def train_init_irm(args):
# prepare data
dsets = {}
dset_loaders = {}
dsets["source"] = ImageList(open(args.source_list).readlines(), \
transform=image_train())
dset_loaders["source"] = DataLoader(dsets["source"], batch_size=args.batch_size, \
shuffle=True, num_workers=4, drop_last=True)
dsets["target"] = ImageList(open(args.target_list).readlines(), \
transform=image_train())
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=args.batch_size, \
shuffle=True, num_workers=4, drop_last=True)
dsets["test"] = ImageList(open(args.target_list).readlines(), \
transform=image_test())
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=2 * args.batch_size, \
shuffle=False, num_workers=4)
#model
model = network.ResNet(class_num=args.num_class,
radius=args.radius,
trainable_radius=args.trainable_radius).cuda()
parameter_list = model.get_parameters()
#pdb.set_trace()
optimizer = torch.optim.SGD(parameter_list,
lr=args.lr,
momentum=0.9,
weight_decay=0.005)
gpus = args.gpu_id.split(',')
if len(gpus) > 1:
adv_net = nn.DataParallel(adv_net, device_ids=[int(i) for i in gpus])
model = nn.DataParallel(model, device_ids=[int(i) for i in gpus])
## train
len_train_source = len(dset_loaders["source"])
len_train_target = len(dset_loaders["target"])
best_acc = 0.0
best_model = copy.deepcopy(model)
Cs_memory = torch.zeros(args.num_class, 256).cuda()
Ct_memory = torch.zeros(args.num_class, 256).cuda()
for i in range(args.max_iter):
if i % args.test_interval == args.test_interval - 1:
model.train(False)
temp_acc = image_classification_test(dset_loaders, model)
if temp_acc > best_acc:
best_acc = temp_acc
best_model = copy.deepcopy(model)
log_str = "\niter: {:05d}, \t precision: {:.4f},\t best_acc:{:.4f}".format(
i, temp_acc, best_acc)
args.log_file.write(log_str)
args.log_file.flush()
print(log_str)
if i % args.snapshot_interval == args.snapshot_interval - 1:
if not os.path.exists('snapshot'):
os.mkdir('snapshot')
if not os.path.exists('snapshot/save'):
os.mkdir('snapshot/save')
torch.save(best_model, 'snapshot/save/initial_model.pk')
model.train(True)
if (args.lr_decay):
optimizer = lr_schedule.inv_lr_scheduler(optimizer, i)
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"])
if i % len_train_target == 0:
iter_target = iter(dset_loaders["target"])
inputs_source, labels_source = iter_source.next()
inputs_target, labels_target = iter_target.next()
inputs_source, inputs_target, labels_source = inputs_source.cuda(
), inputs_target.cuda(), labels_source.cuda()
if (args.irm_type == 'batch'):
scale_source = torch.tensor(1.).cuda().requires_grad_()
scale_target = torch.tensor(1.).cuda().requires_grad_()
elif (args.irm_type == 'sample'):
scale_source = torch.ones(inputs_source.size(0),
1).cuda().requires_grad_()
scale_target = torch.ones(inputs_target.size(0),
1).cuda().requires_grad_()
if (args.irm_feature == 'last_hidden'):
features_source, outputs_source = model.forward_mul(
inputs_source, scale_source)
features_target, outputs_target = model.forward_mul(
inputs_target, scale_target)
elif (args.irm_feature == 'logit'):
features_source, outputs_source = model(inputs_source)
features_target, outputs_target = model(inputs_target)
outputs_source = outputs_source * scale_source
outputs_target = outputs_target * scale_target
features = torch.cat((features_source, features_target), dim=0)
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
if args.baseline == 'MSTN':
lam = network.calc_coeff(i)
elif args.baseline == 'DANN':
lam = 0.0
pseu_labels_target = torch.argmax(outputs_target, dim=1)
loss_sm, Cs_memory, Ct_memory = utils.SM(features_source,
features_target,
labels_source,
pseu_labels_target, Cs_memory,
Ct_memory)
total_loss = classifier_loss + lam * loss_sm
irm_loss = 0
if (i > args.irm_warmup_step):
if ('MSTN' in args.init_method):
irm_loss += sum(
penalty_loss_scales(total_loss,
[scale_source, scale_target]))
else:
source_irm_loss = sum(
