def train_pada(config):
if config['network'] == 'inceptionv1':
extractor_s = InceptionV1(num_classes=32)
extractor_t = InceptionV1(num_classes=32)
elif config['network'] == 'inceptionv1s':
extractor_s = InceptionV1s(num_classes=32)
extractor_t = InceptionV1s(num_classes=32)
else:
extractor_s = Extractor(n_flattens=config['n_flattens'],
n_hiddens=config['n_hiddens'])
extractor_t = Extractor(n_flattens=config['n_flattens'],
n_hiddens=config['n_hiddens'])
classifier_s = Classifier(n_flattens=config['n_flattens'],
n_hiddens=config['n_hiddens'],
n_class=config['n_class'])
classifier_t = Classifier(n_flattens=config['n_flattens'],
n_hiddens=config['n_hiddens'],
n_class=config['n_class'])
if torch.cuda.is_available():
extractor_s = extractor_s.cuda()
classifier_s = classifier_s.cuda()
extractor_t = extractor_t.cuda()
classifier_t = classifier_t.cuda()
cdan_random = config['random_layer']
res_dir = os.path.join(
config['res_dir'],
'normal{}-{}-cons{}-lr{}'.format(config['normal'], config['network'],
config['pada_cons_w'], config['lr']))
if not os.path.exists(res_dir):
os.makedirs(res_dir)
print('train_pada')
print(config)
set_log_config(res_dir)
logging.debug('train_pada')
# logging.debug(extractor)
# logging.debug(classifier)
logging.debug(config)
if config['models'] == 'PADA':
random_layer = None
ad_net = AdversarialNetwork(config['n_flattens'], config['n_hiddens'])
elif cdan_random:
random_layer = RandomLayer([config['n_flattens'], config['n_class']],
config['n_hiddens'])
ad_net = AdversarialNetwork(config['n_hiddens'], config['n_hiddens'])
random_layer.cuda()
else:
random_layer = None
ad_net = AdversarialNetwork(config['n_flattens'] * config['n_class'],
config['n_hiddens'])
ad_net = ad_net.cuda()
optimizer_s = torch.optim.Adam([{
'params': extractor_s.parameters(),
'lr': config['lr']
}, {
'params': classifier_s.parameters(),
'lr': config['lr']
}])
optimizer_t = torch.optim.Adam([{
'params': extractor_t.parameters(),
'lr': config['lr']
}, {
'params': classifier_t.parameters(),
'lr': config['lr']
}])
optimizer_ad = torch.optim.Adam(ad_net.parameters(), lr=config['lr'])
def train_stage1(extractor_s, classifier_s, config, epoch):
extractor_s.train()
classifier_s.train()
# STAGE 1:
# 在labeled source上训练extractor_s和classifier_s
# 训练完成后freeze这两个model
iter_source = iter(config['source_train_loader'])
len_source_loader = len(config['source_train_loader'])
for step in range(1, len_source_loader + 1):
data_source, label_source = iter_source.next()
if torch.cuda.is_available():
data_source, label_source = data_source.cuda(
), label_source.cuda()
optimizer_s.zero_grad()
h_s = extractor_s(data_source)
h_s = h_s.view(h_s.size(0), -1)
source_preds = classifier_s(h_s)
cls_loss = nn.CrossEntropyLoss()(source_preds, label_source)
cls_loss.backward()
optimizer_s.step()
def train(extractor_s, classifier_s, extractor_t, classifier_t, ad_net,
config, epoch):
start_epoch = 0
# extractor_s.train()
# classifier_s.train()
# ad_net.train()
# # STAGE 1:
# # 在labeled source上训练extractor_s和classifier_s
# # 训练完成后freeze这两个model
# iter_source = iter(config['source_train_loader'])
# len_source_loader = len(config['source_train_loader'])
# for step in range(1, len_source_loader + 1):
# data_source, label_source = iter_source.next()
# if torch.cuda.is_available():
# data_source, label_source = data_source.cuda(), label_source.cuda()
# optimizer_s.zero_grad()
# h_s = extractor_s(data_source)
# h_s = h_s.view(h_s.size(0), -1)
# source_preds = classifier_s(h_s)
# cls_loss = nn.CrossEntropyLoss()(source_preds, label_source)
# cls_loss.backward()
# optimizer_s.step()
# for param in extractor_s.parameters():
# param.requires_grad = False
# for param in classifier_s.parameters():
# param.requires_grad = False
# STAGE 2:
# 使用新的extractor和classifier进行DANN训练
# 不同的地方是，每个target 同时使用extractor_s和extractor_t
extractor_t.train()
