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Fixed a warning issue when accessing the internal storage of the torch_geometric dataset and method iteration error. #62

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Fixed a warning issue when accessing the internal storage of the torch_geometric dataset and method iteration error. #62

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26 changes: 13 additions & 13 deletions graphgym/loader_pyg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)

Expand All @@ -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])

Expand All @@ -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)
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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,
Expand All @@ -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,
Expand Down