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
dataset = PygNodePropPredDataset(name=args.dataset)
graph = dataset[0]
if args.self_loop:
graph.edge_index = add_self_loops(edge_index=graph.edge_index,
num_nodes=graph.num_nodes)[0]
split_idx = dataset.get_idx_split()
evaluator = Evaluator(args.dataset)
args.in_channels = graph.x.size(-1)
args.num_tasks = dataset.num_classes
print(args)
model = DeeperGCN(args)
print(model)
model.load_state_dict(torch.load(args.model_load_path)['model_state_dict'])
result = test(model, graph.x, graph.edge_index, graph.y, split_idx,
evaluator)
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')
dataset = PygGraphPropPredDataset(name=args.dataset)
args.num_tasks = dataset.num_tasks
print(args)
if args.feature == 'full':
pass
elif args.feature == 'simple':
print('using simple feature')
# only retain the top two node/edge features
dataset.data.x = dataset.data.x[:, :2]
dataset.data.edge_attr = dataset.data.edge_attr[:, :2]
split_idx = dataset.get_idx_split()
evaluator = Evaluator(args.dataset)
train_loader = DataLoader(dataset[split_idx["train"]],
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
valid_loader = DataLoader(dataset[split_idx["valid"]],
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
test_loader = DataLoader(dataset[split_idx["test"]],
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
model = DeeperGCN(args)
model.load_state_dict(torch.load(args.model_load_path)['model_state_dict'])
model.to(device)
train_result = eval(model, device, train_loader,
evaluator)[dataset.eval_metric]
valid_result = eval(model, device, valid_loader,
evaluator)[dataset.eval_metric]
test_result = eval(model, device, test_loader,
evaluator)[dataset.eval_metric]
print({
'Train': train_result,
'Validation': valid_result,
'Test': test_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().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')
if args.not_extract_node_feature:
dataset = PygGraphPropPredDataset(name=args.dataset,
transform=add_zeros)
else:
extract_node_feature_func = partial(extract_node_feature,
reduce=args.aggr)
dataset = PygGraphPropPredDataset(name=args.dataset,
transform=extract_node_feature_func)
args.num_tasks = dataset.num_classes
evaluator = Evaluator(args.dataset)
split_idx = dataset.get_idx_split()
train_loader = DataLoader(dataset[split_idx["train"]],
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
valid_loader = DataLoader(dataset[split_idx["valid"]],
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
test_loader = DataLoader(dataset[split_idx["test"]],
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
print(args)
model = DeeperGCN(args)
model.load_state_dict(torch.load(args.model_load_path)['model_state_dict'])
model.to(device)
train_accuracy = eval(model, device, train_loader, evaluator)
valid_accuracy = eval(model, device, valid_loader, evaluator)
test_accuracy = eval(model, device, test_loader, evaluator)
print({
'Train': train_accuracy,
'Validation': valid_accuracy,
'Test': test_accuracy
})
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')
dataset = PygNodePropPredDataset(name=args.dataset)
data = dataset[0]
split_idx = dataset.get_idx_split()
evaluator = Evaluator(args.dataset)
x = data.x.to(device)
y_true = data.y.to(device)
edge_index = data.edge_index.to(device)
edge_index = to_undirected(edge_index, data.num_nodes)
if args.self_loop:
edge_index = add_self_loops(edge_index, num_nodes=data.num_nodes)[0]
args.in_channels = data.x.size(-1)
args.num_tasks = dataset.num_classes
print(args)
model = DeeperGCN(args)
model.load_state_dict(torch.load(args.model_load_path)['model_state_dict'])
model.to(device)
result = test(model, x, edge_index, y_true, split_idx, evaluator)
train_accuracy, valid_accuracy, test_accuracy = result
print({
'Train': train_accuracy,
'Validation': valid_accuracy,
'Test': test_accuracy
})
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')
dataset = PygLinkPropPredDataset(name=args.dataset)
data = dataset[0]
# Data(edge_index=[2, 2358104], edge_weight=[2358104, 1], edge_year=[2358104, 1], x=[235868, 128])
split_edge = dataset.get_edge_split()
evaluator = Evaluator(args.dataset)
x = data.x.to(device)
edge_index = data.edge_index.to(device)
args.in_channels = data.x.size(-1)
args.num_tasks = 1
print(args)
model = DeeperGCN(args).to(device)
predictor = LinkPredictor(args).to(device)
model.load_state_dict(torch.load(args.model_load_path)['model_state_dict'])