penalty_loss_scales(classifier_loss, [scale_source]))
irm_loss += source_irm_loss
if ('target' in args.init_method):
classifier_loss_target = nn.CrossEntropyLoss()(
outputs_target, pseu_labels_target.detach())
target_irm_loss = sum(
penalty_loss_scales(classifier_loss_target,
[scale_target]))
irm_loss += target_irm_loss
total_loss += args.irm_weight * irm_loss
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
if (args.trainable_radius):
print(
'step:{: d},\t,class_loss:{:.4f},\t,irm_loss:{:.4f},\tradius:{:.4f}'
.format(i, classifier_loss.item(), float(irm_loss),
float(model.radius)))
else:
print('step:{: d},\t,class_loss:{:.4f},\t,irm_loss:{:.4f}'.format(
i, classifier_loss.item(), float(irm_loss)))
Cs_memory.detach_()
Ct_memory.detach_()
return best_acc, best_model
def train_irm_feat(args):
# prepare data
dsets = {}
dset_loaders = {}
dsets["source"] = ImageList(open(args.source_list).readlines(), \
transform=image_train())
dset_loaders["source"] = DataLoader(dsets["source"], batch_size=args.batch_size, \
shuffle=True, num_workers=4, drop_last=True)
dsets["target"] = ImageList(open(args.save_path).readlines(),
transform=image_train(),
pseudo=True)
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=args.batch_size, \
shuffle=True, num_workers=4, drop_last=True)
dsets["test"] = ImageList(open(args.target_list).readlines(), \
transform=image_test())
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=2 * args.batch_size, \
shuffle=False, num_workers=4)
#model
model = network.ResNet(class_num=args.num_class,
radius=args.radius_refine,
trainable_radius=args.trainable_radius).cuda()
parameter_classifier = model.get_parameters()
optimizer_classifier = torch.optim.SGD(parameter_classifier,
lr=args.lr_refine,
momentum=0.9,
weight_decay=0.005)
gpus = args.gpu_id.split(',')
if len(gpus) > 1:
model = nn.DataParallel(model, device_ids=[int(i) for i in gpus])
## train
len_train_source = len(dset_loaders["source"])
len_train_target = len(dset_loaders["target"])
best_acc = 0.0
best_model = copy.deepcopy(model)
for i in range(args.max_iter):
if i % args.test_interval == args.test_interval - 1:
model.train(False)
temp_acc = image_classification_test(dset_loaders, model)
if temp_acc > best_acc:
best_acc = temp_acc
best_model = copy.deepcopy(model)
log_str = "\n iter: {:05d}, \t precision: {:.4f},\t best_acc:{:.4f}".format(
i, temp_acc, best_acc)
args.log_file.write(log_str)
args.log_file.flush()
print(log_str)
if i % args.snapshot_interval == args.snapshot_interval - 1:
if not os.path.exists('snapshot'):
os.mkdir('snapshot')
if not os.path.exists('snapshot/save'):
os.mkdir('snapshot/save')
torch.save(best_model, 'snapshot/save/best_model.pk')
model.train(True)
if (args.lr_decay_refine):
optimizer_classifier = lr_schedule.inv_lr_scheduler(
optimizer_classifier, i)
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"])
if i % len_train_target == 0:
iter_target = iter(dset_loaders["target"])
inputs_source, labels_source = iter_source.next()
inputs_target, pseudo_labels_target, weights = iter_target.next()
inputs_source, labels_source = inputs_source.cuda(
), labels_source.cuda()
inputs_target, pseudo_labels_target = inputs_target.cuda(
), pseudo_labels_target.cuda()
weights = weights.type(torch.Tensor).cuda()
scale_source = torch.tensor(1.).cuda().requires_grad_()
scale_target = torch.tensor(1.).cuda().requires_grad_()
features_source, outputs_source = model.forward_mul(
inputs_source, scale_source)
features_target, outputs_target = model.forward_mul(
inputs_target, scale_target)
features = torch.cat((features_source, features_target), dim=0)
source_class_loss = nn.CrossEntropyLoss()(outputs_source,
labels_source)
target_robust_loss = utils.robust_pseudo_loss(outputs_target,
pseudo_labels_target,
weights)
classifier_loss = source_class_loss + target_robust_loss
if args.baseline == 'MSTN':
lam = network.calc_coeff(i, max_iter=2000)
elif args.baseline == 'DANN':
lam = 0.0
