Rename TocoConverter to TFLiteConverter.

PiperOrigin-RevId: 214710175

4 files changed, 206 insertions, 78 deletions

diff --git a/tensorflow/contrib/lite/python/convert.py b/tensorflow/contrib/lite/python/convert.py
index 1f48a826d4..627be8f44f 100644
--- a/tensorflow/contrib/lite/python/convert.py
+++ b/tensorflow/contrib/lite/python/convert.py
@@ -343,13 +343,14 @@ def toco_convert_impl(input_data, input_tensors, output_tensors, *args,
   return data
 
 
-@deprecation.deprecated(None, "Use `lite.TocoConverter` instead.")
+@deprecation.deprecated(None, "Use `lite.TFLiteConverter` instead.")
 def toco_convert(input_data, input_tensors, output_tensors, *args, **kwargs):
-  """"Convert a model using TOCO.
+  """Convert a model using TOCO.
 
   Typically this function is used to convert from TensorFlow GraphDef to TFLite.
   Conversion can be customized by providing arguments that are forwarded to
-  `build_toco_convert_protos` (see documentation for details).
+  `build_toco_convert_protos` (see documentation for details). This function has
+  been deprecated. Please use `lite.TFLiteConverter` instead.
 
   Args:
     input_data: Input data (i.e. often `sess.graph_def`),
diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py
index 2be24455d8..09365f101f 100644
--- a/tensorflow/contrib/lite/python/lite.py
+++ b/tensorflow/contrib/lite/python/lite.py
@@ -17,6 +17,7 @@
 EXPERIMENTAL: APIs here are unstable and likely to change without notice.
 
 @@TocoConverter
+@@TFLiteConverter
 @@toco_convert
 @@toco_convert_protos
 @@Interpreter
@@ -62,9 +63,10 @@ from tensorflow.python.framework.importer import import_graph_def as _import_gra
 from tensorflow.python.lib.io import file_io as _file_io
 from tensorflow.python.saved_model import signature_constants as _signature_constants
 from tensorflow.python.saved_model import tag_constants as _tag_constants
+from tensorflow.python.util import deprecation as _deprecation
 
 
-class TocoConverter(object):
+class TFLiteConverter(object):
   """Convert a TensorFlow model into `output_format` using TOCO.
 
   This is used to convert from a TensorFlow GraphDef or SavedModel into either a
@@ -121,22 +123,22 @@ class TocoConverter(object):
 
     ```python
     # Converting a GraphDef from session.
-    converter = lite.TocoConverter.from_session(sess, in_tensors, out_tensors)
+    converter = lite.TFLiteConverter.from_session(sess, in_tensors, out_tensors)
     tflite_model = converter.convert()
     open("converted_model.tflite", "wb").write(tflite_model)
 
     # Converting a GraphDef from file.
-    converter = lite.TocoConverter.from_frozen_graph(
+    converter = lite.TFLiteConverter.from_frozen_graph(
       graph_def_file, input_arrays, output_arrays)
     tflite_model = converter.convert()
     open("converted_model.tflite", "wb").write(tflite_model)
 
     # Converting a SavedModel.
-    converter = lite.TocoConverter.from_saved_model(saved_model_dir)
+    converter = lite.TFLiteConverter.from_saved_model(saved_model_dir)
     tflite_model = converter.convert()
 
     # Converting a tf.keras model.
-    converter = lite.TocoConverter.from_keras_model_file(keras_model)
+    converter = lite.TFLiteConverter.from_keras_model_file(keras_model)
     tflite_model = converter.convert()
     ```
   """
@@ -147,10 +149,9 @@ class TocoConverter(object):
                output_tensors,
                input_arrays_with_shape=None,
                output_arrays=None):
-    """Constructor for TocoConverter.
+    """Constructor for TFLiteConverter.
 
