def test(args):
# load data
num_nodes, num_rels = utils.get_total_number(args.dataset_path, 'stat.txt')
test_data, test_times = utils.load_hexaruples(args.dataset_path,
'test.txt')
total_data, total_times = utils.load_hexaruples(args.dataset_path,
'train.txt', 'valid.txt',
'test.txt')
model_dir = 'models/' + args.dataset + '/{}-{}-{}-{}'.format(
args.dropout, args.n_hidden, args.gamma, args.num_k)
model_state_file = model_dir + '/epoch-{}.pth'.format(args.epoch)
# check cuda
use_cuda = args.gpu >= 0 and torch.cuda.is_available()
if use_cuda:
torch.cuda.set_device(args.gpu)
torch.cuda.manual_seed_all(999)
model = DArtNet(num_nodes,
args.n_hidden,
num_rels,
model=args.model,
seq_len=args.seq_len,
num_k=args.num_k,
gamma=args.gamma)
if use_cuda:
model.cuda()
test_sub_entity = '/test_entity_s_history_data.txt'
test_sub_rel = '/test_rel_s_history_data.txt'
test_sub_att = '/test_att_s_history_data.txt'
test_sub_self_att = '/test_self_att_s_history_data.txt'
test_ob_entity = '/test_entity_o_history_data.txt'
test_ob_rel = '/test_rel_o_history_data.txt'
test_ob_att = '/test_att_o_history_data.txt'
test_ob_self_att = '/test_self_att_o_history_data.txt'
with open(args.dataset_path + test_sub_entity, 'rb') as f:
entity_s_history_data_test = pickle.load(f)
with open(args.dataset_path + test_sub_rel, 'rb') as f:
rel_s_history_data_test = pickle.load(f)
with open(args.dataset_path + test_sub_att, 'rb') as f:
att_s_history_data_test = pickle.load(f)
with open(args.dataset_path + test_sub_self_att, 'rb') as f:
self_att_s_history_data_test = pickle.load(f)
with open(args.dataset_path + test_ob_entity, 'rb') as f:
entity_o_history_data_test = pickle.load(f)
with open(args.dataset_path + test_ob_rel, 'rb') as f:
rel_o_history_data_test = pickle.load(f)
with open(args.dataset_path + test_ob_att, 'rb') as f:
att_o_history_data_test = pickle.load(f)
with open(args.dataset_path + test_ob_self_att, 'rb') as f:
self_att_o_history_data_test = pickle.load(f)
print(f'\nstart testing model file : {model_state_file}')
checkpoint = torch.load(model_state_file,
map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['state_dict'])
model.init_history()
model.latest_time = checkpoint['latest_time']
print("Using epoch: {}".format(checkpoint['epoch']))
total_data = torch.from_numpy(total_data)
test_data = torch.from_numpy(test_data)
model.eval()
total_att_sub_loss = 0
total_ranks = np.array([])
total_ranks_filter = np.array([])
ranks = []
with torch.no_grad():
latest_time = test_times[0]
j = 0
while j < len(test_data):
k = j
while k < len(test_data):
if test_data[k][-1] == test_data[j][-1]:
k += 1
else:
break
start = j
while start < k:
end = min(k, start + args.batch_size)
batch_data = test_data[start:end].clone()
s_hist = entity_s_history_data_test[start:end].copy()
o_hist = entity_o_history_data_test[start:end].copy()
rel_s_hist = rel_s_history_data_test[start:end].copy()
rel_o_hist = rel_o_history_data_test[start:end].copy()
att_s_hist = att_s_history_data_test[start:end].copy()
att_o_hist = att_o_history_data_test[start:end].copy()
self_att_s_hist = self_att_s_history_data_test[start:end].copy(
)
self_att_o_hist = self_att_o_history_data_test[start:end].copy(
)
if use_cuda:
batch_data = batch_data.cuda()
loss_sub = model.predict(batch_data, s_hist, rel_s_hist,
att_s_hist, self_att_s_hist, o_hist,
rel_o_hist, att_o_hist,
self_att_o_hist)