# pseu_labels_target = torch.argmax(outputs_target, dim=1)
# loss_sm, Cs_memory, Ct_memory = utils.SM(features_source, features_target, labels_source, pseu_labels_target,
# Cs_memory, Ct_memory)
# feature_loss = classifier_loss + lam*loss_sm + lam*H
# irm_loss = 0
# if('MSTN' in args.irm_feature):
# if(i>args.irm_warmup_step):
# irm_loss += sum(penalty_loss_scales(feature_loss, [scale_source, scale_target]))
# else:
source_irm_loss = penalty_loss_scale(source_class_loss, scale_source)
target_irm_loss = penalty_loss_scale(target_robust_loss, scale_target)
irm_loss = (source_irm_loss + target_irm_loss)
feature_loss = classifier_loss + args.irm_weight * irm_loss
optimizer_classifier.zero_grad()
classifier_loss.backward()
optimizer_classifier.step()
print(
'step:{: d},\t,source_class_loss:{:.4f},\t,target_robust_loss:{:.4f}'
''.format(i, source_class_loss.item(), target_robust_loss.item()))
#Cs_memory.detach_()
#Ct_memory.detach_()
return best_acc, best_model
def train_init(args):
# prepare data
dsets = {}
dset_loaders = {}
dsets["source"] = ImageList(open(args.source_list).readlines(), \
transform=image_train())
dset_loaders["source"] = DataLoader(dsets["source"], batch_size=args.batch_size, \
shuffle=True, num_workers=4, drop_last=True)
dsets["target"] = ImageList(open(args.target_list).readlines(), \
transform=image_train())
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=args.batch_size, \
shuffle=True, num_workers=4, drop_last=True)
dsets["test"] = ImageList(open(args.target_list).readlines(), \
transform=image_test())
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=2 * args.batch_size, \
shuffle=False, num_workers=4)
#model
model = network.ResNet(class_num=args.num_class,
radius=args.radius,
trainable_radius=args.trainable_radius).cuda()
adv_net = network.AdversarialNetwork(in_feature=model.output_num(),
hidden_size=1024).cuda()
parameter_list = model.get_parameters() + adv_net.get_parameters()
optimizer = torch.optim.SGD(parameter_list,
lr=args.lr,
momentum=0.9,
weight_decay=0.005)
gpus = args.gpu_id.split(',')
if len(gpus) > 1:
adv_net = nn.DataParallel(adv_net, device_ids=[int(i) for i in gpus])
model = nn.DataParallel(model, device_ids=[int(i) for i in gpus])
## train
len_train_source = len(dset_loaders["source"])
len_train_target = len(dset_loaders["target"])
best_acc = 0.0
best_model = copy.deepcopy(model)
Cs_memory = torch.zeros(args.num_class, 256).cuda()
Ct_memory = torch.zeros(args.num_class, 256).cuda()
for i in range(args.max_iter):
if i % args.test_interval == args.test_interval - 1:
model.train(False)
temp_acc = image_classification_test(dset_loaders, model)
if temp_acc > best_acc:
best_acc = temp_acc
best_model = copy.deepcopy(model)
log_str = "\niter: {:05d}, \t precision: {:.4f},\t best_acc:{:.4f}".format(
i, temp_acc, best_acc)
args.log_file.write(log_str)
args.log_file.flush()
print(log_str)
if i % args.snapshot_interval == args.snapshot_interval - 1:
if not os.path.exists('snapshot'):
os.mkdir('snapshot')
if not os.path.exists('snapshot/save'):
os.mkdir('snapshot/save')
torch.save(best_model, 'snapshot/save/initial_model.pk')
model.train(True)
adv_net.train(True)
if (args.lr_decay):
optimizer = lr_schedule.inv_lr_scheduler(optimizer, i)
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"])
if i % len_train_target == 0:
iter_target = iter(dset_loaders["target"])
inputs_source, labels_source = iter_source.next()
inputs_target, labels_target = iter_target.next()
inputs_source, inputs_target, labels_source = inputs_source.cuda(
), inputs_target.cuda(), labels_source.cuda()
features_source, outputs_source = model(inputs_source)
features_target, outputs_target = model(inputs_target)
features = torch.cat((features_source, features_target), dim=0)
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
adv_loss = utils.loss_adv(features, adv_net)
if args.baseline == 'MSTN':
lam = network.calc_coeff(i)
elif args.baseline == 'DANN':
