dataset="cora")
model = GAT(
nfeat=features.shape[1],
nhid=args.hidden,
nhead=args.nb_heads,
nclass=int(labels.max()) + 1,
p_dropout=args.dropout,
)
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
if args.cuda:
model.cuda()
features = features.cuda()
adj = adj.cuda()
labels = labels.cuda()
idx_train = idx_train.cuda()
idx_val = idx_val.cuda()
idx_test = idx_test.cuda()
def train(epoch):
model.train()
optimizer.zero_grad()
output = model(features, adj) # 把特征和图结构一起送入网络
loss_train = F.nll_loss(output[idx_train], labels[idx_train])
acc_train = accuracy(output[idx_train], labels[idx_train])
def train(args):
## load training data
print "loading training data ......"
node_num, class_num = removeIsolated(args.suffix)
label, feature_map, adj_lists = collectGraph_train(node_num, class_num,
args.feat_dim,
args.num_sample,
args.suffix)
label = torch.LongTensor(label)
feature_map = torch.FloatTensor(feature_map)
model = GAT(args.feat_dim, args.embed_dim, class_num, args.alpha,
args.dropout, args.nheads, args.use_cuda)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.learning_rate,
weight_decay=args.weight_decay)
scheduler = StepLR(optimizer,
step_size=args.step_size,
gamma=args.learning_rate_decay)
## train
np.random.seed(2)
random.seed(2)
rand_indices = np.random.permutation(node_num)
train_nodes = rand_indices[:args.train_num]
val_nodes = rand_indices[args.train_num:]
if args.use_cuda:
model.cuda()
label = label.cuda()
feature_map = feature_map.cuda()
epoch_num = args.epoch_num
batch_size = args.batch_size
iter_num = int(math.ceil(args.train_num / float(batch_size)))
check_loss = []
val_accuracy = []
check_step = args.check_step
train_loss = 0.0
iter_cnt = 0
for e in range(epoch_num):
model.train()
scheduler.step()
random.shuffle(train_nodes)
for batch in range(iter_num):
batch_nodes = train_nodes[batch * batch_size:(batch + 1) *
batch_size]
batch_label = label[batch_nodes].squeeze()
batch_neighbors = [adj_lists[node] for node in batch_nodes]
_, logit = model(feature_map, batch_nodes, batch_neighbors)
loss = F.nll_loss(logit, batch_label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
iter_cnt += 1
train_loss += loss.cpu().item()
if iter_cnt % check_step == 0:
check_loss.append(train_loss / check_step)
print time.strftime(
'%Y-%m-%d %H:%M:%S'
), "epoch: {}, iter: {}, loss:{:.4f}".format(
e, iter_cnt, train_loss / check_step)
train_loss = 0.0
## validation
model.eval()
group = int(math.ceil(len(val_nodes) / float(batch_size)))
val_cnt = 0
for batch in range(group):
batch_nodes = val_nodes[batch * batch_size:(batch + 1) *
batch_size]
batch_label = label[batch_nodes].squeeze()
batch_neighbors = [adj_lists[node] for node in batch_nodes]
_, logit = model(feature_map, batch_nodes, batch_neighbors)
batch_predict = np.argmax(logit.cpu().detach().numpy(), axis=1)
val_cnt += np.sum(batch_predict == batch_label.cpu().numpy())
val_accuracy.append(val_cnt / float(len(val_nodes)))
print time.strftime('%Y-%m-%d %H:%M:%S'
), "Epoch: {}, Validation Accuracy: {:.4f}".format(
e, val_cnt / float(len(val_nodes)))
print "******" * 10
checkpoint_path = 'checkpoint/checkpoint_{}.pth'.format(
time.strftime('%Y%m%d%H%M'))
torch.save(
{
'train_num': args.train_num,
'epoch_num': args.epoch_num,
'batch_size': args.batch_size,
'learning_rate': args.learning_rate,
'embed_dim': args.embed_dim,
'num_sample': args.num_sample,
'graph_state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, checkpoint_path)
vis = visdom.Visdom(env='GraphAttention', port='8099')
vis.line(X=np.arange(1,
len(check_loss) + 1, 1) * check_step,
Y=np.array(check_loss),
opts=dict(title=time.strftime('%Y-%m-%d %H:%M:%S'),
xlabel='itr.',
ylabel='loss'))
vis.line(X=np.arange(1,
len(val_accuracy) + 1, 1),
Y=np.array(val_accuracy),
opts=dict(title=time.strftime('%Y-%m-%d %H:%M:%S'),
xlabel='epoch',
ylabel='accuracy'))
return checkpoint_path, class_num
def main(args):
# load and preprocess dataset
data = CoraGraphDataset()
g = data[0]
if args.gpu < 0:
cuda = False
else:
cuda = True
g = g.int().to(args.gpu)
features = g.ndata['feat']
labels = g.ndata['label']
train_mask = g.ndata['train_mask']
val_mask = g.ndata['val_mask']
test_mask = g.ndata['test_mask']
num_feats = features.shape[1]
n_classes = data.num_labels
n_edges = data.graph.number_of_edges()
x = g.nodes().cpu()
print(features)
print(g)
x = input()
