add some more logic before getting to atom radii tomorrow

alphafold3_pytorch/alphafold3.py

@@ -345,6 +345,17 @@ def mean_pool_with_lens(
     lens: Int['b n']
 ) -> Float['b n d']:
 
+    summed, mask = sum_pool_with_lens(feats, lens)
+    avg = einx.divide('b n d, b n', summed, lens.clamp(min = 1))
+    avg = einx.where('b n, b n d, -> b n d', mask, avg, 0.)
+    return avg
+
+@typecheck
+def sum_pool_with_lens(
+    feats: Float['b m d'],
+    lens: Int['b n']
+) -> tuple[Float['b n d'], Bool['b n']]:
+
     seq_len = feats.shape[1]
 
     mask = lens > 0
@@ -364,9 +375,7 @@ def mean_pool_with_lens(
     # subtract cumsum at one index from the previous one
     summed = sel_cumsum[:, 1:] - sel_cumsum[:, :-1]
 
-    avg = einx.divide('b n d, b n', summed, lens.clamp(min = 1))
-    avg = einx.where('b n, b n d, -> b n d', mask, avg, 0.)
-    return avg
+    return summed, mask
 
 @typecheck
 def mean_pool_fixed_windows_with_mask(
@@ -5785,18 +5794,26 @@ def _inhouse_compute_unresolved_rasa(
 
         per_atom_accessible_surface_score = reduce(score * radius ** 2, 'm sd -> m')
 
+        # sum up all surface scores for atoms per residue
+        # the final score seems to be the average of the rsa across all residues (selected by `chain_unresolved_residue_mask`)
+
+        rasa, mask = sum_pool_with_lens(
+            rearrange(per_atom_accessible_surface_score, '... -> 1 ...'),
+            rearrange(chain_molecule_atom_lens, '... -> 1 ...')
+        )
+
+        rasa = einx.where('b n, b n, -> b n', mask, rasa, 0.)
+
+        rasa = rearrange(rasa, '1 ... -> ...')
+
         # rest written by @xluo
 
-        rasa = []
         aatypes = []
         for residue in structure.get_residues():
-            rsa = float(dssp_dict.get((residue.get_full_id()[2], residue.id))[3])
-            rasa.append(rsa)
 
             aatype = dssp_dict.get((residue.get_full_id()[2], residue.id))[1]
             aatypes.append(residue_constants.restype_order[aatype])
 
-        rasa = torch.tensor(rasa, dtype=dtype, device=device)
         aatypes = torch.tensor(aatypes, device=device).int()
 
         unresolved_aatypes = aatypes[chain_unresolved_residue_mask]