Automated g4 rollback of changelist 159583264

PiperOrigin-RevId: 159630408

2 files changed, 8 insertions, 128 deletions

diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index ff44914779..ab49f36e5e 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -357,7 +357,7 @@ class Estimator(object):
               }
 
   def _assert_members_are_not_overridden(self):
-    allowed_overrides = set(['_call_input_fn', '_create_global_step'])
+    allowed_overrides = set(['_create_global_step'])
     estimator_members = set([m for m in Estimator.__dict__.keys()
                              if not m.startswith('__')])
     subclass_members = set(self.__class__.__dict__.keys())
@@ -485,7 +485,7 @@ class Estimator(object):
       return export_dir
 
   def _get_features_from_input_fn(self, input_fn):
-    result = self._call_input_fn(input_fn)
+    result = input_fn()
     if not ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS):
       logging.warning('Input graph does not contain a QueueRunner. '
                       'That means predict yields forever. '
@@ -549,32 +549,6 @@ class Estimator(object):
     assert step.dtype.is_integer
     return step
 
-  def _call_input_fn(self, input_fn):
-    """Calls the input function.
-
-    Args:
-      input_fn: The input function.
-
-    Returns:
-      Either features or (features, labels) where features and labels are:
-        features - `Tensor` or dictionary of string feature name to `Tensor`.
-        labels - `Tensor` or dictionary of `Tensor` with labels.
-
-    Raises:
-      ValueError: if input_fn takes invalid arguments.
-    """
-    input_fn_args = _fn_args(input_fn)
-    for arg in input_fn_args:
-      if arg not in ('config', 'params'):
-        raise ValueError('input_fn should not include argument {}.'.format(arg))
-    kwargs = {}
-    if 'params' in input_fn_args:
-      kwargs['params'] = self.params
-    if 'config' in input_fn_args:
-      kwargs['config'] = self.config
-    with ops.device('/cpu:0'):
-      return input_fn(**kwargs)
-
   def _call_model_fn(self, features, labels, mode):
     """Calls model function.
 
@@ -589,7 +563,7 @@ class Estimator(object):
     Raises:
       ValueError: if model_fn returns invalid objects.
     """
-    model_fn_args = _fn_args(self._model_fn)
+    model_fn_args = _model_fn_args(self._model_fn)
     kwargs = {}
     if 'mode' in model_fn_args:
       kwargs['mode'] = mode
@@ -610,7 +584,8 @@ class Estimator(object):
     with ops.Graph().as_default() as g, g.device(self._device_fn):
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      features, labels = self._call_input_fn(input_fn)
+      with ops.device('/cpu:0'):
+        features, labels = input_fn()
       estimator_spec = self._call_model_fn(features, labels,
                                            model_fn_lib.ModeKeys.TRAIN)
       ops.add_to_collection(ops.GraphKeys.LOSSES, estimator_spec.loss)
@@ -691,7 +666,7 @@ class Estimator(object):
     with ops.Graph().as_default() as g:
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      features, labels = self._call_input_fn(input_fn)
+      features, labels = input_fn()
       estimator_spec = self._call_model_fn(
           features, labels, model_fn_lib.ModeKeys.EVAL)
 
@@ -774,7 +749,7 @@ def _get_replica_device_setter(config):
     return None
 
 
-def _fn_args(fn):
+def _model_fn_args(fn):
   """Get argument names for function-like object.
 
   Args:
@@ -799,7 +774,7 @@ def _fn_args(fn):
 
 def _verify_model_fn_args(model_fn, params):
   """Verifies model fn arguments."""
-  args = set(_fn_args(model_fn))
+  args = set(_model_fn_args(model_fn))
   if 'features' not in args:
     raise ValueError('model_fn (%s) must include features argument.' % model_fn)
   if 'labels' not in args:
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index 338239ca23..b86afece43 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -120,9 +120,6 @@ class EstimatorInheritanceConstraintTest(test.TestCase):
       def __init__(self):
         super(_Estimator, self).__init__(model_fn=dummy_model_fn)
 
-      def _call_input_fn(self, input_fn):
-        return input_fn()
-
       def _create_global_step(self, graph):
         pass
 
@@ -328,48 +325,6 @@ def _make_input_fn(features, labels):
 
 class EstimatorTrainTest(test.TestCase):
 
