Revert the MobileNetV5 changes to make backward compatability with the mobilenetv5 presets

Author: laxmareddyp

keras_hub/src/models/gemma3n/gemma3n_backbone_test.py

@@ -155,7 +155,7 @@ def test_saved_model(self, backbone_type):
         )
 
     @parameterized.named_parameters(
-        ("multimodal", "multimodal", 5426, 7),
+        ("multimodal", "multimodal", 5450, 7),
         ("text_only", "text_only", 350, 4),
     )
     def test_architecture_characteristics(

keras_hub/src/models/mobilenetv5/mobilenetv5_blocks.py

@@ -85,7 +85,7 @@ def build(self, input_shape):
         self.has_skip = (
             in_chs == self.filters and self.stride == 1
         ) and not self.noskip
-        use_bias = False
+        use_bias = self.norm_layer == "rms_norm"
 
         if self.dw_kernel_size_start:
             self.dw_start = ConvNormAct(
@@ -353,7 +353,7 @@ def build(self, input_shape):
         else:
             mid_chs = adjust_channels(in_chs * self.exp_ratio)
         groups = num_groups(self.group_size, mid_chs)
-        use_bias = False
+        use_bias = self.norm_layer == "rms_norm"
         self.conv_exp = ConvNormAct(
             mid_chs,
             self.exp_kernel_size,

keras_hub/src/models/mobilenetv5/mobilenetv5_layers.py

@@ -268,18 +268,14 @@ def __init__(
                 gamma_initializer="ones",
                 dtype=self.dtype_policy,
             )
-        elif norm_layer == "layer_norm":
+        else:
             ln_axis = [1, 2, 3]
             if self.data_format == "channels_first":
                 ln_axis = [2, 3, 1]
             self.norm = keras.layers.LayerNormalization(
                 axis=ln_axis,
                 dtype=self.dtype_policy,
             )
-        else:
-            raise ValueError(
-                f"Unsupported norm_layer in ConvNormAct: {norm_layer}"
-            )
 
         if self.apply_act:
             if act_layer == "gelu":