Update unet.jl

3rd try

src/convnets/unet.jl

@@ -88,25 +88,26 @@ return layers
 end
 function modify_first_conv_layer(encoder_backbone, inchannels)
     for (index, layer) in enumerate(encoder_backbone.layers)
-        if isa(layer, Flux.Conv)  
+        if isa(layer, Flux.Conv)
             # Correctly infer the number of output channels from the layer's weight dimensions
-            outchannels = size(layer.weight, 1)  # The first dimension for Flux.Conv weight is the number of output channels
+            outchannels = size(layer.weight, 1)
 
-            kernel_size = (size(layer.weight, 3), size(layer.weight, 4))  # height and width of the kernel
+            # Recreate the convolutional layer with the new number of input channels
+            kernel_size = (size(layer.weight, 3), size(layer.weight, 4))
             stride = layer.stride
             pad = layer.pad
-            activation = layer.activation
+            new_conv_layer = Flux.Conv(kernel_size, inchannels => outchannels, stride=stride, pad=pad)
 
-            # Create a new convolutional layer with the adjusted number of input channels
-            new_conv_layer = Flux.Conv(kernel_size, inchannels => outchannels, stride=stride, pad=pad, activation=activation)
+            # Update the layer in the backbone
             encoder_backbone.layers[index] = new_conv_layer
-            break  
+            break  # Assume only the first Conv layer needs adjustment
         end
     end
     return encoder_backbone
 end
 
 
+
 """
     UNet(imsize::Dims{2} = (256, 256), inchannels::Integer = 3, outplanes::Integer = 3,
          encoder_backbone = Metalhead.backbone(DenseNet(121)); pretrain::Bool = false)