-  def test_bad_input_fn_args(self):
-    expected_params = {'batch_size': 10}
-    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
-
-    def _model_fn(features, labels, mode, params, config):
-      del params, config
-      return model_fn_global_step_incrementer(features, labels, mode)
-
-    def _input_fn(params, config, not_allowed):
-      del not_allowed
-      self.assertEqual(expected_params, params)
-      self.assertEqual(4321, config.tf_random_seed)
-      return dummy_input_fn()
-
-    est = estimator.Estimator(model_fn=_model_fn,
-                              params=expected_params,
-                              config=expected_config)
-    with self.assertRaisesRegexp(ValueError, 'should not include argument'):
-      est.train(_input_fn, steps=1)
-
-  def test_input_fn_args(self):
-    expected_params = {'batch_size': 10}
-    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
-    input_fn_call_count = [0]
-
-    def _model_fn(features, labels, mode, params, config):
-      del params, config
-      return model_fn_global_step_incrementer(features, labels, mode)
-
-    def _input_fn(params, config):
-      input_fn_call_count[0] += 1
-      self.assertEqual(expected_params, params)
-      self.assertEqual(4321, config.tf_random_seed)
-      return dummy_input_fn()
-
-    est = estimator.Estimator(model_fn=_model_fn,
-                              params=expected_params,
-                              config=expected_config)
-    self.assertEqual(0, input_fn_call_count[0])
-    est.train(_input_fn, steps=1)
-    self.assertEqual(1, input_fn_call_count[0])
-
   def test_minimal_model_fn_args(self):
     expected_features = {'x': 42., 'y': 43.}
     expected_labels = 44.
@@ -710,29 +665,6 @@ class _StepCounterHook(session_run_hook.SessionRunHook):
 
 class EstimatorEvaluateTest(test.TestCase):
 
-  def test_input_fn_args(self):
-    expected_params = {'batch_size': 10}
-    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
-    input_fn_call_count = [0]
-
-    def _model_fn(features, labels, mode, params, config):
-      del params, config
-      return model_fn_global_step_incrementer(features, labels, mode)
-
-    def _input_fn(params, config):
-      input_fn_call_count[0] += 1
-      self.assertEqual(expected_params, params)
-      self.assertEqual(4321, config.tf_random_seed)
-      return dummy_input_fn()
-
-    est = estimator.Estimator(model_fn=_model_fn,
-                              params=expected_params,
-                              config=expected_config)
-    est.train(dummy_input_fn, steps=1)
-    self.assertEqual(0, input_fn_call_count[0])
-    est.evaluate(_input_fn, steps=1)
-    self.assertEqual(1, input_fn_call_count[0])
-
   def test_model_fn_must_return_estimator_spec(self):
     def _model_fn(features, labels, mode):
       _, _ = features, labels
@@ -934,33 +866,6 @@ class EstimatorEvaluateTest(test.TestCase):
 
 class EstimatorPredictTest(test.TestCase):
 
-  def test_input_fn_args(self):
-    expected_params = {'batch_size': 10}
-    expected_config = run_config.RunConfig().replace(tf_random_seed=4321)
-    input_fn_call_count = [0]
-
-    def _model_fn(features, labels, mode, params, config):
-      del features, labels, params, config
-      return model_fn_lib.EstimatorSpec(
-          mode,
-          loss=constant_op.constant(0.),
-          train_op=state_ops.assign_add(training.get_global_step(), 1),
-          predictions=constant_op.constant([[10.]]))
-
-    def _input_fn(params, config):
-      input_fn_call_count[0] += 1
-      self.assertEqual(expected_params, params)
-      self.assertEqual(4321, config.tf_random_seed)
-      return dummy_input_fn()
-
-    est = estimator.Estimator(model_fn=_model_fn,
-                              params=expected_params,
-                              config=expected_config)
-    est.train(dummy_input_fn, steps=1)
-    self.assertEqual(0, input_fn_call_count[0])
-    next(est.predict(_input_fn))
-    self.assertEqual(1, input_fn_call_count[0])
-
   def test_no_trained_model_in_model_dir(self):
     est = estimator.Estimator(model_fn=model_fn_global_step_incrementer)
     with self.assertRaisesRegexp(ValueError,