def main(args):
# load and preprocess dataset
g, graph_labels = load_graphs(
'/yushi/dataset/Amazon2M/Amazon2M_dglgraph.bin')
assert len(g) == 1
g = g[0]
data = g.ndata
features = torch.FloatTensor(data['feat'])
labels = torch.LongTensor(data['label'])
if hasattr(torch, 'BoolTensor'):
train_mask = data['train_mask'].bool()
val_mask = data['val_mask'].bool()
test_mask = data['test_mask'].bool()
num_feats = features.shape[1]
n_classes = 47
n_edges = g.number_of_edges()
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()))
if args.gpu < 0:
cuda = False
else:
cuda = True
torch.cuda.set_device(args.gpu)
features = features.cuda()
labels = labels.cuda()
train_mask = train_mask.cuda()
val_mask = val_mask.cuda()
test_mask = test_mask.cuda()
# add self loop
g = add_self_loop(g)
# g.remove_edges_from(nx.selfloop_edges(g))
# g = DGLGraph(g)
# g.add_edges(g.nodes(), g.nodes())
n_edges = g.number_of_edges()
# create model
heads = ([args.num_heads] * args.num_layers) + [args.num_out_heads]
model = GAT(g, 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 = []
start = time.time()
for epoch in range(args.epochs):
model.train()
if epoch >= 3:
t0 = time.time()
# forward
logits = model(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, 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, features, labels, test_mask)
print("Test Accuracy {:.4f}".format(acc))
print(f"Time Consuming {np.sum(dur)}, Overall time {time.time() - start}")
def main(args):
# load and preprocess dataset
data = load_data(args)
features = torch.FloatTensor(data.features)
labels = torch.LongTensor(data.labels)
train_mask = torch.ByteTensor(data.train_mask)
val_mask = torch.ByteTensor(data.val_mask)
test_mask = torch.ByteTensor(data.test_mask)
num_feats = features.shape[1]
n_classes = data.num_labels
n_edges = data.graph.number_of_edges()
print("""----Data statistics------'
#Edges %d
#Classes %d
#Train samples %d
#Val samples %d
#Test samples %d""" %
(n_edges, n_classes,
train_mask.sum().item(),
val_mask.sum().item(),
test_mask.sum().item()))
if args.gpu < 0:
cuda = False
else:
cuda = True
torch.cuda.set_device(args.gpu)
features = features.cuda()
labels = labels.cuda()
train_mask = train_mask.cuda()
val_mask = val_mask.cuda()
test_mask = test_mask.cuda()
g = data.graph
# add self loop
g.remove_edges_from(g.selfloop_edges())
g = DGLGraph(g)
g.add_edges(g.nodes(), g.nodes())
n_edges = g.number_of_edges()
# create model
heads = ([args.num_heads] * args.num_layers) + [args.num_out_heads]
model = GAT(g,
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)
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(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, features, labels, val_mask)
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()
model.load_state_dict(torch.load('es_checkpoint.pt'))
acc = evaluate(model, features, labels, test_mask)
print("Test Accuracy {:.4f}".format(acc))
def main(args):
# load and preprocess dataset
if args.dataset == 'cora':
data = CoraGraphDataset()
elif args.dataset == 'citeseer':
data = CiteseerGraphDataset()
elif args.dataset == 'pubmed':
data = PubmedGraphDataset()
else:
raise ValueError('Unknown dataset: {}'.format(args.dataset))
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()
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(g, 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(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, 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, features, labels, test_mask)
print("Test Accuracy {:.4f}".format(acc))
def main(args):
# load and preprocess dataset
if args.dataset == 'reddit':
data = RedditDataset()
elif args.dataset in ['photo', "computer"]:
data = MsDataset(args)
else:
data = load_data(args)
features = torch.FloatTensor(data.features)
labels = torch.LongTensor(data.labels)
train_mask = torch.ByteTensor(data.train_mask)
val_mask = torch.ByteTensor(data.val_mask)
test_mask = torch.ByteTensor(data.test_mask)
num_feats = features.shape[1]
n_classes = data.num_labels
