Automated rollback of commit c16517f22b4c4601c1e02ec5cb55193743443878

PiperOrigin-RevId: 209204056

3 files changed, 0 insertions, 514 deletions

diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD
index 77f62df99d..82272bf120 100644
--- a/tensorflow/contrib/estimator/BUILD
+++ b/tensorflow/contrib/estimator/BUILD
@@ -20,7 +20,6 @@ py_library(
         ":dnn_linear_combined",
         ":early_stopping",
         ":export",
-        ":exporter",
         ":extenders",
         ":head",
         ":hooks",
@@ -221,33 +220,6 @@ py_test(
 )
 
 py_library(
-    name = "exporter",
-    srcs = [
-        "python/estimator/exporter.py",
-    ],
-    srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow/python:framework_ops",
-        "//tensorflow/python:platform",
-        "//tensorflow/python:summary",
-        "//tensorflow/python/estimator:exporter",
-    ],
-)
-
-py_test(
-    name = "exporter_test",
-    size = "medium",
-    srcs = ["python/estimator/exporter_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":exporter",
-        "//tensorflow/python:platform",
-        "//tensorflow/python/estimator",
-        "//tensorflow/python/estimator:exporter",
-    ],
-)
-
-py_library(
     name = "head",
     srcs = [
         "python/estimator/head.py",
diff --git a/tensorflow/contrib/estimator/python/estimator/exporter.py b/tensorflow/contrib/estimator/python/estimator/exporter.py
deleted file mode 100644
index 09d7440605..0000000000
--- a/tensorflow/contrib/estimator/python/estimator/exporter.py
+++ /dev/null
@@ -1,280 +0,0 @@
-# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Implements StepsExporter to export the model in user specified steps."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-
-from tensorflow.python.estimator import exporter
-from tensorflow.python.framework import ops
-from tensorflow.python.platform import gfile
-from tensorflow.python.platform import tf_logging
-from tensorflow.python.summary import summary_iterator
-
-DEFAULT_GLOBAL_STEP_KEY = ops.GraphKeys.GLOBAL_STEP
-
-
-class StepsExporter(exporter.Exporter):
-  """This class exports the model in user specified steps.
-
-  This class exports the model at the steps given by the `steps_to_keep`
-  argument. Each number in the list is treated as a lower bound for model
-  exports, to handle the case when evaluation is performed at different steps.
-
-  Consider this example:
-
-  ```
-  steps_to_keep = [1, 2, 3, 6, 7, 10, 12, 25]
-  ```
-
-  The model is evaluated at step increments of 5: `[5, 10, 15, 20, 25, 30]`.
-  The `StepsExporter` will export the model when it has reached steps
-  `[5, 10, 15, 25]`.
-
-  This example illustrates the two cases when the model is exported:
-
-  1. Model is evaluated on a step defined in the list `steps_to_keep`.
-
-     In the example, the model is exported on step `10` and `25`.
-
-  2. Model is evaluated on a step not defined in the list `steps_to_keep`, but
-     is still exported because a step in `steps_to_keep` was missed.
-
-     In the example, when the model reaches step `5`, the model is exported even
-     though  `steps_to_keep` does not contain `5`. Step `5` is exported to make
-     up for step `3`, which was missed. Steps `1` and `2` in `steps_to_keep` are
-     skipped completely (e.g. say the model is evaluated at step `6`. It will
-     **not** be exported to make up for step `2`).
-
-  Using the `steps_to_keep` list as a lower bound allows users to define
-  approximate step boundaries for exporting their models, and avoid frustrating
-  off-by-one calculation errors.
-
-  Sample Use Cases:
-    There are specific points during the training when having a saved version of
-    the model would be useful. One example is at the end of each training phase
-    when the set of freezed weights is changed.
-    Another good use case is saving the model at the end of each epoch for
-    visualization or retraining.
-  """
-
-  def __init__(self,
-               steps_to_keep,
-               name='steps_exporter',
-               serving_input_receiver_fn=None,
-               event_file_pattern='eval/*.tfevents.*',
-               assets_extra=None,
-               as_text=False):
-    """Create an `StepsExporter` to use with `tf.estimator.EvalSpec`.
