def train(args, model, train_loader, criterion, optimizer, lr_schedule, trlog):
model.train()
tl = Averager()
ta = Averager()
global global_count, writer
for i, (data, label) in enumerate(train_loader, 1):
global_count = global_count + 1
if torch.cuda.is_available():
data, label = data.cuda(), label.long().cuda()
elif not args.mixup:
label = label.long()
optimizer.zero_grad()
logits = model(data)
loss = criterion(logits, label)
loss.backward()
optimizer.step()
lr_schedule.step()
writer.add_scalar('data/loss', float(loss), global_count)
tl.add(loss.item())
if not args.mixup:
acc = count_acc(logits, label)
ta.add(acc)
writer.add_scalar('data/acc', float(acc), global_count)
if (i - 1) % 100 == 0 or i == len(train_loader):
if not args.mixup:
print(
'epoch {}, train {}/{}, lr={:.5f}, loss={:.4f} acc={:.4f}'.
format(epoch, i, len(train_loader),
optimizer.param_groups[0]['lr'], loss.item(), acc))
else:
print('epoch {}, train {}/{}, lr={:.5f}, loss={:.4f}'.format(
epoch, i, len(train_loader),
optimizer.param_groups[0]['lr'], loss.item()))
if trlog is not None:
tl = tl.item()
trlog['train_loss'].append(tl)
if not args.mixup:
ta = ta.item()
trlog['train_acc'].append(ta)
else:
trlog['train_acc'].append(0)
return model, trlog
else:
return model
def train_tst(self):
args = self.args
self.model.train()
if self.args.fix_BN:
self.model.encoder.eval()
# Clear evaluation file
with open(osp.join(self.args.save_path, 'eval.jl'), 'w') as fp:
pass
# start FSL training
label, label_aux = self.prepare_label()
for epoch in range(1, args.max_epoch + 1):
self.train_epoch += 1
self.model.train()
if self.args.fix_BN:
self.model.encoder.eval()
tl1 = Averager()
tl2 = Averager()
ta = Averager()
start_tm = time.time()
for batch in self.train_loader:
self.train_step += 1
if torch.cuda.is_available():
data, gt_label = [_.cuda() for _ in batch]
else:
data, gt_label = batch[0], batch[1]
data_tm = time.time()
self.dt.add(data_tm - start_tm)
# get saved centers
logits, reg_logits = self.para_model(data)
if reg_logits is not None:
loss = F.cross_entropy(logits, label)
total_loss = loss + args.balance * F.cross_entropy(reg_logits, label_aux)
else:
loss = F.cross_entropy(logits, label)
total_loss = F.cross_entropy(logits, label)
tl2.add(loss)
forward_tm = time.time()
self.ft.add(forward_tm - data_tm)
acc = count_acc(logits, label)
tl1.add(total_loss.item())
ta.add(acc)
self.optimizer.zero_grad()
total_loss.backward()
backward_tm = time.time()
self.bt.add(backward_tm - forward_tm)
self.optimizer.step()
optimizer_tm = time.time()
self.ot.add(optimizer_tm - backward_tm)
# refresh start_tm
start_tm = time.time()
if args.debug_fast:
print('Debug fast, breaking training after 1 mini-batch')
break
self.lr_scheduler.step()
self.try_evaluate_tst(epoch)
print('ETA:{}/{}'.format(
self.timer.measure(),
self.timer.measure(self.train_epoch / args.max_epoch))
)
torch.save(self.trlog, osp.join(args.save_path, 'trlog'))
def train(self):
args = self.args
# start GFSL training
for epoch in range(1, args.max_epoch + 1):
self.train_epoch += 1
self.model.train()
tl1 = Averager()
tl2 = Averager()
ta = Averager()
start_tm = time.time()
for _, batch in enumerate(
