Merge pull request #331 from dingquanyu/multimer

Batch size fix for tm computation

openfold/utils/loss.py

@@ -691,7 +691,11 @@ def compute_tm(
 
     n = residue_weights.shape[-1]
     pair_mask = residue_weights.new_ones((n, n), dtype=torch.int32)
-    if interface:
+    if interface and (asym_id is not None):
+        if len(asym_id.shape)>1:
+            assert len(asym_id.shape)<=2
+            batch_size = asym_id.shape[0]
+            pair_mask = residue_weights.new_ones((batch_size,n, n), dtype=torch.int32)
         pair_mask *= (asym_id[..., None] != asym_id[..., None, :]).to(dtype=pair_mask.dtype)
     
     predicted_tm_term *= pair_mask
@@ -1440,7 +1444,10 @@ def violation_loss(
         + l_clash
     )
 
-    return loss
+    # Average over the batch dimension
+    mean = torch.mean(loss)
+
+    return mean
 
 
 def compute_renamed_ground_truth(
@@ -1563,7 +1570,7 @@ def experimentally_resolved_loss(
 ) -> torch.Tensor:
     errors = sigmoid_cross_entropy(logits, all_atom_mask)
     loss = torch.sum(errors * atom37_atom_exists, dim=-1)
-    loss = loss / (eps + torch.sum(atom37_atom_exists, dim=(-1, -2)))
+    loss = loss / (eps + torch.sum(atom37_atom_exists, dim=(-1, -2)).unsqueeze(-1))
     loss = torch.sum(loss, dim=-1)
     
     loss = loss * (

openfold/utils/validation_metrics.py

@@ -29,7 +29,7 @@ def drmsd(structure_1, structure_2, mask=None):
     if(mask is not None):
         drmsd = drmsd * (mask[..., None] * mask[..., None, :])
     drmsd = torch.sum(drmsd, dim=(-1, -2))
-    n = d1.shape[-1] if mask is None else torch.sum(mask, dim=-1)
+    n = d1.shape[-1] if mask is None else torch.min(torch.sum(mask, dim=-1))
     drmsd = drmsd * (1 / (n * (n - 1))) if n > 1 else (drmsd * 0.)
     drmsd = torch.sqrt(drmsd)
 

train_openfold.py

@@ -88,9 +88,10 @@ class OpenFoldWrapper(pl.LightningModule):
             )
 
         for k,v in other_metrics.items():
+            assert(len(v.shape) == 1)
             self.log(
                 f"{phase}/{k}",
-                v, 
+                torch.mean(v),
                 on_step=False, on_epoch=True, logger=True
             )