lam = 0.0
pseu_labels_target = torch.argmax(outputs_target, dim=1)
loss_sm, Cs_memory, Ct_memory = utils.SM(features_source,
features_target,
labels_source,
pseu_labels_target, Cs_memory,
Ct_memory)
total_loss = classifier_loss + adv_loss + lam * loss_sm
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
print('step:{: d},\t,class_loss:{:.4f},\t,adv_loss:{:.4f}'.format(
i, classifier_loss.item(), adv_loss.item()))
Cs_memory.detach_()
Ct_memory.detach_()
return best_acc, best_model
def train_distill(teacher, args):
# prepare data
dsets = {}
dset_loaders = {}
dsets["source"] = ImageList(open(args.source_list).readlines(), \
transform=image_train())
dset_loaders["source"] = DataLoader(dsets["source"], batch_size=args.batch_size, \
shuffle=True, num_workers=2, drop_last=True)
dsets["target"] = ImageList(open(args.target_list).readlines(), \
transform=image_train(), params=args)
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=args.batch_size, \
shuffle=True, num_workers=2, drop_last=True)
dsets["test"] = ImageList(open(args.target_list).readlines(), \
transform=image_test())
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=2 * args.batch_size, \
shuffle=False, num_workers=2)
#model
model = network.ResNet(class_num=args.num_class).cuda()
adv_net = network.AdversarialNetwork(in_feature=model.output_num(),hidden_size=1024, max_iter=args.max_iter).cuda()
parameter_list = model.get_parameters() + adv_net.get_parameters()
optimizer = torch.optim.SGD(parameter_list,lr=args.lr,momentum=0.9,weight_decay=0.005)
# model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
gpus = args.gpu_id.split(',')
if len(gpus) > 1:
adv_net = nn.DataParallel(adv_net, device_ids=[int(i) for i in gpus])
model = nn.DataParallel(model, device_ids=[int(i) for i in gpus])
## train
len_train_source = len(dset_loaders["source"])
len_train_target = len(dset_loaders["target"])
best_acc = 0.0
best_model = copy.deepcopy(model)
print_interval = (args.test_interval // 10)
nt_cent = utils.NTXentLoss('cuda', args.batch_size, 0.2, True)
Cs_memory = torch.zeros(args.num_class, 256).cuda()
Ct_memory = torch.zeros(args.num_class, 256).cuda()
max_batch = 100
queue_size = args.batch_size * max_batch
queue_data = [torch.randn(queue_size, 256).cuda(), torch.randn(queue_size, args.num_class).cuda()]
queue_data_w = [torch.randn(queue_size, 256).cuda(), torch.randn(queue_size, args.num_class).cuda()]
# queue_data = [torch.randn(queue_size, 256).cuda(), torch.randn(queue_size, 256).cuda()]
queue_labels = [torch.ones(queue_size).cuda() * (args.num_class+1), torch.ones(queue_size).cuda() * (args.num_class+1)]
queue_ptr = torch.zeros(1, dtype=torch.long)
queue_weight = np.power(np.linspace(.0, 1.0, max_batch), 3)
queue_weight = np.repeat(queue_weight, args.batch_size)
best_ema_acc = 0.0
for i in range(args.max_iter):
if i % args.test_interval == args.test_interval - 1:
model.train(False)
temp_acc = image_classification_test(dset_loaders, model)
if temp_acc > best_acc:
best_acc = temp_acc
best_model = copy.deepcopy(model)
log_str = "\niter: {:05d}, \t precision: {:.4f},\t best_acc:{:.4f}".format(i, temp_acc, best_acc)
args.log_file.write(log_str)
args.log_file.flush()
print(log_str)
temp_acc = image_classification_test(dset_loaders, teacher)
if temp_acc > best_ema_acc:
best_ema_acc = temp_acc
# best_model = copy.deepcopy(model)
log_str = "\niter: {:05d}, \t precision: {:.4f},\t best_ema_acc:{:.4f}".format(i, temp_acc, best_ema_acc)
args.log_file.write(log_str)
args.log_file.flush()
print(log_str)
# if i % args.snapshot_interval == args.snapshot_interval -1:
# if not os.path.exists(args.save_dir):
# os.mkdir(args.save_dir)
# torch.save(best_model,os.path.join(args.save_dir, 'initial_model.pk'))
model.train(True)
adv_net.train(True)
teacher.train(False)
optimizer = lr_schedule.inv_lr_scheduler(optimizer,i)
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"])
if i % len_train_target == 0:
