tf_upgrade_v2 on resnet and utils folders. (#6154)

* Add resnet56 short tests. (#6101)

* Add resnet56 short tests.
- created base benchmark module
- renamed accuracy test class to contain the word Accuracy
which will result in a need to update all the jobs
and a loss of history but is worth it.
- short tests are mostly copied from shining with oss refactor

* Address feedback.

* Move flag_methods to init
- Address setting default flags repeatedly.

* Rename accuracy tests.

* Lint errors resolved.

* fix model_dir set to flags.data_dir.

* fixed not fulling pulling out flag_methods.

* Use core mirrored strategy in official models (#6126)

* Imagenet short tests (#6132)

* Add short imagenet tests (taken from seemuch)
- also rename to match go forward naming

* fix method name

* Update doc strings.

* Fixe gpu number.

* points default data_dir to child folder. (#6131)

Failed test is python2  and was a kokoro failure

* Imagenet short tests (#6136)

* Add short imagenet tests (taken from seemuch)
- also rename to match go forward naming

* fix method name

* Update doc strings.

* Fixe gpu number.

* Add fill_objects

* fixed calling wrong class in super.

* fix lint issue.

* Flag (#6121)

* Fix the turn_off_ds flag problem

* add param names to all args

* Export benchmark stats using tf.test.Benchmark.report_benchmark() (#6103)

* Export benchmark stats using tf.test.Benchmark.report_benchmark()

* Fix python style using pyformat

* Typos. (#6120)

* log verbosity=2 logs every epoch no progress bars (#6142)

* tf_upgrade_v2 on resnet and utils folder.

* tf_upgrade_v2 on resnet and utils folder.

official/utils/flags/_device.py

@@ -39,7 +39,7 @@ def require_cloud_storage(flag_names):
     valid_flags = True
     for key in flag_names:
       if not flag_values[key].startswith("gs://"):
-        tf.logging.error("{} must be a GCS path.".format(key))
+        tf.compat.v1.logging.error("{} must be a GCS path.".format(key))
         valid_flags = False
 
     return valid_flags

official/utils/logs/hooks.py

@@ -25,7 +25,7 @@ import tensorflow as tf  # pylint: disable=g-bad-import-order
 from official.utils.logs import logger
 
 
-class ExamplesPerSecondHook(tf.train.SessionRunHook):
+class ExamplesPerSecondHook(tf.estimator.SessionRunHook):
   """Hook to print out examples per second.
 
   Total time is tracked and then divided by the total number of steps
@@ -66,7 +66,7 @@ class ExamplesPerSecondHook(tf.train.SessionRunHook):
 
     self._logger = metric_logger or logger.BaseBenchmarkLogger()
 
-    self._timer = tf.train.SecondOrStepTimer(
+    self._timer = tf.estimator.SecondOrStepTimer(
         every_steps=every_n_steps, every_secs=every_n_secs)
 
     self._step_train_time = 0
@@ -76,7 +76,7 @@ class ExamplesPerSecondHook(tf.train.SessionRunHook):
 
   def begin(self):
     """Called once before using the session to check global step."""
-    self._global_step_tensor = tf.train.get_global_step()
+    self._global_step_tensor = tf.compat.v1.train.get_global_step()
     if self._global_step_tensor is None:
       raise RuntimeError(
           "Global step should be created to use StepCounterHook.")
@@ -90,7 +90,7 @@ class ExamplesPerSecondHook(tf.train.SessionRunHook):
     Returns:
       A SessionRunArgs object or None if never triggered.
     """
-    return tf.train.SessionRunArgs(self._global_step_tensor)
+    return tf.estimator.SessionRunArgs(self._global_step_tensor)
 
   def after_run(self, run_context, run_values):  # pylint: disable=unused-argument
     """Called after each call to run().

official/utils/logs/hooks_helper.py

@@ -57,7 +57,7 @@ def get_train_hooks(name_list, use_tpu=False, **kwargs):
     return []
 
   if use_tpu:
-    tf.logging.warning("hooks_helper received name_list `{}`, but a TPU is "
+    tf.compat.v1.logging.warning("hooks_helper received name_list `{}`, but a TPU is "
                        "specified. No hooks will be used.".format(name_list))
     return []
 
@@ -89,7 +89,7 @@ def get_logging_tensor_hook(every_n_iter=100, tensors_to_log=None, **kwargs):  #
   if tensors_to_log is None:
     tensors_to_log = _TENSORS_TO_LOG
 
-  return tf.train.LoggingTensorHook(
+  return tf.estimator.LoggingTensorHook(
       tensors=tensors_to_log,
       every_n_iter=every_n_iter)
 
