def split_dataset(self, dataset, args):
# process each graph and add it into Data() as attribute tx
for i, data in enumerate(dataset):
new_feature = get_single_feature(dataset[i], args.num_features,
args.num_classes, args.sample,
args.neighbor, args.stride)
dataset[i].tx = torch.from_numpy(new_feature)
return split_dataset_general(dataset, args)
def split_dataset(cls, dataset, args):
return split_dataset_general(dataset, args)
