Update modeling_super_linear.py

modeling_super_linear.py

@@ -210,13 +210,13 @@ class RLinear(nn.Module):
         if L <  self.seq_len:
             in_features     = L
             W               = self.Linear.weight.detach()
-            fixed_weights   = self.weights[:, :L]
-            dynamic_weights = self.weights[:, L:]
+            fixed_weights   = W[:, :L]
+            dynamic_weights = W[:, L:]
 
-            if in_features != self.weights.size(1) or out_features != self.weights.size(0):
+            if in_features != self.weights.size(1):
                 dynamic_weights = F.interpolate(dynamic_weights.unsqueeze(0).unsqueeze(0), size=(self.horizon, in_features-self.seq_len), mode='bilinear', align_corners=False).squeeze(0).squeeze(0)
             if self.fixed_in != 0:
-                fixed_weights = F.interpolate(fixed_weights.unsqueeze(0).unsqueeze(0), size=(self.horizon, self.fixed_in), mode='bilinear', align_corners=False).squeeze(0).squeeze(0)
+                fixed_weights = F.interpolate(fixed_weights.unsqueeze(0).unsqueeze(0), size=(self.horizon, L), mode='bilinear', align_corners=False).squeeze(0).squeeze(0)
 
             x = self.revin_layer(x, 'norm')
             x = F.linear(x, torch.cat((fixed_weights, dynamic_weights), dim=1))