diff --git a/explaining_framework/metric/robust.py b/explaining_framework/metric/robust.py
index f0795ab..c40509f 100644
--- a/explaining_framework/metric/robust.py
+++ b/explaining_framework/metric/robust.py
@@ -2,15 +2,14 @@ import copy
 
 import torch
 import torch.nn.functional as F
+from explaining_framework.metric.base import Metric
+from explaining_framework.utils.io import obj_config_to_str
 from torch.nn import CrossEntropyLoss, MSELoss
 from torch_geometric.data import Batch, Data
 from torch_geometric.explain.explanation import Explanation
 from torch_geometric.graphgym.config import cfg
 from torch_geometric.utils import add_random_edge, dropout_edge, dropout_node
 
-from explaining_framework.metric.base import Metric
-from explaining_framework.utils.io import obj_config_to_str
-
 
 def compute_gradient(model, inp, target, loss):
     with torch.autograd.set_grad_enabled(True):
@@ -136,7 +135,7 @@ class Attack(Metric):
     def get_attacked_prediction(self, data: Data) -> Data:
         data_ = data.clone()
         data_attacked = self.forward(data_)
-        pred = self.get_prediction(x=data_.x, edge_index=data_.edge_index)
+        pred = self.get_prediction(x=data.x, edge_index=data.edge_index)
         pred_attacked = self.get_prediction(
             x=data_attacked.x, edge_index=data_attacked.edge_index
         )
diff --git a/main.py b/main.py
index 33c6a56..6e40c1e 100644
--- a/main.py
+++ b/main.py
@@ -27,6 +27,7 @@ if __name__ == "__main__":
     args = parse_args()
     outline = ExplainingOutline(args.explaining_cfg_file, args.gpu_id)
     for attack in outline.attacks:
+        logging.info(f"Running {attack.__class__.__name__}: {attack.name}")
         for item, index in tqdm(
             zip(outline.dataset, outline.indexes), total=len(outline.dataset)
         ):
@@ -41,6 +42,7 @@ if __name__ == "__main__":
             )
 
     for attack in outline.attacks:
+        logging.info(f"Running {attack.__class__.__name__}: {attack.name}")
         for item, index in tqdm(
             zip(outline.dataset, outline.indexes), total=len(outline.dataset)
         ):