estimator.model_dir)
+      latest_checkpoint = checkpoint_management.latest_checkpoint(
+          self._estimator.model_dir)
       eval_result = self._call_evaluate(
           input_fn=self._eval_input_fn,
           steps=self._eval_steps,
diff --git a/tensorflow/contrib/learn/python/learn/graph_actions_test.py b/tensorflow/contrib/learn/python/learn/graph_actions_test.py
index 0d039d593b..df156da3f4 100644
--- a/tensorflow/contrib/learn/python/learn/graph_actions_test.py
+++ b/tensorflow/contrib/learn/python/learn/graph_actions_test.py
@@ -35,6 +35,7 @@ from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 from tensorflow.python.summary import summary
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import saver as saver_lib
 
 
@@ -124,7 +125,7 @@ class GraphActionsTest(test.TestCase):
 
   # TODO(ptucker): Test number and contents of checkpoint files.
   def _assert_ckpt(self, output_dir, expected=True):
-    ckpt_state = saver_lib.get_checkpoint_state(output_dir)
+    ckpt_state = checkpoint_management.get_checkpoint_state(output_dir)
     if expected:
       pattern = '%s/model.ckpt-.*' % output_dir
       primary_ckpt_path = ckpt_state.model_checkpoint_path
@@ -434,7 +435,7 @@ class GraphActionsTrainTest(test.TestCase):
 
   # TODO(ptucker): Test number and contents of checkpoint files.
   def _assert_ckpt(self, output_dir, expected=True):
-    ckpt_state = saver_lib.get_checkpoint_state(output_dir)
+    ckpt_state = checkpoint_management.get_checkpoint_state(output_dir)
     if expected:
       pattern = '%s/model.ckpt-.*' % output_dir
       primary_ckpt_path = ckpt_state.model_checkpoint_path
diff --git a/tensorflow/contrib/learn/python/learn/monitors.py b/tensorflow/contrib/learn/python/learn/monitors.py
index 77f7c73d54..3d691d4340 100644
--- a/tensorflow/contrib/learn/python/learn/monitors.py
+++ b/tensorflow/contrib/learn/python/learn/monitors.py
@@ -51,7 +51,7 @@ from tensorflow.python.estimator import estimator as core_estimator
 from tensorflow.python.framework import ops
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.summary import summary as core_summary
-from tensorflow.python.training import saver as saver_lib
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import session_run_hook
 from tensorflow.python.training import training_util
 from tensorflow.python.util import deprecation
@@ -735,7 +735,8 @@ class ValidationMonitor(EveryN):
       return False
     self._last_checkpoint_check_time = current_time
     # Check that we are not running evaluation on the same checkpoint.
-    latest_path = saver_lib.latest_checkpoint(self._estimator.model_dir)
+    latest_path = checkpoint_management.latest_checkpoint(
+        self._estimator.model_dir)
     if latest_path is None:
       logging.debug("Skipping evaluation since model has not been saved yet "
                     "at step %d.", step)
@@ -1059,7 +1060,8 @@ class ExportMonitor(EveryN):
 
   def end(self, session=None):
     super(ExportMonitor, self).end(session=session)
-    latest_path = saver_lib.latest_checkpoint(self._estimator.model_dir)
+    latest_path = checkpoint_management.latest_checkpoint(
+        self._estimator.model_dir)
     if latest_path is None:
       logging.info("Skipping export at the end since model has not been saved "
                    "yet.")
diff --git a/tensorflow/contrib/learn/python/learn/monitors_test.py b/tensorflow/contrib/learn/python/learn/monitors_test.py
index 5c34d0ddb0..ff1da32c21 100644
--- a/tensorflow/contrib/learn/python/learn/monitors_test.py
+++ b/tensorflow/contrib/learn/python/learn/monitors_test.py
@@ -39,9 +39,9 @@ from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.summary import summary
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import gradient_descent
 from tensorflow.python.training import monitored_session
-from tensorflow.python.training import saver
 from tensorflow.python.training import training_util
 
 
@@ -317,7 +317,7 @@ class MonitorsTest(test.TestCase):
         self._run_monitor(monitor)
 
   @test.mock.patch.object(estimators, 'Estimator', autospec=True)
-  @test.mock.patch.object(saver, 'latest_checkpoint')
+  @test.mock.patch.object(checkpoint_management, 'latest_checkpoint')
   def test_validation_monitor_no_ckpt(self, mock_latest_checkpoint,
                                       mock_estimator_class):
     estimator = mock_estimator_class()
@@ -336,7 +336,7 @@ class MonitorsTest(test.TestCase):
       mock_latest_checkpoint.assert_called_with(model_dir)
 