     Args:
-
       graph_def: Frozen TensorFlow GraphDef.
       input_tensors: List of input tensors. Type and shape are computed using
         `foo.get_shape()` and `foo.dtype`.
@@ -158,8 +159,8 @@ class TocoConverter(object):
       input_arrays_with_shape: Tuple of strings representing input tensor names
         and list of integers representing input shapes
         (e.g., [("foo" : [1, 16, 16, 3])]). Use only when graph cannot be loaded
-        into TensorFlow and when `input_tensors` and `output_tensors` are None.
-        (default None)
+          into TensorFlow and when `input_tensors` and `output_tensors` are
+          None. (default None)
       output_arrays: List of output tensors to freeze graph with. Use only when
         graph cannot be loaded into TensorFlow and when `input_tensors` and
         `output_tensors` are None. (default None)
@@ -195,7 +196,7 @@ class TocoConverter(object):
 
   @classmethod
   def from_session(cls, sess, input_tensors, output_tensors):
-    """Creates a TocoConverter class from a TensorFlow Session.
+    """Creates a TFLiteConverter class from a TensorFlow Session.
 
     Args:
       sess: TensorFlow Session.
@@ -204,7 +205,7 @@ class TocoConverter(object):
       output_tensors: List of output tensors (only .name is used from this).
 
     Returns:
-      TocoConverter class.
+      TFLiteConverter class.
     """
     graph_def = _freeze_graph(sess, output_tensors)
     return cls(graph_def, input_tensors, output_tensors)
@@ -215,7 +216,7 @@ class TocoConverter(object):
                         input_arrays,
                         output_arrays,
                         input_shapes=None):
-    """Creates a TocoConverter class from a file containing a frozen GraphDef.
+    """Creates a TFLiteConverter class from a file containing a frozen GraphDef.
 
     Args:
       graph_def_file: Full filepath of file containing frozen GraphDef.
@@ -224,10 +225,10 @@ class TocoConverter(object):
       input_shapes: Dict of strings representing input tensor names to list of
         integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}).
         Automatically determined when input shapes is None (e.g., {"foo" :
-        None}). (default None)
+          None}). (default None)
 
     Returns:
-      TocoConverter class.
+      TFLiteConverter class.
 
     Raises:
       IOError:
@@ -310,7 +311,7 @@ class TocoConverter(object):
                        output_arrays=None,
                        tag_set=None,
                        signature_key=None):
-    """Creates a TocoConverter class from a SavedModel.
+    """Creates a TFLiteConverter class from a SavedModel.
 
     Args:
       saved_model_dir: SavedModel directory to convert.
@@ -319,7 +320,7 @@ class TocoConverter(object):
       input_shapes: Dict of strings representing input tensor names to list of
         integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}).
         Automatically determined when input shapes is None (e.g., {"foo" :
-        None}). (default None)
+          None}). (default None)
       output_arrays: List of output tensors to freeze graph with. Uses output
         arrays from SignatureDef when none are provided. (default None)
       tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to
@@ -328,7 +329,7 @@ class TocoConverter(object):
         (default DEFAULT_SERVING_SIGNATURE_DEF_KEY)
 
     Returns:
-      TocoConverter class.
+      TFLiteConverter class.
     """
     if tag_set is None:
       tag_set = set([_tag_constants.SERVING])
@@ -346,7 +347,7 @@ class TocoConverter(object):
                             input_arrays=None,
                             input_shapes=None,
                             output_arrays=None):
-    """Creates a TocoConverter class from a tf.keras model file.
+    """Creates a TFLiteConverter class from a tf.keras model file.
 
     Args:
       model_file: Full filepath of HDF5 file containing the tf.keras model.
@@ -355,12 +356,12 @@ class TocoConverter(object):
       input_shapes: Dict of strings representing input tensor names to list of
         integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}).
         Automatically determined when input shapes is None (e.g., {"foo" :
-        None}). (default None)
+          None}). (default None)
       output_arrays: List of output tensors to freeze graph with. Uses output
         arrays from SignatureDef when none are provided. (default None)
 
     Returns:
-      TocoConverter class.
+      TFLiteConverter class.
     """
     _keras.backend.clear_session()
     _keras.backend.set_learning_phase(False)
@@ -502,6 +503,59 @@ class TocoConverter(object):
       tensor.set_shape(shape)
 
