Finish up resnet

- Add presets.
- Add converter script.
- Add preprocessing with auto resizing.

keras_nlp/api/layers/__init__.py

@@ -53,6 +53,9 @@
 from keras_nlp.src.models.pali_gemma.pali_gemma_image_converter import (
     PaliGemmaImageConverter,
 )
+from keras_nlp.src.models.resnet.resnet_image_converter import (
+    ResNetImageConverter,
+)
 from keras_nlp.src.models.whisper.whisper_audio_converter import (
     WhisperAudioConverter,
 )

keras_nlp/api/models/__init__.py

@@ -172,6 +172,9 @@
 )
 from keras_nlp.src.models.gpt_neo_x.gpt_neo_x_tokenizer import GPTNeoXTokenizer
 from keras_nlp.src.models.image_classifier import ImageClassifier
+from keras_nlp.src.models.image_classifier_preprocessor import (
+    ImageClassifierPreprocessor,
+)
 from keras_nlp.src.models.llama3.llama3_backbone import Llama3Backbone
 from keras_nlp.src.models.llama3.llama3_causal_lm import Llama3CausalLM
 from keras_nlp.src.models.llama3.llama3_causal_lm_preprocessor import (
@@ -231,6 +234,9 @@
 from keras_nlp.src.models.resnet.resnet_image_classifier import (
     ResNetImageClassifier,
 )
+from keras_nlp.src.models.resnet.resnet_image_classifier_preprocessor import (
+    ResNetImageClassifierPreprocessor,
+)
 from keras_nlp.src.models.roberta.roberta_backbone import RobertaBackbone
 from keras_nlp.src.models.roberta.roberta_masked_lm import RobertaMaskedLM
 from keras_nlp.src.models.roberta.roberta_masked_lm_preprocessor import (

keras_nlp/src/models/image_classifier.py

@@ -33,11 +33,6 @@ class ImageClassifier(Task):
     used to load a pre-trained config and weights.
     """
 
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # Default compilation.
-        self.compile()
-
     def compile(
         self,
         optimizer="auto",

keras_nlp/src/models/image_classifier_preprocessor.py

@@ -0,0 +1,83 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import keras
+
+from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.preprocessor import Preprocessor
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
+
+
+@keras_nlp_export("keras_nlp.models.ImageClassifierPreprocessor")
+class ImageClassifierPreprocessor(Preprocessor):
+    """Base class for image classification preprocessing layers.
+
+    `ImageClassifierPreprocessor` tasks wraps a
+    `keras_nlp.layers.ImageConverter` to create a preprocessing layer for
+    image classification tasks. It is intended to be paired with a
+    `keras_nlp.models.ImageClassifier` task.
+
+    All `ImageClassifierPreprocessor` take inputs three inputs, `x`, `y`, and
+    `sample_weight`. `x`, the first input, should always be included. It can
+    be a image or batch of images. See examples below. `y` and `sample_weight`
+    are optional inputs that will be passed through unaltered. Usually, `y` will
+    be the classification label, and `sample_weight` will not be provided.
+
+    The layer will output either `x`, an `(x, y)` tuple if labels were provided,
+    or an `(x, y, sample_weight)` tuple if labels and sample weight were
+    provided. `x` will be the input images after all model preprocessing has
+    been applied.
+
+    All `ImageClassifierPreprocessor` tasks include a `from_preset()`
+    constructor which can be used to load a pre-trained config and vocabularies.
+    You can call the `from_preset()` constructor directly on this base class, in
+    which case the correct class for your model will be automatically
+    instantiated.
+
+    Examples.
+    ```python
+    preprocessor = keras_nlp.models.ImageClassifierPreprocessor.from_preset(
+        "resnet_50",
+    )
+
+    # Resize a single image for resnet 50.
+    x = np.ones((512, 512, 3))
+    x = preprocessor(x)
+
+    # Resize a labeled image.
+    x, y = np.ones((512, 512, 3)), 1
+    x, y = preprocessor(x, y)
+
+    # Resize a batch of labeled images.
+    x, y = [np.ones((512, 512, 3)), np.zeros((512, 512, 3))], [1, 0]
+    x, y = preprocessor(x, y)
+
+    # Use a `tf.data.Dataset`.
+    ds = tf.data.Dataset.from_tensor_slices((x, y)).batch(2)
+    ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)
+    ```
+    """
+
+    def __init__(
+        self,
+        image_converter=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.image_converter = image_converter
+
+    @preprocessing_function
+    def call(self, x, y=None, sample_weight=None):
+        if self.image_converter:
+            x = self.image_converter(x)
+        return keras.utils.pack_x_y_sample_weight(x, y, sample_weight)

keras_nlp/src/models/resnet/__init__.py

@@ -11,3 +11,9 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+from keras_nlp.src.models.resnet.resnet_backbone import ResNetBackbone
+from keras_nlp.src.models.resnet.resnet_presets import backbone_presets
+from keras_nlp.src.utils.preset_utils import register_presets
+
+register_presets(backbone_presets, ResNetBackbone)

keras_nlp/src/models/resnet/resnet_image_classifier.py

@@ -16,6 +16,9 @@
 from keras_nlp.src.api_export import keras_nlp_export
 from keras_nlp.src.models.image_classifier import ImageClassifier
 from keras_nlp.src.models.resnet.resnet_backbone import ResNetBackbone
+from keras_nlp.src.models.resnet.resnet_image_classifier_preprocessor import (
+    ResNetImageClassifierPreprocessor,
+)
 
