try gtp vit

models.py

@@ -527,6 +527,9 @@ class FouriER(torch.nn.Module):
 
         self.network = nn.ModuleList(network)
         self.norm = norm_layer(embed_dims[-1])
+        self.graph_type = 'Spatial'
+        self.class_token = False
+        self.token_scale = False
         self.head = nn.Linear(
                 embed_dims[-1], num_classes) if num_classes > 0 \
                 else nn.Identity()
@@ -578,10 +581,6 @@ class FouriER(torch.nn.Module):
             degree = torch.diag_embed(degree**(-1/2))
             graph = degree @ graph @ degree
             
-        if self.token_scale:
-            token_scales = self.token_scales.unsqueeze(0).expand(B,-1).to(x.device)
-        else:
-            token_scales = None
         for idx, block in enumerate(self.network):
             x = block(x)
         # output only the features of last layer for image classification