 
+class TocoConverter(object):
+  """Convert a TensorFlow model into `output_format` using TOCO.
+
+  This class has been deprecated. Please use `lite.TFLiteConverter` instead.
+  """
+
+  @classmethod
+  @_deprecation.deprecated(None,
+                           "Use `lite.TFLiteConverter.from_session` instead.")
+  def from_session(cls, sess, input_tensors, output_tensors):
+    """Creates a TocoConverter class from a TensorFlow Session."""
+    return TFLiteConverter.from_session(sess, input_tensors, output_tensors)
+
+  @classmethod
+  @_deprecation.deprecated(
+      None, "Use `lite.TFLiteConverter.from_frozen_graph` instead.")
+  def from_frozen_graph(cls,
+                        graph_def_file,
+                        input_arrays,
+                        output_arrays,
+                        input_shapes=None):
+    """Creates a TocoConverter class from a file containing a frozen graph."""
+    return TFLiteConverter.from_frozen_graph(graph_def_file, input_arrays,
+                                             output_arrays, input_shapes)
+
+  @classmethod
+  @_deprecation.deprecated(
+      None, "Use `lite.TFLiteConverter.from_saved_model` instead.")
+  def from_saved_model(cls,
+                       saved_model_dir,
+                       input_arrays=None,
+                       input_shapes=None,
+                       output_arrays=None,
+                       tag_set=None,
+                       signature_key=None):
+    """Creates a TocoConverter class from a SavedModel."""
+    return TFLiteConverter.from_saved_model(saved_model_dir, input_arrays,
+                                            input_shapes, output_arrays,
+                                            tag_set, signature_key)
+
+  @classmethod
+  @_deprecation.deprecated(
+      None, "Use `lite.TFLiteConverter.from_keras_model_file` instead.")
+  def from_keras_model_file(cls,
+                            model_file,
+                            input_arrays=None,
+                            input_shapes=None,
+                            output_arrays=None):
+    """Creates a TocoConverter class from a tf.keras model file."""
+    return TFLiteConverter.from_keras_model_file(model_file, input_arrays,
+                                                 input_shapes, output_arrays)
+
+
 def _is_frozen_graph(sess):
   """Determines if the graph is frozen.
 
diff --git a/tensorflow/contrib/lite/python/lite_test.py b/tensorflow/contrib/lite/python/lite_test.py
index f112ed5cdd..33f8fc1e8c 100644
--- a/tensorflow/contrib/lite/python/lite_test.py
+++ b/tensorflow/contrib/lite/python/lite_test.py
@@ -50,18 +50,18 @@ class FromConstructor(test_util.TensorFlowTestCase):
 
     # `output_arrays` is not defined.
     with self.assertRaises(ValueError) as error:
-      lite.TocoConverter(
+      lite.TFLiteConverter(
           None, None, [], input_arrays_with_shape=[('input', [3, 9])])
     self.assertEqual(message, str(error.exception))
 
     # `input_arrays_with_shape` is not defined.
     with self.assertRaises(ValueError) as error:
-      lite.TocoConverter(None, [], None, output_arrays=['output'])
+      lite.TFLiteConverter(None, [], None, output_arrays=['output'])
     self.assertEqual(message, str(error.exception))
 
   # Tests valid constructors using a dummy value for the GraphDef.
   def testValidConstructor(self):
-    converter = lite.TocoConverter(
+    converter = lite.TFLiteConverter(
         None,
         None,
         None,
@@ -76,7 +76,7 @@ class FromConstructor(test_util.TensorFlowTestCase):
         'The batch size cannot be set for this model. Please use '
         'input_shapes parameter.', str(error.exception))
 
-    converter = lite.TocoConverter(None, ['input_tensor'], ['output_tensor'])
+    converter = lite.TFLiteConverter(None, ['input_tensor'], ['output_tensor'])
     self.assertTrue(converter._has_valid_tensors())
 
 
@@ -89,7 +89,8 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     sess = session.Session()
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
+    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
+                                                  [out_tensor])
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
@@ -121,7 +122,7 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     sess = session.Session()
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_session(
+    converter = lite.TFLiteConverter.from_session(
         sess, [in_tensor_1, in_tensor_2], [out_tensor])
     converter.inference_type = lite_constants.QUANTIZED_UINT8
     converter.quantized_input_stats = {
@@ -166,7 +167,7 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     sess = session.Session()
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_session(
+    converter = lite.TFLiteConverter.from_session(
         sess, [in_tensor_1, in_tensor_2], [out_tensor])
     converter.inference_type = lite_constants.QUANTIZED_UINT8
     converter.quantized_input_stats = {'inputA': (0., 1.)}  # mean, std_dev
@@ -182,7 +183,8 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     sess = session.Session()
 