total_att_sub_loss += (loss_sub.item() * (end - start + 1))
start += args.batch_size
for i in range(j, k):
batch_data = test_data[i].clone()
s_hist = entity_s_history_data_test[i].copy()
o_hist = entity_o_history_data_test[i].copy()
rel_s_hist = rel_s_history_data_test[i].copy()
rel_o_hist = rel_o_history_data_test[i].copy()
att_s_hist = att_s_history_data_test[i].copy()
att_o_hist = att_o_history_data_test[i].copy()
self_att_s_hist = self_att_s_history_data_test[i].copy()
self_att_o_hist = self_att_o_history_data_test[i].copy()
if use_cuda:
batch_data = batch_data.cuda()
ranks_pred = model.evaluate_filter(batch_data, s_hist,
rel_s_hist, att_s_hist,
self_att_s_hist, o_hist,
rel_o_hist, att_o_hist,
self_att_o_hist, total_data)
total_ranks_filter = np.concatenate(
(total_ranks_filter, ranks_pred))
j = k
ranks.append(total_ranks_filter)
for rank in ranks:
total_ranks = np.concatenate((total_ranks, rank))
mrr = np.mean(1.0 / total_ranks)
mr = np.mean(total_ranks)
hits = []
for hit in [1, 3, 10]:
avg_count = np.mean((total_ranks <= hit))
hits.append(avg_count)
print("Hits (filtered) @ {}: {:.6f}".format(hit, avg_count))
print("MRR (filtered): {:.6f}".format(mrr))
print("MR (filtered): {:.6f}".format(mr))
print("test att sub Loss: {:.6f}".format(total_att_sub_loss /
(len(test_data))))
result_epoch = result(epoch=args.epoch,
MRR=100 * mrr,
sub_att_loss=total_att_sub_loss / len(test_data),
MR=mr,
Hits1=100 * hits[0],
Hits3=100 * hits[1],
Hits10=100 * hits[2])
result_dict[args.epoch] = result_epoch
def train(args):
# load data
num_nodes, num_rels, num_att = utils.get_total_number(
args.dataset_path, 'stat.txt')
train_data, train_times = utils.load_hexaruples(args.dataset_path,
'train.txt')
# check cuda
use_cuda = args.gpu >= 0 and torch.cuda.is_available()
seed = 999
np.random.seed(seed)
torch.manual_seed(seed)
if use_cuda:
torch.cuda.set_device(args.gpu)
model_dir = 'models/' + args.dataset + '/{}-{}-{}-{}'.format(
args.dropout, args.n_hidden, args.gamma, args.num_k)
os.makedirs('models', exist_ok=True)
os.makedirs('models/' + args.dataset, exist_ok=True)
os.makedirs(model_dir, exist_ok=True)
print("start training...")
model = DArtNet(num_nodes,
args.n_hidden,
num_rels,
num_att,
dropout=args.dropout,
model=args.model,
seq_len=args.seq_len,
num_k=args.num_k,
gamma=args.gamma)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=0.00001)
if use_cuda:
model.cuda()
train_sub_entity = '/train_entity_s_history_data.txt'
train_sub_rel = '/train_rel_s_history_data.txt'
train_sub_att = '/train_att_s_history_data.txt'
train_sub_self_att = '/train_self_att_s_history_data.txt'
train_ob_entity = '/train_entity_o_history_data.txt'
train_ob_rel = '/train_rel_o_history_data.txt'
train_ob_att = '/train_att_o_history_data.txt'
train_ob_self_att = '/train_self_att_o_history_data.txt'
with open(args.dataset_path + train_sub_entity, 'rb') as f:
entity_s_history_data_train = pickle.load(f)
with open(args.dataset_path + train_sub_rel, 'rb') as f:
rel_s_history_data_train = pickle.load(f)
with open(args.dataset_path + train_sub_att, 'rb') as f:
att_s_history_data_train = pickle.load(f)
with open(args.dataset_path + train_sub_self_att, 'rb') as f:
self_att_s_history_data_train = pickle.load(f)
with open(args.dataset_path + train_ob_entity, 'rb') as f:
entity_o_history_data_train = pickle.load(f)
with open(args.dataset_path + train_ob_rel, 'rb') as f:
