make sure padded asym_id won't affect permutation steps

openfold/utils/multi_chain_permutation.py

@@ -105,13 +105,12 @@ def get_least_asym_entity_or_longest_length(batch, input_asym_id):
     anchor_pred_asym_ids: list(Tensor(int)) a list of all possible pred anchor candidates
     """
     entity_2_asym_list = get_entity_2_asym_list(batch)
-    unique_entity_ids = torch.unique(batch["entity_id"])
+    unique_entity_ids = [i for i in torch.unique(batch["entity_id"]) if i !=0]# if entity_id is 0, that means this entity_id comes from padding
     entity_asym_count = {}
     entity_length = {}
 
     for entity_id in unique_entity_ids:
         asym_ids = torch.unique(batch["asym_id"][batch["entity_id"] == entity_id])
-
         # Make sure some asym IDs associated with ground truth entity ID exist in cropped prediction
         asym_ids_in_pred = [a for a in asym_ids if a in input_asym_id]
         if not asym_ids_in_pred:
@@ -122,10 +121,8 @@ def get_least_asym_entity_or_longest_length(batch, input_asym_id):
         # Calculate entity length
         entity_mask = (batch["entity_id"] == entity_id)
         entity_length[int(entity_id)] = entity_mask.sum().item()
-
     min_asym_count = min(entity_asym_count.values())
     least_asym_entities = [entity for entity, count in entity_asym_count.items() if count == min_asym_count]
-
     # If multiple entities have the least asym_id count, return those with the longest length
     if len(least_asym_entities) > 1:
         max_length = max([entity_length[entity] for entity in least_asym_entities])
@@ -140,7 +137,6 @@ def get_least_asym_entity_or_longest_length(batch, input_asym_id):
 
     anchor_gt_asym_id = random.choice(entity_2_asym_list[least_asym_entities])
     anchor_pred_asym_ids = [asym_id for asym_id in entity_2_asym_list[least_asym_entities] if asym_id in input_asym_id]
-
     return anchor_gt_asym_id, anchor_pred_asym_ids
 
 
@@ -160,6 +156,7 @@ def greedy_align(
     used = [False for _ in range(len(true_ca_poses))]
     align = []
     unique_asym_ids = [i for i in torch.unique(batch["asym_id"]) if i != 0]
+   
     for cur_asym_id in unique_asym_ids:
         i = int(cur_asym_id - 1)
         asym_mask = batch["asym_id"] == cur_asym_id
@@ -349,6 +346,7 @@ def compute_permutation_alignment(out, features, ground_truth):
     # First select anchors from predicted structures and ground truths
     anchor_gt_asym, anchor_pred_asym_ids = get_least_asym_entity_or_longest_length(ground_truth,
                                                                                    features['asym_id'])
+    
     entity_2_asym_list = get_entity_2_asym_list(ground_truth)
     labels = split_ground_truth_labels(ground_truth)
     assert isinstance(labels, list)