classifier_t.train()
ad_net.train()
iter_source = iter(config['source_train_loader'])
iter_target = iter(config['target_train_loader'])
len_source_loader = len(config['source_train_loader'])
len_target_loader = len(config['target_train_loader'])
num_iter = len_source_loader
for step in range(1, num_iter + 1):
data_source, label_source = iter_source.next()
data_target, label_target = iter_target.next()
if step % len_target_loader == 0:
iter_target = iter(config['target_train_loader'])
if torch.cuda.is_available():
data_source, label_source = data_source.cuda(
), label_source.cuda()
data_target, label_target = data_target.cuda(
), label_target.cuda()
optimizer_t.zero_grad()
optimizer_ad.zero_grad()
h_s = extractor_t(data_source)
h_s = h_s.view(h_s.size(0), -1)
h_t = extractor_t(data_target)
h_t = h_t.view(h_t.size(0), -1)
source_preds = classifier_t(h_s)
cls_loss = nn.CrossEntropyLoss()(source_preds, label_source)
softmax_output_s = nn.Softmax(dim=1)(source_preds)
target_preds = classifier_t(h_t)
softmax_output_t = nn.Softmax(dim=1)(target_preds)
if config['target_labeling'] == 1:
cls_loss += nn.CrossEntropyLoss()(target_preds, label_target)
feature = torch.cat((h_s, h_t), 0)
softmax_output = torch.cat((softmax_output_s, softmax_output_t), 0)
if epoch > start_epoch:
gamma = 2 / (1 + math.exp(-10 *
(epoch) / config['n_epochs'])) - 1
if config['models'] == 'CDAN-E':
entropy = loss_func.Entropy(softmax_output)
d_loss = loss_func.CDAN(
[feature, softmax_output], ad_net, gamma, entropy,
loss_func.calc_coeff(num_iter * (epoch - start_epoch) +
step), random_layer)
elif config['models'] == 'CDAN':
d_loss = loss_func.CDAN([feature, softmax_output], ad_net,
gamma, None, None, random_layer)
elif config['models'] == 'PADA':
d_loss = loss_func.DANN(feature, ad_net, gamma)
else:
raise ValueError('Method cannot be recognized.')
else:
d_loss = 0
# constraints loss
h_s_prev = extractor_s(data_source)
cons_loss = nn.L1Loss()(h_s, h_s_prev)
loss = cls_loss + d_loss + config['pada_cons_w'] * cons_loss
loss.backward()
optimizer_t.step()
if epoch > start_epoch:
optimizer_ad.step()
if (step) % 20 == 0:
print(
'Train Epoch {} closs {:.6f}, dloss {:.6f}, cons_loss {:.6f}, Loss {:.6f}'
.format(epoch, cls_loss.item(), d_loss.item(),
cons_loss.item(), loss.item()))
for epoch in range(1, config['n_epochs'] + 1):
train_stage1(extractor_s, classifier_s, config, epoch)
if epoch % config['TEST_INTERVAL'] == 0:
print('test on source_test_loader')
test(extractor_s, classifier_s, config['source_test_loader'],
epoch)
# print('test on target_test_loader')
# accuracy = test(extractor_s, classifier_s, config['target_test_loader'], epoch)
extractor_t.load_state_dict(extractor_s.state_dict())
classifier_t.load_state_dict(classifier_s.state_dict())
for param in extractor_s.parameters():
param.requires_grad = False
for param in classifier_s.parameters():
param.requires_grad = False
for epoch in range(1, config['n_epochs'] + 1):
train(extractor_s, classifier_s, extractor_t, classifier_t, ad_net,
config, epoch)