@@ -106,7 +106,7 @@ def get_profiler_hook(model_dir, save_steps=1000, **kwargs):  # pylint: disable=
     Returns a ProfilerHook that writes out timelines that can be loaded into
     profiling tools like chrome://tracing.
   """
-  return tf.train.ProfilerHook(save_steps=save_steps, output_dir=model_dir)
+  return tf.estimator.ProfilerHook(save_steps=save_steps, output_dir=model_dir)
 
 
 def get_examples_per_second_hook(every_n_steps=100,

official/utils/logs/hooks_helper_test.py

@@ -45,7 +45,7 @@ class BaseTest(unittest.TestCase):
     returned_hook = hooks_helper.get_train_hooks(
         [test_hook_name], model_dir="", **kwargs)
     self.assertEqual(len(returned_hook), 1)
-    self.assertIsInstance(returned_hook[0], tf.train.SessionRunHook)
+    self.assertIsInstance(returned_hook[0], tf.estimator.SessionRunHook)
     self.assertEqual(returned_hook[0].__class__.__name__.lower(),
                      expected_hook_name)
 

official/utils/logs/hooks_test.py

@@ -26,7 +26,7 @@ import tensorflow as tf  # pylint: disable=g-bad-import-order
 from official.utils.logs import hooks
 from official.utils.testing import mock_lib
 
-tf.logging.set_verbosity(tf.logging.DEBUG)
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG)
 
 
 class ExamplesPerSecondHookTest(tf.test.TestCase):
@@ -44,9 +44,10 @@ class ExamplesPerSecondHookTest(tf.test.TestCase):
 
     self.graph = tf.Graph()
     with self.graph.as_default():
-      tf.train.create_global_step()
-      self.train_op = tf.assign_add(tf.train.get_global_step(), 1)
-      self.global_step = tf.train.get_global_step()
+      tf.compat.v1.train.create_global_step()
+      self.train_op = tf.compat.v1.assign_add(
+          tf.compat.v1.train.get_global_step(), 1)
+      self.global_step = tf.compat.v1.train.get_global_step()
 
   def test_raise_in_both_secs_and_steps(self):
     with self.assertRaises(ValueError):
@@ -71,8 +72,8 @@ class ExamplesPerSecondHookTest(tf.test.TestCase):
         warm_steps=warm_steps,
         metric_logger=self._logger)
 
-    with tf.train.MonitoredSession(
-        tf.train.ChiefSessionCreator(), [hook]) as mon_sess:
+    with tf.compat.v1.train.MonitoredSession(
+        tf.compat.v1.train.ChiefSessionCreator(), [hook]) as mon_sess:
       for _ in range(every_n_steps):
         # Explicitly run global_step after train_op to get the accurate
         # global_step value
@@ -125,8 +126,8 @@ class ExamplesPerSecondHookTest(tf.test.TestCase):
         every_n_secs=every_n_secs,
         metric_logger=self._logger)
 
-    with tf.train.MonitoredSession(
-        tf.train.ChiefSessionCreator(), [hook]) as mon_sess:
+    with tf.compat.v1.train.MonitoredSession(
+        tf.compat.v1.train.ChiefSessionCreator(), [hook]) as mon_sess:
       # Explicitly run global_step after train_op to get the accurate
       # global_step value
       mon_sess.run(self.train_op)

official/utils/logs/logger.py

@@ -119,12 +119,13 @@ class BaseBenchmarkLogger(object):
       eval_results: dict, the result of evaluate.
     """
     if not isinstance(eval_results, dict):
-      tf.logging.warning("eval_results should be dictionary for logging. "
-                         "Got %s", type(eval_results))
+      tf.compat.v1.logging.warning(
+          "eval_results should be dictionary for logging. Got %s",
+          type(eval_results))
       return
-    global_step = eval_results[tf.GraphKeys.GLOBAL_STEP]
+    global_step = eval_results[tf.compat.v1.GraphKeys.GLOBAL_STEP]
     for key in sorted(eval_results):
-      if key != tf.GraphKeys.GLOBAL_STEP:
+      if key != tf.compat.v1.GraphKeys.GLOBAL_STEP:
         self.log_metric(key, eval_results[key], global_step=global_step)
 
   def log_metric(self, name, value, unit=None, global_step=None, extras=None):
@@ -143,12 +144,12 @@ class BaseBenchmarkLogger(object):
     """
     metric = _process_metric_to_json(name, value, unit, global_step, extras)
     if metric:
-      tf.logging.info("Benchmark metric: %s", metric)
+      tf.compat.v1.logging.info("Benchmark metric: %s", metric)
 