-
-    Example of creating a StepsExporter for training and evaluation:
-
-    ```python
-    categorical_feature_a = categorical_column_with_hash_bucket(...)
-    categorical_feature_b = categorical_column_with_hash_bucket(...)
-
-    categorical_feature_a_emb = embedding_column(
-        categorical_column=categorical_feature_a, ...)
-    categorical_feature_b_emb = embedding_column(
-        categorical_column=categorical_feature_b, ...)
-
-    estimator = tf.estimator.DNNClassifier(
-        feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb],
-        hidden_units=[1024, 512, 256])
-
-    # Input pipeline for train and evaluate.
-    def train_input_fn: # returns x, y
-      # please shuffle the data.
-      pass
-    def eval_input_fn_eval: # returns x, y
-      pass
-
-    exporter = tf.contrib.estimator.exporter.StepsExporter(
-        name="steps_exporter",
-        serving_input_receiver_fn=serving_input_receiver_fn,
-        event_file_pattern='eval/*.tfevents.*'
-        steps_to_keep=[...])
-
-    train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=1000)
-
-    eval_spec = [tf.estimator.EvalSpec(
-      input_fn=eval_input_fn,
-      steps=1,
-      exporters=exporter,
-      start_delay_secs=0,
-      throttle_secs=5)]
-
-    tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
-
-    # Models will be exported to estimator.model_dir in timestamped directories,
-    # which can be used for serving, analysis with TFMA, or directly loaded in.
-    # For example:
-    export_dir = os.path.join(estimator.model_dir,
-                              <timestamped directory name>)
-
-    with ops.Graph().as_default() as graph:
-      with session.Session(graph=graph) as sess:
-        tf.saved_model.loader.load(
-            sess, [tf.saved_model.tag_constants.SERVING], export_dir)
-
-    ```
-
-    Args:
-      steps_to_keep: Non-empty list of positive integers containing
-        the step numbers at which the model should be exported. All the exports
-        will be kept, so there is no garbage collection.
-      name: Unique name of this `Exporter` that is going to be used in the
-        export path.
-      serving_input_receiver_fn: A function that takes no arguments and returns
-        a `ServingInputReceiver`.
-      event_file_pattern: Event file name pattern relative to model_dir. If
-        None, however, the exporter would not be preemption-safe. To be
-        preemption-safe, event_file_pattern should be specified.
-      assets_extra: An optional dict specifying how to populate the assets.extra
-        directory within the exported SavedModel.  Each key should give the
-        destination path (including the filename) relative to the assets.extra
-        directory.  The corresponding value gives the full path of the source
-        file to be copied.  For example, the simple case of copying a single
-        file without renaming it is specified as `{'my_asset_file.txt':
-        '/path/to/my_asset_file.txt'}`.
-      as_text: Whether to write the SavedModel proto in text format. Defaults to
-        `False`.
-
-    Raises:
-      ValueError: If any arguments is invalid.
-    """
-    # pylint: disable=protected-access
-    self._saved_model_exporter = exporter._SavedModelExporter(
-        name, serving_input_receiver_fn, assets_extra, as_text)
-    # pylint: enable=protected-access
-
-    self._event_file_pattern = event_file_pattern
-    self._model_dir = None
-
-    self._input_steps_to_keep = steps_to_keep
-    steps_to_keep = [step for step in steps_to_keep if isinstance(step, int)]
-    steps_to_keep = [step for step in steps_to_keep if step > 0]
-    if not steps_to_keep:
-      raise ValueError(
-          '`steps_to_keep` list must have at least one positive integer')
-    elif self._input_steps_to_keep != steps_to_keep:
-      tf_logging.warn('Changed `steps_to_keep`, by omitting non-integer or'
-                      ' less than 1 elements, to [%s]',
-                      ', '.join(str(step) for step in steps_to_keep))
-    self._steps_to_keep = sorted(steps_to_keep)
-    self._steps_kept = []
-
-  @property
-  def name(self):
-    return self._saved_model_exporter.name
-
-  def export(self, estimator, export_path, checkpoint_path, eval_result,
-             is_the_final_export):
-    """Exports the given Estimator to a specific format.
