diff --git a/dataset/shapenet.py b/dataset/shapenet.py
index be243e0..f63a857 100644
--- a/dataset/shapenet.py
+++ b/dataset/shapenet.py
@@ -134,11 +134,12 @@ class CustomShapeNet(InMemoryDataset):
                     y_all = [y_raw] * points.shape[0]
                     y = torch.as_tensor(y_all, dtype=torch.int)
                     if self.collate_per_element:
-                        data = Data(y=y, pos=points[:, :3], points=points, norm=points[:, 3:])
+                        data = Data(y=y, pos=points[:, :3])  # , points=points, norm=points[:, 3:])
                     else:
                         if not data:
                             data = defaultdict(list)
-                        for key, val in dict(y=y, pos=points[:, :3], points=points, norm=points[:, 3:]).items():
+                        # points=points, norm=points[:, 3:]
+                        for key, val in dict(y=y, pos=points[:, :3]).items():
                             data[key].append(val)
 
                     data = self._transform_and_filter(data)
@@ -175,7 +176,7 @@ class ShapeNetPartSegDataset(Dataset):
 
     def __getitem__(self, index):
         data = self.dataset[index]
-        points, labels, _, norm = data.pos, data.y, data.points, data.norm
+        points, labels = data.pos, data.y  # , data.points, data.norm
 
         # Resample to fixed number of points
         try:
@@ -183,14 +184,13 @@ class ShapeNetPartSegDataset(Dataset):
         except ValueError:
             choice = []
 
-        points, labels, norm = points[choice, :], labels[choice], norm[choice]
+        points, labels = points[choice, :], labels[choice]
 
         labels -= 1 if self.num_classes() in labels else 0   # Map label from [1, C] to [0, C-1]
 
         sample = {
             'points': points,  # torch.Tensor (n, 3)
-            'labels': labels,  # torch.Tensor (n,)
-            'normals': norm  # torch.Tensor (n,)
+            'labels': labels  # torch.Tensor (n,)
         }
 
         return sample