iter_target = iter(dset_loaders["target"])
inputs_source, labels_source = iter_source.next()
inputs_target, _, inputs_target_mosaic_w, inputs_target_mosaic_s, labels_target = iter_target.next()
inputs_source, inputs_target, labels_source = inputs_source.cuda(), inputs_target.cuda(), labels_source.cuda()
inputs_target_mosaic_w, inputs_target_mosaic_s = inputs_target_mosaic_w.cuda(), inputs_target_mosaic_s.cuda()
features_source, outputs_source = model(inputs_source)
features_target, outputs_target = model(inputs_target)
features = torch.cat((features_source, features_target), dim=0)
with torch.no_grad():
features_target_teacher, outputs_target_teacher = teacher(inputs_target)
adv_loss = utils.loss_adv(features,adv_net)
H = torch.mean(utils.Entropy(F.softmax(outputs_target, dim=1)))
if args.baseline == 'MSTN':
lam = network.calc_coeff(i)
elif args.baseline =='DANN':
lam = 0.0
prob_max, pseu_labels_target = torch.max(F.softmax(outputs_target, dim=1), dim=1)
loss_sm, Cs_memory, Ct_memory = utils.SM(features_source, features_target, labels_source, pseu_labels_target,
Cs_memory, Ct_memory)
# classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
classifier_loss = 4*utils.cross_entropy_with_logits(outputs_target / 4.0, F.softmax(outputs_target_teacher / 4.0, dim=1)) + nn.CrossEntropyLoss()(outputs_source, labels_source)
total_loss = classifier_loss + lam * loss_sm + adv_loss + network.calc_coeff((i-100), high=0.1, max_iter=100)*H
prob_max, pseu_labels_target = torch.max(F.softmax(outputs_target, dim=1), dim=1)
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
optimizer.zero_grad()
mosaic_loss_target = torch.zeros(1)
if i < args.max_iter // 5 * 2:
alpha = 0.0
else:
alpha = 0.5
with _disable_tracking_bn_stats(model):
mosaic_features_target_w, mosaic_outputs_target_w = model(inputs_target_mosaic_w)
mosaic_features_target_s, mosaic_outputs_target_s = model(inputs_target_mosaic_s)
with torch.no_grad():
features_list_w = [mosaic_features_target_w, F.softmax(mosaic_outputs_target_w, dim=1)]
features_target_, outputs_target_ = model(inputs_target)
outputs_target = alpha * outputs_target_ + (1. - alpha) * outputs_target_teacher
prob_max, pseu_labels_target = torch.max(F.softmax(outputs_target, dim=1), dim=1)
features_list = [features_target_, F.softmax(outputs_target, dim=1)]
labels_list = [pseu_labels_target, pseu_labels_target]
utils.rightshift(queue_weight, args.batch_size)
for j in range(len(features_list)):
queue_data[j][queue_ptr:queue_ptr+args.batch_size, :] = features_list[j]
queue_data_w[j][queue_ptr:queue_ptr+args.batch_size, :] = features_list_w[j]
queue_labels[j][queue_ptr:queue_ptr+args.batch_size] = labels_list[j]
pre_ptr = int(queue_ptr)
ptr = ((i+1) % max_batch) * args.batch_size
queue_ptr[0] = ptr
mosaic_loss_target = (nt_cent(queue_data[1].detach(), F.softmax(mosaic_outputs_target_w, dim=1), queue_labels[1],
pseu_labels_target.float(), queue_weight, pre_ptr, class_level=False) +
1.*nt_cent(queue_data_w[1].detach(), F.softmax(mosaic_outputs_target_s, dim=1), queue_labels[1],
pseu_labels_target.float(), queue_weight, pre_ptr, class_level=False)) * network.calc_coeff(i, high=0.3, max_iter=50)
mosaic_loss = mosaic_loss_target * 1.0
# mosaic_loss = utils.cross_entropy_with_logits(mosaic_outputs_target, F.softmax(outputs_target*1.5, dim=1)) * (network.calc_coeff(i, high=0.5, max_iter=2000))
# mosaic_loss += 0.4*(torch.abs(F.softmax(outputs_target, dim=1).detach() - F.softmax(mosaic_outputs_target, dim=1)).sum(1)).mean(0)
mosaic_loss.backward()
optimizer.step()
if i % print_interval == 0:
log_str = 'step:{: d},\t,class_loss:{:.4f},\t,adv_loss:{:.4f}\t,mosaic_loss:{:.4f}\t,mean_prob:{:.4f}'.format(i, classifier_loss.item(),
adv_loss.item(), mosaic_loss_target.item(),prob_max.mean().item())
print(log_str)
args.log_file.write('\n'+log_str)
args.log_file.flush()
Cs_memory.detach_()
Ct_memory.detach_()
return best_acc, best_model
