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import torch
import numpy as np
import torch.nn.functional as F
import torch.optim as optim
from deeprobust.graph.defense import GCN
from deeprobust.graph.global_attack import PGDAttack
from deeprobust.graph.utils import *
from deeprobust.graph.data import Dataset
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=15, help='Random seed.')
parser.add_argument('--epochs', type=int, default=100,
help='Number of epochs to train.')
parser.add_argument('--dataset', type=str, default='citeseer', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed'], help='dataset')
parser.add_argument('--ptb_rate', type=float, default=0.05, help='pertubation rate')
parser.add_argument('--model', type=str, default='PGD', choices=['PGD', 'min-max'], help='model variant')
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if device != 'cpu':
torch.cuda.manual_seed(args.seed)
# data = Dataset(root='/tmp/', name=args.dataset, setting='gcn')
from torch_geometric.datasets import Planetoid
from deeprobust.graph.data import Pyg2Dpr
dataset = Planetoid('./', name=args.dataset)
data = Pyg2Dpr(dataset)
adj, features, labels = data.adj, data.features, data.labels
features = normalize_feature(features)
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
perturbations = int(args.ptb_rate * (adj.sum()//2))
adj, features, labels = preprocess(adj, features, labels, preprocess_adj=False)
def test(new_adj, gcn=None):
''' test on GCN '''
if gcn is None:
# adj = normalize_adj_tensor(adj)
gcn = GCN(nfeat=features.shape[1],
nhid=16,
nclass=labels.max().item() + 1,
dropout=0.5, device=device)
gcn = gcn.to(device)
# gcn.fit(features, new_adj, labels, idx_train) # train without model picking
gcn.fit(features, new_adj, labels, idx_train, idx_val, patience=30) # train with validation model picking
gcn.eval()
output = gcn.predict().cpu()
else:
gcn.eval()
output = gcn.predict(features.to(device), new_adj.to(device)).cpu()
loss_test = F.nll_loss(output[idx_test], labels[idx_test])
acc_test = accuracy(output[idx_test], labels[idx_test])
print("Test set results:",
"loss= {:.4f}".format(loss_test.item()),
"accuracy= {:.4f}".format(acc_test.item()))
return acc_test.item()
def main():
target_gcn = GCN(nfeat=features.shape[1],
nhid=16,
nclass=labels.max().item() + 1,
dropout=0.5, device=device, lr=0.01)
target_gcn = target_gcn.to(device)
target_gcn.fit(features, adj, labels, idx_train, idx_val, patience=30)
# target_gcn.fit(features, adj, labels, idx_train)
print('=== testing GCN on clean graph ===')
test(adj, target_gcn)
# Setup Attack Model
print('=== setup attack model ===')
model = PGDAttack(model=target_gcn, nnodes=adj.shape[0], loss_type='CE', device=device)
model = model.to(device)
# model.attack(features, adj, labels, idx_train, perturbations, epochs=args.epochs)
# Here for the labels we need to replace it with predicted ones
fake_labels = target_gcn.predict(features.to(device), adj.to(device))
fake_labels = torch.argmax(fake_labels, 1).cpu()
# Besides, we need to add the idx into the whole process
idx_fake = np.concatenate([idx_train,idx_test])
idx_others = list(set(np.arange(len(labels))) - set(idx_train))
fake_labels = torch.cat([labels[idx_train], fake_labels[idx_others]])
model.attack(features, adj, fake_labels, idx_fake, perturbations, epochs=args.epochs)
print('=== testing GCN on Evasion attack ===')
modified_adj = model.modified_adj
test(modified_adj, target_gcn)
# modified_features = model.modified_features
print('=== testing GCN on Poisoning attack ===')
test(modified_adj)
# # if you want to save the modified adj/features, uncomment the code below
# model.save_adj(root='./', name=f'mod_adj')
# model.save_features(root='./', name='mod_features')
if __name__ == '__main__':
main()
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