data_index = np.arange(labels.shape[0])
            test_mask = torch.tensor(np.in1d(data_index, test_index), dtype=torch.bool)
            dev_mask = torch.tensor(np.in1d(data_index, dev_index), dtype=torch.bool)
            train_mask = ~(dev_mask + test_mask)
            test_mask = test_mask.reshape(1, -1)
            dev_mask = dev_mask.reshape(1, -1)
            train_mask = train_mask.reshape(1, -1)

            #if data.train_mask is None:
            if train_mask not in data:
                data.train_mask = train_mask
                data.val_mask = dev_mask
                data.test_mask = test_mask
            else:
                data.train_mask = torch.cat((data.train_mask, train_mask), dim=0)
                data.val_mask = torch.cat((data.val_mask, dev_mask), dim=0)
                data.test_mask = torch.cat((data.test_mask, test_mask), dim=0)

将
if data.train_mask is None
改成
if train_mask not in data:

即可解决