Register VGG presets. (#1935)

* register vgg preset

* nit

* nit

* nit

Author: sachinprasadhs

keras_hub/api/layers/__init__.py

@@ -52,7 +52,7 @@
 from keras_hub.src.models.sam.sam_image_converter import SAMImageConverter
 from keras_hub.src.models.sam.sam_mask_decoder import SAMMaskDecoder
 from keras_hub.src.models.sam.sam_prompt_encoder import SAMPromptEncoder
-from keras_hub.src.models.vgg.vgg_image_classifier import VGGImageConverter
+from keras_hub.src.models.vgg.vgg_image_converter import VGGImageConverter
 from keras_hub.src.models.whisper.whisper_audio_converter import (
     WhisperAudioConverter,
 )

keras_hub/api/models/__init__.py

@@ -299,7 +299,7 @@
 from keras_hub.src.models.text_to_image import TextToImage
 from keras_hub.src.models.vgg.vgg_backbone import VGGBackbone
 from keras_hub.src.models.vgg.vgg_image_classifier import VGGImageClassifier
-from keras_hub.src.models.vgg.vgg_image_classifier import (
+from keras_hub.src.models.vgg.vgg_image_classifier_preprocessor import (
     VGGImageClassifierPreprocessor,
 )
 from keras_hub.src.models.vit_det.vit_det_backbone import ViTDetBackbone

keras_hub/src/models/vgg/__init__.py

@@ -1 +1,5 @@
 from keras_hub.src.models.vgg.vgg_backbone import VGGBackbone
+from keras_hub.src.models.vgg.vgg_presets import backbone_presets
+from keras_hub.src.utils.preset_utils import register_presets
+
+register_presets(backbone_presets, VGGBackbone)

keras_hub/src/models/vgg/vgg_image_classifier.py

@@ -1,24 +1,12 @@
 import keras
 
 from keras_hub.src.api_export import keras_hub_export
-from keras_hub.src.layers.preprocessing.image_converter import ImageConverter
 from keras_hub.src.models.image_classifier import ImageClassifier
-from keras_hub.src.models.image_classifier_preprocessor import (
-    ImageClassifierPreprocessor,
-)
 from keras_hub.src.models.task import Task
 from keras_hub.src.models.vgg.vgg_backbone import VGGBackbone
-
-
-@keras_hub_export("keras_hub.layers.VGGImageConverter")
-class VGGImageConverter(ImageConverter):
-    backbone_cls = VGGBackbone
-
-
-@keras_hub_export("keras_hub.models.VGGImageClassifierPreprocessor")
-class VGGImageClassifierPreprocessor(ImageClassifierPreprocessor):
-    backbone_cls = VGGBackbone
-    image_converter_cls = VGGImageConverter
+from keras_hub.src.models.vgg.vgg_image_classifier_preprocessor import (
+    VGGImageClassifierPreprocessor,
+)
 
 
 @keras_hub_export("keras_hub.models.VGGImageClassifier")
@@ -211,6 +199,7 @@ def __init__(
         self.pooling = pooling
         self.pooling_hidden_dim = pooling_hidden_dim
         self.dropout = dropout
+        self.preprocessor = preprocessor
 
     def get_config(self):
         # Backbone serialized in `super`

keras_hub/src/models/vgg/vgg_image_classifier_preprocessor.py

@@ -0,0 +1,12 @@
+from keras_hub.src.api_export import keras_hub_export
+from keras_hub.src.models.image_classifier_preprocessor import (
+    ImageClassifierPreprocessor,
+)
+from keras_hub.src.models.vgg.vgg_backbone import VGGBackbone
+from keras_hub.src.models.vgg.vgg_image_converter import VGGImageConverter
+
+
+@keras_hub_export("keras_hub.models.VGGImageClassifierPreprocessor")
+class VGGImageClassifierPreprocessor(ImageClassifierPreprocessor):
+    backbone_cls = VGGBackbone
+    image_converter_cls = VGGImageConverter

keras_hub/src/models/vgg/vgg_image_classifier_test.py

@@ -3,34 +3,40 @@
 
 from keras_hub.src.models.vgg.vgg_backbone import VGGBackbone
 from keras_hub.src.models.vgg.vgg_image_classifier import VGGImageClassifier
+from keras_hub.src.models.vgg.vgg_image_classifier_preprocessor import (
+    VGGImageClassifierPreprocessor,
+)
+from keras_hub.src.models.vgg.vgg_image_converter import VGGImageConverter
 from keras_hub.src.tests.test_case import TestCase
 
 
 class VGGImageClassifierTest(TestCase):
     def setUp(self):
         # Setup model.
         self.images = np.ones((2, 8, 8, 3), dtype="float32")
-        self.labels = [0, 3]
+        self.labels = [0, 1]
         self.backbone = VGGBackbone(
             stackwise_num_repeats=[2, 4, 4],
             stackwise_num_filters=[2, 16, 16],
             image_shape=(8, 8, 3),
         )
+        image_converter = VGGImageConverter(image_size=(8, 8))
+        self.preprocessor = VGGImageClassifierPreprocessor(
+            image_converter=image_converter,
+        )
         self.init_kwargs = {
             "backbone": self.backbone,
             "num_classes": 2,
             "activation": "softmax",
             "pooling": "flatten",
+            "preprocessor": self.preprocessor,
         }
         self.train_data = (
             self.images,
             self.labels,
         )
 
     def test_classifier_basics(self):
-        pytest.skip(
-            reason="TODO: enable after preprocessor flow is figured out"
-        )
         self.run_task_test(
             cls=VGGImageClassifier,
             init_kwargs=self.init_kwargs,

keras_hub/src/models/vgg/vgg_image_converter.py

@@ -0,0 +1,8 @@
+from keras_hub.src.api_export import keras_hub_export
+from keras_hub.src.layers.preprocessing.image_converter import ImageConverter
+from keras_hub.src.models.vgg.vgg_backbone import VGGBackbone
+
+
+@keras_hub_export("keras_hub.layers.VGGImageConverter")
+class VGGImageConverter(ImageConverter):
+    backbone_cls = VGGBackbone