def test_target(args):
dset_loaders = digit_load(args)
## set base network
if args.dset == 'u2m':
netF = network.LeNetBase().cuda()
elif args.dset == 'm2u':
netF = network.LeNetBase().cuda()
elif args.dset == 's2m':
netF = network.DTNBase().cuda()
elif args.dset == 'm2mm':
netF = network.DTNBase().cuda()
netB = network.feat_bootleneck(type=args.classifier, feature_dim=netF.in_features, bottleneck_dim=args.bottleneck).cuda()
netC = network.feat_classifier(type=args.layer, class_num = args.class_num, bottleneck_dim=args.bottleneck).cuda()
args.modelpath = args.output_dir + '/source_F.pt'
netF.load_state_dict(torch.load(args.modelpath))
args.modelpath = args.output_dir + '/source_B.pt'
netB.load_state_dict(torch.load(args.modelpath))
args.modelpath = args.output_dir + '/source_C.pt'
netC.load_state_dict(torch.load(args.modelpath))
netF.eval()
netB.eval()
netC.eval()
acc, _ = cal_acc(dset_loaders['test'], netF, netB, netC)
log_str = 'Task: {}, Accuracy = {:.2f}%'.format(args.dset, acc)
args.out_file.write(log_str + '\n')
args.out_file.flush()
print(log_str+'\n')
def train_target(args):
dset_loaders = digit_load(args)
## set base network
if args.dset == 'u2m':
netF = network.LeNetBase().cuda()
elif args.dset == 'm2u':
netF = network.LeNetBase().cuda()
elif args.dset == 's2m':
netF = network.DTNBase().cuda()
elif args.dset == 'm2mm':
netF = network.DTNBase_c().cuda()
elif args.dset == 's2u':
netF = network.DTNBase_c().cuda()
netB = network.feat_bootleneck_c().cuda()
netC = network.feat_classifier_c().cuda()
args.modelpath = args.output_dir + '/source_F.pt'
netF.load_state_dict(torch.load(args.modelpath))
args.modelpath = args.output_dir + '/source_B.pt'
netB.load_state_dict(torch.load(args.modelpath))
args.modelpath = args.output_dir + '/source_C.pt'
netC.load_state_dict(torch.load(args.modelpath))
netC.eval()
for k, v in netC.named_parameters():
v.requires_grad = False
param_group = []
for k, v in netF.named_parameters():
param_group += [{'params': v, 'lr': args.lr}]
for k, v in netB.named_parameters():
param_group += [{'params': v, 'lr': args.lr}]
# optimizer = optim.SGD(param_group)
optimizer = optim.Adam(param_group)
optimizer = op_copy(optimizer)
max_iter = args.max_epoch * len(dset_loaders["target"])
interval_iter = len(dset_loaders["target"])
# interval_iter = max_iter // args.interval
iter_num = 0
while iter_num < max_iter:
optimizer.zero_grad()
try:
inputs_test, _, tar_idx = iter_test.next()
except:
iter_test = iter(dset_loaders["target"])
inputs_test, _, tar_idx = iter_test.next()
if inputs_test.size(0) == 1:
continue
if iter_num % interval_iter == 0 and args.cls_par > 0:
netF.eval()
netF.eval()
mem_label = obtain_label(dset_loaders['target_te'], netF, netB,
netC, args)
mem_label = torch.from_numpy(mem_label).cuda()
netF.train()
netB.train()
iter_num += 1
# lr_scheduler(optimizer, iter_num=iter_num, max_iter=max_iter)
inputs_test = inputs_test.cuda()
features_test = netB(netF(inputs_test))
outputs_test = netC(features_test)
if args.cls_par > 0:
pred = mem_label[tar_idx]
classifier_loss = args.cls_par * nn.CrossEntropyLoss()(
outputs_test, pred)
else:
classifier_loss = torch.tensor(0.0).cuda()
if args.ent:
softmax_out = nn.Softmax(dim=1)(outputs_test)
entropy_loss = torch.mean(loss.Entropy(softmax_out))
if args.gent:
msoftmax = softmax_out.mean(dim=0)
entropy_loss -= torch.sum(-msoftmax *
torch.log(msoftmax + 1e-5))
im_loss = entropy_loss * args.ent_par
classifier_loss += im_loss
optimizer.zero_grad()
classifier_loss.backward()
optimizer.step()
if iter_num % interval_iter == 0 or iter_num == max_iter:
netF.eval()
netB.eval()
acc, _ = cal_acc(dset_loaders['test'], netF, netB, netC)
log_str = 'Task: {}, Iter:{}/{}; Accuracy = {:.2f}%'.format(
args.dset, iter_num, max_iter, acc)
args.out_file.write(log_str + '\n')
args.out_file.flush()
