From 9086f84aaacafa3e166f496883d7290a1615efda Mon Sep 17 00:00:00 2001
From: koguhnhyeok <peter1222@stu.jejunu.ac.kr>
Date: Thu, 9 Nov 2023 22:22:49 +0900
Subject: [PATCH] Fixed a warning issue when accessing the internal storage of
 the torch_geometric dataset and method iteration error.

---
 graphgym/loader_pyg.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/graphgym/loader_pyg.py b/graphgym/loader_pyg.py
index 7b7e6735..240f31b0 100644
--- a/graphgym/loader_pyg.py
+++ b/graphgym/loader_pyg.py
@@ -75,7 +75,7 @@ def load_pyg(name, dataset_dir):
 
 def set_dataset_attr(dataset, name, value, size):
     dataset._data_list = None
-    dataset.data[name] = value
+    dataset._data[name] = value
     if dataset.slices is not None:
         dataset.slices[name] = torch.tensor([0, size], dtype=torch.long)
 
@@ -102,9 +102,9 @@ def load_ogb(name, dataset_dir):
         splits = dataset.get_idx_split()
         split_names = ['train_mask', 'val_mask', 'test_mask']
         for i, key in enumerate(splits.keys()):
-            mask = index_to_mask(splits[key], size=dataset.data.y.shape[0])
+            mask = index_to_mask(splits[key], size=dataset._data.y.shape[0])
             set_dataset_attr(dataset, split_names[i], mask, len(mask))
-        edge_index = to_undirected(dataset.data.edge_index)
+        edge_index = to_undirected(dataset._data.edge_index)
         set_dataset_attr(dataset, 'edge_index', edge_index,
                          edge_index.shape[1])
 
@@ -127,7 +127,7 @@ def load_ogb(name, dataset_dir):
             dataset.transform = neg_sampling_transform
         else:
             id_neg = negative_sampling(edge_index=id,
-                                       num_nodes=dataset.data.num_nodes,
+                                       num_nodes=dataset._data.num_nodes,
                                        num_neg_samples=id.shape[1])
             id_all = torch.cat([id, id_neg], dim=-1)
             label = create_link_label(id, id_neg)
@@ -190,24 +190,24 @@ def set_dataset_info(dataset):
 
     # get dim_in and dim_out
     try:
-        cfg.share.dim_in = dataset.data.x.shape[1]
+        cfg.share.dim_in = dataset._data.x.shape[1]
     except Exception:
         cfg.share.dim_in = 1
     try:
         if cfg.dataset.task_type == 'classification':
-            cfg.share.dim_out = torch.unique(dataset.data.y).shape[0]
+            cfg.share.dim_out = torch.unique(dataset._data.y).shape[0]
         else:
-            cfg.share.dim_out = dataset.data.y.shape[1]
+            cfg.share.dim_out = dataset._data.y.shape[1]
     except Exception:
         cfg.share.dim_out = 1
 
     # count number of dataset splits
     cfg.share.num_splits = 1
-    for key in dataset.data.keys:
+    for key in dataset._data.keys():
         if 'val' in key:
             cfg.share.num_splits += 1
             break
-    for key in dataset.data.keys:
+    for key in dataset._data.keys():
         if 'test' in key:
             cfg.share.num_splits += 1
             break
@@ -297,14 +297,14 @@ def create_loader():
     dataset = create_dataset()
     # train loader
     if cfg.dataset.task == 'graph':
-        id = dataset.data['train_graph_index']
+        id = dataset._data['train_graph_index']
         loaders = [
             get_loader(dataset[id],
                        cfg.train.sampler,
                        cfg.train.batch_size,
                        shuffle=True)
         ]
-        delattr(dataset.data, 'train_graph_index')
+        delattr(dataset._data, 'train_graph_index')
     else:
         loaders = [
             get_loader(dataset,
@@ -317,13 +317,13 @@ def create_loader():
     for i in range(cfg.share.num_splits - 1):
         if cfg.dataset.task == 'graph':
             split_names = ['val_graph_index', 'test_graph_index']
-            id = dataset.data[split_names[i]]
+            id = dataset._data[split_names[i]]
             loaders.append(
                 get_loader(dataset[id],
                            cfg.val.sampler,
                            cfg.train.batch_size,
                            shuffle=False))
-            delattr(dataset.data, split_names[i])
+            delattr(dataset._data, split_names[i])
         else:
             loaders.append(
                 get_loader(dataset,