def main(args: argparse.Namespace):
logger = CompleteLogger(args.log, args.phase)
print(args)
if seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
# warnings.warn('You have chosen to seed training. '
# 'This will turn on the CUDNN deterministic setting, '
# 'which can slow down your training considerably! '
# 'You may see unexpected behavior when restarting '
# 'from checkpoints.')
cudnn.benchmark = True
# Data loading code
normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
if args.center_crop:
train_transform = T.Compose([
ResizeImage(256),
T.CenterCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
else:
train_transform = T.Compose([
ResizeImage(256),
T.RandomResizedCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
val_transform = T.Compose([
ResizeImage(256),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
dataset = datasets.__dict__[args.data]
source_dataset = open_set(dataset, source=True)
target_dataset = open_set(dataset, source=False)
train_source_dataset = source_dataset(root=args.root, task=args.source, download=True, transform=train_transform)
train_source_loader = DataLoader(train_source_dataset, batch_size=args.batch_size,
shuffle=True, num_workers=args.workers, drop_last=True)
train_target_dataset = target_dataset(root=args.root, task=args.target, download=True, transform=train_transform)
train_target_loader = DataLoader(train_target_dataset, batch_size=args.batch_size,
shuffle=True, num_workers=args.workers, drop_last=True)
val_dataset = target_dataset(root=args.root, task=args.target, download=True, transform=val_transform)
val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers)
if args.data == 'DomainNet':
test_dataset = target_dataset(root=args.root, task=args.target, split='test', download=True,
transform=val_transform)
test_loader = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers)
else:
test_loader = val_loader
train_source_iter = ForeverDataIterator(train_source_loader)
train_target_iter = ForeverDataIterator(train_target_loader)
# create model
print("=> using pre-trained model '{}'".format(args.arch))
num_classes = train_source_dataset.num_classes
backbone = models.__dict__[args.arch](pretrained=True)
classifier = ImageClassifier(backbone, num_classes, bottleneck_dim=args.bottleneck_dim).to(device)
domain_discri = DomainDiscriminator(in_feature=classifier.features_dim, hidden_size=1024).to(device)
# define optimizer and lr scheduler
optimizer = SGD(classifier.get_parameters() + domain_discri.get_parameters(),
args.lr, momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True)
lr_scheduler = LambdaLR(optimizer, lambda x: args.lr * (1. + args.lr_gamma * float(x)) ** (-args.lr_decay))
# define loss function
domain_adv = DomainAdversarialLoss(domain_discri).to(device)
# analysis the model
if args.phase == 'analysis':
# extract features from both domains
feature_extractor = nn.Sequential(classifier.backbone, classifier.bottleneck).to(device)
source_feature = collect_feature(train_source_loader, feature_extractor, device)
target_feature = collect_feature(train_target_loader, feature_extractor, device)
# plot t-SNE
tSNE_filename = osp.join(logger.visualize_directory, 'TSNE.png')
tsne.visualize(source_feature, target_feature, tSNE_filename)
print("Saving t-SNE to", tSNE_filename)
# calculate A-distance, which is a measure for distribution discrepancy
A_distance = a_distance.calculate(source_feature, target_feature, device)
print("A-distance =", A_distance)
return
if args.phase == 'test':
acc1 = validate(test_loader, classifier, args)
print(acc1)
return
# start training
best_h_score = 0.
