def evaluate(model, dataloader, device, args):
model.eval()
relation_type = dataloader.dataset.relation_type
score = 0
upper_bound = 0
num_data = 0
N = len(dataloader.dataset)
entropy = None
if model.module.fusion == "ban":
entropy = torch.Tensor(model.module.glimpse).zero_().to(device)
pbar = tqdm(total=len(dataloader))
for i, (v, norm_bb, q, q_target, target, _, _, bb, spa_adj_matrix,
sem_adj_matrix) in enumerate(dataloader):
batch_size = v.size(0)
num_objects = v.size(1)
v = Variable(v).to(device)
norm_bb = Variable(norm_bb).to(device)
q = Variable(q).to(device)
target = Variable(target).to(device)
pos_emb, sem_adj_matrix, spa_adj_matrix = prepare_graph_variables(
relation_type, bb, sem_adj_matrix, spa_adj_matrix, num_objects,
args.nongt_dim, args.imp_pos_emb_dim, args.spa_label_num,
args.sem_label_num, device)
q_type, pred, att = model(v, norm_bb, q, pos_emb, sem_adj_matrix,
spa_adj_matrix, target)
batch_score = compute_score_with_logits(pred, target, device).sum()
score += batch_score
upper_bound += (target.max(1)[0]).sum()
num_data += pred.size(0)
if att is not None and 0 < model.module.glimpse\
and entropy is not None:
entropy += calc_entropy(att.data)[:model.module.glimpse]
pbar.update(1)
score = score / len(dataloader.dataset)
upper_bound = upper_bound / len(dataloader.dataset)
if entropy is not None:
entropy = entropy / len(dataloader.dataset)
model.train()
return score, upper_bound, entropy
def train(model, train_loader, eval_loader, args, device=torch.device("cuda")):
N = len(train_loader.dataset)
lr_default = args.base_lr
num_epochs = args.epochs
lr_decay_epochs = range(args.lr_decay_start, num_epochs,
args.lr_decay_step)
gradual_warmup_steps = [
0.5 * lr_default, 1.0 * lr_default, 1.5 * lr_default, 2.0 * lr_default
]
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad,
model.parameters()),
lr=lr_default,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=args.weight_decay)
logger = utils.Logger(os.path.join(args.output, 'log.txt'))
best_eval_score = 0
utils.print_model(model, logger)
logger.write('optim: adamax lr=%.4f, decay_step=%d, decay_rate=%.2f,' %
(lr_default, args.lr_decay_step, args.lr_decay_rate) +
'grad_clip=%.2f' % args.grad_clip)
logger.write('LR decay epochs: ' +
','.join([str(i) for i in lr_decay_epochs]))
last_eval_score, eval_score = 0, 0
relation_type = train_loader.dataset.relation_type
for epoch in range(0, num_epochs):
pbar = tqdm(total=len(train_loader))
total_norm, count_norm = 0, 0
total_loss, train_score = 0, 0
count, average_loss, att_entropy = 0, 0, 0
t = time.time()
if epoch < len(gradual_warmup_steps):
for i in range(len(optim.param_groups)):
optim.param_groups[i]['lr'] = gradual_warmup_steps[epoch]
logger.write('gradual warmup lr: %.4f' %
optim.param_groups[-1]['lr'])
elif (epoch in lr_decay_epochs
or eval_score < last_eval_score and args.lr_decay_based_on_val):
for i in range(len(optim.param_groups)):
optim.param_groups[i]['lr'] *= args.lr_decay_rate
logger.write('decreased lr: %.4f' % optim.param_groups[-1]['lr'])
else:
logger.write('lr: %.4f' % optim.param_groups[-1]['lr'])
last_eval_score = eval_score
mini_batch_count = 0
batch_multiplier = args.grad_accu_steps
for i, (v, norm_bb, q, q_target, target, _, _, bb, spa_adj_matrix,
sem_adj_matrix) in enumerate(train_loader):
batch_size = v.size(0)
num_objects = v.size(1)
if mini_batch_count == 0:
optim.step()
optim.zero_grad()
mini_batch_count = batch_multiplier
### Debugging ###
# with autograd.detect_anomaly():
v = Variable(v).to(device)
norm_bb = Variable(norm_bb).to(device)
q = Variable(q).to(device)
q_target = Variable(q_target).to(device)
target = Variable(target).to(device)
pos_emb, sem_adj_matrix, spa_adj_matrix = prepare_graph_variables(
relation_type, bb, sem_adj_matrix, spa_adj_matrix, num_objects,
args.nongt_dim, args.imp_pos_emb_dim, args.spa_label_num,
args.sem_label_num, device)