model.to(device)
predictor.load_state_dict(
torch.load(args.predictor_load_path)['model_state_dict'])
predictor.to(device)
hits = ['Hits@10', 'Hits@50', 'Hits@100']
result = test(model, predictor, x, edge_index, split_edge, evaluator,
args.batch_size)
for k in hits:
train_result, valid_result, test_result = result[k]
print('{}--Train: {}, Validation: {}, Test: {}'.format(
k, train_result, valid_result, test_result))
def test_model(model_path):
args = ArgsInit().args
dataset = PygNodePropPredDataset(name=args.dataset)
graph = dataset[0]
num_parts = 10
data_list = list(
RandomNodeSampler(graph, num_parts=num_parts, shuffle=True))
number_of_train = int(0.9 * num_parts)
train_data_list = data_list[0:number_of_train]
test_data_list = data_list[number_of_train:]
args.in_channels = graph.x.size(-1)
args.num_tasks = dataset.num_classes
model = DeeperGCN(args)
model.load_state_dict(torch.load(model_path))
print(test(model, test_data_list))
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')
sub_dir = 'BS_{}-NF_{}'.format(args.batch_size, args.feature)
dataset = PygGraphPropPredDataset(name=args.dataset)
args.num_tasks = dataset.num_tasks
logging.info('%s' % args)
if args.feature == 'full':
pass
elif args.feature == 'simple':
print('using simple feature')
# only retain the top two node/edge features
dataset.data.x = dataset.data.x[:, :2]
dataset.data.edge_attr = dataset.data.edge_attr[:, :2]
evaluator = Evaluator(args.dataset)
split_idx = dataset.get_idx_split()
train_loader = DataLoader(dataset[split_idx["train"]],
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers)
valid_loader = DataLoader(dataset[split_idx["valid"]],
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
test_loader = DataLoader(dataset[split_idx["test"]],
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
model = DeeperGCN(args).to(device)
logging.info(model)
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):
logging.info("=====Epoch {}".format(epoch))
logging.info('Training...')
# epoch_loss = train(model, device, train_loader, optimizer, dataset.task_type)
epoch_loss = train_flag(model, device, train_loader, optimizer,
dataset.task_type, args)
logging.info('Evaluating...')
train_result = eval(model, device, train_loader,
evaluator)[dataset.eval_metric]
valid_result = eval(model, device, valid_loader,
evaluator)[dataset.eval_metric]
test_result = eval(model, device, test_loader,
evaluator)[dataset.eval_metric]
logging.info({
'Train': train_result,
'Validation': valid_result,
'Test': test_result
})
model.print_params(epoch=epoch)
if train_result > results['highest_train']:
results['highest_train'] = train_result
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')
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()
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 = PygNodePropPredDataset(name=args.dataset)
graph = dataset[0]
adj = SparseTensor(row=graph.edge_index[0], col=graph.edge_index[1])
if args.self_loop:
adj = adj.set_diag()
graph.edge_index = add_self_loops(edge_index=graph.edge_index,
num_nodes=graph.num_nodes)[0]
split_idx = dataset.get_idx_split()
train_idx = split_idx["train"].tolist()
evaluator = Evaluator(args.dataset)
sub_dir = 'random-train_{}-full_batch_test'.format(args.cluster_number)
logging.info(sub_dir)
args.in_channels = graph.x.size(-1)
args.num_tasks = dataset.num_classes
logging.info('%s' % args)
model = DeeperGCN(args).to(device)
logging.info(model)
optimizer = torch.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):
# generate batches
parts = random_partition_graph(graph.num_nodes,
cluster_number=args.cluster_number)
data = generate_sub_graphs(adj,
parts,
cluster_number=args.cluster_number)
epoch_loss = train(data, model, graph.x, graph.y, train_idx, optimizer,
device)
logging.info('Epoch {}, training loss {:.4f}'.format(
epoch, epoch_loss))
model.print_params(epoch=epoch)
if epoch == args.epochs:
result = test(model, graph.x, graph.edge_index, graph.y, split_idx,
evaluator)
logging.info(result)
train_accuracy, valid_accuracy, test_accuracy = result
if train_accuracy > results['highest_train']:
results['highest_train'] = train_accuracy
if valid_accuracy > results['highest_valid']:
results['highest_valid'] = valid_accuracy
results['final_train'] = train_accuracy
results['final_test'] = test_accuracy
save_ckpt(model,
optimizer,
round(epoch_loss, 4),
epoch,
args.model_save_path,
sub_dir,
name_post='valid_best')
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()