if epoch % config['TEST_INTERVAL'] == 0:
# print('test on source_test_loader')
# test(extractor, classifier, config['source_test_loader'], epoch)
print('test on target_test_loader')
accuracy = test(extractor_t, classifier_t,
config['target_test_loader'], epoch)
if epoch % config['VIS_INTERVAL'] == 0:
title = config['models']
draw_confusion_matrix(extractor_t, classifier_t,
config['target_test_loader'], res_dir, epoch,
title)
draw_tsne(extractor_t,
classifier_t,
config['source_train_loader'],
config['target_test_loader'],
res_dir,
epoch,
title,
separate=True)
def train_cdan_vat(config):
extractor = Extractor(n_flattens=config['n_flattens'], n_hiddens=config['n_hiddens'])
classifier = Classifier(n_flattens=config['n_flattens'], n_hiddens=config['n_hiddens'], n_class=config['n_class'])
if torch.cuda.is_available():
extractor = extractor.cuda()
classifier = classifier.cuda()
xi = 1e-06
ip = 1
eps = 15
vat = VirtualAdversarialPerturbationGenerator(extractor, classifier, xi=xi, eps=eps, ip=ip)
cdan_random = config['random_layer']
res_dir = os.path.join(config['res_dir'], 'random{}-bs{}-lr{}'.format(cdan_random, config['batch_size'], config['lr']))
if not os.path.exists(res_dir):
os.makedirs(res_dir)
print('train_cdan')
print(extractor)
print(classifier)
print(config)
set_log_config(res_dir)
logging.debug('train_cdan')
logging.debug(extractor)
logging.debug(classifier)
logging.debug(config)
if config['models'] == 'DANN':
random_layer = None
ad_net = AdversarialNetwork(config['n_flattens'], config['n_hiddens'])
elif cdan_random:
random_layer = RandomLayer([config['n_flattens'], config['n_class']], config['n_hiddens'])
ad_net = AdversarialNetwork(config['n_hiddens'], config['n_hiddens'])
random_layer.cuda()
else:
random_layer = None
ad_net = AdversarialNetwork(config['n_flattens'] * config['n_class'], config['n_hiddens'])
ad_net = ad_net.cuda()
optimizer = torch.optim.Adam([
{'params': extractor.parameters(), 'lr': config['lr']},
{'params': classifier.parameters(), 'lr': config['lr']}
])
optimizer_ad = torch.optim.Adam(ad_net.parameters(), lr=config['lr'])
extractor_path = os.path.join(res_dir, "extractor.pth")
classifier_path = os.path.join(res_dir, "classifier.pth")
adnet_path = os.path.join(res_dir, "adnet.pth")
def train(extractor, classifier, ad_net, config, epoch):
start_epoch = 0
extractor.train()
classifier.train()
ad_net.train()
iter_source = iter(config['source_train_loader'])
iter_target = iter(config['target_train_loader'])
len_source_loader = len(config['source_train_loader'])
len_target_loader = len(config['target_train_loader'])
num_iter = len_source_loader
for step in range(1, num_iter + 1):
data_source, label_source = iter_source.next()
data_target, _ = iter_target.next()
if step % len_target_loader == 0:
iter_target = iter(config['target_train_loader'])
if torch.cuda.is_available():
data_source, label_source = data_source.cuda(), label_source.cuda()
data_target = data_target.cuda()
optimizer.zero_grad()
optimizer_ad.zero_grad()
h_s = extractor(data_source)
h_s = h_s.view(h_s.size(0), -1)
h_t = extractor(data_target)
h_t = h_t.view(h_t.size(0), -1)
source_preds = classifier(h_s)
cls_loss = nn.CrossEntropyLoss()(source_preds, label_source)
softmax_output_s = nn.Softmax(dim=1)(source_preds)
target_preds = classifier(h_t)
softmax_output_t = nn.Softmax(dim=1)(target_preds)
feature = torch.cat((h_s, h_t), 0)
softmax_output = torch.cat((softmax_output_s, softmax_output_t), 0)