   def log_run_info(self, model_name, dataset_name, run_params, test_id=None):
-    tf.logging.info("Benchmark run: %s",
-                    _gather_run_info(model_name, dataset_name, run_params,
-                                     test_id))
+    tf.compat.v1.logging.info(
+        "Benchmark run: %s", _gather_run_info(model_name, dataset_name,
+                                              run_params, test_id))
 
   def on_finish(self, status):
     pass
@@ -160,9 +161,9 @@ class BenchmarkFileLogger(BaseBenchmarkLogger):
   def __init__(self, logging_dir):
     super(BenchmarkFileLogger, self).__init__()
     self._logging_dir = logging_dir
-    if not tf.gfile.IsDirectory(self._logging_dir):
-      tf.gfile.MakeDirs(self._logging_dir)
-    self._metric_file_handler = tf.gfile.GFile(
+    if not tf.io.gfile.isdir(self._logging_dir):
+      tf.io.gfile.makedirs(self._logging_dir)
+    self._metric_file_handler = tf.io.gfile.GFile(
         os.path.join(self._logging_dir, METRIC_LOG_FILE_NAME), "a")
 
   def log_metric(self, name, value, unit=None, global_step=None, extras=None):
@@ -186,8 +187,9 @@ class BenchmarkFileLogger(BaseBenchmarkLogger):
         self._metric_file_handler.write("\n")
         self._metric_file_handler.flush()
       except (TypeError, ValueError) as e:
-        tf.logging.warning("Failed to dump metric to log file: "
-                           "name %s, value %s, error %s", name, value, e)
+        tf.compat.v1.logging.warning(
+            "Failed to dump metric to log file: name %s, value %s, error %s",
+            name, value, e)
 
   def log_run_info(self, model_name, dataset_name, run_params, test_id=None):
     """Collect most of the TF runtime information for the local env.
@@ -204,14 +206,14 @@ class BenchmarkFileLogger(BaseBenchmarkLogger):
     """
     run_info = _gather_run_info(model_name, dataset_name, run_params, test_id)
 
-    with tf.gfile.GFile(os.path.join(
+    with tf.io.gfile.GFile(os.path.join(
         self._logging_dir, BENCHMARK_RUN_LOG_FILE_NAME), "w") as f:
       try:
         json.dump(run_info, f)
         f.write("\n")
       except (TypeError, ValueError) as e:
-        tf.logging.warning("Failed to dump benchmark run info to log file: %s",
-                           e)
+        tf.compat.v1.logging.warning(
+            "Failed to dump benchmark run info to log file: %s", e)
 
   def on_finish(self, status):
     self._metric_file_handler.flush()
@@ -324,7 +326,7 @@ def _process_metric_to_json(
     name, value, unit=None, global_step=None, extras=None):
   """Validate the metric data and generate JSON for insert."""
   if not isinstance(value, numbers.Number):
-    tf.logging.warning(
+    tf.compat.v1.logging.warning(
         "Metric value to log should be a number. Got %s", type(value))
     return None
 
@@ -341,7 +343,7 @@ def _process_metric_to_json(
 
 def _collect_tensorflow_info(run_info):
   run_info["tensorflow_version"] = {
-      "version": tf.VERSION, "git_hash": tf.GIT_VERSION}
+      "version": tf.version.VERSION, "git_hash": tf.version.GIT_VERSION}
 
 
 def _collect_run_params(run_info, run_params):
@@ -385,7 +387,8 @@ def _collect_cpu_info(run_info):
 
     run_info["machine_config"]["cpu_info"] = cpu_info
   except ImportError:
-    tf.logging.warn("'cpuinfo' not imported. CPU info will not be logged.")
+    tf.compat.v1.logging.warn(
+        "'cpuinfo' not imported. CPU info will not be logged.")
 
 
 def _collect_gpu_info(run_info, session_config=None):
@@ -415,7 +418,8 @@ def _collect_memory_info(run_info):
     run_info["machine_config"]["memory_total"] = vmem.total
     run_info["machine_config"]["memory_available"] = vmem.available
   except ImportError:
-    tf.logging.warn("'psutil' not imported. Memory info will not be logged.")
+    tf.compat.v1.logging.warn(
+        "'psutil' not imported. Memory info will not be logged.")
 
 
 def _collect_test_environment(run_info):

official/utils/logs/logger_test.py

@@ -78,7 +78,7 @@ class BenchmarkLoggerTest(tf.test.TestCase):
     mock_logger = mock.MagicMock()
     mock_config_benchmark_logger.return_value = mock_logger
     with logger.benchmark_context(None):
-      tf.logging.info("start benchmarking")
+      tf.compat.v1.logging.info("start benchmarking")
     mock_logger.on_finish.assert_called_once_with(logger.RUN_STATUS_SUCCESS)
 