     # Test invalid shape. None after 1st dimension.
-    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
+    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
+                                                  [out_tensor])
     with self.assertRaises(ValueError) as error:
       converter.convert()
     self.assertEqual('Provide an input shape for input array \'Placeholder\'.',
@@ -195,7 +197,8 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     sess = session.Session()
 
     # Test invalid shape. None after 1st dimension.
-    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
+    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
+                                                  [out_tensor])
     with self.assertRaises(ValueError) as error:
       converter.convert()
     self.assertEqual(
@@ -210,7 +213,8 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     sess = session.Session()
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
+    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
+                                                  [out_tensor])
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
@@ -242,7 +246,8 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     sess.run(_global_variables_initializer())
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
+    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
+                                                  [out_tensor])
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
@@ -272,7 +277,8 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     sess = session.Session()
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
+    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
+                                                  [out_tensor])
     converter.output_format = lite_constants.GRAPHVIZ_DOT
     graphviz_output = converter.convert()
     self.assertTrue(graphviz_output)
@@ -285,7 +291,8 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     sess = session.Session()
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
+    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
+                                                  [out_tensor])
     graphviz_dir = self.get_temp_dir()
     converter.dump_graphviz_dir = graphviz_dir
     tflite_model = converter.convert()
@@ -299,7 +306,8 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     self.assertTrue(num_items_graphviz)
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
+    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
+                                                  [out_tensor])
     graphviz_dir = self.get_temp_dir()
     converter.dump_graphviz_dir = graphviz_dir
     converter.dump_graphviz_video = True
@@ -317,7 +325,8 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     sess = session.Session()
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
+    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
+                                                  [out_tensor])
     converter.inference_input_type = lite_constants.QUANTIZED_UINT8
     converter.quantized_input_stats = {'Placeholder': (0., 1.)}  # mean, std_dev
     tflite_model = converter.convert()
@@ -347,7 +356,8 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     sess = session.Session()
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
+    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
+                                                  [out_tensor])
     converter.inference_type = lite_constants.QUANTIZED_UINT8
     converter.quantized_input_stats = {'Placeholder': (0., 1.)}  # mean, std_dev
     converter.default_ranges_stats = (0, 6)  # min, max
@@ -387,13 +397,13 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     sess = session.Session()
 
     # Convert float model.
-    float_converter = lite.TocoConverter.from_session(sess, [in_tensor_1],
-                                                      [out_tensor])
+    float_converter = lite.TFLiteConverter.from_session(sess, [in_tensor_1],
+                                                        [out_tensor])
     float_tflite = float_converter.convert()
     self.assertTrue(float_tflite)
 
     # Convert quantized weights model.
-    quantized_converter = lite.TocoConverter.from_session(
+    quantized_converter = lite.TFLiteConverter.from_session(
         sess, [in_tensor_1], [out_tensor])
     quantized_converter.post_training_quantize = True
     quantized_tflite = quantized_converter.convert()
@@ -409,7 +419,8 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     sess = session.Session()
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
+    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
+                                                  [out_tensor])
     converter.converter_mode = lite.ConverterMode.TOCO_EXTENDED_ALL
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
@@ -424,6 +435,22 @@ class FromSessionTest(test_util.TensorFlowTestCase):
         'sure you invoke the Eager delegate before inference.',
         str(error.exception))
 
+  def testFloatTocoConverter(self):
+    """Tests deprecated test TocoConverter."""
+    in_tensor = array_ops.placeholder(
+        shape=[1, 16, 16, 3], dtype=dtypes.float32)
+    out_tensor = in_tensor + in_tensor
+    sess = session.Session()
+
+    # Convert model and ensure model is not None.
+    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
+    tflite_model = converter.convert()
+    self.assertTrue(tflite_model)
+
+    # Ensure the interpreter is able to load.
+    interpreter = Interpreter(model_content=tflite_model)
+    interpreter.allocate_tensors()
+
 
 class FromFrozenGraphFile(test_util.TensorFlowTestCase):
 
@@ -439,8 +466,8 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
     sess.close()
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_frozen_graph(graph_def_file,
-                                                     ['Placeholder'], ['add'])
+    converter = lite.TFLiteConverter.from_frozen_graph(graph_def_file,
+                                                       ['Placeholder'], ['add'])
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
@@ -474,7 +501,7 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
     sess.close()
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_frozen_graph(
+    converter = lite.TFLiteConverter.from_frozen_graph(
         graph_def_file, ['Placeholder'], ['add'],
         input_shapes={'Placeholder': [1, 16, 16, 3]})
     tflite_model = converter.convert()
@@ -503,8 +530,8 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
 