print("""----Data statistics------'
#Edges %d
#Classes %d
#Train samples %d
#Val samples %d
#Test samples %d""" %
(n_edges, n_classes,
train_mask.int().sum().item(),
val_mask.int().sum().item(),
test_mask.int().sum().item()))
# add self loop
g = dgl.remove_self_loop(g)
g = dgl.add_self_loop(g)
n_edges = g.number_of_edges()
# create model
heads = ([args.num_heads] * args.num_layers) + [args.num_out_heads]
model = GAT(args.num_layers,
num_feats,
args.num_hidden,
n_classes,
heads,
F.elu,
args.in_drop,
args.attn_drop,
args.negative_slope,
args.residual)
print(model)
if args.early_stop:
stopper = EarlyStopping(patience=100)
if cuda:
model.cuda()
loss_fcn = torch.nn.CrossEntropyLoss()
# use optimizer
optimizer = torch.optim.Adam(
model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
# initialize graph
dur = []
for epoch in range(args.epochs):
model.train()
if epoch >= 3:
t0 = time.time()
# forward
logits = model(g, features)
loss = loss_fcn(logits[train_mask], labels[train_mask])
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch >= 3:
dur.append(time.time() - t0)
train_acc = accuracy(logits[train_mask], labels[train_mask])
if args.fastmode:
val_acc = accuracy(logits[val_mask], labels[val_mask])
else:
val_acc = evaluate(model, g, features, labels, val_mask)
if args.early_stop:
if stopper.step(val_acc, model):
break
print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | TrainAcc {:.4f} |"
" ValAcc {:.4f} | ETputs(KTEPS) {:.2f}".
format(epoch, np.mean(dur), loss.item(), train_acc,
val_acc, n_edges / np.mean(dur) / 1000))
print()
if args.early_stop:
model.load_state_dict(torch.load('es_checkpoint.pt'))
acc = evaluate(model, g, features, labels, test_mask)
print("Test Accuracy {:.4f}".format(acc))
def test(checkpoint_path, class_num, args):
model = GAT(args.feat_dim, args.embed_dim, class_num, args.alpha,
args.dropout, args.nheads, args.use_cuda)
checkpoint = torch.load(checkpoint_path)
model.load_state_dict(checkpoint['graph_state_dict'])
if args.use_cuda:
model.cuda()
model.eval()
for key in building.keys():
node_num = test_dataset[key]['node_num']
old_feature_map, adj_lists = collectGraph_test(
test_dataset[key]['feature_path'], node_num, args.feat_dim,
args.num_sample, args.suffix)
old_feature_map = torch.FloatTensor(old_feature_map)
if args.use_cuda:
old_feature_map = old_feature_map.cuda()
batch_num = int(math.ceil(node_num / float(args.batch_size)))
new_feature_map = torch.FloatTensor()
for batch in tqdm(range(batch_num)):
start_node = batch * args.batch_size
end_node = min((batch + 1) * args.batch_size, node_num)
batch_nodes = range(start_node, end_node)
batch_neighbors = [adj_lists[node] for node in batch_nodes]
new_feature, _ = model(old_feature_map, batch_nodes,
batch_neighbors)
new_feature = F.normalize(new_feature, p=2, dim=1)
new_feature_map = torch.cat(
(new_feature_map, new_feature.cpu().detach()), dim=0)
new_feature_map = new_feature_map.numpy()
old_similarity = np.dot(old_feature_map.cpu().numpy(),
old_feature_map.cpu().numpy().T)
new_similarity = np.dot(new_feature_map, new_feature_map.T)
mAP_old = building[key].evalRetrieval(old_similarity, retrieval_result)
mAP_new = building[key].evalRetrieval(new_similarity, retrieval_result)
print time.strftime('%Y-%m-%d %H:%M:%S'), 'eval {}'.format(key)
print 'base feature: {}, new feature: {}'.format(
old_feature_map.size(), new_feature_map.shape)
print 'base mAP: {:.4f}, new mAP: {:.4f}, improve: {:.4f}'.format(
mAP_old, mAP_new, mAP_new - mAP_old)
## directly update node's features by mean pooling features of its neighbors.
meanAggregator = model.attentions[0]
mean_feature_map = torch.FloatTensor()
for batch in tqdm(range(batch_num)):
start_node = batch * args.batch_size
end_node = min((batch + 1) * args.batch_size, node_num)
batch_nodes = range(start_node, end_node)
batch_neighbors = [adj_lists[node] for node in batch_nodes]
mean_feature = meanAggregator.meanAggregate(
old_feature_map, batch_nodes, batch_neighbors)
mean_feature = F.normalize(mean_feature, p=2, dim=1)
mean_feature_map = torch.cat(
(mean_feature_map, mean_feature.cpu().detach()), dim=0)
mean_feature_map = mean_feature_map.numpy()
mean_similarity = np.dot(mean_feature_map, mean_feature_map.T)
mAP_mean = building[key].evalRetrieval(mean_similarity,
retrieval_result)
print 'mean aggregation mAP: {:.4f}'.format(mAP_mean)
print ""