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 __init__(self, args):
if args.dataset == 'CUB':
self.VAL_SETTING = [(5, 1), (5, 5), (5, 20)]
else:
self.VAL_SETTING = [(5, 1), (5, 5), (5, 20), (5, 50)]
if args.eval_dataset == 'CUB':
self.TEST_SETTINGS = [(5, 1), (5, 5), (5, 20)]
else:
self.TEST_SETTINGS = [(5, 1), (5, 5), (5, 20), (5, 50)]
self.args = args
# ensure_path(
# self.args.save_path,
# scripts_to_save=['model/models', 'model/networks', __file__],
# )
self.logger = Logger(args, osp.join(args.save_path))
self.train_step = 0
self.train_epoch = 0
self.max_steps = args.episodes_per_epoch * args.max_epoch
self.dt, self.ft = Averager(), Averager()
self.bt, self.ot = Averager(), Averager()
self.timer = Timer()
# train statistics
self.trlog = {}
self.trlog['max_acc'] = 0.0
self.trlog['max_acc_epoch'] = 0
self.trlog['max_acc_interval'] = 0.0
def __init__(self, args):
self.args = args
# ensure_path(
# self.args.save_path,
# scripts_to_save=['model/models', 'model/networks', __file__],
# )
self.logger = Logger(args, osp.join(args.save_path))
self.train_step = 0
self.train_epoch = 0
self.max_steps = args.episodes_per_epoch * args.max_epoch
self.dt, self.ft = Averager(), Averager()
self.bt, self.ot = Averager(), Averager()
self.timer = Timer()
# train statistics
self.trlog = {}
self.trlog['max_acc'] = 0.0
self.trlog['max_acc_epoch'] = 0
self.trlog['max_acc_interval'] = 0.0
# For tst
if args.tst_free:
self.trlog['max_tst_criterion'] = 0.0
self.trlog['max_tst_criterion_interval'] = 0.
self.trlog['max_tst_criterion_epoch'] = 0
self.trlog['tst_criterion'] = args.tst_criterion
def __init__(self, args):
self.args = args
self.logger = Logger(args, osp.join(args.save_path))
self.train_step = 0
self.train_epoch = 0
self.max_steps = args.episodes_per_epoch * args.max_epoch
self.dt, self.ft = Averager(), Averager()
self.bt, self.ot = Averager(), Averager()
self.timer = Timer()
# train statistics
self.trlog = {}
self.trlog['max_auc'] = 0.0
self.trlog['max_auc_epoch'] = 0
self.trlog['max_auc_interval'] = 0.0
def __init__(self, args):
self.args = args
ensure_path(
self.args.save_path,
scripts_to_save=['model/models', 'model/networks', __file__],
)
self.logger = Logger(args, osp.join(args.save_path))
self.train_step = 0
self.train_epoch = 0
self.dt, self.ft = Averager(), Averager()
self.bt, self.ot = Averager(), Averager()
self.timer = Timer()
# train statistics
self.trlog = {}
self.trlog['max_acc'] = 0.0
self.trlog['max_acc_epoch'] = 0
self.trlog['max_acc_interval'] = 0.0
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'))
class Trainer(object, metaclass=abc.ABCMeta):
def __init__(self, args):
self.args = args
# ensure_path(
# self.args.save_path,
# scripts_to_save=['model/models', 'model/networks', __file__],
# )
self.logger = Logger(args, osp.join(args.save_path))
self.train_step = 0
self.train_epoch = 0
self.max_steps = args.episodes_per_epoch * args.max_epoch
self.dt, self.ft = Averager(), Averager()
self.bt, self.ot = Averager(), Averager()
self.timer = Timer()
# train statistics
self.trlog = {}
self.trlog['max_acc'] = 0.0
self.trlog['max_acc_epoch'] = 0
self.trlog['max_acc_interval'] = 0.0
@abc.abstractmethod
def train(self):
pass
@abc.abstractmethod
def evaluate(self, data_loader):
pass
@abc.abstractmethod
def evaluate_test(self, data_loader):
pass
@abc.abstractmethod
def final_record(self):
pass
def try_evaluate(self, epoch):
args = self.args
if self.train_epoch % args.eval_interval == 0:
vl, va, vap = self.evaluate(self.val_loader)
self.logger.add_scalar('val_loss', float(vl), self.train_epoch)
self.logger.add_scalar('val_acc', float(va), self.train_epoch)