zip(self.train_fsl_loader, self.train_gfsl_loader)):
self.train_step += 1
if torch.cuda.is_available():
support_data, support_label = batch[0][0].cuda(
), batch[0][1].cuda()
query_data, query_label = batch[1][0].cuda(
), batch[1][1].cuda()
else:
support_data, support_label = batch[0][0], batch[0][1]
query_data, query_label = batch[1][0], batch[1][1]
data_tm = time.time()
self.dt.add(data_tm - start_tm)
logits = self.model(support_data, query_data, support_label)
loss = F.cross_entropy(
logits,
query_label.view(-1, 1).repeat(1, args.num_tasks).view(-1))
tl2.add(loss.item())
forward_tm = time.time()
self.ft.add(forward_tm - data_tm)
acc = count_acc(
logits,
query_label.view(-1, 1).repeat(1, args.num_tasks).view(-1))
tl1.add(loss.item())
ta.add(acc)
self.optimizer.zero_grad()
loss.backward()
backward_tm = time.time()
self.bt.add(backward_tm - forward_tm)
self.optimizer.step()
self.lr_scheduler.step()
optimizer_tm = time.time()
self.ot.add(optimizer_tm - backward_tm)
self.try_logging(tl1, tl2, ta)
# refresh start_tm
start_tm = time.time()
del logits, loss
torch.cuda.empty_cache()
self.try_evaluate(epoch)
print('ETA:{}/{}'.format(
self.timer.measure(),
self.timer.measure(self.train_epoch / args.max_epoch)))
torch.save(self.trlog, osp.join(args.save_path, 'trlog'))
self.save_model('epoch-last')
def validate(args, model, val_loader, epoch, trlog=None):
model.eval()
global writer
vl_dist, va_dist, vl_sim, va_sim = Averager(), Averager(), Averager(
), Averager()
if trlog is not None:
print('[Dist] best epoch {}, current best val acc={:.4f}'.format(
trlog['max_acc_dist_epoch'], trlog['max_acc_dist']))
print('[Sim] best epoch {}, current best val acc={:.4f}'.format(
trlog['max_acc_sim_epoch'], trlog['max_acc_sim']))
# test performance with Few-Shot
label = torch.arange(args.num_val_class).repeat(args.query).long()
if torch.cuda.is_available():
label = label.cuda()
with torch.no_grad():
for i, batch in tqdm(enumerate(val_loader, 1), total=len(val_loader)):
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data, _ = batch
data_shot, data_query = data[:args.num_val_class], data[
args.num_val_class:] # 16-way test
logits_dist, logits_sim = model.forward_proto(
data_shot, data_query, args.num_val_class)
loss_dist = F.cross_entropy(logits_dist, label)
acc_dist = count_acc(logits_dist, label)
loss_sim = F.cross_entropy(logits_sim, label)
acc_sim = count_acc(logits_sim, label)
vl_dist.add(loss_dist.item())
va_dist.add(acc_dist)
vl_sim.add(loss_sim.item())
va_sim.add(acc_sim)
vl_dist = vl_dist.item()
va_dist = va_dist.item()
vl_sim = vl_sim.item()
va_sim = va_sim.item()
print(
'epoch {}, val, loss_dist={:.4f} acc_dist={:.4f} loss_sim={:.4f} acc_sim={:.4f}'
.format(epoch, vl_dist, va_dist, vl_sim, va_sim))
if trlog is not None:
writer.add_scalar('data/val_loss_dist', float(vl_dist), epoch)
writer.add_scalar('data/val_acc_dist', float(va_dist), epoch)
writer.add_scalar('data/val_loss_sim', float(vl_sim), epoch)
writer.add_scalar('data/val_acc_sim', float(va_sim), epoch)
if va_dist > trlog['max_acc_dist']:
trlog['max_acc_dist'] = va_dist
trlog['max_acc_dist_epoch'] = epoch
save_model('max_acc_dist', model, args)
save_checkpoint(True)