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 = PygNodePropPredDataset(name=args.dataset, root=args.data_folder)
graph = dataset[0]
adj = SparseTensor(row=graph.edge_index[0], col=graph.edge_index[1])
if args.self_loop:
adj = adj.set_diag()
graph.edge_index = add_self_loops(edge_index=graph.edge_index,
num_nodes=graph.num_nodes)[0]
split_idx = dataset.get_idx_split()
train_idx = split_idx["train"].tolist()
evaluator = Evaluator(args.dataset)
sub_dir = 'random-train_{}-full_batch_test'.format(args.cluster_number)
logging.info(sub_dir)
log_dir = os.path.join(args.save, "tensorboard/")
writer = SummaryWriter(log_dir=log_dir)
args.in_channels = graph.x.size(-1)
args.num_tasks = dataset.num_classes
logging.info('%s' % args)
model = DeeperGCN(args).to(device)
logging.info(model)
optimizer = torch.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):
# generate batches
parts = random_partition_graph(graph.num_nodes,
cluster_number=args.cluster_number)
data = generate_sub_graphs(adj,
parts,
cluster_number=args.cluster_number)
# epoch_loss = train(data, model, graph.x, graph.y, train_idx, optimizer, device)
epoch_loss = train_flag(data, model, graph.x, graph.y, train_idx,
optimizer, device, args)
logging.info('Epoch {}, training loss {:.4f}'.format(
epoch, epoch_loss))
writer.add_scalar("Loss/train", epoch_loss, epoch)
model.print_params(epoch=epoch)
# if epoch % args.eval_epochs == 0:
# save_ckpt(model, optimizer,
# round(epoch_loss, 4), epoch,
# args.model_save_path,
# sub_dir, name_post=f'epoch{epoch}')
# logging.info(f"Epoch {epoch}, saved model to checkpoint folder {args.model_save_path}")
if epoch % args.eval_epochs == 0:
# save_ckpt(model, optimizer,
# round(epoch_loss, 4), epoch,
# args.model_save_path,
# sub_dir, name_post='final')
# logging.info(f"Saved model to checkpoint folder {args.model_save_path}")
logging.info(f'---- Evaluating at epoch {epoch} ----')
res = test_with_partition(model,
graph,
adj,
split_idx,
num_clusters=args.eval_cluster_number,
partition_method=args.partition_method,
evaluator=evaluator,
device=device)
# result = test(model, graph.x, graph.edge_index, graph.y, split_idx, evaluator)
logging.info(res)
logging.info(f"---------------------------------")
train_accuracy, valid_accuracy, test_accuracy = res[
"train_acc"], res["valid_acc"], res["test_acc"]
writer.add_scalar("Acc/train", train_accuracy)
writer.add_scalar("Acc/dev", valid_accuracy)
if train_accuracy > results['highest_train']:
results['highest_train'] = train_accuracy
if valid_accuracy > results['highest_valid']:
results['highest_valid'] = valid_accuracy
results['final_train'] = train_accuracy
results['final_test'] = test_accuracy
save_ckpt(model,
optimizer,
round(epoch_loss, 4),
epoch,
args.model_save_path,
sub_dir,
name_post='valid_best')
logging.info(
f"Saved better model to checkpoint folder {args.model_save_path}"
)
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))))
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')
sub_dir = 'BS_{}'.format(args.batch_size)
if args.not_extract_node_feature:
dataset = PygGraphPropPredDataset(name=args.dataset,
transform=add_zeros)
else:
if args.aggr == 'add':
dataset = PygGraphPropPredDataset(
name=args.dataset, transform=extract_node_feature_add)
elif args.aggr == 'mean':
dataset = PygGraphPropPredDataset(
name=args.dataset, transform=extract_node_feature_mean)
elif args.aggr == 'max':
dataset = PygGraphPropPredDataset(
name=args.dataset, transform=extract_node_feature_max)
else:
raise Exception('Unknown Aggregation Type')
sub_dir = sub_dir + '-NF_{}'.format(args.aggr)
args.num_tasks = dataset.num_classes
evaluator = Evaluator(args.dataset)
logging.info('%s' % args)
split_idx = dataset.get_idx_split()
train_loader = DataLoader(dataset[split_idx["train"]],
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers)
valid_loader = DataLoader(dataset[split_idx["valid"]],
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
test_loader = DataLoader(dataset[split_idx["test"]],
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
model = DeeperGCN(args).to(device)
logging.info(model)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
criterion = torch.nn.CrossEntropyLoss()
results = {
'highest_valid': 0,
'final_train': 0,
'final_test': 0,
'highest_train': 0
}
start_time = time.time()
evaluate = True
for epoch in range(1, args.epochs + 1):
logging.info("=====Epoch {}".format(epoch))
logging.info('Training...')