if epoch > start_epoch:
gamma = 2 / (1 + math.exp(-10 * (epoch) / config['n_epochs'])) - 1
if config['models'] == 'CDAN-E':
entropy = loss_func.Entropy(softmax_output)
d_loss = loss_func.CDAN([feature, softmax_output], ad_net, gamma, entropy, loss_func.calc_coeff(num_iter*(epoch-start_epoch)+step), random_layer)
elif config['models'] == 'CDAN':
d_loss = loss_func.CDAN([feature, softmax_output], ad_net, gamma, None, None, random_layer)
elif config['models'] == 'DANN':
d_loss = loss_func.DANN(feature, ad_net, gamma)
elif config['models'] == 'CDAN_VAT':
# entropy = loss_func.Entropy(softmax_output)
# d_loss = loss_func.CDAN([feature, softmax_output], ad_net, gamma, entropy, loss_func.calc_coeff(num_iter*(epoch-start_epoch)+step), random_layer)
d_loss = loss_func.CDAN([feature, softmax_output], ad_net, gamma, None, None, random_layer)
# vat_loss = loss_func.VAT(vat, data_target, extractor, classifier, target_consistency_criterion)
# vat_adv, clean_vat_logits = vat(data_target)
# vat_adv_inputs = data_target + vat_adv
# adv_vat_features = extractor(vat_adv_inputs)
# adv_vat_logits = classifier(adv_vat_features)
# target_vat_loss = target_consistency_criterion(adv_vat_logits, clean_vat_logits)
# vat_loss = target_vat_loss_weight * target_vat_loss
else:
raise ValueError('Method cannot be recognized.')
else:
d_loss = 0
loss = cls_loss + d_loss
loss.backward()
optimizer.step()
vat_adv, clean_vat_logits = vat(data_target)
vat_adv_inputs = data_target + vat_adv
adv_vat_features = extractor(vat_adv_inputs)
adv_vat_logits = classifier(adv_vat_features)
target_vat_loss = target_consistency_criterion(adv_vat_logits, clean_vat_logits)
vat_loss = target_vat_loss_weight * target_vat_loss
vat_loss.backward()
# optimizer.step()
if epoch > start_epoch:
optimizer_ad.step()
if (step) % 20 == 0:
print('Train Epoch {} closs {:.6f}, dloss {:.6f}, vat_loss {:.6f}, Loss {:.6f}'.format(epoch, cls_loss.item(), d_loss.item(), vat_loss.item(), loss.item()))
if config['testonly'] == 0:
best_accuracy = 0
best_model_index = -1
for epoch in range(1, config['n_epochs'] + 1):
train(extractor, classifier, ad_net, config, epoch)
if epoch % config['TEST_INTERVAL'] == 0:
print('test on source_test_loader')
test(extractor, classifier, config['source_test_loader'], epoch)
print('test on target_test_loader')
accuracy = test(extractor, classifier, config['target_test_loader'], epoch)
if accuracy > best_accuracy:
best_accuracy = accuracy
best_model_index = epoch
torch.save(extractor.state_dict(), extractor_path)
torch.save(classifier.state_dict(), classifier_path)
torch.save(ad_net.state_dict(), adnet_path)
print('epoch {} accuracy: {:.6f}, best accuracy {:.6f} on epoch {}'.format(epoch, accuracy, best_accuracy, best_model_index))
if epoch % config['VIS_INTERVAL'] == 0:
title = config['models']
draw_confusion_matrix(extractor, classifier, config['target_test_loader'], res_dir, epoch, title)
draw_tsne(extractor, classifier, config['source_test_loader'], config['target_test_loader'], res_dir, epoch, title, separate=True)
# draw_tsne(extractor, classifier, config['source_test_loader'], config['target_test_loader'], res_dir, epoch, title, separate=False)
else:
if os.path.exists(extractor_path) and os.path.exists(classifier_path) and os.path.exists(adnet_path):
extractor.load_state_dict(torch.load(extractor_path))
classifier.load_state_dict(torch.load(classifier_path))
ad_net.load_state_dict(torch.load(adnet_path))
print('Test only mode, model loaded')
print('test on source_test_loader')
test(extractor, classifier, config['source_test_loader'], -1)
print('test on target_test_loader')
test(extractor, classifier, config['target_test_loader'], -1)
title = config['models']
draw_confusion_matrix(extractor, classifier, config['target_test_loader'], res_dir, -1, title)
draw_tsne(extractor, classifier, config['source_test_loader'], config['target_test_loader'], res_dir, -1, title, separate=True)
else:
print('no saved model found')
def train_cdan(config):
if config['network'] == 'inceptionv1':
extractor = InceptionV1(num_classes=32)
elif config['network'] == 'inceptionv1s':
extractor = InceptionV1s(num_classes=32)
else:
extractor = Extractor(n_flattens=config['n_flattens'], n_hiddens=config['n_hiddens'], bn=config['bn'])
classifier = Classifier(n_flattens=config['n_flattens'], n_hiddens=config['n_hiddens'], n_class=config['n_class'])
vat_loss = VAT(extractor, classifier, n_power=1, radius=3.5).cuda()
if torch.cuda.is_available():
extractor = extractor.cuda()
classifier = classifier.cuda()
cdan_random = config['random_layer']
res_dir = os.path.join(config['res_dir'], 'slim{}-targetLabel{}-snr{}-snrp{}-lr{}'.format(config['slim'],
config['target_labeling'],
config['snr'],
config['snrp'],
config['lr']))
if not os.path.exists(res_dir):
os.makedirs(res_dir)
print('train_cdan')
#print(extractor)
#print(classifier)
print(config)
set_log_config(res_dir)
logging.debug('train_cdan')
logging.debug(extractor)
logging.debug(classifier)
logging.debug(config)
if config['models'] == 'DANN':
random_layer = None
ad_net = AdversarialNetwork(config['n_flattens'], config['n_hiddens'])
elif cdan_random:
random_layer = RandomLayer([config['n_flattens'], config['n_class']], config['n_hiddens'])
ad_net = AdversarialNetwork(config['n_hiddens'], config['n_hiddens'])
random_layer.cuda()
else:
random_layer = None
ad_net = AdversarialNetwork(config['n_flattens'] * config['n_class'], config['n_hiddens'])
ad_net = ad_net.cuda()
optimizer = torch.optim.Adam([
{'params': extractor.parameters(), 'lr': config['lr']},
{'params': classifier.parameters(), 'lr': config['lr']}
])
optimizer_ad = torch.optim.Adam(ad_net.parameters(), lr=config['lr'])
extractor_path = os.path.join(res_dir, "extractor.pth")
classifier_path = os.path.join(res_dir, "classifier.pth")
adnet_path = os.path.join(res_dir, "adnet.pth")
def train(extractor, classifier, ad_net, config, epoch):
start_epoch = 0
extractor.train()
classifier.train()
ad_net.train()
iter_source = iter(config['source_train_loader'])
iter_target = iter(config['target_train_loader'])
len_source_loader = len(config['source_train_loader'])
len_target_loader = len(config['target_train_loader'])
if config['slim'] > 0:
iter_target_semi = iter(config['target_train_semi_loader'])
len_target_semi_loader = len(config['target_train_semi_loader'])
num_iter = len_source_loader
for step in range(1, num_iter + 1):
data_source, label_source = iter_source.next()
data_target, label_target = iter_target.next()
if config['slim'] > 0:
data_target_semi, label_target_semi = iter_target_semi.next()
if step % len_target_semi_loader == 0:
iter_target_semi = iter(config['target_train_semi_loader'])
if step % len_target_loader == 0:
iter_target = iter(config['target_train_loader'])
if torch.cuda.is_available():
data_source, label_source = data_source.cuda(), label_source.cuda()
data_target, label_target = data_target.cuda(), label_target.cuda()
