Add InceptionNeXt and MobileViTV2 and change naming of `MobileNet…

…*V2` and `MobileNet*V3` (#30)

* Add `InceptionNeXt`

* Add `MobileViTV2`

* Fix tests

* Fix test

* Fix format

* Fix github runner

* Fix requirements

* Update version and nit

README.md

@@ -167,11 +167,13 @@ Reference: [Grad-CAM class activation visualization (keras.io)](https://keras.io
 |EfficientNetV2|[ICML 2021](https://arxiv.org/abs/2104.00298)|`timm`|`kimm.models.EfficientNetV2*`|
 |GhostNet|[CVPR 2020](https://arxiv.org/abs/1911.11907)|`timm`|`kimm.models.GhostNet*`|
 |GhostNetV2|[NeurIPS 2022](https://arxiv.org/abs/2211.12905)|`timm`|`kimm.models.GhostNetV2*`|
+|InceptionNeXt|[arXiv 2023](https://arxiv.org/abs/2303.16900)|`timm`|`kimm.models.InceptionNeXt*`|
 |InceptionV3|[CVPR 2016](https://arxiv.org/abs/1512.00567)|`timm`|`kimm.models.InceptionV3`|
 |LCNet|[arXiv 2021](https://arxiv.org/abs/2109.15099)|`timm`|`kimm.models.LCNet*`|
 |MobileNetV2|[CVPR 2018](https://arxiv.org/abs/1801.04381)|`timm`|`kimm.models.MobileNetV2*`|
 |MobileNetV3|[ICCV 2019](https://arxiv.org/abs/1905.02244)|`timm`|`kimm.models.MobileNetV3*`|
 |MobileViT|[ICLR 2022](https://arxiv.org/abs/2110.02178)|`timm`|`kimm.models.MobileViT*`|
+|MobileViTV2|[arXiv 2022](https://arxiv.org/abs/2206.02680)|`timm`|`kimm.models.MobileViTV2*`|
 |RegNet|[CVPR 2020](https://arxiv.org/abs/2003.13678)|`timm`|`kimm.models.RegNet*`|
 |ResNet|[CVPR 2015](https://arxiv.org/abs/1512.03385)|`timm`|`kimm.models.ResNet*`|
 |TinyNet|[NeurIPS 2020](https://arxiv.org/abs/2010.14819)|`timm`|`kimm.models.TinyNet*`|

kimm/__init__.py

@@ -2,4 +2,4 @@
 from kimm import models  # force to add models to the registry
 from kimm.utils.model_registry import list_models
 
-__version__ = "0.1.4"
+__version__ = "0.1.5"

kimm/export/export_onnx_test.py

@@ -10,7 +10,7 @@
 class ExportOnnxTest(testing.TestCase, parameterized.TestCase):
     def get_model(self):
         input_shape = [3, 224, 224]  # channels_first
-        model = models.MobileNet050V3Small(include_preprocessing=False)
+        model = models.MobileNetV3W050Small(include_preprocessing=False)
         return input_shape, model
 
     @classmethod

kimm/export/export_tflite_test.py

@@ -12,7 +12,7 @@
 class ExportTFLiteTest(testing.TestCase, parameterized.TestCase):
     def get_model_and_representative_dataset(self):
         input_shape = [224, 224, 3]
-        model = models.MobileNet050V3Small(include_preprocessing=False)
+        model = models.MobileNetV3W050Small(include_preprocessing=False)
 
         def representative_dataset():
             for _ in range(10):

kimm/models/__init__.py

@@ -4,6 +4,7 @@
 from kimm.models.densenet import *  # noqa:F403
 from kimm.models.efficientnet import *  # noqa:F403
 from kimm.models.ghostnet import *  # noqa:F403
+from kimm.models.inception_next import *  # noqa:F403
 from kimm.models.inception_v3 import *  # noqa:F403
 from kimm.models.mobilenet_v2 import *  # noqa:F403
 from kimm.models.mobilenet_v3 import *  # noqa:F403

kimm/models/convnext.py

@@ -24,8 +24,8 @@ def apply_convnext_block(
 ):
     channels_axis = -1 if backend.image_data_format() == "channels_last" else -3
     input_channels = inputs.shape[channels_axis]
-
     hidden_channels = int(mlp_ratio * output_channels)
+
     x = inputs
     shortcut = inputs