VideoSwin Backbone

keras_nlp/api/models/__init__.py

@@ -50,6 +50,12 @@
 from keras_nlp.src.models.bloom.bloom_tokenizer import BloomTokenizer
 from keras_nlp.src.models.causal_lm import CausalLM
 from keras_nlp.src.models.classifier import Classifier
+from keras_nlp.src.models.csp_darknet.csp_darknet_backbone import (
+    CSPDarkNetBackbone,
+)
+from keras_nlp.src.models.csp_darknet.csp_darknet_image_classifier import (
+    CSPDarkNetImageClassifier,
+)
 from keras_nlp.src.models.deberta_v3.deberta_v3_backbone import (
     DebertaV3Backbone,
 )
@@ -68,6 +74,10 @@
 from keras_nlp.src.models.deberta_v3.deberta_v3_tokenizer import (
     DebertaV3Tokenizer,
 )
+from keras_nlp.src.models.densenet.densenet_backbone import DenseNetBackbone
+from keras_nlp.src.models.densenet.densenet_image_classifier import (
+    DenseNetImageClassifier,
+)
 from keras_nlp.src.models.distil_bert.distil_bert_backbone import (
     DistilBertBackbone,
 )
@@ -106,6 +116,7 @@
 )
 from keras_nlp.src.models.falcon.falcon_preprocessor import FalconPreprocessor
 from keras_nlp.src.models.falcon.falcon_tokenizer import FalconTokenizer
+from keras_nlp.src.models.feature_pyramid_backbone import FeaturePyramidBackbone
 from keras_nlp.src.models.gemma.gemma_backbone import GemmaBackbone
 from keras_nlp.src.models.gemma.gemma_causal_lm import GemmaCausalLM
 from keras_nlp.src.models.gemma.gemma_causal_lm_preprocessor import (
@@ -129,6 +140,7 @@
     GPTNeoXPreprocessor,
 )
 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.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 (
@@ -180,6 +192,10 @@
 from keras_nlp.src.models.phi3.phi3_preprocessor import Phi3Preprocessor
 from keras_nlp.src.models.phi3.phi3_tokenizer import Phi3Tokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
+from keras_nlp.src.models.resnet.resnet_backbone import ResNetBackbone
+from keras_nlp.src.models.resnet.resnet_image_classifier import (
+    ResNetImageClassifier,
+)
 from keras_nlp.src.models.roberta.roberta_backbone import RobertaBackbone
 from keras_nlp.src.models.roberta.roberta_classifier import RobertaClassifier
 from keras_nlp.src.models.roberta.roberta_masked_lm import RobertaMaskedLM
@@ -194,6 +210,8 @@
 from keras_nlp.src.models.t5.t5_backbone import T5Backbone
 from keras_nlp.src.models.t5.t5_tokenizer import T5Tokenizer
 from keras_nlp.src.models.task import Task
+from keras_nlp.src.models.vgg.vgg_backbone import VGGBackbone
+from keras_nlp.src.models.vgg.vgg_image_classifier import VGGImageClassifier
 from keras_nlp.src.models.whisper.whisper_audio_feature_extractor import (
     WhisperAudioFeatureExtractor,
 )

keras_nlp/src/models/backbone.py

@@ -30,6 +30,7 @@
 from keras_nlp.src.utils.preset_utils import save_metadata
 from keras_nlp.src.utils.preset_utils import save_serialized_object
 from keras_nlp.src.utils.python_utils import classproperty
+from keras_nlp.src.utils.timm.convert import load_timm_backbone
 from keras_nlp.src.utils.transformers.convert import load_transformers_backbone
 
 
@@ -204,6 +205,8 @@ class like `keras_nlp.models.Backbone.from_preset()`, or from
 
         if format == "transformers":
             return load_transformers_backbone(cls, preset, load_weights)
+        elif format == "timm":
+            return load_timm_backbone(cls, preset, load_weights, **kwargs)
 
         preset_cls = check_config_class(preset)
         if not issubclass(preset_cls, cls):

keras_nlp/src/models/csp_darknet/__init__.py

@@ -0,0 +1,13 @@
+# 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.