-
-    Args:
-      estimator: A `tf.estimator.Estimator` instance to export.
-      export_path: A string containing a directory where to write the export.
-      checkpoint_path: The checkpoint path to export.
-      eval_result: The output of Estimator.evaluate on this checkpoint.
-      is_the_final_export: This boolean is True when this is an export in the
-        end of training. It is False for the intermediate exports during the
-        training. When passing Exporter to tf.estimator.train_and_evaluate
-        is_the_final_export is always False if TrainSpec.max_steps is None.
-
-    Returns:
-      The string path to the exported directory or None if export is skipped.
-
-    Raises:
-      ValueError: If `eval_result` is None or doesn't have
-        `ops.GraphKeys.GLOBAL_STEP` as a key.
-    """
-    export_result = None
-
-    if not eval_result or DEFAULT_GLOBAL_STEP_KEY not in eval_result:
-      raise ValueError(
-          '`eval_result` is empty, or does not have global step. This'
-          ' should never happen as Estimator always sets the global step in '
-          '`eval_result`. Please file a bug report. Got eval_result: %s'
-          % str(eval_result))
-
-    if self._model_dir != estimator.model_dir and self._event_file_pattern:
-      tf_logging.info('Loads the steps that the model was already evaluated at,'
-                      'from event files')
-      self._model_dir = estimator.model_dir
-      full_event_file_pattern = os.path.join(self._model_dir,
-                                             self._event_file_pattern)
-      self._steps_kept = self._get_kept_steps(full_event_file_pattern)
-
-      if self._steps_kept:
-        self._steps_kept = sorted(self._steps_kept)
-        self._steps_to_keep = [step for step in self._steps_to_keep if
-                               step > self._steps_kept[-1]]
-    # It is assumed that the model is exported at any evaluated step 'n' if
-    # there is any `steps_missed` lower than 'n'. As a result, all the steps in
-    # `_steps_to_keep` lower than the last evaluated step will be removed.
-    steps_missed = [step for step in self._steps_to_keep
-                    if step <= eval_result[DEFAULT_GLOBAL_STEP_KEY]]
-
-    if steps_missed:
-      # update the `_steps_to_keep` list by omitting all steps smaller than the
-      # current global step which are missed to be exported
-      export_result = self._saved_model_exporter.export(estimator, export_path,
-                                                        checkpoint_path,
-                                                        eval_result,
-                                                        is_the_final_export)
-      self._steps_to_keep = [step for step in self._steps_to_keep if step
-                             not in steps_missed]
-      # contains all the steps in which export has happened.
-      self._steps_kept.append(eval_result[DEFAULT_GLOBAL_STEP_KEY])
-      # Show warning for all the missed steps except the last one
-      if steps_missed[:-1]:
-        tf_logging.warn('Missed steps [%s] for exporting, as no evaluation'
-                        ' took place at them.', ', '.join(str(step) for step in
-                                                          steps_missed[:-1]))
-      # Log model export if the last missed step is the same as the current step
-      if steps_missed[-1] == eval_result[DEFAULT_GLOBAL_STEP_KEY]:
-        tf_logging.info('Performing model export at step %d.',
-                        eval_result[DEFAULT_GLOBAL_STEP_KEY])
-      # Show warning for exporting model at another step instead of the user
-      #   specified one
-      else:
-        tf_logging.warn('Performing model export at step %d instead of %d, as'
-                        ' no evaluation took place at step %d.',
-                        eval_result[DEFAULT_GLOBAL_STEP_KEY], steps_missed[-1],
-                        steps_missed[-1])
-    return export_result
-
-  def _get_kept_steps(self, event_files):
-    """Get the steps that the model was evaluated at, from event files.
-
-    Args:
-      event_files: Absolute pattern of event files.
-
-    Returns:
-      steps_kept: A list of steps in which the model was evaluated.