 
 @keras_nlp_export("keras_nlp.models.ResNetImageClassifier")
@@ -88,21 +91,22 @@ class ResNetImageClassifier(ImageClassifier):
     """
 
     backbone_cls = ResNetBackbone
+    preprocessor_cls = ResNetImageClassifierPreprocessor
 
     def __init__(
         self,
         backbone,
         num_classes,
-        activation="softmax",
+        preprocessor=None,
+        activation=None,
         head_dtype=None,
-        preprocessor=None,  # adding this dummy arg for saved model test
-        # TODO: once preprocessor flow is figured out, this needs to be updated
         **kwargs,
     ):
         head_dtype = head_dtype or backbone.dtype_policy
 
         # === Layers ===
         self.backbone = backbone
+        self.preprocessor = preprocessor
         self.output_dense = keras.layers.Dense(
             num_classes,
             activation=activation,

keras_nlp/src/models/resnet/resnet_image_classifier_preprocessor.py

@@ -0,0 +1,28 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.image_classifier_preprocessor import (
+    ImageClassifierPreprocessor,
+)
+from keras_nlp.src.models.resnet.resnet_backbone import ResNetBackbone
+from keras_nlp.src.models.resnet.resnet_image_converter import (
+    ResNetImageConverter,
+)
+
+
+@keras_nlp_export("keras_nlp.models.ResNetImageClassifierPreprocessor")
+class ResNetImageClassifierPreprocessor(ImageClassifierPreprocessor):
+    backbone_cls = ResNetBackbone
+    image_converter_cls = ResNetImageConverter

keras_nlp/src/models/resnet/resnet_image_converter.py

@@ -0,0 +1,23 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.layers.preprocessing.resizing_image_converter import (
+    ResizingImageConverter,
+)
+from keras_nlp.src.models.resnet.resnet_backbone import ResNetBackbone
+
+
+@keras_nlp_export("keras_nlp.layers.ResNetImageConverter")
+class ResNetImageConverter(ResizingImageConverter):
+    backbone_cls = ResNetBackbone

keras_nlp/src/models/resnet/resnet_presets.py

@@ -0,0 +1,95 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""ResNet preset configurations."""
+
+backbone_presets = {
+    "resnet_18_imagenet": {
+        "metadata": {
+            "description": (
+                "18-layer ResNet model pre-trained on the ImageNet 1k dataset "
+                "at a 224x224 resolution."
+            ),
+            "params": 11186112,
+            "official_name": "ResNet",
+            "path": "resnet",
+            "model_card": "https://arxiv.org/abs/2110.00476",
+        },
+        "kaggle_handle": "kaggle://kerashub/resnetv1/keras/resnet_18_imagenet/1",
+    },
+    "resnet_50_imagenet": {
+        "metadata": {
+            "description": (
+                "50-layer ResNet model pre-trained on the ImageNet 1k dataset "
+                "at a 224x224 resolution."
+            ),
+            "params": 23561152,
+            "official_name": "ResNet",
+            "path": "resnet",
+            "model_card": "https://arxiv.org/abs/2110.00476",
+        },
+        "kaggle_handle": "kaggle://kerashub/resnetv1/keras/resnet_50_imagenet/1",
+    },
+    "resnet_101_imagenet": {
+        "metadata": {
+            "description": (
+                "101-layer ResNet model pre-trained on the ImageNet 1k dataset "
+                "at a 224x224 resolution."
+            ),
+            "params": 42605504,
+            "official_name": "ResNet",
+            "path": "resnet",
+            "model_card": "https://arxiv.org/abs/2110.00476",
+        },
+        "kaggle_handle": "kaggle://kerashub/resnetv1/keras/resnet_101_imagenet/1",
+    },
+    "resnet_152_imagenet": {
+        "metadata": {
+            "description": (
+                "152-layer ResNet model pre-trained on the ImageNet 1k dataset "
+                "at a 224x224 resolution."
+            ),
+            "params": 58295232,
+            "official_name": "ResNet",
+            "path": "resnet",
+            "model_card": "https://arxiv.org/abs/2110.00476",
+        },
+        "kaggle_handle": "kaggle://kerashub/resnetv1/keras/resnet_152_imagenet/1",
+    },
+    "resnet_v2_50_imagenet": {
+        "metadata": {
+            "description": (
+                "50-layer ResNetV2 model pre-trained on the ImageNet 1k "
+                "dataset at a 224x224 resolution."
+            ),
+            "params": 23561152,
+            "official_name": "ResNet",
+            "path": "resnet",
+            "model_card": "https://arxiv.org/abs/2110.00476",
+        },
+        "kaggle_handle": "kaggle://kerashub/resnetv2/keras/resnet_v2_50_imagenet/1",
+    },
+    "resnet_v2_101_imagenet": {
+        "metadata": {
+            "description": (
+                "101-layer ResNetV2 model pre-trained on the ImageNet 1k "
+                "dataset at a 224x224 resolution."
+            ),
+            "params": 42605504,
+            "official_name": "ResNet",
+            "path": "resnet",
+            "model_card": "https://arxiv.org/abs/2110.00476",
+        },
+        "kaggle_handle": "kaggle://kerashub/resnetv2/keras/resnet_v2_101_imagenet/1",
+    },
+}

keras_nlp/src/models/text_classifier.py

@@ -63,9 +63,6 @@ class TextClassifier(Task):
     ```
     """
 
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
     def compile(
         self,
         optimizer="auto",