     # Ensure the graph with variables cannot be converted.
     with self.assertRaises(ValueError) as error:
-      lite.TocoConverter.from_frozen_graph(graph_def_file, ['Placeholder'],
-                                           ['add'])
+      lite.TFLiteConverter.from_frozen_graph(graph_def_file, ['Placeholder'],
+                                             ['add'])
     self.assertEqual('Please freeze the graph using freeze_graph.py.',
                      str(error.exception))
 
@@ -520,8 +547,8 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
     sess.close()
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_frozen_graph(graph_def_file,
-                                                     ['Placeholder'], ['add'])
+    converter = lite.TFLiteConverter.from_frozen_graph(graph_def_file,
+                                                       ['Placeholder'], ['add'])
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
@@ -545,8 +572,8 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
 
   def testInvalidFileNotFound(self):
     with self.assertRaises(IOError) as error:
-      lite.TocoConverter.from_frozen_graph('invalid_file', ['Placeholder'],
-                                           ['add'])
+      lite.TFLiteConverter.from_frozen_graph('invalid_file', ['Placeholder'],
+                                             ['add'])
     self.assertEqual('File \'invalid_file\' does not exist.',
                      str(error.exception))
 
@@ -558,8 +585,8 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
 
     # Attempts to convert the invalid model.
     with self.assertRaises(IOError) as error:
-      lite.TocoConverter.from_frozen_graph(graph_def_file, ['Placeholder'],
-                                           ['add'])
+      lite.TFLiteConverter.from_frozen_graph(graph_def_file, ['Placeholder'],
+                                             ['add'])
     self.assertEqual(
         'Unable to parse input file \'{}\'.'.format(graph_def_file),
         str(error.exception))
@@ -580,7 +607,7 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
     # Tests the object detection model that cannot be loaded in TensorFlow.
     self._initObjectDetectionArgs()
 
-    converter = lite.TocoConverter.from_frozen_graph(
+    converter = lite.TFLiteConverter.from_frozen_graph(
         self._graph_def_file, self._input_arrays, self._output_arrays,
         self._input_shapes)
     converter.allow_custom_ops = True
@@ -621,7 +648,7 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
 
     # Missing `input_shapes`.
     with self.assertRaises(ValueError) as error:
-      lite.TocoConverter.from_frozen_graph(
+      lite.TFLiteConverter.from_frozen_graph(
           self._graph_def_file, self._input_arrays, self._output_arrays)
     self.assertEqual('input_shapes must be defined for this model.',
                      str(error.exception))
@@ -632,7 +659,7 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
 
     # `input_shapes` does not contain the names in `input_arrays`.
     with self.assertRaises(ValueError) as error:
-      lite.TocoConverter.from_frozen_graph(
+      lite.TFLiteConverter.from_frozen_graph(
           self._graph_def_file,
           self._input_arrays,
           self._output_arrays,
@@ -641,6 +668,27 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
         'input_shapes must contain a value for each item in input_array.',
         str(error.exception))
 
+  def testFloatTocoConverter(self):
+    in_tensor = array_ops.placeholder(
+        shape=[1, 16, 16, 3], dtype=dtypes.float32)
+    _ = in_tensor + in_tensor
+    sess = session.Session()
+
+    # Write graph to file.
+    graph_def_file = os.path.join(self.get_temp_dir(), 'model.pb')
+    write_graph(sess.graph_def, '', graph_def_file, False)
+    sess.close()
+
+    # Convert model and ensure model is not None.
+    converter = lite.TocoConverter.from_frozen_graph(graph_def_file,
+                                                     ['Placeholder'], ['add'])
+    tflite_model = converter.convert()
+    self.assertTrue(tflite_model)
+
+    # Ensure the model is able to load.
+    interpreter = Interpreter(model_content=tflite_model)
+    interpreter.allocate_tensors()
+
 
 class FromSavedModelTest(test_util.TensorFlowTestCase):
 
@@ -663,7 +711,7 @@ class FromSavedModelTest(test_util.TensorFlowTestCase):
     saved_model_dir = self._createSavedModel(shape=[1, 16, 16, 3])
 
