fix facornet validation loss aggregation (#71)

ours/tasks/facornet.py

@@ -54,7 +54,7 @@ def predict(model, val_loader, device: int | str = 0) -> tuple[torch.Tensor, tor
 
         current_index += batch_size_current
 
-    return loss, similarities, y_true, pred_kin_relations, y_true_kin_relations
+    return loss_values, similarities, y_true, pred_kin_relations, y_true_kin_relations
 
 
 def validate(model, dataloader, device=0, threshold=None):
@@ -83,6 +83,7 @@ def validate(model, dataloader, device=0, threshold=None):
         )
     # kin_acc = tm.functional.accuracy(pred_kin_relations, y_true_kin_relations, task="multiclass", num_classes=12)
     # return loss, auc, threshold, acc, acc_kin_relations, kin_acc
+    loss = loss.mean().item()
     return loss, auc, threshold, acc, acc_kin_relations
 
 
@@ -127,12 +128,12 @@ def train(args):
     out = acc_kr_to_str(out, acc_kv)
     print(out)
 
-    ce_loss = torch.nn.CrossEntropyLoss()
+    # ce_loss = torch.nn.CrossEntropyLoss()
 
     for epoch in range(args.num_epoch):
         model.train()
         contrastive_loss_epoch = 0.0
-        kin_loss_epoch = 0.0
+        # kin_loss_epoch = 0.0
         for step, data in enumerate(train_loader):
             global_step = step + epoch * args.steps_per_epoch