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train_GCA.py
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import argparse
import os.path as osp
import random
import nni
import time
import pickle as pkl
import torch
from torch_geometric.utils import dropout_adj, degree, to_undirected
from simple_param.sp import SimpleParam
from pGRACE.model import Encoder, GRACE
from pGRACE.functional import drop_feature, drop_edge_weighted, \
degree_drop_weights, evc_drop_weights, pr_drop_weights, \
feature_drop_weights, drop_feature_weighted, feature_drop_weights_dense
from pGRACE.eval import log_regression, MulticlassEvaluator
from pGRACE.utils import get_base_model, get_activation, \
generate_split, compute_pr, eigenvector_centrality
from pGRACE.dataset import get_dataset
def train():
model.train()
optimizer.zero_grad()
def drop_edge(idx: int):
global drop_weights
if param['drop_scheme'] == 'uniform':
return dropout_adj(data.edge_index, p=param[f'drop_edge_rate_{idx}'])[0]
elif param['drop_scheme'] in ['degree', 'evc', 'pr']:
return drop_edge_weighted(data.edge_index, drop_weights, p=param[f'drop_edge_rate_{idx}'], threshold=0.7)
else:
raise Exception(f'undefined drop scheme: {param["drop_scheme"]}')
edge_index_1 = drop_edge(1)
edge_index_2 = drop_edge(2)
x_1 = drop_feature(data.x, param['drop_feature_rate_1'])
x_2 = drop_feature(data.x, param['drop_feature_rate_2'])
if param['drop_scheme'] in ['pr', 'degree', 'evc']:
x_1 = drop_feature_weighted(data.x, feature_weights, param['drop_feature_rate_1'])
x_2 = drop_feature_weighted(data.x, feature_weights, param['drop_feature_rate_2'])
z1 = model(x_1, edge_index_1)
z2 = model(x_2, edge_index_2)
loss = model.loss(z1, z2, batch_size=None)
loss.backward()
optimizer.step()
return loss.item()
def test(final=False):
model.eval()
z = model(data.x, data.edge_index)
evaluator = MulticlassEvaluator()
if args.dataset == 'Cora':
acc = log_regression(z, data, evaluator, split='cora', num_epochs=3000)['acc']
elif args.dataset == 'CiteSeer':
acc = log_regression(z, data, evaluator, split='citeseer', num_epochs=3000)['acc']
else:
raise ValueError('Please check the split first!')
if final and use_nni:
nni.report_final_result(acc)
elif use_nni:
nni.report_intermediate_result(acc)
return acc
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cuda:0')
parser.add_argument('--dataset', type=str, default='Cora')
parser.add_argument('--param', type=str, default='local:general.json')
parser.add_argument('--seed', type=int, default=39788)
parser.add_argument('--verbose', type=str, default='train,eval,final')
parser.add_argument('--save_split', action="store_true")
parser.add_argument('--load_split', action="store_true")
parser.add_argument('--perturb', action="store_true")
parser.add_argument('--attack_method', type=str, default=None)
parser.add_argument('--attack_rate', type=float, default=0.10)
default_param = {
'learning_rate': 0.01,
'num_hidden': 256,
'num_proj_hidden': 32,
'activation': 'prelu',
'base_model': 'GCNConv',
'num_layers': 2,
'drop_edge_rate_1': 0.3,
'drop_edge_rate_2': 0.4,
'drop_feature_rate_1': 0.1,
'drop_feature_rate_2': 0.0,
'tau': 0.4,
'num_epochs': 3000,
'weight_decay': 1e-5,
'drop_scheme': 'degree',
}
# add hyper-parameters into parser
param_keys = default_param.keys()
for key in param_keys:
parser.add_argument(f'--{key}', type=type(default_param[key]), nargs='?')