epoch_loss = train(model, device, train_loader, optimizer, criterion)
if args.num_layers > args.num_layers_threshold:
if epoch % args.eval_steps != 0:
evaluate = False
else:
evaluate = True
model.print_params(epoch=epoch)
if evaluate:
logging.info('Evaluating...')
train_accuracy = eval(model, device, train_loader, evaluator)
valid_accuracy = eval(model, device, valid_loader, evaluator)
test_accuracy = eval(model, device, test_loader, evaluator)
logging.info({
'Train': train_accuracy,
'Validation': valid_accuracy,
'Test': test_accuracy
})
if train_accuracy > results['highest_train']:
results['highest_train'] = train_accuracy
if valid_accuracy > results['highest_valid']:
results['highest_valid'] = valid_accuracy
results['final_train'] = train_accuracy
results['final_test'] = test_accuracy
save_ckpt(model,
optimizer,
round(epoch_loss, 4),
epoch,
args.model_save_path,
sub_dir,
name_post='valid_best')
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()
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 = PygNodePropPredDataset(name=args.dataset)
data = dataset[0]
split_idx = dataset.get_idx_split()
evaluator = Evaluator(args.dataset)
x = data.x.to(device)
y_true = data.y.to(device)
train_idx = split_idx['train'].to(device)
edge_index = data.edge_index.to(device)
edge_index = to_undirected(edge_index, data.num_nodes)
if args.self_loop:
edge_index = add_self_loops(edge_index, num_nodes=data.num_nodes)[0]
sub_dir = 'SL_{}'.format(args.self_loop)
args.in_channels = data.x.size(-1)
args.num_tasks = dataset.num_classes
logging.info('%s' % args)
model = DeeperGCN(args).to(device)
logging.info(model)
optimizer = torch.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):
epoch_loss = train(model, x, edge_index, y_true, train_idx, optimizer)
logging.info('Epoch {}, training loss {:.4f}'.format(
epoch, epoch_loss))
model.print_params(epoch=epoch)
result = test(model, x, edge_index, y_true, split_idx, evaluator)
logging.info(result)
train_accuracy, valid_accuracy, test_accuracy = result
if train_accuracy > results['highest_train']:
results['highest_train'] = train_accuracy
if valid_accuracy > results['highest_valid']:
results['highest_valid'] = valid_accuracy
results['final_train'] = train_accuracy
results['final_test'] = test_accuracy
save_ckpt(model,
optimizer,
round(epoch_loss, 4),
epoch,
args.model_save_path,
sub_dir,
name_post='valid_best')
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():
EPOCHS = 1
NUMBER_OF_SUBGRAPHS = 10
CKPT_PATH = f'neighbor_deeper_num_{NUMBER_OF_SUBGRAPHS}.pt'
EXPERIMENT_RES_PATH = f'neighbor_deeper_num_{NUMBER_OF_SUBGRAPHS}_experiment_res.pk'
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 = PygNodePropPredDataset(name=args.dataset)
graph = dataset[0]
print(graph)
num_parts = NUMBER_OF_SUBGRAPHS
data_list = list(
NeighborSubgraphLoader(graph, num_parts=NUMBER_OF_SUBGRAPHS))
print(f'len of datalist: {len(data_list)}')
number_of_train = int(0.9 * num_parts)
train_data_list = data_list[0:number_of_train]
test_data_list = data_list[number_of_train:]
print(
f'Train test split successful, number of train: {len(train_data_list)} | number of test: {len(test_data_list)}'
)
args.in_channels = graph.x.size(-1)
args.num_tasks = dataset.num_classes
logging.info('%s' % args)
model = DeeperGCN(args).to(device)
logging.info(model)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
start_time = time.time()
