Resolve the problem with conv weights (#183)

Author: fardinayar

loralib/layers.py

@@ -247,6 +247,8 @@ class ConvLoRA(nn.Module, LoRALayer):
     def __init__(self, conv_module, in_channels, out_channels, kernel_size, r=0, lora_alpha=1, lora_dropout=0., merge_weights=True, **kwargs):
         super(ConvLoRA, self).__init__()
         self.conv = conv_module(in_channels, out_channels, kernel_size, **kwargs)
+        for name, param in self.conv.named_parameters():
+            self.register_parameter(name, param)
         LoRALayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights)
         assert isinstance(kernel_size, int)
         # Actual trainable parameters