Update base image aug layer tensor conversion (#2281)

keras_cv/layers/preprocessing/base_image_augmentation_layer.py

@@ -23,6 +23,7 @@
 
 from keras_cv import bounding_box
 from keras_cv.api_export import keras_cv_export
+from keras_cv.backend import config
 from keras_cv.backend import keras
 from keras_cv.backend import ops
 from keras_cv.backend import scope
@@ -411,14 +412,16 @@ def get_random_transformation(
     def call(self, inputs):
         # try to convert a given backend native tensor to TensorFlow tensor
         # before passing it over to TFDataScope
+        is_tf_backend = config.backend() == "tensorflow"
+        is_in_tf_graph = not tf.executing_eagerly()
         contains_ragged = lambda y: any(
             tree.map_structure(
                 lambda x: isinstance(x, (tf.RaggedTensor, tf.SparseTensor)),
                 tree.flatten(y),
             )
         )
         inputs_contain_ragged = contains_ragged(inputs)
-        if not inputs_contain_ragged:
+        if not is_tf_backend and not inputs_contain_ragged:
             inputs = tree.map_structure(
                 lambda x: tf.convert_to_tensor(x), inputs
             )
@@ -444,13 +447,15 @@ def call(self, inputs):
         # backend native tensors. This is to avoid breaking TF data
         # pipelines that can't easily be ported to become backend
         # agnostic.
-        if not inputs_contain_ragged and not contains_ragged(outputs):
-            outputs = tree.map_structure(
-                # some layers return None, handle that case when
-                # converting to tensors
-                lambda x: ops.convert_to_tensor(x) if x is not None else x,
-                outputs,
-            )
+        # Skip this step for TF backend or if in `tf.graph` like `tf.data`.
+        if not is_tf_backend and not is_in_tf_graph:
+            if not inputs_contain_ragged and not contains_ragged(outputs):
+                outputs = tree.map_structure(
+                    # some layers return None, handle that case when
+                    # converting to tensors
+                    lambda x: ops.convert_to_tensor(x) if x is not None else x,
+                    outputs,
+                )
         return outputs
 
     def _augment(self, inputs):

keras_cv/layers/preprocessing/base_image_augmentation_layer_test.py

@@ -265,3 +265,11 @@ def in_tf_function(inputs):
         self.assertNotAllClose(
             segmentation_mask_diff[0], segmentation_mask_diff[1]
         )
+
+    def test_augment_tf_data_pipeline(self):
+        image = np.random.random(size=(1, 8, 8, 3)).astype("float32")
+        tf_dataset = tf.data.Dataset.from_tensor_slices(image).map(
+            RandomAddLayer(fixed_value=2.0)
+        )
+        output = iter(tf_dataset).get_next()
+        self.assertAllClose(image[0] + 2.0, output)