n_edges = data.graph.number_of_edges()
current_time = time.strftime('%d_%H:%M:%S', localtime())
writer = SummaryWriter(log_dir='runs/' + current_time + '_' + args.sess, flush_secs=30)
print("""----Data statistics------'
#Edges %d
#Classes %d
#Train samples %d
#Val samples %d
#Test samples %d""" %
(n_edges, n_classes,
train_mask.sum().item(),
val_mask.sum().item(),
test_mask.sum().item()))
if args.gpu < 0:
cuda = False
else:
cuda = True
torch.cuda.set_device(args.gpu)
features = features.cuda()
labels = labels.cuda()
train_mask = train_mask.bool().cuda()
val_mask = val_mask.bool().cuda()
test_mask = test_mask.bool().cuda()
g = data.graph
# add self loop
if args.dataset != 'reddit':
g.remove_edges_from(nx.selfloop_edges(g))
g = DGLGraph(g)
g.add_edges(g.nodes(), g.nodes())
n_edges = g.number_of_edges()
print('edge number %d'%(n_edges))
# create model
heads = ([args.num_heads] * args.num_layers) + [args.num_out_heads]
model = GAT(g,
args.num_layers,
num_feats,
args.num_hidden,
n_classes,
heads,
F.elu,
args.idrop,
args.adrop,
args.alpha,
args.bias,
args.residual, args.l0)
print(model)
if args.early_stop:
stopper = EarlyStopping(patience=150)
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)
dur = []
time_used = 0
for epoch in range(args.epochs):
model.train()
if epoch >= 3:
t0 = time.time()
# forward
logits = model(features)
loss = loss_fcn(logits[train_mask], labels[train_mask])
loss_l0 = args.loss_l0*( model.gat_layers[0].loss)
optimizer.zero_grad()
(loss + loss_l0).backward()
optimizer.step()
if epoch >= 3:
dur.append(time.time() - t0)
train_acc = accuracy(logits[train_mask], labels[train_mask])
writer.add_scalar('edge_num/0', model.gat_layers[0].num, epoch)
if args.fastmode:
val_acc, loss = accuracy(logits[val_mask], labels[val_mask], loss_fcn)
else:
val_acc,_ = evaluate(model, features, labels, val_mask, loss_fcn)
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))
writer.add_scalar('loss', loss.item(), epoch)
writer.add_scalar('f1/train_f1_mic', train_acc, epoch)
writer.add_scalar('f1/test_f1_mic', val_acc, epoch)
writer.add_scalar('time/time', time_used, epoch)
writer.close()
if args.early_stop:
model.load_state_dict(torch.load('es_checkpoint.pt'))
acc, _ = evaluate(model,features, labels, test_mask, loss_fcn)
print("Test Accuracy {:.4f}".format(acc))
def main(args):
# load and preprocess dataset
g, features, labels, n_classes, train_mask, val_mask, test_mask, lp_dict, ind_features, ind_labels = load_reg_data(args)
num_feats = features.shape[1]
n_edges = g.number_of_edges()
print("""----Data statistics------'
#use cuda: %d
#Edges %d
#Classes %d
#Train samples %d
#Val samples %d
#Test samples %d""" %
(args.gpu, n_edges, n_classes,
train_mask.int().sum().item(),
val_mask.int().sum().item(),
test_mask.int().sum().item()))
if args.gpu < 0:
cuda = False
else:
cuda = True
torch.cuda.set_device(args.gpu)
features = features.cuda()
ind_features = ind_features.cuda()
labels = labels.cuda()
ind_labels = ind_labels.cuda()
train_mask = train_mask.cuda()
val_mask = val_mask.cuda()
test_mask = test_mask.cuda()
# create model
heads = ([args.num_heads] * args.num_layers) + [args.num_out_heads]
model = GAT(g,
args.num_layers,
num_feats,
args.num_hidden,
n_classes,
heads,
F.elu,
args.in_drop,
args.attn_drop,
args.negative_slope,
args.residual,
args.bias)
print(model)
if args.early_stop:
stopper = EarlyStopping(patience=100)
if cuda:
model.cuda()
# 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
pred = model(features)
loss = loss_fcn(pred[train_mask], labels[train_mask])
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch >= 3:
dur.append(time.time() - t0)
train_r2 = compute_r2(pred[train_mask], labels[train_mask])
if args.fastmode:
val_r2 = compute_r2(pred[val_mask], labels[val_mask])
else:
val_r2 = evaluate(model, features, labels, val_mask)
if args.early_stop:
if stopper.step(val_r2, model):
break
if epoch > 3:
print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | TrainR2 {:.4f} |"
" Val R2 {:.4f} | ETputs(KTEPS) {:.2f}".