for epoch in range(args.epochs):
# train for one epoch
train(train_source_iter, train_target_iter, classifier, domain_adv, optimizer,
lr_scheduler, epoch, args)
# evaluate on validation set
h_score = validate(val_loader, classifier, args)
# remember best acc@1 and save checkpoint
torch.save(classifier.state_dict(), logger.get_checkpoint_path('latest'))
if h_score > best_h_score:
shutil.copy(logger.get_checkpoint_path('latest'), logger.get_checkpoint_path('best'))
best_h_score = max(h_score, best_h_score)
print("best_h_score = {:3.1f}".format(best_h_score))
# evaluate on test set
classifier.load_state_dict(torch.load(logger.get_checkpoint_path('best')))
h_score = validate(test_loader, classifier, args)
print("test_h_score = {:3.1f}".format(h_score))
logger.close()
def train(train_source_iter: ForeverDataIterator,
train_target_iter: ForeverDataIterator, model: ImageClassifier,
domain_discri: DomainDiscriminator,
domain_adv: DomainAdversarialLoss, gl, optimizer: SGD,
lr_scheduler: LambdaLR, optimizer_d: SGD, lr_scheduler_d: LambdaLR,
epoch: int, args: argparse.Namespace):
batch_time = AverageMeter('Time', ':5.2f')
data_time = AverageMeter('Data', ':5.2f')
losses_s = AverageMeter('Cls Loss', ':6.2f')
losses_transfer = AverageMeter('Transfer Loss', ':6.2f')
losses_discriminator = AverageMeter('Discriminator Loss', ':6.2f')
cls_accs = AverageMeter('Cls Acc', ':3.1f')
domain_accs = AverageMeter('Domain Acc', ':3.1f')
progress = ProgressMeter(args.iters_per_epoch, [
batch_time, data_time, losses_s, losses_transfer, losses_discriminator,
cls_accs, domain_accs
],
prefix="Epoch: [{}]".format(epoch))
end = time.time()
for i in range(args.iters_per_epoch):
x_s, labels_s = next(train_source_iter)
x_t, _ = next(train_target_iter)
x_s = x_s.to(device)
x_t = x_t.to(device)
labels_s = labels_s.to(device)
# measure data loading time
data_time.update(time.time() - end)
# Step 1: Train the classifier, freeze the discriminator
model.train()
domain_discri.eval()
set_requires_grad(model, True)
set_requires_grad(domain_discri, False)
x = torch.cat((x_s, x_t), dim=0)
y, f = model(x)
y_s, y_t = y.chunk(2, dim=0)
loss_s = F.cross_entropy(y_s, labels_s)
# adversarial training to fool the discriminator
d = domain_discri(gl(f))
d_s, d_t = d.chunk(2, dim=0)
loss_transfer = 0.5 * (domain_adv(d_s, 'target') +
domain_adv(d_t, 'source'))
optimizer.zero_grad()
(loss_s + loss_transfer * args.trade_off).backward()
optimizer.step()
lr_scheduler.step()
# Step 2: Train the discriminator
model.eval()
domain_discri.train()
set_requires_grad(model, False)
set_requires_grad(domain_discri, True)
d = domain_discri(f.detach())
d_s, d_t = d.chunk(2, dim=0)
loss_discriminator = 0.5 * (domain_adv(d_s, 'source') +
domain_adv(d_t, 'target'))
optimizer_d.zero_grad()
loss_discriminator.backward()
optimizer_d.step()
lr_scheduler_d.step()
losses_s.update(loss_s.item(), x_s.size(0))
losses_transfer.update(loss_transfer.item(), x_s.size(0))
losses_discriminator.update(loss_discriminator.item(), x_s.size(0))
cls_acc = accuracy(y_s, labels_s)[0]
cls_accs.update(cls_acc.item(), x_s.size(0))
domain_acc = 0.5 * (binary_accuracy(d_s, torch.ones_like(d_s)) +
binary_accuracy(d_t, torch.zeros_like(d_t)))
domain_accs.update(domain_acc.item(), x_s.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
def main(args: argparse.Namespace):
logger = CompleteLogger(args.log, args.phase)
print(args)
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
cudnn.benchmark = True
# Data loading code
train_transform = utils.get_train_transform(
args.train_resizing,
random_horizontal_flip=not args.no_hflip,
random_color_jitter=False,
resize_size=args.resize_size,
norm_mean=args.norm_mean,
