Add HGNet and HGNetV2

README.md

@@ -154,6 +154,8 @@ 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*`|
+|HGNet||`timm`|`kimm.models.HGNet*`|
+|HGNetV2||`timm`|`kimm.models.HGNetV2*`|
 |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*`|

kimm/blocks/base_block.py

@@ -55,7 +55,8 @@ def apply_conv2d_block(
         if strides > 1:
             padding = "valid"
             x = layers.ZeroPadding2D(
-                (kernel_size[0] // 2, kernel_size[1] // 2), name=f"{name}_pad"
+                ((kernel_size[0] - 1) // 2, (kernel_size[1] - 1) // 2),
+                name=f"{name}_pad",
             )(x)
 
     if not use_depthwise:

kimm/layers/__init__.py

@@ -1,5 +1,6 @@
 from kimm.layers.attention import Attention
 from kimm.layers.layer_scale import LayerScale
+from kimm.layers.learnable_affine import LearnableAffine
 from kimm.layers.mobile_one_conv2d import MobileOneConv2D
 from kimm.layers.position_embedding import PositionEmbedding
 from kimm.layers.rep_conv2d import RepConv2D

kimm/layers/learnable_affine.py

@@ -0,0 +1,50 @@
+import keras
+from keras import layers
+from keras import ops
+
+
+@keras.saving.register_keras_serializable(package="kimm")
+class LearnableAffine(layers.Layer):
+    def __init__(self, scale_value=1.0, bias_value=0.0, **kwargs):
+        super().__init__(**kwargs)
+        if isinstance(scale_value, int):
+            raise ValueError(
+                f"scale_value must be a integer. Received: {scale_value}"
+            )
+        if isinstance(bias_value, int):
+            raise ValueError(
+                f"bias_value must be a integer. Received: {bias_value}"
+            )
+        self.scale_value = scale_value
+        self.bias_value = bias_value
+
+    def build(self, input_shape):
+        self.scale = self.add_weight(
+            shape=(1,),
+            initializer=lambda shape, dtype: ops.cast(self.scale_value, dtype),
+            trainable=True,
+            name="scale",
+        )
+        self.bias = self.add_weight(
+            shape=(1,),
+            initializer=lambda shape, dtype: ops.cast(self.bias_value, dtype),
+            trainable=True,
+            name="bias",
+        )
+        self.built = True
+
+    def call(self, inputs, training=None, mask=None):
+        scale = ops.cast(self.scale, self.compute_dtype)
+        bias = ops.cast(self.bias, self.compute_dtype)
+        return ops.add(ops.multiply(inputs, scale), bias)
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "scale_value": self.scale_value,
+                "bias_value": self.bias_value,
+                "name": self.name,
+            }
+        )
+        return config

kimm/layers/learnable_affine_test.py

@@ -0,0 +1,20 @@
+import pytest
+from absl.testing import parameterized
+from keras.src import testing
+
+from kimm.layers.learnable_affine import LearnableAffine
+
+
+class LearnableAffineTest(testing.TestCase, parameterized.TestCase):
+    @pytest.mark.requires_trainable_backend
+    def test_layer_scale_basic(self):
+        self.run_layer_test(
+            LearnableAffine,
+            init_kwargs={"scale_value": 1.0, "bias_value": 0.0},
+            input_shape=(1, 10),
+            expected_output_shape=(1, 10),
+            expected_num_trainable_weights=2,
+            expected_num_non_trainable_weights=0,
+            expected_num_losses=0,
+            supports_masking=False,
+        )

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.hgnet 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