rel_o_history_data_train = pickle.load(f)
with open(args.dataset_path + train_ob_att, 'rb') as f:
att_o_history_data_train = pickle.load(f)
with open(args.dataset_path + train_ob_self_att, 'rb') as f:
self_att_o_history_data_train = pickle.load(f)
entity_s_history_train = entity_s_history_data_train
rel_s_history_train = rel_s_history_data_train
att_s_history_train = att_s_history_data_train
self_att_s_history_train = self_att_s_history_data_train
entity_o_history_train = entity_o_history_data_train
rel_o_history_train = rel_o_history_data_train
att_o_history_train = att_o_history_data_train
self_att_o_history_train = self_att_o_history_data_train
epoch = 0
if args.retrain != 0:
try:
checkpoint = torch.load(model_dir + '/checkpoint.pth',
map_location=f"cuda:{args.gpu}")
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint['epoch']
model.latest_time = checkpoint['latest_time']
model.to(torch.device(f"cuda:{args.gpu}"))
except FileNotFoundError as _:
try:
e = sorted([
int(file[6:-4])
for file in os.listdir(model_dir) if file[-4:] == '.pth'
],
reverse=True)[0]
checkpoint = torch.load(model_dir + '/epoch-{}.pth'.format(e),
map_location=f"cuda:{args.gpu}")
model.load_state_dict(checkpoint['state_dict'])
epoch = checkpoint['epoch']
model.latest_time = checkpoint['latest_time']
model.to(torch.device(f"cuda:{args.gpu}"))
except Exception as _:
print('no model found')
print('training from scratch')
while True:
model.train()
if epoch == args.max_epochs:
break
epoch += 1
loss_epoch = 0
loss_att_sub_epoch = 0
# loss_att_ob_epoch = 0
t0 = time.time()
train_data, entity_s_history_train, rel_s_history_train, entity_o_history_train, rel_o_history_train, att_s_history_train, self_att_s_history_train, att_o_history_train, self_att_o_history_train = shuffle(
train_data, entity_s_history_train, rel_s_history_train,
entity_o_history_train, rel_o_history_train, att_s_history_train,
self_att_s_history_train, att_o_history_train,
self_att_o_history_train)
iteration = 0
for batch_data, s_hist, rel_s_hist, o_hist, rel_o_hist, att_s_hist, self_att_s_hist, att_o_hist, self_att_o_hist in utils.make_batch3(
train_data, entity_s_history_train, rel_s_history_train,
entity_o_history_train, rel_o_history_train,
att_s_history_train, self_att_s_history_train,
att_o_history_train, self_att_o_history_train,
args.batch_size):
iteration += 1
print(f'iteration {iteration}', end='\r')
batch_data = torch.from_numpy(batch_data)
if use_cuda:
batch_data = batch_data.cuda()
loss, loss_att_sub = model.get_loss(batch_data, s_hist, rel_s_hist,
att_s_hist, self_att_s_hist,
o_hist, rel_o_hist, att_o_hist,
self_att_o_hist)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.grad_norm) # clip gradients
optimizer.step()
optimizer.zero_grad()
loss_epoch += loss.item()
loss_att_sub_epoch += loss_att_sub.item()
# loss_att_ob_epoch += loss_att_ob.item()
t3 = time.time()
print(
"Epoch {:04d} | Loss {:.4f} | Loss_att_sub {:.4f} | time {:.4f} ".
format(epoch, loss_epoch / (len(train_data) / args.batch_size),
loss_att_sub_epoch / (len(train_data) / args.batch_size),
t3 - t0))
torch.save(
{
'state_dict': model.state_dict(),
'epoch': epoch,
'latest_time': model.latest_time,
}, model_dir + '/epoch-{}.pth'.format(epoch))
torch.save(
{
'state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'epoch': epoch,
'latest_time': model.latest_time,
}, model_dir + '/checkpoint.pth')
print("training done")