   @mock.patch("official.utils.logs.logger.config_benchmark_logger")
@@ -95,18 +95,18 @@ class BaseBenchmarkLoggerTest(tf.test.TestCase):
 
   def setUp(self):
     super(BaseBenchmarkLoggerTest, self).setUp()
-    self._actual_log = tf.logging.info
+    self._actual_log = tf.compat.v1.logging.info
     self.logged_message = None
 
     def mock_log(*args, **kwargs):
       self.logged_message = args
       self._actual_log(*args, **kwargs)
 
-    tf.logging.info = mock_log
+    tf.compat.v1.logging.info = mock_log
 
   def tearDown(self):
     super(BaseBenchmarkLoggerTest, self).tearDown()
-    tf.logging.info = self._actual_log
+    tf.compat.v1.logging.info = self._actual_log
 
   def test_log_metric(self):
     log = logger.BaseBenchmarkLogger()
@@ -128,16 +128,16 @@ class BenchmarkFileLoggerTest(tf.test.TestCase):
 
   def tearDown(self):
     super(BenchmarkFileLoggerTest, self).tearDown()
-    tf.gfile.DeleteRecursively(self.get_temp_dir())
+    tf.io.gfile.rmtree(self.get_temp_dir())
     os.environ.clear()
     os.environ.update(self.original_environ)
 
   def test_create_logging_dir(self):
     non_exist_temp_dir = os.path.join(self.get_temp_dir(), "unknown_dir")
-    self.assertFalse(tf.gfile.IsDirectory(non_exist_temp_dir))
+    self.assertFalse(tf.io.gfile.isdir(non_exist_temp_dir))
 
     logger.BenchmarkFileLogger(non_exist_temp_dir)
-    self.assertTrue(tf.gfile.IsDirectory(non_exist_temp_dir))
+    self.assertTrue(tf.io.gfile.isdir(non_exist_temp_dir))
 
   def test_log_metric(self):
     log_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
@@ -145,8 +145,8 @@ class BenchmarkFileLoggerTest(tf.test.TestCase):
     log.log_metric("accuracy", 0.999, global_step=1e4, extras={"name": "value"})
 
     metric_log = os.path.join(log_dir, "metric.log")
-    self.assertTrue(tf.gfile.Exists(metric_log))
-    with tf.gfile.GFile(metric_log) as f:
+    self.assertTrue(tf.io.gfile.exists(metric_log))
+    with tf.io.gfile.GFile(metric_log) as f:
       metric = json.loads(f.readline())
       self.assertEqual(metric["name"], "accuracy")
       self.assertEqual(metric["value"], 0.999)
@@ -161,8 +161,8 @@ class BenchmarkFileLoggerTest(tf.test.TestCase):
     log.log_metric("loss", 0.02, global_step=1e4)
 
     metric_log = os.path.join(log_dir, "metric.log")
-    self.assertTrue(tf.gfile.Exists(metric_log))
-    with tf.gfile.GFile(metric_log) as f:
+    self.assertTrue(tf.io.gfile.exists(metric_log))
+    with tf.io.gfile.GFile(metric_log) as f:
       accuracy = json.loads(f.readline())
       self.assertEqual(accuracy["name"], "accuracy")
       self.assertEqual(accuracy["value"], 0.999)
@@ -184,7 +184,7 @@ class BenchmarkFileLoggerTest(tf.test.TestCase):
     log.log_metric("accuracy", const)
 
     metric_log = os.path.join(log_dir, "metric.log")
-    self.assertFalse(tf.gfile.Exists(metric_log))
+    self.assertFalse(tf.io.gfile.exists(metric_log))
 
   def test_log_evaluation_result(self):
     eval_result = {"loss": 0.46237424,
@@ -195,8 +195,8 @@ class BenchmarkFileLoggerTest(tf.test.TestCase):
     log.log_evaluation_result(eval_result)
 
     metric_log = os.path.join(log_dir, "metric.log")
-    self.assertTrue(tf.gfile.Exists(metric_log))
-    with tf.gfile.GFile(metric_log) as f:
+    self.assertTrue(tf.io.gfile.exists(metric_log))
+    with tf.io.gfile.GFile(metric_log) as f:
       accuracy = json.loads(f.readline())
       self.assertEqual(accuracy["name"], "accuracy")
       self.assertEqual(accuracy["value"], 0.9285)
@@ -216,7 +216,7 @@ class BenchmarkFileLoggerTest(tf.test.TestCase):
     log.log_evaluation_result(eval_result)
 
     metric_log = os.path.join(log_dir, "metric.log")
-    self.assertFalse(tf.gfile.Exists(metric_log))
+    self.assertFalse(tf.io.gfile.exists(metric_log))
 