print('epoch {}, val, loss={:.4f} acc={:.4f}+{:.4f}'.format(
epoch, vl, va, vap))
if va >= self.trlog['max_acc']:
self.trlog['max_acc'] = va
self.trlog['max_acc_interval'] = vap
self.trlog['max_acc_epoch'] = self.train_epoch
self.save_model('max_acc')
def try_logging(self, tl1, tl2, ta, tg=None):
args = self.args
if self.train_step % args.log_interval == 0:
print(
'epoch {}, train {:06g}/{:06g}, total loss={:.4f}, loss={:.4f} acc={:.4f}, lr={:.4g}'
.format(self.train_epoch, self.train_step, self.max_steps,
tl1.item(), tl2.item(), ta.item(),
self.optimizer.param_groups[0]['lr']))
self.logger.add_scalar('train_total_loss', tl1.item(),
self.train_step)
self.logger.add_scalar('train_loss', tl2.item(), self.train_step)
self.logger.add_scalar('train_acc', ta.item(), self.train_step)
if tg is not None:
self.logger.add_scalar('grad_norm', tg.item(), self.train_step)
print('data_timer: {:.2f} sec, ' \
'forward_timer: {:.2f} sec,' \
'backward_timer: {:.2f} sec, ' \
'optim_timer: {:.2f} sec'.format(
self.dt.item(), self.ft.item(),
self.bt.item(), self.ot.item())
)
self.logger.dump()
def save_model(self, name):
torch.save(dict(params=self.model.state_dict()),
osp.join(self.args.save_path, name + '.pth'))
def __str__(self):
return "{}({})".format(self.__class__.__name__,
self.model.__class__.__name__)
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')
class Trainer(object, metaclass=abc.ABCMeta):
def __init__(self, args):
if args.dataset == 'CUB':
self.VAL_SETTING = [(5, 1), (5, 5), (5, 20)]
else:
self.VAL_SETTING = [(5, 1), (5, 5), (5, 20), (5, 50)]
if args.eval_dataset == 'CUB':
self.TEST_SETTINGS = [(5, 1), (5, 5), (5, 20)]
else:
self.TEST_SETTINGS = [(5, 1), (5, 5), (5, 20), (5, 50)]
self.args = args
# ensure_path(
# self.args.save_path,
# scripts_to_save=['model/models', 'model/networks', __file__],
# )
self.logger = Logger(args, osp.join(args.save_path))
self.train_step = 0
self.train_epoch = 0
self.max_steps = args.episodes_per_epoch * args.max_epoch
self.dt, self.ft = Averager(), Averager()
self.bt, self.ot = Averager(), Averager()
self.timer = Timer()
# train statistics
self.trlog = {}
self.trlog['max_acc'] = 0.0
self.trlog['max_acc_epoch'] = 0
self.trlog['max_acc_interval'] = 0.0
@abc.abstractmethod
def train(self):
pass
@abc.abstractmethod
def evaluate(self, data_loader):
pass
@abc.abstractmethod
def evaluate_test(self, data_loader):
pass
@abc.abstractmethod
def final_record(self):
pass
def try_evaluate(self, epoch):
args = self.args
if self.train_epoch % args.eval_interval == 0:
if args.eval_all:
for i, (args.eval_way, args.eval_shot) in enumerate(self.VAL_SETTING):
if i == 0:
vl, va, vap = self.eval_process(args, epoch)
else:
self.eval_process(args, epoch)
else:
vl, va, vap = self.eval_process(args, epoch)
if va >= self.trlog['max_acc']:
self.trlog['max_acc'] = va
self.trlog['max_acc_interval'] = vap
self.trlog['max_acc_epoch'] = self.train_epoch
self.save_model('max_acc')
print('best epoch {}, best val acc={:.4f} + {:.4f}'.format(
self.trlog['max_acc_epoch'],
self.trlog['max_acc'],
self.trlog['max_acc_interval']))
def eval_process(self, args, epoch):
valset = self.valset
if args.model_class in ['QsimProtoNet', 'QsimMatchNet']:
val_sampler = NegativeSampler(args, valset.label,
args.num_eval_episodes,
args.eval_way, args.eval_shot + args.eval_query)
else:
val_sampler = CategoriesSampler(valset.label,
args.num_eval_episodes,
args.eval_way, args.eval_shot + args.eval_query)
val_loader = DataLoader(dataset=valset,
batch_sampler=val_sampler,
num_workers=args.num_workers,
pin_memory=True)
vl, va, vap = self.evaluate(val_loader)