norm_std=args.norm_std)
val_transform = utils.get_val_transform(args.val_resizing,
resize_size=args.resize_size,
norm_mean=args.norm_mean,
norm_std=args.norm_std)
print("train_transform: ", train_transform)
print("val_transform: ", val_transform)
train_source_dataset, train_target_dataset, val_dataset, test_dataset, num_classes, args.class_names = \
utils.get_dataset(args.data, args.root, args.source, args.target, train_transform, val_transform)
train_source_loader = DataLoader(train_source_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
drop_last=True)
train_target_loader = DataLoader(train_target_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
drop_last=True)
val_loader = DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers)
test_loader = DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers)
train_source_iter = ForeverDataIterator(train_source_loader)
train_target_iter = ForeverDataIterator(train_target_loader)
# create model
print("=> using model '{}'".format(args.arch))
backbone = utils.get_model(args.arch, pretrain=not args.scratch)
pool_layer = nn.Identity() if args.no_pool else None
classifier = ImageClassifier(backbone,
num_classes,
bottleneck_dim=args.bottleneck_dim,
pool_layer=pool_layer,
finetune=not args.scratch).to(device)
domain_discri = DomainDiscriminator(in_feature=classifier.features_dim,
hidden_size=1024).to(device)
# define loss function
domain_adv = DomainAdversarialLoss().to(device)
gl = WarmStartGradientLayer(alpha=1.,
lo=0.,
hi=1.,
max_iters=1000,
auto_step=True)
# define optimizer and lr scheduler
optimizer = SGD(classifier.get_parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=True)
optimizer_d = SGD(domain_discri.get_parameters(),
args.lr_d,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=True)
lr_scheduler = LambdaLR(
optimizer, lambda x: args.lr *
(1. + args.lr_gamma * float(x))**(-args.lr_decay))
lr_scheduler_d = LambdaLR(
optimizer_d, lambda x: args.lr_d *
(1. + args.lr_gamma * float(x))**(-args.lr_decay))
# resume from the best checkpoint
if args.phase != 'train':
checkpoint = torch.load(logger.get_checkpoint_path('best'),
map_location='cpu')
classifier.load_state_dict(checkpoint)
# analysis the model
if args.phase == 'analysis':
# extract features from both domains
feature_extractor = nn.Sequential(classifier.backbone,
classifier.pool_layer,
classifier.bottleneck).to(device)
source_feature = collect_feature(train_source_loader,
feature_extractor, device)
target_feature = collect_feature(train_target_loader,
feature_extractor, device)
# plot t-SNE
tSNE_filename = osp.join(logger.visualize_directory, 'TSNE.pdf')
tsne.visualize(source_feature, target_feature, tSNE_filename)
print("Saving t-SNE to", tSNE_filename)
# calculate A-distance, which is a measure for distribution discrepancy
A_distance = a_distance.calculate(source_feature, target_feature,
device)
print("A-distance =", A_distance)
return
if args.phase == 'test':
acc1 = utils.validate(test_loader, classifier, args, device)
print(acc1)
return
# start training
best_acc1 = 0.
for epoch in range(args.epochs):
print("lr classifier:", lr_scheduler.get_lr())
print("lr discriminator:", lr_scheduler_d.get_lr())
# train for one epoch
train(train_source_iter, train_target_iter, classifier, domain_discri,
domain_adv, gl, optimizer, lr_scheduler, optimizer_d,
lr_scheduler_d, epoch, args)
# evaluate on validation set
acc1 = utils.validate(val_loader, classifier, args, device)
# remember best acc@1 and save checkpoint
torch.save(classifier.state_dict(),
logger.get_checkpoint_path('latest'))
if acc1 > best_acc1:
shutil.copy(logger.get_checkpoint_path('latest'),
logger.get_checkpoint_path('best'))
best_acc1 = max(acc1, best_acc1)