-    """
-    if not event_files:
-      return None
-
-    steps_kept = []
-    for event_file in gfile.Glob(os.path.join(event_files)):
-      for event in summary_iterator.summary_iterator(event_file):
-        if event.step not in steps_kept:
-          steps_kept.append(event.step)
-    return steps_kept
diff --git a/tensorflow/contrib/estimator/python/estimator/exporter_test.py b/tensorflow/contrib/estimator/python/estimator/exporter_test.py
deleted file mode 100644
index 0d009b945e..0000000000
--- a/tensorflow/contrib/estimator/python/estimator/exporter_test.py
+++ /dev/null
@@ -1,206 +0,0 @@
-# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for `StepsExporter`."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import shutil
-import tempfile
-
-from tensorflow.contrib.estimator.python.estimator import exporter as exporter_lib
-from tensorflow.python.estimator import estimator as estimator_lib
-from tensorflow.python.platform import gfile
-from tensorflow.python.platform import test
-
-
-class StepsExporterTest(test.TestCase):
-
-  def test_error_out_if_steps_to_keep_has_no_positive_integers(self):
-
-    def _serving_input_receiver_fn():
-      pass
-
-    with self.assertRaisesRegexp(ValueError, "positive integer"):
-      exporter = exporter_lib.StepsExporter(
-          name="specified_steps_exporter",
-          serving_input_receiver_fn=_serving_input_receiver_fn,
-          steps_to_keep=[-1, 0, 1.1])
-      self.assertEqual("specified_steps_exporter", exporter.name)
-
-  def test_steps_exporter(self):
-
-    def _serving_input_receiver_fn():
-      pass
-
-    export_dir_base = tempfile.mkdtemp()
-    gfile.MkDir(export_dir_base)
-    gfile.MkDir(export_dir_base + "/export")
-    gfile.MkDir(export_dir_base + "/eval")
-
-    exporter = exporter_lib.StepsExporter(
-        name="steps_exporter",
-        serving_input_receiver_fn=_serving_input_receiver_fn,
-        assets_extra={"from/path": "to/path"},
-        as_text=False,
-        steps_to_keep=[1])
-    estimator = test.mock.Mock(spec=estimator_lib.Estimator)
-    estimator.export_savedmodel.return_value = "export_result_path"
-    estimator.model_dir = export_dir_base
-
-    export_result = exporter.export(estimator, export_dir_base,
-                                    "checkpoint_path", {"global_step": 1},
-                                    False)
-
-    self.assertEqual("export_result_path", export_result)
-    estimator.export_savedmodel.assert_called_with(
-        export_dir_base,
-        _serving_input_receiver_fn,
-        assets_extra={"from/path": "to/path"},
-        as_text=False,
-        checkpoint_path="checkpoint_path",
-        strip_default_attrs=True)
-
-    shutil.rmtree(export_dir_base, ignore_errors=True)
-
-  def test_steps_exporter_with_preemption(self):
-
-    def _serving_input_receiver_fn():
-      pass
-
-    export_dir_base = tempfile.mkdtemp()
-    gfile.MkDir(export_dir_base)
-    gfile.MkDir(export_dir_base + "/export")
-    gfile.MkDir(export_dir_base + "/eval")
-
-    eval_dir_base = os.path.join(export_dir_base, "eval_continuous")
-    estimator_lib._write_dict_to_summary(eval_dir_base, {}, 1)
-    estimator_lib._write_dict_to_summary(eval_dir_base, {}, 2)
-
-    exporter = exporter_lib.StepsExporter(
-        name="steps_exporter",
-        serving_input_receiver_fn=_serving_input_receiver_fn,
-        event_file_pattern="eval_continuous/*.tfevents.*",
-        assets_extra={"from/path": "to/path"},
-        as_text=False,
-        steps_to_keep=[1, 2, 6, 8])
-
-    estimator = test.mock.Mock(spec=estimator_lib.Estimator)
-    estimator.model_dir = export_dir_base
-    estimator.export_savedmodel.return_value = "export_result_path"
-
-    export_result = exporter.export(estimator, export_dir_base,
-                                    "checkpoint_path", {"global_step": 3},
-                                    False)
-    self.assertEqual(None, export_result)