   @test.mock.patch.object(estimators, 'Estimator', autospec=True)
-  @test.mock.patch.object(saver, 'latest_checkpoint')
+  @test.mock.patch.object(checkpoint_management, 'latest_checkpoint')
   def test_validation_monitor_no_early_stopping_rounds(self,
                                                        mock_latest_checkpoint,
                                                        mock_estimator_class):
@@ -356,7 +356,7 @@ class MonitorsTest(test.TestCase):
       self._assert_validation_monitor(monitor)
 
   @test.mock.patch.object(estimators, 'Estimator', autospec=True)
-  @test.mock.patch.object(saver, 'latest_checkpoint')
+  @test.mock.patch.object(checkpoint_management, 'latest_checkpoint')
   def test_validation_monitor_invalid_metric(self, mock_latest_checkpoint,
                                              mock_estimator_class):
     estimator = mock_estimator_class()
@@ -375,7 +375,7 @@ class MonitorsTest(test.TestCase):
         self._run_monitor(monitor, num_epochs=1, num_steps_per_epoch=1)
 
   @test.mock.patch.object(estimators, 'Estimator', autospec=True)
-  @test.mock.patch.object(saver, 'latest_checkpoint')
+  @test.mock.patch.object(checkpoint_management, 'latest_checkpoint')
   def test_validation_monitor(self, mock_latest_checkpoint,
                               mock_estimator_class):
     estimator = mock_estimator_class()
@@ -464,7 +464,7 @@ class MonitorsTest(test.TestCase):
       monitor.epoch_end(epoch=0)
       monitor.end()
 
-  @test.mock.patch.object(saver, 'latest_checkpoint')
+  @test.mock.patch.object(checkpoint_management, 'latest_checkpoint')
   def test_validation_monitor_with_core_estimator(self, mock_latest_checkpoint):
     estimator = test.mock.Mock(spec=core_estimator.Estimator)
     model_dir = 'model/dir'
@@ -495,7 +495,7 @@ class MonitorsTest(test.TestCase):
           expected_best_metrics={'loss': 42.0, 'auc': 0.5})
       monitor.post_step(step=step, session=None)
 
-  @test.mock.patch.object(saver, 'latest_checkpoint')
+  @test.mock.patch.object(checkpoint_management, 'latest_checkpoint')
   def test_validation_monitor_fail_with_core_estimator_and_metrics(
       self, mock_latest_checkpoint):
     estimator = test.mock.Mock(spec=core_estimator.Estimator)
diff --git a/tensorflow/contrib/learn/python/learn/utils/export.py b/tensorflow/contrib/learn/python/learn/utils/export.py
index 3eacac7a3d..0144b93814 100644
--- a/tensorflow/contrib/learn/python/learn/utils/export.py
+++ b/tensorflow/contrib/learn/python/learn/utils/export.py
@@ -35,6 +35,7 @@ from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import saver as tf_saver
 from tensorflow.python.training import training_util
 
@@ -298,7 +299,8 @@ def _export_estimator(estimator,
 
   # If checkpoint_path is specified, use the specified checkpoint path.
   checkpoint_path = (checkpoint_path or
-                     tf_saver.latest_checkpoint(estimator._model_dir))
+                     checkpoint_management.latest_checkpoint(
+                         estimator._model_dir))
   with ops.Graph().as_default() as g:
     training_util.create_global_step(g)
 
diff --git a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
index f8106d1e4a..66af6833da 100644
--- a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
+++ b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
@@ -55,7 +55,7 @@ from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.saved_model import signature_constants
 from tensorflow.python.saved_model import signature_def_utils
 from tensorflow.python.summary import summary_iterator
-from tensorflow.python.training import saver
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.util import compat
 from tensorflow.python.util.deprecation import deprecated
 
@@ -714,7 +714,8 @@ def make_best_model_export_strategy(
       #  as soon as contrib is cleaned up and we can thus be sure that
       #  estimator is a tf.estimator.Estimator and not a
       #  tf.contrib.learn.Estimator
-      checkpoint_path = saver.latest_checkpoint(estimator.model_dir)
+      checkpoint_path = checkpoint_management.latest_checkpoint(
+          estimator.model_dir)
     export_checkpoint_path, export_eval_result = best_model_selector.update(
         checkpoint_path, eval_result)