if config['slim'] > 0:
data_target_semi, label_target_semi = data_target_semi.cuda(), label_target_semi.cuda()
optimizer.zero_grad()
optimizer_ad.zero_grad()
h_s = extractor(data_source)
h_s = h_s.view(h_s.size(0), -1)
h_t = extractor(data_target)
h_t = h_t.view(h_t.size(0), -1)
source_preds = classifier(h_s)
cls_loss = nn.CrossEntropyLoss()(source_preds, label_source)
softmax_output_s = nn.Softmax(dim=1)(source_preds)
target_preds = classifier(h_t)
softmax_output_t = nn.Softmax(dim=1)(target_preds)
if config['target_labeling'] == 1:
cls_loss += nn.CrossEntropyLoss()(target_preds, label_target)
feature = torch.cat((h_s, h_t), 0)
softmax_output = torch.cat((softmax_output_s, softmax_output_t), 0)
if epoch > start_epoch:
gamma = 2 / (1 + math.exp(-10 * (epoch) / config['n_epochs'])) - 1
if config['models'] == 'CDAN-E':
entropy = loss_func.Entropy(softmax_output)
d_loss = loss_func.CDAN([feature, softmax_output], ad_net, gamma, entropy, loss_func.calc_coeff(num_iter*(epoch-start_epoch)+step), random_layer)
elif config['models'] == 'CDAN':
d_loss = loss_func.CDAN([feature, softmax_output], ad_net, gamma, None, None, random_layer)
elif config['models'] == 'DANN':
d_loss = loss_func.DANN(feature, ad_net, gamma)
else:
raise ValueError('Method cannot be recognized.')
else:
d_loss = 0
loss = cls_loss + d_loss
err_t_bnm = get_loss_bnm(target_preds)
err_s_vat = vat_loss(data_source, source_preds)
err_t_vat = vat_loss(data_target, target_preds)
loss += 1.0 * err_s_vat + 1.0 * err_t_vat + 1.0 * err_t_bnm
if config['slim'] > 0:
feature_target_semi = extractor(data_target_semi)
feature_target_semi = feature_target_semi.view(feature_target_semi.size(0), -1)
preds_target_semi = classifier(feature_target_semi)
loss += nn.CrossEntropyLoss()(preds_target_semi, label_target_semi)
loss.backward()
optimizer.step()
if epoch > start_epoch:
optimizer_ad.step()
if (step) % 100 == 0:
print('Train Epoch {} closs {:.6f}, dloss {:.6f}, Loss {:.6f}'.format(epoch, cls_loss.item(), d_loss.item(), loss.item()))
if config['testonly'] == 0:
best_accuracy = 0
best_model_index = -1
for epoch in range(1, config['n_epochs'] + 1):
train(extractor, classifier, ad_net, config, epoch)
if epoch % config['TEST_INTERVAL'] == 0:
# print('test on source_test_loader')
# test(extractor, classifier, config['source_test_loader'], epoch)
print('test on target_test_loader')
accuracy = test(extractor, classifier, config['target_test_loader'], epoch)
if accuracy > best_accuracy:
best_accuracy = accuracy
best_model_index = epoch
torch.save(extractor.state_dict(), extractor_path)
torch.save(classifier.state_dict(), classifier_path)
torch.save(ad_net.state_dict(), adnet_path)
print('epoch {} accuracy: {:.6f}, best accuracy {:.6f} on epoch {}'.format(epoch, accuracy, best_accuracy, best_model_index))
if epoch % config['VIS_INTERVAL'] == 0:
title = config['models']
draw_confusion_matrix(extractor, classifier, config['target_test_loader'], res_dir, epoch, title)
draw_tsne(extractor, classifier, config['source_train_loader'], config['target_test_loader'], res_dir, epoch, title, separate=True)
# draw_tsne(extractor, classifier, config['source_test_loader'], config['target_test_loader'], res_dir, epoch, title, separate=False)
else:
if os.path.exists(extractor_path) and os.path.exists(classifier_path) and os.path.exists(adnet_path):
extractor.load_state_dict(torch.load(extractor_path))
classifier.load_state_dict(torch.load(classifier_path))