   @mock.patch("official.utils.logs.logger._gather_run_info")
   def test_log_run_info(self, mock_gather_run_info):
@@ -229,8 +229,8 @@ class BenchmarkFileLoggerTest(tf.test.TestCase):
     log.log_run_info("model_name", "dataset_name", {})
 
     run_log = os.path.join(log_dir, "benchmark_run.log")
-    self.assertTrue(tf.gfile.Exists(run_log))
-    with tf.gfile.GFile(run_log) as f:
+    self.assertTrue(tf.io.gfile.exists(run_log))
+    with tf.io.gfile.GFile(run_log) as f:
       run_info = json.loads(f.readline())
       self.assertEqual(run_info["model_name"], "model_name")
       self.assertEqual(run_info["dataset"], "dataset_name")
@@ -240,8 +240,10 @@ class BenchmarkFileLoggerTest(tf.test.TestCase):
     run_info = {}
     logger._collect_tensorflow_info(run_info)
     self.assertNotEqual(run_info["tensorflow_version"], {})
-    self.assertEqual(run_info["tensorflow_version"]["version"], tf.VERSION)
-    self.assertEqual(run_info["tensorflow_version"]["git_hash"], tf.GIT_VERSION)
+    self.assertEqual(run_info["tensorflow_version"]["version"],
+                     tf.version.VERSION)
+    self.assertEqual(run_info["tensorflow_version"]["git_hash"],
+                     tf.version.GIT_VERSION)
 
   def test_collect_run_params(self):
     run_info = {}
@@ -315,7 +317,7 @@ class BenchmarkBigQueryLoggerTest(tf.test.TestCase):
 
   def tearDown(self):
     super(BenchmarkBigQueryLoggerTest, self).tearDown()
-    tf.gfile.DeleteRecursively(self.get_temp_dir())
+    tf.io.gfile.rmtree(self.get_temp_dir())
     os.environ.clear()
     os.environ.update(self.original_environ)
 

official/utils/logs/metric_hook.py

@@ -21,7 +21,7 @@ from __future__ import print_function
 import tensorflow as tf  # pylint: disable=g-bad-import-order
 
 
-class LoggingMetricHook(tf.train.LoggingTensorHook):
+class LoggingMetricHook(tf.estimator.LoggingTensorHook):
   """Hook to log benchmark metric information.
 
   This hook is very similar as tf.train.LoggingTensorHook, which logs given
@@ -68,7 +68,7 @@ class LoggingMetricHook(tf.train.LoggingTensorHook):
 
   def begin(self):
     super(LoggingMetricHook, self).begin()
-    self._global_step_tensor = tf.train.get_global_step()
+    self._global_step_tensor = tf.compat.v1.train.get_global_step()
     if self._global_step_tensor is None:
       raise RuntimeError(
           "Global step should be created to use LoggingMetricHook.")

official/utils/logs/metric_hook_test.py

@@ -39,7 +39,7 @@ class LoggingMetricHookTest(tf.test.TestCase):
 
   def tearDown(self):
     super(LoggingMetricHookTest, self).tearDown()
-    tf.gfile.DeleteRecursively(self.get_temp_dir())
+    tf.io.gfile.rmtree(self.get_temp_dir())
 
   def test_illegal_args(self):
     with self.assertRaisesRegexp(ValueError, "nvalid every_n_iter"):
@@ -55,15 +55,15 @@ class LoggingMetricHookTest(tf.test.TestCase):
       metric_hook.LoggingMetricHook(tensors=["t"], every_n_iter=5)
 
   def test_print_at_end_only(self):
-    with tf.Graph().as_default(), tf.Session() as sess:
-      tf.train.get_or_create_global_step()
+    with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
+      tf.compat.v1.train.get_or_create_global_step()
       t = tf.constant(42.0, name="foo")
       train_op = tf.constant(3)
       hook = metric_hook.LoggingMetricHook(
           tensors=[t.name], at_end=True, metric_logger=self._logger)
       hook.begin()
       mon_sess = monitored_session._HookedSession(sess, [hook])  # pylint: disable=protected-access
-      sess.run(tf.global_variables_initializer())
+      sess.run(tf.compat.v1.global_variables_initializer())
 
       for _ in range(3):
         mon_sess.run(train_op)
@@ -88,8 +88,8 @@ class LoggingMetricHookTest(tf.test.TestCase):
         hook.begin()
 
