Add RepVGG (#32)

* Add `RepVGG`

* Update tests

* Add test for `get_reparameterized_model`

* Update README

* Minor update

* Fix test

* Fix readme

* Fix readme

README.md

@@ -17,66 +17,73 @@
 
 **K**eras **Im**age **M**odels (`kimm`) is a collection of image models, blocks and layers written in Keras 3. The goal is to offer SOTA models with pretrained weights in a user-friendly manner.
 
-`kimm` is:
+KIMM is:
 
-- 🚀 A model zoo where almost all models come with pre-trained weights on ImageNet.
+🚀 A model zoo where almost all models come with pre-trained weights on ImageNet.
 
-  > **Note:**
-  > The accuracy of the converted models can be found at [results-imagenet.csv (timm)](https://github.com/huggingface/pytorch-image-models/blob/main/results/results-imagenet.csv) and [https://keras.io/api/applications/ (keras)](https://keras.io/api/applications/),
-  > and the numerical differences of the converted models can be verified in `tools/convert_*.py`
+> [!NOTE]
+> The accuracy of the converted models can be found at [results-imagenet.csv (timm)](https://github.com/huggingface/pytorch-image-models/blob/main/results/results-imagenet.csv) and [https://keras.io/api/applications/ (keras)](https://keras.io/api/applications/),
+> and the numerical differences of the converted models can be verified in `tools/convert_*.py`.
 
-- ✨ Exposing a common API identical to offcial `keras.applications.*`.
+✨ Exposing a common API identical to offcial `keras.applications.*`.
 
-  ```python
-  model = kimm.models.RegNetY002(
-      input_tensor: keras.KerasTensor = None,
-      input_shape: typing.Optional[typing.Sequence[int]] = None,
-      include_preprocessing: bool = True,
-      include_top: bool = True,
-      pooling: typing.Optional[str] = None,
-      dropout_rate: float = 0.0,
-      classes: int = 1000,
-      classifier_activation: str = "softmax",
-      weights: typing.Optional[str] = "imagenet",
-      name: str = "RegNetY002",
-  )
-  ```
-
-- 🔥 Integrated with feature extraction capability.
-
-  ```python
-  from keras import random
-  import kimm
-
-  model = kimm.models.ConvNeXtAtto(feature_extractor=True)
-  x = random.uniform([1, 224, 224, 3])
-  y = model(x, training=False)
-  # y becomes a dict
-  for k, v in y.items():
-      print(k, v.shape)
-  ```
-
-- 🧰 Providing APIs to export models to `.tflite` and `.onnx`.
-
-  ```python
-  # in tensorflow backend
-  from keras import backend
-  import kimm
-
-  backend.set_image_data_format("channels_last")
-  model = kimm.models.MobileNet050V3Small()
-  kimm.export.export_tflite(model, [224, 224, 3], "model.tflite")
-  ```
-
-  ```python
-  # in torch backend
-  from keras import backend
-  import kimm
-
-  backend.set_image_data_format("channels_first")
-  model = kimm.models.MobileNet050V3Small()
-  kimm.export.export_onnx(model, [3, 224, 224], "model.onnx")
-  ```
+```python
+model = kimm.models.RegNetY002(
+    input_tensor: keras.KerasTensor = None,
+    input_shape: typing.Optional[typing.Sequence[int]] = None,
+    include_preprocessing: bool = True,
+    include_top: bool = True,
+    pooling: typing.Optional[str] = None,
+    dropout_rate: float = 0.0,
+    classes: int = 1000,
+    classifier_activation: str = "softmax",
+    weights: typing.Optional[str] = "imagenet",
+    name: str = "RegNetY002",
+)
+```
+
+🔥 Integrated with feature extraction capability.
+
+```python
+model = kimm.models.ConvNeXtAtto(feature_extractor=True)
+x = keras.random.uniform([1, 224, 224, 3])
+y = model.predict(x)
+# y becomes a dict
+for k, v in y.items():
+    print(k, v.shape)
+```
+
+🧰 Providing APIs to export models to `.tflite` and `.onnx`.
+
+```python
+# tensorflow backend
+keras.backend.set_image_data_format("channels_last")
+model = kimm.models.MobileNet050V3Small()
+kimm.export.export_tflite(model, [224, 224, 3], "model.tflite")
+```
+
+```python
+# torch backend
+keras.backend.set_image_data_format("channels_first")
+model = kimm.models.MobileNet050V3Small()
+kimm.export.export_onnx(model, [3, 224, 224], "model.onnx")
+```
+
+> [!IMPORTANT]
+> `kimm.export.export_tflite` is currently restricted to `tensorflow` backend and `channels_last`.
+> `kimm.export.export_onnx` is currently restricted to `torch` backend and `channels_first`.
+
+🔧 Supporting the reparameterization technique.
+
+```python
+model = kimm.models.RepVGGA0()
+reparameterized_model = kimm.utils.get_reparameterized_model(model)
+# or
+# reparameterized_model = model.get_reparameterized_model()
+y1 = model.predict(x)
+y2 = model.predict(x)
+np.testing.assert_allclose(y1, y2, atol=1e-5)
+```
 
 ## Installation
 
@@ -175,6 +182,7 @@ Reference: [Grad-CAM class activation visualization (keras.io)](https://keras.io
 |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*`|
+|RepVGG|[CVPR 2021](https://arxiv.org/abs/2101.03697)|`timm`|`kimm.models.RepVGG*`|
 |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*`|
 |VGG|[ICLR 2015](https://arxiv.org/abs/1409.1556)|`timm`|`kimm.models.VGG*`|

kimm/layers/__init__.py

@@ -1,3 +1,4 @@
 from kimm.layers.attention import Attention
 from kimm.layers.layer_scale import LayerScale
 from kimm.layers.position_embedding import PositionEmbedding
+from kimm.layers.rep_conv2d import RepConv2D

kimm/layers/attention.py

@@ -30,7 +30,7 @@ def __init__(
         self.qkv = layers.Dense(
             hidden_dim * 3,
             use_bias=use_qkv_bias,
-            dtype=self.dtype,
+            dtype=self.dtype_policy,
             name=f"{name}_qkv",
         )
         if use_qk_norm: