def main():
args = ArgsInit().args
if args.use_gpu:
device = torch.device("cuda:" + str(args.device)) if torch.cuda.is_available() else torch.device("cpu")
else:
device = torch.device("cpu")
dataset = OGBNDataset(dataset_name=args.dataset)
args.num_tasks = dataset.num_tasks
args.nf_path = dataset.extract_node_features(args.aggr)
evaluator = Evaluator(args.dataset)
valid_data_list = []
for i in range(args.num_evals):
parts = dataset.random_partition_graph(dataset.total_no_of_nodes,
cluster_number=args.valid_cluster_number)
valid_data = dataset.generate_sub_graphs(parts,
cluster_number=args.valid_cluster_number)
valid_data_list.append(valid_data)
model = DeeperGCN(args)
model.load_state_dict(torch.load(args.model_load_path)['model_state_dict'])
model.to(device)
result = multi_evaluate(valid_data_list, dataset, model, evaluator, device)
print(result)
model.print_params(final=True)
def main():
args = ArgsInit().args
if args.use_gpu:
device = torch.device("cuda:" +
str(args.device)) if torch.cuda.is_available(
) else torch.device("cpu")
else:
device = torch.device("cpu")
args.device = device
dataset = OGBNDataset(dataset_name=args.dataset)
args.num_tasks = dataset.num_tasks
args.nf_path = dataset.extract_node_features(args.aggr)
evaluator = Evaluator(args.dataset)
valid_data_list = []
for i in range(args.num_evals):
parts = dataset.random_partition_graph(
dataset.total_no_of_nodes,
cluster_number=args.valid_cluster_number)
valid_data = dataset.generate_sub_graphs(
parts, cluster_number=args.valid_cluster_number)
valid_data_list.append(valid_data)
if args.backbone == 'deepergcn':
# model = DeeperGCN(args).to(device)
pass
# elif args.backbone == 'deq':
# model = DEQGCN(args).to(device)
# elif args.backbone == 'revwt':
# model = WTRevGCN(args).to(device)
elif args.backbone == 'rev':
model = RevGCN(args).to(device)
pass
else:
raise Exception("unkown backbone")
if torch.cuda.is_available():
model.load_state_dict(
torch.load(args.model_load_path)['model_state_dict'])
else:
model.load_state_dict(
torch.load(args.model_load_path,
map_location=torch.device('cpu'))['model_state_dict'])
model.to(device)
with torch.cuda.amp.autocast():
result = multi_evaluate(valid_data_list, dataset, model, evaluator,
device)
print(result)
model.print_params(final=True)
peak_memuse = torch.cuda.max_memory_allocated(device) / float(1024**3)
print('Peak memuse {:.2f} G'.format(peak_memuse))
def main():
args = ArgsInit().save_exp()
if args.use_gpu:
device = torch.device("cuda:" +
str(args.device)) if torch.cuda.is_available(
) else torch.device("cpu")
else:
device = torch.device("cpu")
logging.info('%s' % device)
dataset = OGBNDataset(dataset_name=args.dataset)
# extract initial node features
nf_path = dataset.extract_node_features(args.aggr)
args.num_tasks = dataset.num_tasks
args.nf_path = nf_path
logging.info('%s' % args)
evaluator = Evaluator(args.dataset)
criterion = torch.nn.BCEWithLogitsLoss()
valid_data_list = []
for i in range(args.num_evals):
parts = dataset.random_partition_graph(
dataset.total_no_of_nodes,
cluster_number=args.valid_cluster_number)
valid_data = dataset.generate_sub_graphs(
parts, cluster_number=args.valid_cluster_number)
valid_data_list.append(valid_data)
sub_dir = 'random-train_{}-test_{}-num_evals_{}'.format(
args.cluster_number, args.valid_cluster_number, args.num_evals)
logging.info(sub_dir)
model = DeeperGCN(args).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
results = {
'highest_valid': 0,
'final_train': 0,
'final_test': 0,
'highest_train': 0
}
start_time = time.time()
for epoch in range(1, args.epochs + 1):
# do random partition every epoch
train_parts = dataset.random_partition_graph(
dataset.total_no_of_nodes, cluster_number=args.cluster_number)
data = dataset.generate_sub_graphs(train_parts,
cluster_number=args.cluster_number,
ifmask=True)
epoch_loss = train(data, dataset, model, optimizer, criterion, device)
logging.info('Epoch {}, training loss {:.4f}'.format(
epoch, epoch_loss))
model.print_params(epoch=epoch)
result = multi_evaluate(valid_data_list, dataset, model, evaluator,
device)
if epoch % 5 == 0:
logging.info('%s' % result)
train_result = result['train']['rocauc']
valid_result = result['valid']['rocauc']
test_result = result['test']['rocauc']
if valid_result > results['highest_valid']:
results['highest_valid'] = valid_result
results['final_train'] = train_result
results['final_test'] = test_result
save_ckpt(model,
optimizer,
round(epoch_loss, 4),
epoch,
args.model_save_path,
sub_dir,
name_post='valid_best')
if train_result > results['highest_train']:
results['highest_train'] = train_result
logging.info("%s" % results)
end_time = time.time()
total_time = end_time - start_time
logging.info('Total time: {}'.format(
time.strftime('%H:%M:%S', time.gmtime(total_time))))
def main():
args = ArgsInit().save_exp()
logging.getLogger().setLevel(logging.INFO)
writer = SummaryWriter(log_dir=args.save)
if args.use_gpu:
device = torch.device("cuda:" +
str(args.device)) if torch.cuda.is_available(
) else torch.device("cpu")
else:
device = torch.device("cpu")
logging.info('%s' % device)
dataset = OGBNDataset(dataset_name=args.dataset)
# extract initial node features
nf_path = dataset.extract_node_features(args.aggr)
args.num_tasks = dataset.num_tasks
args.nf_path = nf_path
logging.info('%s' % args)
evaluator = Evaluator(args.dataset)
criterion = torch.nn.BCEWithLogitsLoss()
valid_data_list = []
for i in range(args.num_evals):
parts = dataset.random_partition_graph(
dataset.total_no_of_nodes,
cluster_number=args.valid_cluster_number)
valid_data = dataset.generate_sub_graphs(
parts, cluster_number=args.valid_cluster_number)
valid_data_list.append(valid_data)
sub_dir = 'random-train_{}-test_{}-num_evals_{}'.format(
args.cluster_number, args.valid_cluster_number, args.num_evals)
logging.info(sub_dir)
if args.backbone == 'deepergcn':
# model = DeeperGCN(args).to(device)
pass
# elif args.backbone == 'deq':
# model = DEQGCN(args).to(device)
# elif args.backbone == 'revwt':
# model = WTRevGCN(args).to(device)
elif args.backbone == 'rev':
model = RevGCN(args).to(device)
else:
raise Exception("unkown backbone")
logging.info('# of params: {}'.format(
sum(p.numel() for p in model.parameters())))
logging.info('# of learnable params: {}'.format(
sum(p.numel() for p in model.parameters() if p.requires_grad)))
optimizer = optim.Adam(model.parameters(), lr=args.lr)
results = {
'highest_valid': 0,
'final_train': 0,
'final_test': 0,
'highest_train': 0
}
start_time = time.time()
for epoch in range(1, args.epochs + 1):
# do random partition every epoch
train_parts = dataset.random_partition_graph(
dataset.total_no_of_nodes, cluster_number=args.cluster_number)
data = dataset.generate_sub_graphs(train_parts,
cluster_number=args.cluster_number)
epoch_loss = train(data,
dataset,
model,
optimizer,
criterion,
device,
epoch=epoch)
logging.info('Epoch {}, training loss {:.4f}'.format(
epoch, epoch_loss))
if epoch == 1:
peak_memuse = torch.cuda.max_memory_allocated(device) / float(1024
**3)
logging.info('Peak memuse {:.2f} G'.format(peak_memuse))
torch.cuda.empty_cache()
model.print_params(epoch=epoch)
with torch.cuda.amp.autocast():
result = multi_evaluate(valid_data_list,
dataset,
model,
evaluator,
device,
epoch=epoch)
if epoch % 5 == 0:
logging.info('%s' % result)
train_result = result['train']['rocauc']
valid_result = result['valid']['rocauc']
test_result = result['test']['rocauc']
writer.add_scalar('stats/train_rocauc', train_result, epoch)
writer.add_scalar('stats/valid_rocauc', valid_result, epoch)
writer.add_scalar('stats/test_rocauc', test_result, epoch)
if valid_result > results['highest_valid']:
results['highest_valid'] = valid_result
results['final_train'] = train_result
results['final_test'] = test_result
save_ckpt(model,
optimizer,
round(epoch_loss, 4),
epoch,
args.model_save_path,
sub_dir,
name_post='valid_best')
if train_result > results['highest_train']:
results['highest_train'] = train_result
logging.info("%s" % results)
end_time = time.time()
total_time = end_time - start_time
logging.info('Total time: {}'.format(
time.strftime('%d-%H:%M:%S', time.gmtime(total_time))))