     # Convert model and ensure model is not None.
-    converter = lite.TocoConverter.from_saved_model(saved_model_dir)
+    converter = lite.TFLiteConverter.from_saved_model(saved_model_dir)
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
@@ -693,7 +741,7 @@ class FromSavedModelTest(test_util.TensorFlowTestCase):
     """Test a SavedModel, with None in input tensor's shape."""
     saved_model_dir = self._createSavedModel(shape=[None, 16, 16, 3])
 
-    converter = lite.TocoConverter.from_saved_model(saved_model_dir)
+    converter = lite.TFLiteConverter.from_saved_model(saved_model_dir)
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
@@ -724,7 +772,7 @@ class FromSavedModelTest(test_util.TensorFlowTestCase):
     """Test a SavedModel ordering of input arrays."""
     saved_model_dir = self._createSavedModel(shape=[1, 16, 16, 3])
 
-    converter = lite.TocoConverter.from_saved_model(
+    converter = lite.TFLiteConverter.from_saved_model(
         saved_model_dir, input_arrays=['inputB', 'inputA'])
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
@@ -757,7 +805,7 @@ class FromSavedModelTest(test_util.TensorFlowTestCase):
     saved_model_dir = self._createSavedModel(shape=[1, 16, 16, 3])
 
     # Check case where input shape is given.
-    converter = lite.TocoConverter.from_saved_model(
+    converter = lite.TFLiteConverter.from_saved_model(
         saved_model_dir,
         input_arrays=['inputA'],
         input_shapes={'inputA': [1, 16, 16, 3]})
@@ -766,12 +814,25 @@ class FromSavedModelTest(test_util.TensorFlowTestCase):
     self.assertTrue(tflite_model)
 
     # Check case where input shape is None.
-    converter = lite.TocoConverter.from_saved_model(
+    converter = lite.TFLiteConverter.from_saved_model(
         saved_model_dir, input_arrays=['inputA'], input_shapes={'inputA': None})
 
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
+  def testSimpleModelTocoConverter(self):
+    """Test a SavedModel with deprecated TocoConverter."""
+    saved_model_dir = self._createSavedModel(shape=[1, 16, 16, 3])
+
+    # Convert model and ensure model is not None.
+    converter = lite.TocoConverter.from_saved_model(saved_model_dir)
+    tflite_model = converter.convert()
+    self.assertTrue(tflite_model)
+
+    # Ensure the model is able to load.
+    interpreter = Interpreter(model_content=tflite_model)
+    interpreter.allocate_tensors()
+
 
 class FromKerasFile(test_util.TensorFlowTestCase):
 
@@ -805,7 +866,7 @@ class FromKerasFile(test_util.TensorFlowTestCase):
     """Test a Sequential tf.keras model with default inputs."""
     keras_file = self._getSequentialModel()
 
-    converter = lite.TocoConverter.from_keras_model_file(keras_file)
+    converter = lite.TFLiteConverter.from_keras_model_file(keras_file)
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
@@ -845,13 +906,13 @@ class FromKerasFile(test_util.TensorFlowTestCase):
 
     # Invalid input array raises error.
     with self.assertRaises(ValueError) as error:
-      lite.TocoConverter.from_keras_model_file(
+      lite.TFLiteConverter.from_keras_model_file(
           keras_file, input_arrays=['invalid-input'])
     self.assertEqual("Invalid tensors 'invalid-input' were found.",
                      str(error.exception))
 
     # Valid input array.
-    converter = lite.TocoConverter.from_keras_model_file(
+    converter = lite.TFLiteConverter.from_keras_model_file(
         keras_file, input_arrays=['dense_input'])
     tflite_model = converter.convert()
     os.remove(keras_file)
@@ -863,13 +924,13 @@ class FromKerasFile(test_util.TensorFlowTestCase):
 
     # Passing in shape of invalid input array has no impact as long as all input
     # arrays have a shape.
-    converter = lite.TocoConverter.from_keras_model_file(
+    converter = lite.TFLiteConverter.from_keras_model_file(
         keras_file, input_shapes={'invalid-input': [2, 3]})
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
     # Passing in shape of valid input array.
-    converter = lite.TocoConverter.from_keras_model_file(
+    converter = lite.TFLiteConverter.from_keras_model_file(
         keras_file, input_shapes={'dense_input': [2, 3]})
     tflite_model = converter.convert()
     os.remove(keras_file)
@@ -890,13 +951,13 @@ class FromKerasFile(test_util.TensorFlowTestCase):
 
