Make sure that keras conversions work when storing to saved model is failed

PiperOrigin-RevId: 317028099
Change-Id: I544d2c5170644791fe644a595d7f4e91b9cd9d3d

tensorflow/lite/python/lite.py

@@ -690,21 +690,20 @@ class TFLiteKerasModelConverterV2(TFLiteConverterBaseV2):
     self._keras_model = keras_model
     self._trackable_obj = trackable_obj
 
-  def convert(self):
+  def _convert_as_saved_model(self):
-    """Converts a keras model based on instance variables.
+    """Converts a Keras model as a saved model.
 
     Returns:
       The converted data in serialized format.
-
-    Raises:
-      ValueError:
-        Multiple concrete functions are specified.
-        Input shape is not specified.
-        Invalid quantization parameters.
     """
     temp_dir = tempfile.mkdtemp()
+    try:
       try:
         self._keras_model.save(temp_dir, save_format="tf")
+      except Exception:  # pylint: disable=broad-except
+        # When storing the given keras model to a saved model is failed, let's
+        # use original keras model conversion pipeline.
+        return None
       self.saved_model_dir = temp_dir
       self._saved_model_tags = set([_tag_constants.SERVING])
       self._saved_model_exported_names = [
@@ -735,6 +734,22 @@ class TFLiteKerasModelConverterV2(TFLiteConverterBaseV2):
     finally:
       shutil.rmtree(temp_dir, True)
 
+  def convert(self):
+    """Converts a keras model based on instance variables.
+
+    Returns:
+      The converted data in serialized format.
+
+    Raises:
+      ValueError:
+        Multiple concrete functions are specified.
+        Input shape is not specified.
+        Invalid quantization parameters.
+    """
+    saved_model_convert_result = self._convert_as_saved_model()
+    if saved_model_convert_result:
+      return saved_model_convert_result
+
     input_signature = None
     # If the model's call is not a `tf.function`, then we need to first get its
     # input signature from `model_input_signature` method. We can't directly
@@ -1473,21 +1488,20 @@ class TFLiteKerasModelConverter(TFLiteConverterBaseV1):
     self._output_tensors = output_tensors
     self._debug_info_func = _build_debug_info_func(sess.graph)
 
-  def convert(self):
+  def _convert_as_saved_model(self):
-    """Converts a Keras model based on instance variables.
+    """Converts a Keras model as a saved model.
 
     Returns:
-      The converted data in serialized format. Either a TFLite Flatbuffer or a
+      The converted data in serialized format.
-      Graphviz graph depending on value in `output_format`.
-
-    Raises:
-      ValueError:
-        Input shape is not specified.
-        None value for dimension in input_tensor.
     """
     temp_dir = tempfile.mkdtemp()
+    try:
       try:
         self._keras_model.save(temp_dir, save_format="tf")
+      except Exception:  # pylint: disable=broad-except
+        # When storing the given keras model to a saved model is failed, let's
+        # use original keras model conversion pipeline.
+        return None
       tag_set = set([_tag_constants.SERVING])
       signature_key = _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
       result = _freeze_saved_model(temp_dir, None, None, None, tag_set,
@@ -1506,6 +1520,22 @@ class TFLiteKerasModelConverter(TFLiteConverterBaseV1):
     finally:
       shutil.rmtree(temp_dir, True)
 
+  def convert(self):
+    """Converts a Keras model based on instance variables.
+
+    Returns:
+      The converted data in serialized format. Either a TFLite Flatbuffer or a
+      Graphviz graph depending on value in `output_format`.
+
+    Raises:
+      ValueError:
+        Input shape is not specified.
+        None value for dimension in input_tensor.
+    """
+    saved_model_convert_result = self._convert_as_saved_model()
+    if saved_model_convert_result:
+      return saved_model_convert_result
+
     return super(TFLiteKerasModelConverter, self).convert()
 
 

tensorflow/lite/python/lite_v2_test.py

@@ -769,6 +769,37 @@ class FromKerasModelTest(lite_v2_test_util.ModelTest):
     converter.convert()
     self._assertValidDebugInfo(converter._debug_info)
 
+  @test_util.run_v2_only
+  def testKerasFallbackPath(self):
+    """Test keras model which failed when exporting to the saved model."""
+    input_data = tf.constant(
+        np.array(np.random.random_sample((20)), dtype=np.float32))
+
+    class Model(tf.keras.Model):
+
+      def __init__(self):
+        super(Model, self).__init__()
+        # A None name will cause a failure in exporting to a saved model.
+        self.shared_weights = self.add_weight(
+            name=None,
+            shape=(20, 1),
+            dtype=tf.float32,
+            initializer=tf.random_normal_initializer(
+                mean=0.0, stddev=300**(-0.5)))
+
+      def call(self, x):
+        return tf.add(self.shared_weights, x)
+
+    # Building the model.
+    model = Model()
+    model.compile(optimizer='sgd', loss='mean_squared_error')
+    model.fit(input_data, input_data, epochs=1)
+
+    # Convert model.
+    converter = lite.TFLiteConverterV2.from_keras_model(model)
+    tflite_model = converter.convert()
+    self.assertTrue(tflite_model)
+
 
 class ControlFlowTest(lite_v2_test_util.ModelTest):