-
-    export_result = exporter.export(estimator, export_dir_base,
-                                    "checkpoint_path", {"global_step": 6},
-                                    False)
-    self.assertEqual("export_result_path", export_result)
-
-    export_result = exporter.export(estimator, export_dir_base,
-                                    "checkpoint_path", {"global_step": 7},
-                                    False)
-    self.assertEqual(None, export_result)
-
-    shutil.rmtree(export_dir_base, ignore_errors=True)
-
-  def test_specified_step_is_saved(self):
-
-    def _serving_input_receiver_fn():
-      pass
-
-    export_dir_base = tempfile.mkdtemp()
-    gfile.MkDir(export_dir_base)
-    gfile.MkDir(export_dir_base + "/export")
-    gfile.MkDir(export_dir_base + "/eval")
-
-    exporter = exporter_lib.StepsExporter(
-        name="steps_exporter",
-        serving_input_receiver_fn=_serving_input_receiver_fn,
-        assets_extra={"from/path": "to/path"},
-        as_text=False,
-        steps_to_keep=[1, 5, 8, 10, 11])
-    estimator = test.mock.Mock(spec=estimator_lib.Estimator)
-    estimator.export_savedmodel.return_value = "export_result_path"
-    estimator.model_dir = export_dir_base
-
-    export_result = exporter.export(estimator, export_dir_base,
-                                    "checkpoint_path", {"global_step": 1},
-                                    False)
-
-    self.assertTrue(estimator.export_savedmodel.called)
-    self.assertEqual("export_result_path", export_result)
-
-    export_result = exporter.export(estimator, export_dir_base,
-                                    "checkpoint_path", {"global_step": 2},
-                                    False)
-    self.assertEqual(None, export_result)
-
-    export_result = exporter.export(estimator, export_dir_base,
-                                    "checkpoint_path", {"global_step": 5},
-                                    False)
-    self.assertTrue(estimator.export_savedmodel.called)
-    self.assertEqual("export_result_path", export_result)
-
-    export_result = exporter.export(estimator, export_dir_base,
-                                    "checkpoint_path", {"global_step": 10},
-                                    False)
-    self.assertTrue(estimator.export_savedmodel.called)
-    self.assertEqual("export_result_path", export_result)
-
-    export_result = exporter.export(estimator, export_dir_base,
-                                    "checkpoint_path", {"global_step": 15},
-                                    False)
-    self.assertTrue(estimator.export_savedmodel.called)
-    self.assertEqual("export_result_path", export_result)
-
-    export_result = exporter.export(estimator, export_dir_base,
-                                    "checkpoint_path", {"global_step": 20},
-                                    False)
-    self.assertEqual(None, export_result)
-
-    shutil.rmtree(export_dir_base, ignore_errors=True)
-
-  def test_steps_exporter_with_no_global_step_key(self):
-
-    def _serving_input_receiver_fn():
-      pass
-
-    export_dir_base = tempfile.mkdtemp()
-    gfile.MkDir(export_dir_base)
-    gfile.MkDir(export_dir_base + "/export")
-    gfile.MkDir(export_dir_base + "/eval")
-
-    exporter = exporter_lib.StepsExporter(
-        name="steps_exporter",
-        serving_input_receiver_fn=_serving_input_receiver_fn,
-        assets_extra={"from/path": "to/path"},
-        as_text=False,
-        steps_to_keep=[1])
-    estimator = test.mock.Mock(spec=estimator_lib.Estimator)
-    estimator.export_savedmodel.return_value = "export_result_path"
-    estimator.model_dir = export_dir_base
-
-    with self.assertRaisesRegexp(ValueError, "does not have global step"):
-      exporter.export(estimator, export_dir_base, "checkpoint_path", {}, False)
-
-    shutil.rmtree(export_dir_base, ignore_errors=True)
-
-
-if __name__ == "__main__":
-  test.main()