fix(autoencoderkl): handle proj_attn→out_proj key mapping in load_old_state_dict

monai/networks/nets/autoencoderkl.py

@@ -715,13 +715,25 @@ def load_old_state_dict(self, old_state_dict: dict, verbose=False) -> None:
             new_state_dict[f"{block}.attn.to_k.bias"] = old_state_dict.pop(f"{block}.to_k.bias")
             new_state_dict[f"{block}.attn.to_v.bias"] = old_state_dict.pop(f"{block}.to_v.bias")
 
-            # old version did not have a projection so set these to the identity
-            new_state_dict[f"{block}.attn.out_proj.weight"] = torch.eye(
-                new_state_dict[f"{block}.attn.out_proj.weight"].shape[0]
-            )
-            new_state_dict[f"{block}.attn.out_proj.bias"] = torch.zeros(
-                new_state_dict[f"{block}.attn.out_proj.bias"].shape
-            )
+            out_w = f"{block}.attn.out_proj.weight"
+            out_b = f"{block}.attn.out_proj.bias"
+            proj_w = f"{block}.proj_attn.weight"
+            proj_b = f"{block}.proj_attn.bias"
+
+            if out_w in new_state_dict:
+                if proj_w in old_state_dict:
+                    new_state_dict[out_w] = old_state_dict.pop(proj_w)
+                    if proj_b in old_state_dict:
+                        new_state_dict[out_b] = old_state_dict.pop(proj_b)
+                else:
+                    # weights pre-date proj_attn: initialise to identity / zero
+                    new_state_dict[out_w] = torch.eye(new_state_dict[out_w].shape[0])
+                    new_state_dict[out_b] = torch.zeros(new_state_dict[out_b].shape)
+            elif proj_w in old_state_dict:
+                # new model has no out_proj at all – discard the legacy keys so they
+                # don't surface as "unexpected keys" during load_state_dict
+                old_state_dict.pop(proj_w)
+                old_state_dict.pop(proj_b)
 
         # fix the upsample conv blocks which were renamed postconv
         for k in new_state_dict: