vl.add(loss.item())
va.add(acc)
proto = None
logits = None
loss = None
vl = vl.item()
va = va.item()
print('epoch {}, val, loss={:.4f} acc={:.4f}'.format(epoch, vl, va))
if va > trlog['max_acc']:
trlog['max_acc'] = va
save_model('max-acc')
trlog['train_loss'].append(tl)
trlog['train_acc'].append(ta)
trlog['val_loss'].append(vl)
trlog['val_acc'].append(va)
torch.save(trlog, osp.join(save_path, 'trlog'))
save_model('epoch-last')
if epoch % save_epoch == 0:
save_model('epoch-{}'.format(epoch))
print('ETA:{}/{}'.format(timer.measure(),
timer.measure(epoch / max_epoch)))
if va > trlog["max_acc"]:
trlog["max_acc"] = va
trlog["max_acc_epoch"] = epoch
save_model("max_acc")
trlog["train_loss"].append(tl)
trlog["train_acc"].append(ta)
trlog["val_loss"].append(vl)
trlog["val_acc"].append(va)
torch.save(trlog, osp.join(args.save_path, "trlog"))
save_model("epoch-last")
print(
"ETA:{}/{}".format(timer.measure(), timer.measure(epoch / args.max_epoch))
)
writer.close()
# Test Phase
trlog = torch.load(osp.join(args.save_path, "trlog"))
test_set = Dataset("test", args)
sampler = CategoriesSampler(
test_set.label, 10000, args.validation_way, args.shot + args.query
)
loader = DataLoader(test_set, batch_sampler=sampler, num_workers=8, pin_memory=True)
test_acc_record = np.zeros((10000,))
model.load_state_dict(
torch.load(osp.join(args.save_path, "max_acc" + ".pth"))["params"]
)
val_accuracies.append(acc.item())
val_losses.append(loss.item())
val_acc_avg = np.mean(np.array(val_accuracies))
val_acc_ci95 = 1.96 * np.std(np.array(val_accuracies)) / np.sqrt(
opt.val_episode)
val_loss_avg = np.mean(np.array(val_losses))
if val_acc_avg > max_val_acc:
max_val_acc = val_acc_avg
torch.save({'embedding': embedding_net.state_dict(), 'head': cls_head.state_dict()},\
os.path.join(opt.save_path, 'best_model.pth'))
log(log_file_path, 'Validation Epoch: {}\t\t\tLoss: {:.4f}\tAccuracy: {:.2f} ± {:.2f} % (Best)'\
.format(epoch, val_loss_avg, val_acc_avg, val_acc_ci95))
else:
log(log_file_path, 'Validation Epoch: {}\t\t\tLoss: {:.4f}\tAccuracy: {:.2f} ± {:.2f} %'\
.format(epoch, val_loss_avg, val_acc_avg, val_acc_ci95))
torch.save({'embedding': embedding_net.state_dict(), 'head': cls_head.state_dict()}\
, os.path.join(opt.save_path, 'last_epoch.pth'))
if epoch % opt.save_epoch == 0:
torch.save({'embedding': embedding_net.state_dict(), 'head': cls_head.state_dict()}\
, os.path.join(opt.save_path, 'epoch_{}.pth'.format(epoch)))
log(
log_file_path, 'Elapsed Time: {}/{}\n'.format(
timer.measure(), timer.measure(epoch / float(opt.num_epoch))))
loss = x_entropy(logit_query.reshape(-1, opt.test_way), labels_query.reshape(-1))
acc = count_accuracy(logit_query.reshape(-1, opt.test_way), labels_query.reshape(-1))
val_accuracies.append(acc.item())
val_losses.append(loss.item())
val_acc_avg = np.mean(np.array(val_accuracies))
val_acc_ci95 = 1.96 * np.std(np.array(val_accuracies)) / np.sqrt(opt.val_episode)
val_loss_avg = np.mean(np.array(val_losses))
if val_acc_avg > max_val_acc:
max_val_acc = val_acc_avg
torch.save({'embedding': embedding_net.state_dict(), 'head': cls_head.state_dict()},\
os.path.join(opt.save_path, 'best_model.pth'))
log(log_file_path, 'Validation Epoch: {}\t\t\tLoss: {:.4f}\tAccuracy: {:.2f} ± {:.2f} % (Best)'\
.format(epoch, val_loss_avg, val_acc_avg, val_acc_ci95))
else:
log(log_file_path, 'Validation Epoch: {}\t\t\tLoss: {:.4f}\tAccuracy: {:.2f} ± {:.2f} %'\
.format(epoch, val_loss_avg, val_acc_avg, val_acc_ci95))
torch.save({'embedding': embedding_net.state_dict(), 'head': cls_head.state_dict()}\
, os.path.join(opt.save_path, 'last_epoch.pth'))
if epoch % opt.save_epoch == 0:
torch.save({'embedding': embedding_net.state_dict(), 'head': cls_head.state_dict()}\
, os.path.join(opt.save_path, 'epoch_{}.pth'.format(epoch)))
log(log_file_path, 'Elapsed Time: {}/{}\n'.format(timer.measure(), timer.measure(epoch / float(opt.num_epoch))))
def main(args):
device = torch.device(args.device)
ensure_path(args.save_path)
data = Data(args.dataset, args.n_batches, args.train_way, args.test_way, args.shot, args.query)
train_loader = data.train_loader
val_loader = data.valid_loader
model = Convnet(x_dim=2).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
def save_model(name):
torch.save(model.state_dict(), osp.join(args.save_path, name + '.pth'))
trlog = dict(
args=vars(args),
train_loss=[],
val_loss=[],
train_acc=[],
val_acc=[],
max_acc=0.0,
)
timer = Timer()
for epoch in range(1, args.max_epoch + 1):
lr_scheduler.step()
model.train()
tl = Averager()
ta = Averager()
for i, batch in enumerate(train_loader, 1):
data, _ = [_.to(device) for _ in batch]
data = data.reshape(-1, 2, 105, 105)
p = args.shot * args.train_way
embedded = model(data)
embedded_shot, embedded_query = embedded[:p], embedded[p:]
proto = embedded_shot.reshape(args.shot, args.train_way, -1).mean(dim=0)
label = torch.arange(args.train_way).repeat(args.query).to(device)
logits = euclidean_metric(embedded_query, proto)
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
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()
model.eval()
vl = Averager()
va = Averager()
for i, batch in enumerate(val_loader, 1):
data, _ = [_.cuda() for _ in batch]
data = data.reshape(-1, 2, 105, 105)
p = args.shot * args.test_way
data_shot, data_query = data[:p], data[p:]
proto = model(data_shot)
proto = proto.reshape(args.shot, args.test_way, -1).mean(dim=0)
label = torch.arange(args.test_way).repeat(args.query).to(device)
logits = euclidean_metric(model(data_query), proto)
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
vl.add(loss.item())
va.add(acc)
vl = vl.item()
va = va.item()
print('epoch {}, val, loss={:.4f} acc={:.4f}'.format(epoch, vl, va))
if va > trlog['max_acc']:
trlog['max_acc'] = va
save_model('max-acc')
trlog['train_loss'].append(tl)
trlog['train_acc'].append(ta)
trlog['val_loss'].append(vl)
trlog['val_acc'].append(va)
torch.save(trlog, osp.join(args.save_path, 'trlog'))
save_model('epoch-last')
if epoch % args.save_epoch == 0:
save_model('epoch-{}'.format(epoch))
print('ETA:{}/{}'.format(timer.measure(), timer.measure(epoch / args.max_epoch)))
def main():
timer = Timer()
args, writer = init()
train_file = args.dataset_dir + 'train.json'
val_file = args.dataset_dir + 'val.json'
few_shot_params = dict(n_way=args.n_way, n_support=args.n_shot, n_query=args.n_query)
n_episode = 10 if args.debug else 100
if args.method_type is Method_type.baseline:
train_datamgr = SimpleDataManager(train_file, args.dataset_dir, args.image_size, batch_size=64)
train_loader = train_datamgr.get_data_loader(aug = True)
else:
train_datamgr = SetDataManager(train_file, args.dataset_dir, args.image_size,
n_episode=n_episode, mode='train', **few_shot_params)
train_loader = train_datamgr.get_data_loader(aug=True)
val_datamgr = SetDataManager(val_file, args.dataset_dir, args.image_size,
n_episode=n_episode, mode='val', **few_shot_params)
val_loader = val_datamgr.get_data_loader(aug=False)
if args.model_type is Model_type.ConvNet:
pass
elif args.model_type is Model_type.ResNet12:
from methods.backbone import ResNet12
encoder = ResNet12()
else:
raise ValueError('')
if args.method_type is Method_type.baseline:
from methods.baselinetrain import BaselineTrain
model = BaselineTrain(encoder, args)
elif args.method_type is Method_type.protonet:
from methods.protonet import ProtoNet
model = ProtoNet(encoder, args)
else:
raise ValueError('')
from torch.optim import SGD,lr_scheduler
if args.method_type is Method_type.baseline:
optimizer = SGD(model.encoder.parameters(), lr=args.lr, momentum=0.9, weight_decay=args.weight_decay)
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.max_epoch, eta_min=0, last_epoch=-1)
else:
optimizer = torch.optim.SGD(model.encoder.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4,
nesterov=True)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.step_size, gamma=0.5)
args.ngpu = torch.cuda.device_count()
torch.backends.cudnn.benchmark = True
model = model.cuda()
label = torch.from_numpy(np.repeat(range(args.n_way), args.n_query))
label = label.cuda()
if args.test:
test(model, label, args, few_shot_params)
return
if args.resume:
resume_OK = resume_model(model, optimizer, args, scheduler)
else:
resume_OK = False
if (not resume_OK) and (args.warmup is not None):
load_pretrained_weights(model, args)
if args.debug:
args.max_epoch = args.start_epoch + 1
for epoch in range(args.start_epoch, args.max_epoch):
train_one_epoch(model, optimizer, args, train_loader, label, writer, epoch)
scheduler.step()
vl, va = val(model, args, val_loader, label)
if writer is not None:
writer.add_scalar('data/val_acc', float(va), epoch)
print('epoch {}, val, loss={:.4f} acc={:.4f}'.format(epoch, vl, va))
if va >= args.max_acc:
args.max_acc = va
print('saving the best model! acc={:.4f}'.format(va))
save_model(model, optimizer, args, epoch, args.max_acc, 'max_acc', scheduler)
save_model(model, optimizer, args, epoch, args.max_acc, 'epoch-last', scheduler)
if epoch != 0:
print('ETA:{}/{}'.format(timer.measure(), timer.measure(epoch / args.max_epoch)))
if writer is not None:
writer.close()
test(model, label, args, few_shot_params)
def train(self):
"""The function for the pre-train phase."""
# Set the timer
timer = Timer()
# Set global count to zero
global_count = self.Learner.epoch * len(self.train_loader)
# Set tensorboardX
writer = SummaryWriter(log_dir=self.Learner.pretrain_save_dir)
# Start pretrain
for epoch in range(self.Learner.epoch, self.max_epoch_pre + 1):
# update learning rate
self.Learner.scheduler_pre.step()
# set averager classes to record training losses and accuracies
train_loss_averager = Averager()
train_acc_averager = Averager()
# using tqdm to read samples from train loader
tqdm_gen = tqdm.tqdm(self.train_loader)
for i, batch in enumerate(tqdm_gen, 1):
# update global count number
global_count = global_count + 1
if self.use_gpu:
batch = [_.cuda() for _ in batch]
data, label = batch
# set to right phase
self.Learner.set_phase('pretrain')
# output logits for model
logits = self.Learner(data)
# calculate train loss
loss = F.cross_entropy(logits, label)
# calculate train accuracy
acc = count_acc(logits, label)
# write the tensorboardX records
writer.add_scalar('train/loss', float(loss), global_count)
writer.add_scalar('train/acc', float(acc), global_count)
# Print loss and accuracy for this step
tqdm_gen.set_description('Epoch {}, Loss={:.4f} Acc={:.4f}'.format(epoch, loss.item(), acc))
# Add loss and accuracy for the averagers
train_loss_averager.add(loss.item())
train_acc_averager.add(acc)
# Loss backwards and optimizer updates
self.Learner.optimizer_pre.zero_grad()
loss.backward()
self.Learner.optimizer_pre.step()
# update the averagers
train_loss_averager = train_loss_averager.item()
train_acc_averager = train_acc_averager.item()
# Start validation for this epoch, set model to eval mode
self.Learner.set_phase('preval')
# Set averager classes to record validation losses and accuracies
val_loss_averager = Averager()
val_acc_averager = Averager()
# run meta-validation
for i, batch in enumerate(self.val_loader, 1):
data, _ = batch
if self.use_gpu:
data = data.cuda()
p = self.shot_pre * self.way_pre
data_shot, data_query = data[:p], data[p:]
logits = self.Learner(data_shot, self.label_shot, data_query)
loss = F.cross_entropy(logits, self.label_query)
acc = count_acc(logits, self.label_query)
val_loss_averager.add(loss.item())
val_acc_averager.add(acc)
# update validation averagers
val_loss_averager = val_loss_averager.item()
val_acc_averager = val_acc_averager.item()
# write the tensorboardX records
writer.add_scalar('val/loss', float(val_loss_averager), epoch)
writer.add_scalar('val/acc', float(val_acc_averager), epoch)
# print loss and accuracy for this epoch
print('Epoch {}, Val, Loss={:.4f} Acc={:.4f}'.format(epoch, val_loss_averager, val_acc_averager))
# update best saved model
if val_acc_averager > self.train_log['max_acc']:
self.train_log['max_acc'] = val_acc_averager
self.train_log['max_acc_epoch'] = epoch
self.Learner.save_pretrained_model(epoch=epoch, max_metric=val_acc_averager, is_best=True)
# save model every 10 epochs
if epoch % 10 == 0:
self.Learner.save_pretrained_model(epoch=epoch, max_metric=val_acc_averager, is_best=False)
# update the logs
self.train_log['train_loss'].append(train_loss_averager)
self.train_log['train_acc'].append(train_acc_averager)
self.train_log['val_loss'].append(val_loss_averager)
self.train_log['val_acc'].append(val_acc_averager)
# Print previous information
if epoch % 10 == 0:
print('Best Epoch {}, Best Val acc={:.4f}'.format(self.train_log['max_acc_epoch'],
self.train_log['max_acc']))
if epoch % 10 == 0:
print('Running Time: {}, Estimated Time: {}'.format(timer.measure(),
timer.measure(epoch / self.max_epoch_pre)))
writer.close()
'classifier': classifier.state_dict()
}, os.path.join(save_path, '1_stage_best_model.pth'))
log(log_file_path, 'Best model saving!!!')
log(
log_file_path,
'Val_Epoch: [{}/{}]\tAccuracyBoth: {:.2f} +- {:.2f} %\tAccuracyBase: {:.2f} +- {:.2f} %\tAccuracyNovel: {:.2f} +- {:.2f} %'
.format(epoch, params.num_epoch, val_acc_both, val_acc_both_ci95,
val_acc_base, val_acc_base_ci95, val_acc_novel,
val_acc_novel_ci95))
else:
log(
log_file_path,
'Val_Epoch: [{}/{}]\tAccuracyBoth: {:.2f} +- {:.2f} %\tAccuracyBase: {:.2f} +- {:.2f} %\tAccuracyNovel: {:.2f} +- {:.2f} %'
.format(epoch, params.num_epoch, val_acc_both, val_acc_both_ci95,
val_acc_base, val_acc_base_ci95, val_acc_novel,
val_acc_novel_ci95))
torch.save(
{
'embedding': embedding_model.state_dict(),
'classifier': classifier.state_dict()
}, os.path.join(save_path, '1_stage_last_model.pth'))
log(
log_file_path, 'Elapsed Time: {}/{}\n'.format(
timer.measure(), timer.measure(epoch / float(params.num_epoch))))
log(
log_file_path,
'Best model saving!!!\tBest_Epoch: [{}/{}]\tAccuracyNovel: {:.2f} %'.
format(max_val_epoch, params.num_epoch, max_val_acc))