self.logger.add_scalar('%dw%ds_val_loss' % (args.eval_way, args.eval_shot), float(vl),
self.train_epoch)
self.logger.add_scalar('%dw%ds_val_acc' % (args.eval_way, args.eval_shot), float(va),
self.train_epoch)
print('epoch {},{} way {} shot, val, loss={:.4f} acc={:.4f}+{:.4f}'.format(epoch, args.eval_way,
args.eval_shot, vl, va,
vap))
return vl, va, vap
def try_logging(self, tl1, tl2, ta, tg=None):
args = self.args
if self.train_step % args.log_interval == 0:
print('epoch {}, train {:06g}/{:06g}, total loss={:.4f}, loss={:.4f} acc={:.4f}, lr={:.4g}'
.format(self.train_epoch,
self.train_step,
self.max_steps,
tl1.item(), tl2.item(), ta.item(),
self.optimizer.param_groups[0]['lr']))
self.logger.add_scalar('train_total_loss', tl1.item(), self.train_step)
self.logger.add_scalar('train_loss', tl2.item(), self.train_step)
self.logger.add_scalar('train_acc', ta.item(), self.train_step)
if tg is not None:
self.logger.add_scalar('grad_norm', tg.item(), self.train_step)
print('data_timer: {:.2f} sec, ' \
'forward_timer: {:.2f} sec,' \
'backward_timer: {:.2f} sec, ' \
'optim_timer: {:.2f} sec'.format(
self.dt.item(), self.ft.item(),
self.bt.item(), self.ot.item())
)
self.logger.dump()
def save_model(self, name):
torch.save(
dict(params=self.model.state_dict()),
osp.join(self.args.save_path, name + '.pth')
)
def __str__(self):
return "{}({})".format(
self.__class__.__name__,
self.model.__class__.__name__
)
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
trlog['max_acc_sim'] = 0.0
trlog['max_acc_sim_epoch'] = 0
initial_lr = args.lr
global_count = 0
timer = Timer()
writer = SummaryWriter(logdir=args.save_path)
for epoch in range(init_epoch, args.max_epoch + 1):
# refine the step-size
if epoch in args.schedule:
initial_lr *= args.gamma
for param_group in optimizer.param_groups:
param_group['lr'] = initial_lr
model.train()
tl = Averager()
ta = Averager()
for i, batch in enumerate(train_loader, 1):
global_count = global_count + 1
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)
pin_memory=True)
# test_set = Dataset('test', args.unsupervised, args)
test_set = get_dataset(n, 'test', args.unsupervised, args)
sampler = CategoriesSampler(test_set.label, args.num_test_episodes,
args.way, args.shot + args.query)
loader = DataLoader(dataset=test_set,
batch_sampler=sampler,
num_workers=8,
pin_memory=True)
shot_label = torch.arange(min(args.way, valset.num_class)).repeat(
args.shot).numpy()
query_label = torch.arange(min(args.way, valset.num_class)).repeat(
args.query).numpy()
val_acc_record = np.zeros((500, len(c_list)))
ave_acc = Averager()
with torch.no_grad():
for i, batch in tqdm(enumerate(val_loader, 1),
total=len(val_loader),
desc='val eval'):
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
data_emb = model(data, is_emb=True)
if args.centralize:
data_emb = data_emb - class_mean
if args.normalize:
data_emb = F.normalize(data_emb, dim=1, p=2)
class Trainer(object, metaclass=abc.ABCMeta):
def __init__(self, args):
self.args = args
# ensure_path(
# self.args.save_path,
# scripts_to_save=['model/models', 'model/networks', __file__],
# )
self.logger = Logger(args, osp.join(args.save_path))
self.train_step = 0
self.train_epoch = 0
self.max_steps = args.episodes_per_epoch * args.max_epoch
self.dt, self.ft = Averager(), Averager()
self.bt, self.ot = Averager(), Averager()
self.timer = Timer()
# train statistics
self.trlog = {}
self.trlog['max_acc'] = 0.0
self.trlog['max_acc_epoch'] = 0
self.trlog['max_acc_interval'] = 0.0
# For tst
if args.tst_free:
self.trlog['max_tst_criterion'] = 0.0
self.trlog['max_tst_criterion_interval'] = 0.