print("best_acc1 = {:3.1f}".format(best_acc1))
# evaluate on test set
classifier.load_state_dict(torch.load(logger.get_checkpoint_path('best')))
acc1 = utils.validate(test_loader, classifier, args, device)
print("test_acc1 = {:3.1f}".format(acc1))
logger.close()
def main(args: argparse.Namespace):
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
cudnn.benchmark = True
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_transform = transforms.Compose([
ResizeImage(256),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize
])
val_tranform = transforms.Compose([
ResizeImage(256),
transforms.CenterCrop(224),
transforms.ToTensor(), normalize
])
dataset = datasets.__dict__[args.data]
train_source_dataset = dataset(root=args.root,
task=args.source,
download=True,
transform=train_transform)
train_source_loader = DataLoader(train_source_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
drop_last=True)
train_target_dataset = dataset(root=args.root,
task=args.target,
download=True,
transform=train_transform)
train_target_loader = DataLoader(train_target_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
drop_last=True)
val_dataset = dataset(root=args.root,
task=args.target,
download=True,
transform=val_tranform)
val_loader = DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers)
if args.data == 'DomainNet':
test_dataset = dataset(root=args.root,
task=args.target,
evaluate=True,
download=True,
transform=val_tranform)
test_loader = DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers)
else:
test_loader = val_loader
train_source_iter = ForeverDataIterator(train_source_loader)
train_target_iter = ForeverDataIterator(train_target_loader)
# create model
print("=> using pre-trained model '{}'".format(args.arch))
backbone = models.__dict__[args.arch](pretrained=True)
classifier = ImageClassifier(backbone,
train_source_dataset.num_classes).to(device)
domain_discri = DomainDiscriminator(in_feature=classifier.features_dim,
hidden_size=1024).to(device)
# define optimizer and lr scheduler
optimizer = SGD(classifier.get_parameters() +
domain_discri.get_parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=True)
lr_scheduler = StepwiseLR(optimizer,
init_lr=args.lr,
gamma=0.001,
decay_rate=0.75)
# define loss function
domain_adv = DomainAdversarialLoss(domain_discri).to(device)
# start training
best_acc1 = 0.
for epoch in range(args.epochs):
# train for one epoch
train(train_source_iter, train_target_iter, classifier, domain_adv,
optimizer, lr_scheduler, epoch, args)
# evaluate on validation set
acc1 = validate(val_loader, classifier, args)
# remember best acc@1 and save checkpoint
if acc1 > best_acc1:
best_model = copy.deepcopy(classifier.state_dict())
best_acc1 = max(acc1, best_acc1)
print("best_acc1 = {:3.1f}".format(best_acc1))
# evaluate on test set
classifier.load_state_dict(best_model)
acc1 = validate(test_loader, classifier, args)
print("test_acc1 = {:3.1f}".format(acc1))
def da_methods(discr, frame, ag, dim_x, dim_y, device, ckpt, discr_src=None):
if ag.mode not in MODES_GEN:
celossobj, celossfn = get_ce_or_bce_loss(discr, dim_y, ag.reduction)
if ag.mode == "dann":
domdisc = DomainDiscriminator(
in_feature=discr.dim_s, hidden_size=ag.domdisc_dimh).to(device)
dalossobj = DomainAdversarialLoss(
domdisc, reduction=ag.reduction).to(device)
auto_load(locals(), ['domdisc', 'dalossobj'], ckpt)
def lossfn(x, y, xt):
logit, feat = discr.ys1x(x)
featt = discr.s1x(xt)
if feat.shape[0] < featt.shape[0]:
featt = featt[:feat.shape[0]]
elif feat.shape[0] > featt.shape[0]:
feat = feat[:featt.shape[0]]
return celossobj(logit,
y.float() if dim_y == 1 else
y) + ag.wda * dalossobj(feat, featt)
elif ag.mode == "cdan":
# In both randomized and not randomized versions, the code has problems.
# For randomized, the dim_s*num_cls is fed to domdisc.
# For not rand, `tc.mm` receives the wrong input order in `RandomizedMultiLinearMap.forward()`
num_classes = (2 if dim_y == 1 else dim_y)
domdisc = DomainDiscriminator(
in_feature=discr.dim_s *
(1 if ag.cdan_rand else num_classes), # confusing `in_feature`
hidden_size=ag.domdisc_dimh).to(device)
dalossobj = ConditionalDomainAdversarialLoss(
domdisc,
reduction=ag.reduction,
randomized=ag.cdan_rand,
num_classes=num_classes,
features_dim=discr.dim_s,
randomized_dim=discr.dim_s).to(device)
auto_load(locals(), ['domdisc', 'dalossobj'], ckpt)
def lossfn(x, y, xt):
logit, feat = discr.ys1x(x)
logitt, featt = discr.ys1x(xt)
logit_stack = tc.stack([tc.zeros_like(logit), logit],
dim=-1) if dim_y == 1 else logit
logitt_stack = tc.stack([tc.zeros_like(logitt), logitt],
dim=-1) if dim_y == 1 else logitt
return celossobj(
logit,
y.float() if dim_y == 1 else y) + ag.wda * dalossobj(
logit_stack, feat, logitt_stack, featt)
elif ag.mode == "dan":
domdisc = None
dalossobj = MultipleKernelMaximumMeanDiscrepancy([
GaussianKernel(alpha=alpha) for alpha in ag.ker_alphas
]).to(device)
def lossfn(x, y, xt):
logit, feat = discr.ys1x(x)
featt = discr.s1x(xt)
if feat.shape[0] < featt.shape[0]:
featt = featt[:feat.shape[0]]
elif feat.shape[0] > featt.shape[0]:
feat = feat[:featt.shape[0]]
return celossobj(logit,
y.float() if dim_y == 1 else
y) + ag.wda * dalossobj(feat, featt)
elif ag.mode == "mdd":
num_classes = (2 if dim_y == 1 else dim_y)
domdisc = mlp.MLP([
dim_x, ag.domdisc_dimh, ag.domdisc_dimh, num_classes
]).to(
device
) # actually not domain discriminator but an auxiliary (adversarial) classifier
dalossobj = MarginDisparityDiscrepancy(
margin=ag.mdd_margin, reduction=ag.reduction).to(device)
auto_load(locals(), ['domdisc', 'dalossobj'], ckpt)
def lossfn(x, y, xt):
logit, logitt = discr(x), discr(xt)
logit_adv, logitt_adv = domdisc(x.reshape(-1, dim_x)), domdisc(
xt.reshape(-1, dim_x))
logit_stack = tc.stack([tc.zeros_like(logit), logit],
dim=-1) if dim_y == 1 else logit
logitt_stack = tc.stack([tc.zeros_like(logitt), logitt],
dim=-1) if dim_y == 1 else logitt
return celossobj(
logit,
y.float() if dim_y == 1 else y) + ag.wda * dalossobj(
logit_stack, logit_adv, logitt_stack, logitt_adv)
elif ag.mode == "bnm":
domdisc = None
dalossobj = None
def lossfn(x, y, xt):
logit, logitt = discr(x), discr(xt)
logitt_stack = tc.stack([tc.zeros_like(logitt), logitt],
dim=-1) if dim_y == 1 else logitt
softmax_tgt = logitt_stack.softmax(dim=1)
_, s_tgt, _ = tc.svd(softmax_tgt)
# if config["method"]=="BNM":
transfer_loss = -tc.mean(s_tgt)
# elif config["method"]=="BFM":
# transfer_loss = -tc.sqrt(tc.sum(s_tgt*s_tgt)/s_tgt.shape[0])
# elif config["method"]=="ENT":
# transfer_loss = -tc.mean(tc.sum(softmax_tgt*tc.log(softmax_tgt+1e-8),dim=1))/tc.log(softmax_tgt.shape[1])
return celossobj(
logit,
y.float() if dim_y == 1 else y) + ag.wda * transfer_loss
else:
pass
for obj in [dalossobj, domdisc]:
if obj is not None: obj.train()
else:
if ag.mode.endswith("-da2") and discr_src is not None:
true_discr = discr_src
elif ag.mode in MODES_TWIST and ag.true_sup:
true_discr = partial(frame.logit_y1x_src, n_mc_q=ag.n_mc_q)
else:
true_discr = discr
celossfn = get_ce_or_bce_loss(true_discr, dim_y, ag.reduction)[1]
lossobj = frame.get_lossfn(ag.n_mc_q,
ag.reduction,
"defl",
weight_da=ag.wda / ag.wgen,
wlogpi=ag.wlogpi / ag.wgen)
lossfn = add_ce_loss(lossobj, celossfn, ag)
domdisc, dalossobj = None, None
return lossfn, domdisc, dalossobj
def main(args: argparse.Namespace):
logger = CompleteLogger(args.log, args.phase)
print(args)
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
cudnn.benchmark = True
# Data loading code
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_transform = T.Compose(
[T.Resize(args.resize_size),
T.ToTensor(), normalize])
val_transform = T.Compose(
[T.Resize(args.resize_size),
T.ToTensor(), normalize])
dataset = datasets.__dict__[args.data]
train_source_dataset = dataset(root=args.root,
task=args.source,
split='train',
download=True,
transform=train_transform)
train_source_loader = DataLoader(train_source_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
drop_last=True)
train_target_dataset = dataset(root=args.root,
task=args.target,
split='train',
download=True,
transform=train_transform)
train_target_loader = DataLoader(train_target_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
drop_last=True)
val_dataset = dataset(root=args.root,
task=args.target,
split='test',
download=True,
transform=val_transform)
val_loader = DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers)
train_source_iter = ForeverDataIterator(train_source_loader)
train_target_iter = ForeverDataIterator(train_target_loader)
# create model
print("=> using pre-trained model '{}'".format(args.arch))
backbone = models.__dict__[args.arch](pretrained=True)
if args.normalization == 'IN':
backbone = convert_model(backbone)
num_factors = train_source_dataset.num_factors
bottleneck = nn.Sequential(nn.AdaptiveAvgPool2d(output_size=(1, 1)),
nn.Flatten(),
nn.Linear(backbone.out_features, 256),
nn.ReLU())
regressor = Regressor(backbone=backbone,
num_factors=num_factors,
bottleneck=bottleneck,
bottleneck_dim=256).to(device)
print(regressor)
domain_discri = DomainDiscriminator(in_feature=regressor.features_dim,
hidden_size=1024).to(device)
# define optimizer and lr scheduler
optimizer = SGD(regressor.get_parameters() +
domain_discri.get_parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.wd,
nesterov=True)
lr_scheduler = LambdaLR(
optimizer, lambda x: args.lr *
(1. + args.lr_gamma * float(x))**(-args.lr_decay))
# define loss function
dann = DomainAdversarialLoss(domain_discri).to(device)
# resume from the best checkpoint
if args.phase != 'train':
checkpoint = torch.load(logger.get_checkpoint_path('best'),
map_location='cpu')
regressor.load_state_dict(checkpoint)
# analysis the model
if args.phase == 'analysis':
train_source_loader = DataLoader(train_source_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
drop_last=True)
train_target_loader = DataLoader(train_target_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
drop_last=True)
# extract features from both domains
feature_extractor = nn.Sequential(regressor.backbone,
regressor.bottleneck).to(device)
source_feature = collect_feature(train_source_loader,
feature_extractor, device)
target_feature = collect_feature(train_target_loader,
feature_extractor, device)
# plot t-SNE
tSNE_filename = osp.join(logger.visualize_directory, 'TSNE.pdf')
tsne.visualize(source_feature, target_feature, tSNE_filename)
print("Saving t-SNE to", tSNE_filename)
# calculate A-distance, which is a measure for distribution discrepancy
A_distance = a_distance.calculate(source_feature, target_feature,
device)
print("A-distance =", A_distance)
return
if args.phase == 'test':
mae = validate(val_loader, regressor, args,
train_source_dataset.factors, device)
print(mae)
return
# start training
best_mae = 100000.