   def test_log_tensors(self):
-    with tf.Graph().as_default(), tf.Session() as sess:
-      tf.train.get_or_create_global_step()
+    with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
+      tf.compat.v1.train.get_or_create_global_step()
       t1 = tf.constant(42.0, name="foo")
       t2 = tf.constant(43.0, name="bar")
       train_op = tf.constant(3)
@@ -97,7 +97,7 @@ class LoggingMetricHookTest(tf.test.TestCase):
           tensors=[t1, t2], at_end=True, metric_logger=self._logger)
       hook.begin()
       mon_sess = monitored_session._HookedSession(sess, [hook])  # pylint: disable=protected-access
-      sess.run(tf.global_variables_initializer())
+      sess.run(tf.compat.v1.global_variables_initializer())
 
       for _ in range(3):
         mon_sess.run(train_op)
@@ -126,7 +126,7 @@ class LoggingMetricHookTest(tf.test.TestCase):
         metric_logger=self._logger)
     hook.begin()
     mon_sess = monitored_session._HookedSession(sess, [hook])  # pylint: disable=protected-access
-    sess.run(tf.global_variables_initializer())
+    sess.run(tf.compat.v1.global_variables_initializer())
     mon_sess.run(train_op)
     self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
     for _ in range(3):
@@ -153,15 +153,15 @@ class LoggingMetricHookTest(tf.test.TestCase):
       self.assertEqual(str(self._logger.logged_metric).find(t.name), -1)
 
   def test_print_every_n_steps(self):
-    with tf.Graph().as_default(), tf.Session() as sess:
-      tf.train.get_or_create_global_step()
+    with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
+      tf.compat.v1.train.get_or_create_global_step()
       self._validate_print_every_n_steps(sess, at_end=False)
       # Verify proper reset.
       self._validate_print_every_n_steps(sess, at_end=False)
 
   def test_print_every_n_steps_and_end(self):
-    with tf.Graph().as_default(), tf.Session() as sess:
-      tf.train.get_or_create_global_step()
+    with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
+      tf.compat.v1.train.get_or_create_global_step()
       self._validate_print_every_n_steps(sess, at_end=True)
       # Verify proper reset.
       self._validate_print_every_n_steps(sess, at_end=True)
@@ -175,7 +175,7 @@ class LoggingMetricHookTest(tf.test.TestCase):
         metric_logger=self._logger)
     hook.begin()
     mon_sess = monitored_session._HookedSession(sess, [hook])  # pylint: disable=protected-access
-    sess.run(tf.global_variables_initializer())
+    sess.run(tf.compat.v1.global_variables_initializer())
 
     mon_sess.run(train_op)
     self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
@@ -199,15 +199,15 @@ class LoggingMetricHookTest(tf.test.TestCase):
       self.assertEqual(str(self._logger.logged_metric).find(t.name), -1)
 
   def test_print_every_n_secs(self):
-    with tf.Graph().as_default(), tf.Session() as sess:
-      tf.train.get_or_create_global_step()
+    with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
+      tf.compat.v1.train.get_or_create_global_step()
       self._validate_print_every_n_secs(sess, at_end=False)
       # Verify proper reset.
       self._validate_print_every_n_secs(sess, at_end=False)
 
   def test_print_every_n_secs_and_end(self):
-    with tf.Graph().as_default(), tf.Session() as sess:
-      tf.train.get_or_create_global_step()
+    with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
+      tf.compat.v1.train.get_or_create_global_step()
       self._validate_print_every_n_secs(sess, at_end=True)
       # Verify proper reset.
       self._validate_print_every_n_secs(sess, at_end=True)

official/utils/logs/mlperf_helper.py

@@ -94,7 +94,7 @@ def get_mlperf_log():
       version = pkg_resources.get_distribution("mlperf_compliance")
       version = tuple(int(i) for i in version.version.split("."))
       if version < _MIN_VERSION:
-        tf.logging.warning(
+        tf.compat.v1.logging.warning(
             "mlperf_compliance is version {}, must be >= {}".format(
                 ".".join([str(i) for i in version]),
                 ".".join([str(i) for i in _MIN_VERSION])))
@@ -187,6 +187,6 @@ def clear_system_caches():
 
 
 if __name__ == "__main__":
-  tf.logging.set_verbosity(tf.logging.INFO)
+  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
   with LOGGER(True):
     ncf_print(key=TAGS.RUN_START)

official/utils/misc/model_helpers.py

@@ -48,7 +48,7 @@ def past_stop_threshold(stop_threshold, eval_metric):
                      "must be a number.")
 