     # Invalid output array raises error.
     with self.assertRaises(ValueError) as error:
-      lite.TocoConverter.from_keras_model_file(
+      lite.TFLiteConverter.from_keras_model_file(
           keras_file, output_arrays=['invalid-output'])
     self.assertEqual("Invalid tensors 'invalid-output' were found.",
                      str(error.exception))
 
     # Valid output array.
-    converter = lite.TocoConverter.from_keras_model_file(
+    converter = lite.TFLiteConverter.from_keras_model_file(
         keras_file, output_arrays=['time_distributed/Reshape_1'])
     tflite_model = converter.convert()
     os.remove(keras_file)
@@ -926,7 +987,7 @@ class FromKerasFile(test_util.TensorFlowTestCase):
         os.close(fd)
 
     # Convert to TFLite model.
-    converter = lite.TocoConverter.from_keras_model_file(keras_file)
+    converter = lite.TFLiteConverter.from_keras_model_file(keras_file)
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
@@ -991,7 +1052,7 @@ class FromKerasFile(test_util.TensorFlowTestCase):
         os.close(fd)
 
     # Convert to TFLite model.
-    converter = lite.TocoConverter.from_keras_model_file(keras_file)
+    converter = lite.TFLiteConverter.from_keras_model_file(keras_file)
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
@@ -1052,7 +1113,7 @@ class FromKerasFile(test_util.TensorFlowTestCase):
         os.close(fd)
 
     # Convert to TFLite model.
-    converter = lite.TocoConverter.from_keras_model_file(keras_file)
+    converter = lite.TFLiteConverter.from_keras_model_file(keras_file)
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
@@ -1086,6 +1147,18 @@ class FromKerasFile(test_util.TensorFlowTestCase):
     np.testing.assert_almost_equal(tflite_result, keras_result, 5)
     os.remove(keras_file)
 
+  def testSequentialModelTocoConverter(self):
+    """Test a Sequential tf.keras model with deprecated TocoConverter."""
+    keras_file = self._getSequentialModel()
+
+    converter = lite.TocoConverter.from_keras_model_file(keras_file)
+    tflite_model = converter.convert()
+    self.assertTrue(tflite_model)
+
+    # Ensure the model is able to load.
+    interpreter = Interpreter(model_content=tflite_model)
+    interpreter.allocate_tensors()
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/lite/python/tflite_convert.py b/tensorflow/contrib/lite/python/tflite_convert.py
index c0ff7f37f9..d6d9052a4e 100644
--- a/tensorflow/contrib/lite/python/tflite_convert.py
+++ b/tensorflow/contrib/lite/python/tflite_convert.py
@@ -40,13 +40,13 @@ def _parse_set(values):
 
 
 def _get_toco_converter(flags):
-  """Makes a TocoConverter object based on the flags provided.
+  """Makes a TFLiteConverter object based on the flags provided.
 
   Args:
     flags: argparse.Namespace object containing TFLite flags.
 
   Returns:
-    TocoConverter object.
+    TFLiteConverter object.
 
   Raises:
     ValueError: Invalid flags.
@@ -68,17 +68,17 @@ def _get_toco_converter(flags):
       "output_arrays": output_arrays
   }
 
-  # Create TocoConverter.
+  # Create TFLiteConverter.
   if flags.graph_def_file:
-    converter_fn = lite.TocoConverter.from_frozen_graph
+    converter_fn = lite.TFLiteConverter.from_frozen_graph
     converter_kwargs["graph_def_file"] = flags.graph_def_file
   elif flags.saved_model_dir:
-    converter_fn = lite.TocoConverter.from_saved_model
+    converter_fn = lite.TFLiteConverter.from_saved_model
     converter_kwargs["saved_model_dir"] = flags.saved_model_dir
     converter_kwargs["tag_set"] = _parse_set(flags.saved_model_tag_set)
     converter_kwargs["signature_key"] = flags.saved_model_signature_key
   elif flags.keras_model_file:
-    converter_fn = lite.TocoConverter.from_keras_model_file
+    converter_fn = lite.TFLiteConverter.from_keras_model_file
     converter_kwargs["model_file"] = flags.keras_model_file
   else:
     raise ValueError("--graph_def_file, --saved_model_dir, or "