Code cleaning

graphium/nn/architectures/global_architectures.py

@@ -666,8 +666,9 @@ def _parse_subset_in_dim(
         """
 
         # Parse the subset_in_dim, make sure value is between 0 and 1
+        subset_idx = None
         if subset_in_dim is None:
-            subset_in_dim = 1.0
+            return 1.0, None
         if isinstance(subset_in_dim, int):
             assert (
                 subset_in_dim > 0 and subset_in_dim <= in_dim
@@ -681,9 +682,7 @@ def _parse_subset_in_dim(
                 subset_in_dim = 1
 
         # Create the subset_idx, which is a list of indices to use for each ensemble
-        if subset_in_dim == in_dim:
-            subset_idx = None
-        else:
+        if subset_in_dim != in_dim:
             subset_idx = torch.stack([torch.randperm(in_dim)[:subset_in_dim] for _ in range(num_ensemble)])
 
         return subset_in_dim, subset_idx
@@ -719,7 +718,9 @@ def forward(self, h: torch.Tensor) -> torch.Tensor:
         # Subset the input features for each MLP in the ensemble
         if self.subset_idx is not None:
             if len(h.shape) != 2:
-                assert h.shape[-3] == 1, f"Expected shape to be [B, Din] or [..., 1, B, Din], got {h.shape}."
+                assert (
+                    h.shape[-3] == 1
+                ), f"Expected shape to be [B, Din] or [..., 1, B, Din] when using `subset_in_dim`, got {h.shape}."
             h = h[..., self.subset_idx].transpose(-2, -3)
 
         # Run the standard forward pass