   if eval_metric >= stop_threshold:
-    tf.logging.info(
+    tf.compat.v1.logging.info(
         "Stop threshold of {} was passed with metric value {}.".format(
             stop_threshold, eval_metric))
     return True
@@ -87,7 +87,7 @@ def generate_synthetic_data(
 
 
 def apply_clean(flags_obj):
-  if flags_obj.clean and tf.gfile.Exists(flags_obj.model_dir):
-    tf.logging.info("--clean flag set. Removing existing model dir: {}".format(
+  if flags_obj.clean and tf.io.gfile.exists(flags_obj.model_dir):
+    tf.compat.v1.logging.info("--clean flag set. Removing existing model dir: {}".format(
         flags_obj.model_dir))
-    tf.gfile.DeleteRecursively(flags_obj.model_dir)
+    tf.io.gfile.rmtree(flags_obj.model_dir)

official/utils/misc/model_helpers_test.py

@@ -69,13 +69,13 @@ class SyntheticDataTest(tf.test.TestCase):
   """Tests for generate_synthetic_data."""
 
   def test_generate_synethetic_data(self):
-    input_element, label_element = model_helpers.generate_synthetic_data(
-        input_shape=tf.TensorShape([5]),
-        input_value=123,
-        input_dtype=tf.float32,
-        label_shape=tf.TensorShape([]),
-        label_value=456,
-        label_dtype=tf.int32).make_one_shot_iterator().get_next()
+    input_element, label_element = tf.compat.v1.data.make_one_shot_iterator(
+        model_helpers.generate_synthetic_data(input_shape=tf.TensorShape([5]),
+                                              input_value=123,
+                                              input_dtype=tf.float32,
+                                              label_shape=tf.TensorShape([]),
+                                              label_value=456,
+                                              label_dtype=tf.int32)).get_next()
 
     with self.test_session() as sess:
       for n in range(5):
@@ -89,7 +89,7 @@ class SyntheticDataTest(tf.test.TestCase):
         input_value=43.5,
         input_dtype=tf.float32)
 
-    element = d.make_one_shot_iterator().get_next()
+    element = tf.compat.v1.data.make_one_shot_iterator(d).get_next()
     self.assertFalse(isinstance(element, tuple))
 
     with self.test_session() as sess:
@@ -102,7 +102,7 @@ class SyntheticDataTest(tf.test.TestCase):
                      'b': {'c': tf.TensorShape([3]), 'd': tf.TensorShape([])}},
         input_value=1.1)
 
-    element = d.make_one_shot_iterator().get_next()
+    element = tf.compat.v1.data.make_one_shot_iterator(d).get_next()
     self.assertIn('a', element)
     self.assertIn('b', element)
     self.assertEquals(len(element['b']), 2)

official/utils/testing/reference_data.py

@@ -170,12 +170,12 @@ class BaseTest(tf.test.TestCase):
     # Serialize graph for comparison.
     graph_bytes = graph.as_graph_def().SerializeToString()
     expected_file = os.path.join(data_dir, "expected_graph")
-    with tf.gfile.Open(expected_file, "wb") as f:
+    with tf.io.gfile.GFile(expected_file, "wb") as f:
       f.write(graph_bytes)
 
     with graph.as_default():
-      init = tf.global_variables_initializer()
-      saver = tf.train.Saver()
+      init = tf.compat.v1.global_variables_initializer()
+      saver = tf.compat.v1.train.Saver()
 
     with self.test_session(graph=graph) as sess:
       sess.run(init)
@@ -191,11 +191,11 @@ class BaseTest(tf.test.TestCase):
 
       if correctness_function is not None:
         results = correctness_function(*eval_results)
-        with tf.gfile.Open(os.path.join(data_dir, "results.json"), "w") as f:
+        with tf.io.gfile.GFile(os.path.join(data_dir, "results.json"), "w") as f:
           json.dump(results, f)
 
-      with tf.gfile.Open(os.path.join(data_dir, "tf_version.json"), "w") as f:
-        json.dump([tf.VERSION, tf.GIT_VERSION], f)
+      with tf.io.gfile.GFile(os.path.join(data_dir, "tf_version.json"), "w") as f:
+        json.dump([tf.version.VERSION, tf.version.GIT_VERSION], f)
 
   def _evaluate_test_case(self, name, graph, ops_to_eval, correctness_function):
     """Determine if a graph agrees with the reference data.
@@ -216,7 +216,7 @@ class BaseTest(tf.test.TestCase):
     # Serialize graph for comparison.
     graph_bytes = graph.as_graph_def().SerializeToString()
     expected_file = os.path.join(data_dir, "expected_graph")
-    with tf.gfile.Open(expected_file, "rb") as f:
+    with tf.io.gfile.GFile(expected_file, "rb") as f:
       expected_graph_bytes = f.read()
       # The serialization is non-deterministic byte-for-byte. Instead there is
       # a utility which evaluates the semantics of the two graphs to test for
@@ -228,19 +228,19 @@ class BaseTest(tf.test.TestCase):
         graph_bytes, expected_graph_bytes).decode("utf-8")
 
     with graph.as_default():
-      init = tf.global_variables_initializer()
-      saver = tf.train.Saver()
+      init = tf.compat.v1.global_variables_initializer()
+      saver = tf.compat.v1.train.Saver()
 
