# we need to set the random seed to be the same as that when you generate the perturbed graph
# data = Dataset(root='/tmp/', name=args.dataset, setting='nettack', seed=15)
# Or we can just use setting='prognn' to get the splits
# data = Dataset(root='./tmp/', name=args.dataset, setting='prognn')
# adj, features, labels_1 = data.adj, data.features, data.labels
# idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
# load pre-attacked graph
# perturbed_data = PrePtbDataset(root='./tmp/',
# name=args.dataset,
# attack_method='meta',
# ptb_rate=args.ptb_rate)
# use data splist provided by prognn
data = Dataset(root='./tmp/', name=args.dataset, setting='prognn')
data.adj = sp.csr_matrix(adj_per)
data.features = sp.csr_matrix(base_feat)
data.labels = labels
data.idx_train = idx_train
data.idx_val = idx_val
data.idx_test = idx_test
perturbed_adj = sp.csr_matrix(adj_per, dtype=float)
features = sp.csr_matrix(base_feat, dtype=float)
# Setup Defense Model
gat = GAT(nfeat=features.shape[1],
nhid=8,
heads=8,
nclass=labels.max().item() + 1,
dropout=0.5,
features,
labels,
preprocess_adj=False,
device=device)
labels = labels.cuda()
perturbed_adj = to_sparse(perturbed_adj.cuda())
features = features.cuda()
perturbed_adj = ut.normalize_adj_tensor(perturbed_adj, True)
features[features > 1] = 1
data = load_data(args.data,
normalize_feature=args.no_fea_norm,
missing_rate=args.missing_rate,
cuda=True)
data.x = features
data.y = labels
data.adj = perturbed_adj
data.train_mask, data.val_mask, data.test_mask = idx_train, idx_val, idx_test
nfeat = data.x.size(1)
nclass = int(data.y.max()) + 1
net = getattr(models, args.model)(nfeat,
args.hid,
nclass,
dropout=args.dropout,
nhead=args.nhead,
nlayer=args.nlayer,
norm_mode=args.norm_mode,
norm_scale=args.norm_scale,
residual=args.residual)
net = net.cuda()
optimizer = torch.optim.Adam(net.parameters(), args.lr, weight_decay=args.wd)