Add MobileOne

README.md

@@ -193,6 +193,7 @@ Reference: [Grad-CAM class activation visualization (keras.io)](https://keras.io
 |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*`|
+|MobileOne|[CVPR 2023](https://arxiv.org/abs/2206.04040)|`timm`|`kimm.models.MobileOne*`|
 |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*`|

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__ = "0.1.6"
+__version__ = "0.1.7"

kimm/layers/__init__.py

@@ -1,4 +1,5 @@
 from kimm.layers.attention import Attention
 from kimm.layers.layer_scale import LayerScale
+from kimm.layers.mobile_one_conv2d import MobileOneConv2D
 from kimm.layers.position_embedding import PositionEmbedding
 from kimm.layers.rep_conv2d import RepConv2D

kimm/layers/attention.py

@@ -13,7 +13,6 @@ def __init__(
         use_qk_norm: bool = False,
         attention_dropout_rate: float = 0.0,
         projection_dropout_rate: float = 0.0,
-        name: str = "attention",
         **kwargs,
     ):
         super().__init__(**kwargs)
@@ -25,20 +24,19 @@ def __init__(
         self.use_qk_norm = use_qk_norm
         self.attention_dropout_rate = attention_dropout_rate
         self.projection_dropout_rate = projection_dropout_rate
-        self.name = name
 
         self.qkv = layers.Dense(
             hidden_dim * 3,
             use_bias=use_qkv_bias,
             dtype=self.dtype_policy,
-            name=f"{name}_qkv",
+            name=f"{self.name}_qkv",
         )
         if use_qk_norm:
             self.q_norm = layers.LayerNormalization(
-                dtype=self.dtype_policy, name=f"{name}_q_norm"
+                dtype=self.dtype_policy, name=f"{self.name}_q_norm"
             )
             self.k_norm = layers.LayerNormalization(
-                dtype=self.dtype_policy, name=f"{name}_k_norm"
+                dtype=self.dtype_policy, name=f"{self.name}_k_norm"
             )
         else:
             self.q_norm = layers.Identity(dtype=self.dtype_policy)
@@ -47,15 +45,15 @@ def __init__(
         self.attention_dropout = layers.Dropout(
             attention_dropout_rate,
             dtype=self.dtype_policy,
-            name=f"{name}_attn_drop",
+            name=f"{self.name}_attn_drop",
         )
         self.projection = layers.Dense(
-            hidden_dim, dtype=self.dtype_policy, name=f"{name}_proj"
+            hidden_dim, dtype=self.dtype_policy, name=f"{self.name}_proj"
         )
         self.projection_dropout = layers.Dropout(
             projection_dropout_rate,
             dtype=self.dtype_policy,
-            name=f"{name}_proj_drop",
+            name=f"{self.name}_proj_drop",
         )
 
     def build(self, input_shape):

kimm/layers/layer_scale.py

@@ -11,13 +11,11 @@ def __init__(
         self,
         axis: int = -1,
         initializer: Initializer = initializers.Constant(1e-5),
-        name: str = "layer_scale",
         **kwargs,
     ):
         super().__init__(**kwargs)
         self.axis = axis
         self.initializer = initializer
-        self.name = name
 
     def build(self, input_shape):
         if isinstance(self.axis, list):