-    with tf.gfile.Open(os.path.join(data_dir, "tf_version.json"), "r") as f:
+    with tf.io.gfile.GFile(os.path.join(data_dir, "tf_version.json"), "r") as f:
       tf_version_reference, tf_git_version_reference = json.load(f)  # pylint: disable=unpacking-non-sequence
 
     tf_version_comparison = ""
-    if tf.GIT_VERSION != tf_git_version_reference:
+    if tf.version.GIT_VERSION != tf_git_version_reference:
       tf_version_comparison = (
           "Test was built using:     {} (git = {})\n"
           "Local TensorFlow version: {} (git = {})"
           .format(tf_version_reference, tf_git_version_reference,
-                  tf.VERSION, tf.GIT_VERSION)
+                  tf.version.VERSION, tf.version.GIT_VERSION)
       )
 
     with self.test_session(graph=graph) as sess:
@@ -249,7 +249,7 @@ class BaseTest(tf.test.TestCase):
         saver.restore(sess=sess, save_path=os.path.join(
             data_dir, self.ckpt_prefix))
         if differences:
-          tf.logging.warn(
+          tf.compat.v1.logging.warn(
               "The provided graph is different than expected:\n  {}\n"
               "However the weights were still able to be loaded.\n{}".format(
                   differences, tf_version_comparison)
@@ -262,7 +262,7 @@ class BaseTest(tf.test.TestCase):
       eval_results = [op.eval() for op in ops_to_eval]
       if correctness_function is not None:
         results = correctness_function(*eval_results)
-        with tf.gfile.Open(os.path.join(data_dir, "results.json"), "r") as f:
+        with tf.io.gfile.GFile(os.path.join(data_dir, "results.json"), "r") as f:
           expected_results = json.load(f)
         self.assertAllClose(results, expected_results)
 
@@ -298,7 +298,7 @@ class BaseTest(tf.test.TestCase):
             correctness_function=correctness_function
         )
       except:
-        tf.logging.error("Failed unittest {}".format(name))
+        tf.compat.v1.logging.error("Failed unittest {}".format(name))
         raise
     else:
       self._construct_and_save_reference_files(

official/utils/testing/reference_data_test.py

@@ -63,12 +63,12 @@ class GoldenBaseTest(reference_data.BaseTest):
     with g.as_default():
       seed = self.name_to_seed(name)
       seed = seed + 1 if bad_seed else seed
-      tf.set_random_seed(seed)
+      tf.compat.v1.set_random_seed(seed)
       tensor_name = "wrong_tensor" if wrong_name else "input_tensor"
       tensor_shape = (1, 2) if wrong_shape else (1, 1)
-      input_tensor = tf.get_variable(
+      input_tensor = tf.compat.v1.get_variable(
           tensor_name, dtype=tf.float32,
-          initializer=tf.random_uniform(tensor_shape, maxval=1)
+          initializer=tf.random.uniform(tensor_shape, maxval=1)
       )
 
     def correctness_function(tensor_result):
@@ -86,13 +86,13 @@ class GoldenBaseTest(reference_data.BaseTest):
 
     g = tf.Graph()
     with g.as_default():
-      tf.set_random_seed(self.name_to_seed(name))
-      input_tensor = tf.get_variable(
+      tf.compat.v1.set_random_seed(self.name_to_seed(name))
+      input_tensor = tf.compat.v1.get_variable(
           "input_tensor", dtype=tf.float32,
-          initializer=tf.random_uniform((1, 2), maxval=1)
+          initializer=tf.random.uniform((1, 2), maxval=1)
       )
-      layer = tf.layers.dense(inputs=input_tensor, units=4)
-      layer = tf.layers.dense(inputs=layer, units=1)
+      layer = tf.compat.v1.layers.dense(inputs=input_tensor, units=4)
+      layer = tf.compat.v1.layers.dense(inputs=layer, units=1)
 
     self._save_or_test_ops(
         name=name, graph=g, ops_to_eval=[layer], test=test,