Fix tests

keras_hub/src/models/differential_binarization/differential_binarization_backbone.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import keras
 from keras import layers
 
 from keras_hub.src.api_export import keras_hub_export
@@ -53,9 +54,16 @@ def __init__(
     def get_config(self):
         config = super().get_config()
         config["fpn_channels"] = self.fpn_channels
-        config["image_encoder"] = self.image_encoder
+        config["image_encoder"] = keras.layers.serialize(self.image_encoder)
         return config
 
+    @classmethod
+    def from_config(cls, config):
+        config["image_encoder"] = keras.layers.deserialize(
+            config["image_encoder"]
+        )
+        return cls(**config)
+
 
 def diffbin_fpn_model(inputs, out_channels):
     in2 = layers.Conv2D(

keras_hub/src/models/differential_binarization/differential_binarization_test.py

@@ -18,33 +18,40 @@
 from keras_hub.src.models.differential_binarization.differential_binarization import (
     DifferentialBinarization,
 )
+from keras_hub.src.models.differential_binarization.differential_binarization_backbone import (
+    DifferentialBinarizationBackbone,
+)
 from keras_hub.src.models.resnet.resnet_backbone import ResNetBackbone
+from keras_hub.src.models.resnet.resnet_image_classifier_preprocessor import (
+    ResNetImageClassifierPreprocessor,
+)
 from keras_hub.src.tests.test_case import TestCase
 
 
 class DifferentialBinarizationTest(TestCase):
     def setUp(self):
         self.images = ops.ones((2, 224, 224, 3))
         self.labels = ops.zeros((2, 224, 224, 4))
-        self.backbone = ResNetBackbone(
+        image_encoder = ResNetBackbone(
             input_conv_filters=[64],
             input_conv_kernel_sizes=[7],
             stackwise_num_filters=[64, 128, 256, 512],
             stackwise_num_blocks=[3, 4, 6, 3],
             stackwise_num_strides=[1, 2, 2, 2],
             block_type="bottleneck_block",
             image_shape=(224, 224, 3),
-            include_rescaling=False,
         )
+        self.backbone = DifferentialBinarizationBackbone(
+            image_encoder=image_encoder
+        )
+        self.preprocessor = ResNetImageClassifierPreprocessor()
         self.init_kwargs = {
             "backbone": self.backbone,
+            "preprocessor": self.preprocessor,
         }
         self.train_data = (self.images, self.labels)
 
     def test_basics(self):
-        pytest.skip(
-            reason="TODO: enable after preprocessor flow is figured out"
-        )
         self.run_task_test(
             cls=DifferentialBinarization,
             init_kwargs=self.init_kwargs,