format(epoch, np.mean(dur), loss.item(), train_r2,
val_r2, n_edges / np.mean(dur) / 1000))
print()
if args.early_stop:
model.load_state_dict(torch.load('es_checkpoint.pt'))
evaluate_test(model, features, labels, test_mask, lp_dict, meta="2012")
evaluate_test(model, ind_features, ind_labels, test_mask, lp_dict, meta="2016")
def train(args):
# load and preprocess dataset
#data = load_data(args)
#data = CoraFull()
#data = Coauthor('cs')
#FIRST, CHECK DATASET
path = './dataset/' + str(args.dataset) + '/'
'''
edges = np.loadtxt(path + 'edges.txt')
edges = edges.astype(int)
features = np.loadtxt(path + 'features.txt')
train_mask = np.loadtxt(path + 'train_mask.txt')
train_mask = train_mask.astype(int)
labels = np.loadtxt(path + 'labels.txt')
labels = labels.astype(int)
'''
edges = np.load(path + 'edges.npy')
features = np.load(path + 'features.npy')
train_mask = np.load(path + 'train_mask.npy')
labels = np.load(path + 'labels.npy')
num_edges = edges.shape[0]
num_nodes = features.shape[0]
num_feats = features.shape[1]
n_classes = max(labels) - min(labels) + 1
assert train_mask.shape[0] == num_nodes
print('dataset {}'.format(args.dataset))
print('# of edges : {}'.format(num_edges))
print('# of nodes : {}'.format(num_nodes))
print('# of features : {}'.format(num_feats))
features = torch.FloatTensor(features)
labels = torch.LongTensor(labels)
if hasattr(torch, 'BoolTensor'):
train_mask = torch.BoolTensor(train_mask)
else:
train_mask = torch.ByteTensor(train_mask)
if args.gpu < 0:
cuda = False
else:
cuda = True
torch.cuda.set_device(args.gpu)
features = features.cuda()
labels = labels.cuda()
train_mask = train_mask.cuda()
u = edges[:, 0]
v = edges[:, 1]
#initialize a DGL graph
g = DGLGraph()
g.add_nodes(num_nodes)
g.add_edges(u, v)
# add self loop
if isinstance(g, nx.classes.digraph.DiGraph):
g.remove_edges_from(nx.selfloop_edges(g))
g = DGLGraph(g)
g.add_edges(g.nodes(), g.nodes())
elif isinstance(g, DGLGraph):
g = transform.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(g, args.num_layers, num_feats, args.num_hidden, n_classes,
heads, F.elu, args.in_drop, args.attn_drop,
args.negative_slope, args.residual)
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 = []
record_time = 0
avg_run_time = 0
Used_memory = 0
for epoch in range(args.num_epochs):
#print('epoch = ', epoch)
#print('mem0 = {}'.format(mem0))
torch.cuda.synchronize()
tf = time.time()
model.train()
if epoch >= 3:
t0 = time.time()
# forward
logits = model(features)
loss = loss_fcn(logits[train_mask], labels[train_mask])
now_mem = torch.cuda.max_memory_allocated(0)
print('now_mem : ', now_mem)
Used_memory = max(now_mem, Used_memory)
tf1 = time.time()
optimizer.zero_grad()
torch.cuda.synchronize()
t1 = time.time()
loss.backward()
torch.cuda.synchronize()
optimizer.step()
t2 = time.time()
run_time_this_epoch = t2 - tf
if epoch >= 3:
dur.append(time.time() - t0)
record_time += 1
avg_run_time += run_time_this_epoch
train_acc = accuracy(logits[train_mask], labels[train_mask])
#log for each step
print(
'Epoch {:05d} | Time(s) {:.4f} | train_acc {:.6f} | Used_Memory {:.6f} mb'
.format(epoch, run_time_this_epoch, train_acc,
(now_mem * 1.0 / (1024**2))))
'''
if args.fastmode:
val_acc = accuracy(logits[val_mask], labels[val_mask])
else:
val_acc = evaluate(model, 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))
'''
if args.early_stop:
model.load_state_dict(torch.load('es_checkpoint.pt'))
#OUTPUT we need
avg_run_time = avg_run_time * 1. / record_time
Used_memory /= (1024**3)
print('^^^{:6f}^^^{:6f}'.format(Used_memory, avg_run_time))