args = parser.parse_args()
# parse param
sp = SimpleParam(default=default_param)
param = sp(source=args.param, preprocess='nni')
# merge cli arguments and parsed param
for key in param_keys:
if getattr(args, key) is not None:
param[key] = getattr(args, key)
use_nni = args.param == 'nni'
if use_nni and args.device != 'cpu':
args.device = 'cuda'
torch_seed = args.seed
torch.manual_seed(torch_seed)
random.seed(12345)
device = torch.device(args.device)
path = osp.expanduser('dataset')
path = osp.join(path, args.dataset)
dataset = get_dataset(path, args.dataset)
data = dataset[0]
if args.perturb:
try:
perturbed_adj = pkl.load(open('poisoned_adj/%s_%s_%f_adj.pkl' % (args.dataset, args.attack_method, args.attack_rate), 'rb')).to(device)
except:
perturbed_adj = torch.load('poisoned_adj/%s_%s_%f_adj.pkl' % (args.dataset, args.attack_method, args.attack_rate), map_location=device)
data.edge_index = perturbed_adj.nonzero().T
data = data.to(device)
edge_index = data.edge_index
edge_sp_adj = torch.sparse.FloatTensor(edge_index,
torch.ones(edge_index.shape[1]).to(device),
[data.num_nodes, data.num_nodes])
edge_adj = edge_sp_adj.to_dense().to(device)
# generate split
split = generate_split(data.num_nodes, train_ratio=0.1, val_ratio=0.1)
if args.save_split:
torch.save(split, 'split/%s_split.pkl'%args.dataset)
elif args.load_split:
split = torch.load('split/%s_split.pkl'%args.dataset)
encoder = Encoder(data.num_features, param['num_hidden'], get_activation(param['activation']),
base_model=get_base_model(param['base_model']), k=param['num_layers']).to(device)
model = GRACE(encoder, param['num_hidden'], param['num_proj_hidden'], param['tau']).to(device)
optimizer = torch.optim.Adam(
model.parameters(),
lr=param['learning_rate'],
weight_decay=param['weight_decay']
)
if param['drop_scheme'] == 'degree':
drop_weights = degree_drop_weights(data.edge_index).to(device)
elif param['drop_scheme'] == 'pr':
drop_weights = pr_drop_weights(data.edge_index, aggr='sink', k=200).to(device)
elif param['drop_scheme'] == 'evc':
drop_weights = evc_drop_weights(data).to(device)
else:
drop_weights = None
if param['drop_scheme'] == 'degree':
print(data.edge_index.shape)
edge_index_ = to_undirected(data.edge_index)
print(edge_index_.shape)
node_deg = degree(edge_index_[1], num_nodes=data.num_nodes)
print(node_deg.shape)
if args.dataset == 'WikiCS':
feature_weights = feature_drop_weights_dense(data.x, node_c=node_deg).to(device)
else:
feature_weights = feature_drop_weights(data.x, node_c=node_deg).to(device)
elif param['drop_scheme'] == 'pr':
node_pr = compute_pr(data.edge_index)
if args.dataset == 'WikiCS':
feature_weights = feature_drop_weights_dense(data.x, node_c=node_pr).to(device)
else:
feature_weights = feature_drop_weights(data.x, node_c=node_pr).to(device)
elif param['drop_scheme'] == 'evc':
node_evc = eigenvector_centrality(data)
if args.dataset == 'WikiCS':
feature_weights = feature_drop_weights_dense(data.x, node_c=node_evc).to(device)
else:
feature_weights = feature_drop_weights(data.x, node_c=node_evc).to(device)
else:
feature_weights = torch.ones((data.x.size(1),)).to(device)
log = args.verbose.split(',')
print('Begin training....')
best_acc = 0
for epoch in range(1, param['num_epochs'] + 1):
start = time.time()
loss = train()
end = time.time()
if 'train' in log:
print(f'(T) | Epoch={epoch:03d}, loss={loss:.4f}, training time={end-start}')
acc = test(final=True)
if 'final' in log:
print(f'{acc}')
if acc > best_acc:
best_acc = acc
print(f'best accuracy = {best_acc}')