self.trlog['max_tst_criterion_epoch'] = 0
self.trlog['tst_criterion'] = args.tst_criterion
@abc.abstractmethod
def train(self):
pass
@abc.abstractmethod
def evaluate(self, data_loader):
pass
@abc.abstractmethod
def evaluate_test(self, data_loader):
pass
@abc.abstractmethod
def final_record(self):
pass
def print_metric_summaries(self, metric_summaries, prefix='\t'):
for key, (mean, std) in metric_summaries.items():
print('{}{}: {:.4f} +/- {:.4f}'.format(prefix, key, mean, std))
def log_metric_summaries(self, metric_summaries, epoch, prefix=''):
for key, (mean, std) in metric_summaries.items():
self.logger.add_scalar('{}{}'.format(prefix, key), mean, epoch)
def try_evaluate(self, epoch):
args = self.args
if self.train_epoch % args.eval_interval == 0:
if not args.tst_free:
vl, va, vap = self.evaluate(self.val_loader)
self.logger.add_scalar('val_loss', float(vl), self.train_epoch)
self.logger.add_scalar('val_acc', float(va), self.train_epoch)
print('epoch {}, val, loss={:.4f} acc={:.4f}+{:.4f}'.format(
epoch, vl, va, vap))
else:
vl, va, vap, metrics = self.evaluate(self.val_loader)
self.logger.add_scalar('val_loss', float(vl), self.train_epoch)
self.logger.add_scalar('val_acc', float(va), self.train_epoch)
print('epoch {}, val, loss={:.4f} acc={:.4f}+{:.4f}'.format(
epoch, vl, va, vap))
self.print_metric_summaries(metrics, prefix='\tval_')
self.log_metric_summaries(metrics, epoch=epoch, prefix='val_')
if va >= self.trlog['max_acc']:
self.trlog['max_acc'] = va
self.trlog['max_acc_interval'] = vap
self.trlog['max_acc_epoch'] = self.train_epoch
self.save_model('max_acc')
# Probably a different criterion for TST -> optimize here.
if args.tst_free and args.tst_criterion:
assert args.tst_criterion in metrics, 'Criterion {} not found in {}'.format(
args.tst_criterion, metrics.keys())
criterion, criterion_interval = metrics[args.tst_criterion]
if criterion >= self.trlog['max_tst_criterion']:
self.trlog['max_tst_criterion'] = criterion
self.trlog[
'max_tst_criterion_interval'] = criterion_interval
self.trlog['max_tst_criterion_epoch'] = self.train_epoch
self.save_model('max_tst_criterion')
print(
'Found new best model at Epoch {} : Validation {} = {:.4f} +/- {:4f}'
.format(self.train_epoch, args.tst_criterion,
criterion, criterion_interval))
def try_logging(self, tl1, tl2, ta, tg=None):
args = self.args
if self.train_step % args.log_interval == 0:
print(
'epoch {}, train {:06g}/{:06g}, total loss={:.4f}, loss={:.4f} acc={:.4f}, lr={:.4g}'
.format(self.train_epoch, self.train_step, self.max_steps,
tl1.item(), tl2.item(), ta.item(),
self.optimizer.param_groups[0]['lr']))
self.logger.add_scalar('train_total_loss', tl1.item(),
self.train_step)
self.logger.add_scalar('train_loss', tl2.item(), self.train_step)
self.logger.add_scalar('train_acc', ta.item(), self.train_step)
if tg is not None:
self.logger.add_scalar('grad_norm', tg.item(), self.train_step)
print('data_timer: {:.2f} sec, ' \
'forward_timer: {:.2f} sec,' \
'backward_timer: {:.2f} sec, ' \
'optim_timer: {:.2f} sec'.format(
self.dt.item(), self.ft.item(),
self.bt.item(), self.ot.item())
)
self.logger.dump()
def save_model(self, name):
torch.save(dict(params=self.model.state_dict()),
osp.join(self.args.save_path, name + '.pth'))
def __str__(self):
return "{}({})".format(self.__class__.__name__,
self.model.__class__.__name__)
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')