print(log_str + '\n')
if args.issave:
torch.save(
netF.state_dict(),
osp.join(args.output_dir, "target_F_" + args.savename + ".pt"))
torch.save(
netB.state_dict(),
osp.join(args.output_dir, "target_B_" + args.savename + ".pt"))
torch.save(
netC.state_dict(),
osp.join(args.output_dir, "target_C_" + args.savename + ".pt"))
return netF, netB, netC
def train_source(args):
dset_loaders = digit_load(args)
## set base network
if args.dset == 'u2m':
netF = network.LeNetBase().cuda()
elif args.dset == 'm2u':
netF = network.LeNetBase().cuda()
elif args.dset == 's2m':
netF = network.DTNBase().cuda()
elif args.dset == 'm2mm':
netF = network.DTNBase_c().cuda()
elif args.dset == 's2u':
netF = network.DTNBase_c().cuda()
netB = network.feat_bootleneck_c().cuda()
netC = network.feat_classifier_c().cuda()
param_group = []
learning_rate = args.lr
for k, v in netF.named_parameters():
param_group += [{'params': v, 'lr': learning_rate}]
for k, v in netB.named_parameters():
param_group += [{'params': v, 'lr': learning_rate}]
for k, v in netC.named_parameters():
param_group += [{'params': v, 'lr': learning_rate}]
# optimizer = optim.SGD(param_group)
optimizer = optim.Adam(param_group)
optimizer = op_copy(optimizer)
acc_init = 0
max_iter = args.max_epoch * len(dset_loaders["source_tr"])
interval_iter = max_iter // 10
iter_num = 0
netF.train()
netB.train()
netC.train()
while iter_num < max_iter:
try:
inputs_source, labels_source = iter_source.next()
except:
iter_source = iter(dset_loaders["source_tr"])
inputs_source, labels_source = iter_source.next()
if inputs_source.size(0) == 1:
continue
iter_num += 1
# lr_scheduler(optimizer, iter_num=iter_num, max_iter=max_iter)
inputs_source, labels_source = inputs_source.cuda(
), labels_source.cuda()
outputs_source = netC(netB(netF(inputs_source)))
classifier_loss = loss.CrossEntropyLabelSmooth(
num_classes=args.class_num, epsilon=args.smooth)(outputs_source,
labels_source)
optimizer.zero_grad()
classifier_loss.backward()
optimizer.step()
if iter_num % interval_iter == 0 or iter_num == max_iter:
netF.eval()
netB.eval()
netC.eval()
acc_s_tr, _ = cal_acc(dset_loaders['source_tr'], netF, netB, netC)
acc_s_te, _ = cal_acc(dset_loaders['source_te'], netF, netB, netC)
log_str = 'Task: {}, Iter:{}/{}; Accuracy = {:.2f}%/ {:.2f}%'.format(
args.dset, iter_num, max_iter, acc_s_tr, acc_s_te)
args.out_file.write(log_str + '\n')
args.out_file.flush()
print(log_str + '\n')
if acc_s_te >= acc_init:
acc_init = acc_s_te
best_netF = netF.state_dict()
best_netB = netB.state_dict()
best_netC = netC.state_dict()
torch.save(best_netF, osp.join(args.output_dir, "source_F.pt"))
torch.save(best_netB, osp.join(args.output_dir, "source_B.pt"))
torch.save(best_netC, osp.join(args.output_dir, "source_C.pt"))
return netF, netB, netC
#sigma 0.25 0.932 0.842 0 0 0 0 0
# 0.5 0.808 0.538 0.245 0 0 0 0
# 1 0.262 0.122 0.049 0.020 0.002 0.000 0.000
if __name__ == "__main__":
# load the base classifier
device = 'cuda:0'
args.dset = 'm2mm'
# args.dset = 's2u'
print(args.dset, args.sigma)
if args.dset == 'u2m':
netF = network.LeNetBase().cuda()
elif args.dset == 'm2u':
netF = network.LeNetBase().cuda()
elif args.dset == 's2m':
netF = network.DTNBase().cuda()
elif args.dset == 'm2mm':
netF = network.DTNBase_c().cuda()
elif args.dset == 's2u':
netF = network.DTNBase_c().cuda()
print(args.dset)
netB = network.feat_bootleneck_c().cuda()
netC = network.feat_classifier_c().cuda()
args.output_dir = osp.join(args.output, 'seed' + str(args.seed), args.dset)
args.modelpath = args.output_dir + '/target_F_par_0.3.pt'
netF.load_state_dict(torch.load(args.modelpath))
args.modelpath = args.output_dir + '/target_B_par_0.3.pt'
netB.load_state_dict(torch.load(args.modelpath))
args.modelpath = args.output_dir + '/target_C_par_0.3.pt'
netC.load_state_dict(torch.load(args.modelpath))