fixed the fape and backbone loss errors

openfold/utils/loss.py

@@ -185,7 +185,13 @@ def backbone_loss(
     eps: float = 1e-4,
     **kwargs,
 ) -> torch.Tensor:
+    
+    ### need to check if the traj belongs to 4*4 matrix or a tensor_7
+    if traj.shape[-1]==7:
         pred_aff = Rigid.from_tensor_7(traj)
+    elif traj.shape[-1]==4:
+        pred_aff = Rigid.from_tensor_4x4(traj)
+
     pred_aff = Rigid(
         Rotation(rot_mats=pred_aff.get_rots().get_rot_mats(), quats=None),
         pred_aff.get_trans(),
@@ -304,10 +310,10 @@ def fape_loss(
         interface_bb_loss = backbone_loss(
             traj=traj,
             pair_mask=1. - intra_chain_mask,
-            **{**batch, **config.interface_backbone},
+            **{**batch, **config.intra_chain_backbone},
         )
         weighted_bb_loss = (intra_chain_bb_loss * config.intra_chain_backbone.weight
-                            + interface_bb_loss * config.interface_backbone.weight)
+                            + interface_bb_loss * config.intra_chain_backbone.weight)
     else:
         bb_loss = backbone_loss(
             traj=traj,
@@ -1865,7 +1871,6 @@ def greedy_align(
                     if (rmsd is not None) and (rmsd < best_rmsd):   
                         best_rmsd = rmsd
                         best_idx = j
-            print(f"now best_idx is {best_idx} and rmsd is {rmsd} and j is {j}")
         assert best_idx is not None
         used[best_idx] = True
         align.append((i, best_idx))
@@ -1920,7 +1925,7 @@ class AlphaFoldLoss(nn.Module):
             out["violation"] = find_structural_violations(
                 batch,
                 out["sm"]["positions"][-1],
-                **self.config.loss.violation,
+                **self.config.violation,
             )
 
         if "renamed_atom14_gt_positions" not in out.keys():
@@ -2110,12 +2115,13 @@ class AlphaFoldMultimerLoss(AlphaFoldLoss):
         batch: a pair of input features and its corresponding ground truth structure
         """
         features,labels = batch
+        features['resolution'] = labels[2]['resolution'] # firstly update the resolution feature
         # first remove the recycling dimention of input features
         features = tensor_tree_map(lambda t: t[..., -1], features)
         # then permutate ground truth chains before calculating the loss
         permutated_labels = self.multi_chain_perm_align(out,features,labels)
         permutated_labels.pop('aatype')
-        logger.info("finished multi-chain permutation")
+        logger.info("finished multi-chain permutation ")
         features.update(permutated_labels)
         move_to_cpu = lambda t: (t.to('cpu'))
         features = tensor_tree_map(move_to_cpu,features)