epoch_loss_list = []
epoch_acc_list = []
highest_acc = 0
best_model_dict = None
for epoch in range(1, EPOCHS + 1):
epoch_loss, epoch_acc = train(data_list, model, optimizer, device)
epoch_loss_list.append(epoch_loss)
epoch_acc_list.append(epoch_acc)
print('Epoch {}, training loss {:.4f} | training acc {}'.format(
epoch, epoch_loss, epoch_acc))
test_acc = test(model, test_data_list)
if test_acc > highest_acc:
highest_acc = test_acc
best_model_dict = model.state_dict()
logging.info(
f'best test acc: {highest_acc} | saved to path {CKPT_PATH}')
end_time = time.time()
total_time = end_time - start_time
logging.info('Total time: {}'.format(
time.strftime('%H:%M:%S', time.gmtime(total_time))))
experiment_result = {}
experiment_result['Total training time'] = total_time
experiment_result['Epoch loss list'] = epoch_loss_list
experiment_result['Epoch acc list'] = epoch_acc_list
experiment_result['Best test acc'] = highest_acc
torch.save(best_model_dict, CKPT_PATH)
with open(EXPERIMENT_RES_PATH, 'wb') as f:
pk.dump(experiment_result, f)
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()
if args.use_tensor_board:
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')
dataset = PygLinkPropPredDataset(name=args.dataset)
data = dataset[0]
# Data(edge_index=[2, 2358104], edge_weight=[2358104, 1], edge_year=[2358104, 1], x=[235868, 128])
split_edge = dataset.get_edge_split()
evaluator = Evaluator(args.dataset)
x = data.x.to(device)
edge_index = data.edge_index.to(device)
args.in_channels = data.x.size(-1)
args.num_tasks = 1
logging.info('%s' % args)
model = DeeperGCN(args).to(device)
predictor = LinkPredictor(args).to(device)
logging.info(model)
logging.info(predictor)
optimizer = torch.optim.Adam(list(model.parameters()) +
list(predictor.parameters()),
lr=args.lr)
results = {}
keys = ['highest_valid', 'final_train', 'final_test', 'highest_train']
hits = ['Hits@10', 'Hits@50', 'Hits@100']
for key in keys:
results[key] = {k: 0 for k in hits}
start_time = time.time()
for epoch in range(1, args.epochs + 1):
epoch_loss = train(model, predictor, x, edge_index, split_edge,
optimizer, args.batch_size)
logging.info('Epoch {}, training loss {:.4f}'.format(
epoch, epoch_loss))
model.print_params(epoch=epoch)
result = test(model, predictor, x, edge_index, split_edge, evaluator,
args.batch_size)
for k in hits:
# return a tuple
train_result, valid_result, test_result = result[k]
if args.use_tensor_board and k == 'Hits@50':
writer.add_scalar('stats/train_loss', epoch_loss, epoch)
writer.add_scalar('stats/train_Hits@50', train_result, epoch)
writer.add_scalar('stats/valid_Hits@50', valid_result, epoch)
writer.add_scalar('stats/test_Hits@50', test_result, epoch)
if train_result > results['highest_train'][k]:
results['highest_train'][k] = train_result
if valid_result > results['highest_valid'][k]:
results['highest_valid'][k] = valid_result
results['final_train'][k] = train_result
results['final_test'][k] = test_result
save_ckpt(model,
optimizer,
round(epoch_loss, 4),
epoch,
args.model_save_path,
k,
name_post='valid_best')
save_ckpt(predictor,
optimizer,
round(epoch_loss, 4),
epoch,
args.model_save_path,
k,
name_post='valid_best_link_predictor')
logging.info(result)
logging.info("%s" % results)
end_time = time.time()
total_time = end_time - start_time
time_used = 'Total time: {}'.format(
time.strftime('%H:%M:%S', time.gmtime(total_time)))
logging.info(time_used)