if va_sim > trlog['max_acc_sim']:
trlog['max_acc_sim'] = va_sim
trlog['max_acc_sim_epoch'] = epoch
save_model('max_acc_sim', model, args)
save_checkpoint(True)
trlog['val_loss_dist'].append(vl_dist)
trlog['val_acc_dist'].append(va_dist)
trlog['val_loss_sim'].append(vl_sim)
trlog['val_acc_sim'].append(va_sim)
return trlog
if torch.cuda.is_available():
data, label = [_.cuda() for _ in batch]
label = label.type(torch.cuda.LongTensor)
else:
data, label = batch
label = label.type(torch.LongTensor)
logits = model(data)
loss = criterion(logits, label)
acc = count_acc(logits, label)
writer.add_scalar('data/loss', float(loss), global_count)
writer.add_scalar('data/acc', float(acc), global_count)
if (i - 1) % 100 == 0:
print('epoch {}, train {}/{}, loss={:.4f} acc={:.4f}'.format(
epoch, i, len(train_loader), loss.item(), acc))
tl.add(loss.item())
ta.add(acc)
optimizer.zero_grad()
loss.backward()
optimizer.step()
tl = tl.item()
ta = ta.item()
# do not do validation in first 500 epoches
if epoch > 100 or (epoch - 1) % 5 == 0:
model.eval()
vl_dist = Averager()
va_dist = Averager()
vl_sim = Averager()
def train(self):
args = self.args
self.model.train()
if self.args.fix_BN:
self.model.encoder.eval()
# start FSL training
label, label_aux = self.prepare_label()
for epoch in range(1, args.max_epoch + 1):
self.train_epoch += 1
self.model.train()
if self.args.fix_BN:
self.model.encoder.eval()
tl1 = Averager()
tl2 = Averager()
ta = Averager()
start_tm = time.time()
for batch in tqdm(self.train_loader):
data, gt_label = [_.cuda() for _ in batch]
data_tm = time.time()
self.dt.add(data_tm - start_tm)
# get saved centers
logits, reg_logits = self.para_model(data)
logits = logits.view(-1, args.way)
oh_query = torch.nn.functional.one_hot(label, args.way)
sims = logits
temp = (sims * oh_query).sum(-1)
e_sim_p = temp - self.model.margin
e_sim_p_pos = F.relu(e_sim_p)
e_sim_p_neg = F.relu(-e_sim_p)
l_open_margin = e_sim_p_pos.mean(-1)
l_open = e_sim_p_neg.mean(-1)
if reg_logits is not None:
loss = F.cross_entropy(logits, label)
total_loss = loss + args.balance * F.cross_entropy(reg_logits, label_aux)
else:
loss = F.cross_entropy(logits, label)
total_loss = total_loss + args.open_balance * l_open
tl2.add(loss)
forward_tm = time.time()
self.ft.add(forward_tm - data_tm)
acc = count_acc(logits, label)
tl1.add(total_loss.item())
ta.add(acc)
self.optimizer.zero_grad()
total_loss.backward(retain_graph=True)
self.optimizer_margin.zero_grad()
l_open_margin.backward()
self.optimizer.step()
self.optimizer_margin.step()
backward_tm = time.time()
self.bt.add(backward_tm - forward_tm)
optimizer_tm = time.time()
self.ot.add(optimizer_tm - backward_tm)
# refresh start_tm
start_tm = time.time()
print('lr: {:.4f} Total_loss: {:.4f} ce_loss {:.4f} l_open: {:4f} R: {:4f}\n'.format(self.optimizer_margin.param_groups[0]['lr'],\
total_loss.item(), loss.item(), l_open.item(), self.model.margin.item()))
self.lr_scheduler.step()
self.lr_scheduler_margin.step()
self.try_evaluate(epoch)
print('ETA:{}/{}'.format(
self.timer.measure(),
self.timer.measure(self.train_epoch / args.max_epoch))
)
torch.save(self.trlog, osp.join(args.save_path, 'trlog'))
self.save_model('epoch-last')