for epoch in range(args.epochs):
# train for one epoch
print("lr", lr_scheduler.get_lr())
train(train_source_iter, train_target_iter, regressor, dann, optimizer,
lr_scheduler, epoch, args)
# evaluate on validation set
mae = validate(val_loader, regressor, args,
train_source_dataset.factors, device)
# remember best mae and save checkpoint
torch.save(regressor.state_dict(),
logger.get_checkpoint_path('latest'))
if mae < best_mae:
shutil.copy(logger.get_checkpoint_path('latest'),
logger.get_checkpoint_path('best'))
best_mae = min(mae, best_mae)
print("mean MAE {:6.3f} best MAE {:6.3f}".format(mae, best_mae))
print("best_mae = {:6.3f}".format(best_mae))
logger.close()
def main(args: argparse.Namespace):
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
cudnn.benchmark = True
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_transform = transforms.Compose([
ResizeImage(256),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
])
val_transform = transforms.Compose([
ResizeImage(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize
])
dataset = datasets.__dict__[args.data]
train_source_dataset = dataset(root=args.root, task=args.source, download=True, transform=train_transform)
train_source_loader = DataLoader(train_source_dataset, batch_size=args.batch_size,
shuffle=True, num_workers=args.workers, drop_last=True)
train_target_dataset = dataset(root=args.root, task=args.target, download=True, transform=train_transform)
train_target_loader = DataLoader(train_target_dataset, batch_size=args.batch_size,
shuffle=True, num_workers=args.workers, drop_last=True)
val_dataset = dataset(root=args.root, task=args.target, download=True, transform=val_transform)
val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers)
train_source_iter = ForeverDataIterator(train_source_loader)
train_target_iter = ForeverDataIterator(train_target_loader)
# create model
print("=> using pre-trained model '{}'".format(args.arch))
backbone = models.__dict__[args.arch](pretrained=True)
classifier = ImageClassifier(backbone, train_source_dataset.num_classes).to(device)
domain_discri = DomainDiscriminator(in_feature=classifier.features_dim, hidden_size=1024).to(device)
# define optimizer and lr scheduler
optimizer = SGD(classifier.get_parameters() + domain_discri.get_parameters(),
args.lr, momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True)
lr_scheduler = StepwiseLR(optimizer, init_lr=args.lr, gamma=0.001, decay_rate=0.75)
# define loss function
domain_adv = DomainAdversarialLoss(domain_discri).to(device)
# start training
best_acc1 = 0.
best_model = classifier.state_dict()
for epoch in range(args.epochs):
# train for one epoch
train(train_source_iter, train_target_iter, classifier, domain_adv, optimizer,
lr_scheduler, epoch, args)
# evaluate on validation set
acc1 = validate(val_loader, classifier, args)
# remember best acc@1 and save checkpoint
if acc1 > best_acc1:
best_model = classifier.state_dict()
torch.save(best_model, 'best_model.pth.tar')
best_acc1 = max(acc1, best_acc1)
print("best_acc1 = {:3.1f}".format(best_acc1))
# visualize the results using T-SNE
classifier.load_state_dict(best_model)
classifier.eval()
features, labels, domains = [], [], []
source_val_dataset = dataset(root=args.root, task=args.source, download=True, transform=val_transform)
source_val_loader = DataLoader(source_val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers)
with torch.no_grad():
for loader in [source_val_loader, val_loader]:
for i, (images, target) in enumerate(loader):
images = images.to(device)
target = target.to(device)
# compute output
_, f = classifier(images)
features.extend(f.cpu().numpy().tolist())
labels.extend(target)
domains = np.concatenate((np.ones(len(source_val_dataset)), np.zeros(len(val_dataset))))
features, labels = np.array(features), np.array(labels)
print("source:", len(source_val_dataset), "target:", len(val_dataset))
X_tsne = TSNE(n_components=2, random_state=33).fit_transform(features)
plt.figure(figsize=(10, 10))
plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=domains, cmap=col.ListedColormap(["r", "b"]), s=2)
plt.savefig(os.path.join('{}_{}2{}.pdf'.format("dann", args.source, args.target)))
tar_val_loader,
5,
device,
type='tar')
global_logger.info(f'test on test set nmi: {nmi}')
if nmi > best_nmi:
global_logger.info(f"save best model to {model_dir}")
torch.save(backbone.state_dict(),
os.path.join(model_dir, 'minst_best_model.pth'))
best_nmi = nmi
if __name__ == "__main__":
# setup model
backbone = FeatureExtractor().to(device)
domain_discri = DomainDiscriminator(in_feature=128,
hidden_size=256).to(device)
domain_adv = DomainAdversarialLoss(domain_discri).to(device)
# TODO feature reconstruction loss
# TODO feautre transfer module
src_domain_class = [0, 1, 2, 3, 4]
tar_domain_class = [5, 6, 7, 8, 9]
# setup dataloader
src_train_loader = get_mnist_m_loader(
dataset_root='./dataset/MNIST-M',
label_filter=lambda x: x in src_domain_class,
sample_per_cls=sample_num_per_cls,
cls_num=cls_num)
tar_train_loader = get_mnist_loader(
dataset_root='./dataset/MNIST',
