added test3 to test if permutated tensors end up as expected

openfold/utils/loss.py

@@ -2095,6 +2095,7 @@ class AlphaFoldMultimerLoss(AlphaFoldLoss):
             per_asym_residue_index[int(cur_asym_id)] = torch.masked_select(batch["residue_index"], asym_mask)
         if permutate_chains:
             anchor_gt_asym, anchor_pred_asym = get_least_asym_entity_or_longest_length(batch)
+            print(f"anchor_gt_asym:{anchor_gt_asym} anchor_pred_asym:{anchor_pred_asym}")
             anchor_gt_idx = int(anchor_gt_asym) - 1
 
             unique_entity_ids = torch.unique(batch["entity_id"])
@@ -2154,22 +2155,19 @@ class AlphaFoldMultimerLoss(AlphaFoldLoss):
         out: the output of model.forward()
         batch: a pair of input features and its corresponding ground truth structure
         """
-        _is_monomer = len(torch.unique(features['asym_id']))==1 or torch.unique(features['asym_id']).tolist()==[0,1]
+        # first determin which dimension in the tensor to split into individual ground truth labels
+        dim_dict = AlphaFoldMultimerLoss.determine_split_dim(features) 
+
+        # Then permutate ground truth chains before calculating the loss
+        align, per_asym_residue_index = AlphaFoldMultimerLoss.multi_chain_perm_align(out, features,dim_dict=dim_dict,
+                                                                                     permutate_chains=permutate_chains)
         
-        if not _is_monomer:
-            # first determin which dimension in the tensor to split into individual ground truth labels
-            dim_dict = AlphaFoldMultimerLoss.determine_split_dim(features) 
-
-            # Then permutate ground truth chains before calculating the loss
-            align, per_asym_residue_index = AlphaFoldMultimerLoss.multi_chain_perm_align(out, features,dim_dict=dim_dict,
-                                                                                        permutate_chains=True)
-            
-            labels = AlphaFoldMultimerLoss.split_ground_truth_labels(features,dim_dict=dim_dict,
-                                                                    REQUIRED_FEATURES=[i for i in features.keys() if i in dim_dict])
-            # reorder ground truth labels according to permutation results
-            labels = merge_labels(per_asym_residue_index,labels,align,
-                                original_nres=features['aatype'].shape[-1])
-            features.update(labels)
+        labels = AlphaFoldMultimerLoss.split_ground_truth_labels(features,dim_dict=dim_dict,
+                                                                 REQUIRED_FEATURES=[i for i in features.keys() if i in dim_dict])
+        # reorder ground truth labels according to permutation results
+        labels = merge_labels(per_asym_residue_index,labels,align,
+                              original_nres=features['aatype'].shape[-1])
+        features.update(labels)
 
         if (not _return_breakdown):
             cum_loss = self.loss(out, features, _return_breakdown)

tests/test_permutation.py

@@ -151,7 +151,19 @@ class TestPermutation(unittest.TestCase):
         tensor_to_cuda = lambda t: t.to('cuda')
         batch = tensor_tree_map(tensor_to_cuda,batch)
         dim_dict = AlphaFoldMultimerLoss.determine_split_dim(batch) 
-        aligns,_ = AlphaFoldMultimerLoss.multi_chain_perm_align(out,
+        aligns,per_asym_residue_index = AlphaFoldMultimerLoss.multi_chain_perm_align(out,
                                                                 batch,
                                                                 dim_dict,
-                                                                permutate_chains=True)
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+                                                                permutate_chains=True)
+        
+        labels = AlphaFoldMultimerLoss.split_ground_truth_labels(batch,dim_dict=dim_dict,
+                                                                 REQUIRED_FEATURES=[i for i in batch.keys() if i in dim_dict])
+        
+        labels = merge_labels(per_asym_residue_index,labels,aligns,
+                              original_nres=batch['aatype'].shape[-1])
+
+        self.assertTrue(torch.equal(labels['residue_index'],batch['residue_index']))
+        
+        expected_permutated_gt_pos = torch.cat((chain_a2_pos,chain_a1_pos,chain_b2_pos,chain_b3_pos,chain_b1_pos),dim=1)
+        expected_permutated_gt_pos = pad_features(expected_permutated_gt_pos,nres_pad,pad_dim=1)
+        self.assertTrue(torch.equal(labels['all_atom_positions'],expected_permutated_gt_pos))
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