perturbed_adj,
labels,
idx_train,
train_iters=200,
verbose=True)
# # using validation to pick model
# model.fit(features, perturbed_adj, labels, idx_train, idx_val, train_iters=200, verbose=True)
model.eval()
# You can use the inner function of model to test
model.test(idx_test)
print('==================')
print(
'=== load graph perturbed by DeepRobust 5% metattack (under seed 15) ===')
perturbed_data = PtbDataset(root='/tmp/',
name=args.dataset,
attack_method='meta')
perturbed_adj = perturbed_data.adj
model.fit(features,
perturbed_adj,
labels,
idx_train,
train_iters=200,
verbose=True)
# # using validation to pick model
# model.fit(features, perturbed_adj, labels, idx_train, idx_val, train_iters=200, verbose=True)
model.eval()
# You can use the inner function of model to test
model.test(idx_test)
print('cuda: %s' % args.cuda)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# make sure you use the same data splits as you generated attacks
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
# load original dataset (to get clean features and labels)
data = Dataset(root='/tmp/', name=args.dataset)
adj, features, labels = 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 = PtbDataset(root='/tmp/', name=args.dataset)
perturbed_adj = perturbed_data.adj
# Setup RGCN Model
model = RGCN(nnodes=perturbed_adj.shape[0],
nfeat=features.shape[1],
nclass=labels.max() + 1,
nhid=64,
device=device)
model = model.to(device)
model.fit(features,
perturbed_adj,
labels,
idx_train,
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
# Setup GCN Model
model = GCN(nfeat=features.shape[1], nhid=16, nclass=labels.max()+1, device=device)
model = model.to(device)
model.fit(features, perturbed_adj, labels, idx_train, train_iters=200, verbose=True)
# # using validation to pick model
# model.fit(features, perturbed_adj, labels, idx_train, idx_val, train_iters=200, verbose=True)
model.eval()
# You can use the inner function of model to test
model.test(idx_test)
print('=== load graph perturbed by DeepRobust 5% metattack (under seed 15) ===')
perturbed_data = PtbDataset(root='/tmp/',
name='cora',
attack_method='meta')
perturbed_adj = perturbed_data.adj
model.fit(features, perturbed_adj, labels, idx_train, train_iters=200, verbose=True)
# # using validation to pick model
# model.fit(features, perturbed_adj, labels, idx_train, idx_val, train_iters=200, verbose=True)
model.eval()
# You can use the inner function of model to test
model.test(idx_test)