q_type, pred, att = model(v, norm_bb, q, pos_emb, sem_adj_matrix,
spa_adj_matrix, target)
loss = instance_bce_with_logits(
pred, target) + instance_bce_with_logits(q_type, q_target)
loss /= batch_multiplier
loss.backward()
mini_batch_count -= 1
total_norm += nn.utils.clip_grad_norm_(model.parameters(),
args.grad_clip)
count_norm += 1
batch_score = compute_score_with_logits(pred, target, device).sum()
total_loss += loss.data.item() * batch_multiplier * v.size(0)
train_score += batch_score
pbar.update(1)
if args.log_interval > 0:
average_loss += loss.data.item() * batch_multiplier
if model.module.fusion == "ban":
current_att_entropy = torch.sum(calc_entropy(att.data))
att_entropy += current_att_entropy / batch_size / att.size(
1)
count += 1
if i % args.log_interval == 0:
att_entropy /= count
average_loss /= count
print(
"step {} / {} (epoch {}), ave_loss {:.3f},".format(
i, len(train_loader), epoch, average_loss),
"att_entropy {:.3f}".format(att_entropy))
average_loss = 0
count = 0
att_entropy = 0
total_loss /= N
train_score = 100 * train_score / N
if eval_loader is not None:
eval_score, bound, entropy = evaluate(model, eval_loader, device,
args)
logger.write('epoch %d, time: %.2f' % (epoch, time.time() - t))
logger.write('\ttrain_loss: %.2f, norm: %.4f, score: %.2f' %
(total_loss, total_norm / count_norm, train_score))
if eval_loader is not None:
logger.write('\teval score: %.2f (%.2f)' %
(100 * eval_score, 100 * bound))
if entropy is not None:
info = ''
for i in range(entropy.size(0)):
info = info + ' %.2f' % entropy[i]
logger.write('\tentropy: ' + info)
if (eval_loader is not None)\
or (eval_loader is None and epoch >= args.saving_epoch):
logger.write("saving current model weights to folder")
model_path = os.path.join(args.output, 'model_%d.pth' % epoch)
opt = optim if args.save_optim else None
utils.save_model(model_path, model, epoch, opt)
def evaluate(model, dataloader, model_hps, args, device):
model.eval()
label2ans = dataloader.dataset.label2ans
num_answers = len(label2ans)
relation_type = dataloader.dataset.relation_type
N = len(dataloader.dataset)
results = []
score = 0
pbar = tqdm(total=len(dataloader))
if args.save_logits:
idx = 0
pred_logits = np.zeros((N, num_answers))
gt_logits = np.zeros((N, num_answers))
for i, (v, norm_bb, q, target, qid, _, bb,
spa_adj_matrix, sem_adj_matrix) in enumerate(dataloader):
batch_size = v.size(0)
num_objects = v.size(1)
v = Variable(v).to(device)
norm_bb = Variable(norm_bb).to(device)
q = Variable(q).to(device)
pos_emb, sem_adj_matrix, spa_adj_matrix = prepare_graph_variables(
relation_type, bb, sem_adj_matrix, spa_adj_matrix, num_objects,
model_hps.nongt_dim, model_hps.imp_pos_emb_dim,
model_hps.spa_label_num, model_hps.sem_label_num, device)
pred, att = model(v, norm_bb, q, pos_emb, sem_adj_matrix,
spa_adj_matrix, None)
# Check if target is a placeholder or actual targets
if target.size(-1) == num_answers:
target = Variable(target).to(device)
batch_score = compute_score_with_logits(
pred, target, device).sum()
score += batch_score
if args.save_logits:
gt_logits[idx:batch_size+idx, :] = target.cpu().numpy()
if args.save_logits:
pred_logits[idx:batch_size+idx, :] = pred.cpu().numpy()
idx += batch_size
if args.save_answers:
qid = qid.cpu()
pred = pred.cpu()
current_results = make_json(pred, qid, dataloader)
results.extend(current_results)
pbar.update(1)
score = score / N
results_folder = f"{args.output_folder}/results"
if args.save_logits:
utils.create_dir(results_folder)
save_to = f"{results_folder}/logits_{args.dataset}" +\
f"_{args.split}.npy"
np.save(save_to, pred_logits)
utils.create_dir("./gt_logits")
save_to = f"./gt_logits/{args.dataset}_{args.split}_gt.npy"
if not os.path.exists(save_to):
np.save(save_to, gt_logits)
if args.save_answers:
utils.create_dir(results_folder)
save_to = f"{results_folder}/{args.dataset}_" +\
f"{args.split}.json"
json.dump(results, open(save_to, "w"))
return score