ad_net.load_state_dict(torch.load(adnet_path))
print('Test only mode, model loaded')
# print('test on source_test_loader')
# test(extractor, classifier, config['source_test_loader'], -1)
print('test on target_test_loader')
test(extractor, classifier, config['target_test_loader'], -1)
title = config['models']
draw_confusion_matrix(extractor, classifier, config['target_test_loader'], res_dir, -1, title)
# draw_tsne(extractor, classifier, config['source_test_loader'], config['target_test_loader'], res_dir, -1, title, separate=True)
draw_tsne(extractor, classifier, config['source_train_loader'], config['target_test_loader'], res_dir, -1, title, separate=True)
else:
print('no saved model found')
def train_tcl_vat(config):
if config['network'] == 'inceptionv1':
extractor = InceptionV1(num_classes=32)
elif config['network'] == 'inceptionv1s':
extractor = InceptionV1s(num_classes=32)
else:
extractor = Extractor(n_flattens=config['n_flattens'],
n_hiddens=config['n_hiddens'])
classifier = Classifier(n_flattens=config['n_flattens'],
n_hiddens=config['n_hiddens'],
n_class=config['n_class'])
if torch.cuda.is_available():
extractor = extractor.cuda()
classifier = classifier.cuda()
#summary(extractor, (1, 5120))
res_dir = os.path.join(
config['res_dir'],
'slim{}-snr{}-snrp{}-Lythred{}-Ldthred{}-lambdad{}-lr{}'.format(
config['slim'], config['snr'], config['snrp'], config['Lythred'],
config['Ldthred'], config['lambdad'], config['lr']))
if not os.path.exists(res_dir):
os.makedirs(res_dir)
print('train_tcl')
#print(extractor)
#print(classifier)
print(config)
set_log_config(res_dir)
logging.debug('train_tcl')
logging.debug(extractor)
logging.debug(classifier)
logging.debug(config)
ad_net = AdversarialNetwork(config['n_flattens'], config['n_hiddens'])
ad_net = ad_net.cuda()
optimizer = torch.optim.Adam([{
'params': extractor.parameters(),
'lr': config['lr']
}, {
'params': classifier.parameters(),
'lr': config['lr']
}],
weight_decay=0.0001)
optimizer_ad = torch.optim.Adam(ad_net.parameters(),
lr=config['lr'],
weight_decay=0.0001)
print(ad_net)
extractor_path = os.path.join(res_dir, "extractor.pth")
classifier_path = os.path.join(res_dir, "classifier.pth")
adnet_path = os.path.join(res_dir, "adnet.pth")
def cal_Ly(source_y_softmax, source_d, label):
#
# source_y_softmax, category预测结果带softmax
# source_d，domain预测结果
# label: 实际category标签
#
agey = -math.log(config['Lythred'])
aged = -math.log(1.0 - config['Ldthred'])
age = agey + config['lambdad'] * aged
# print('agey {}, labmdad {}, aged {}, age {}'.format(agey, config['lambdad'], aged, age))
y_softmax = source_y_softmax
the_index = torch.LongTensor(np.array(range(
config['batch_size']))).cuda()
# 这是什么意思？对于每个样本，只取出实际label对应的softmax值
# 与softmax loss有什么区别？
y_label = y_softmax[the_index, label]
# print('y_softmax {}, the_index {}, y_label shape {}'.format(y_softmax.shape, the_index.shape, y_label.shape))
y_loss = -torch.log(y_label + 1e-8)
d_loss = -torch.log(1.0 - source_d)
d_loss = d_loss.view(config['batch_size'])
weight_loss = y_loss + config['lambdad'] * d_loss
# print('y_loss {}'.format(torch.mean(y_loss)))
# print('lambdad {}'.format(config['lambdad']))
# print('d_loss {}'.format(torch.mean(d_loss)))
# print('y_loss {}'.format(y_loss.item()))
# print('lambdad {}'.format(config['lambdad']))
# print('d_loss {}'.format(d_loss.item()))
weight_var = (weight_loss < age).float().detach()
Ly = torch.mean(y_loss * weight_var)
source_weight = weight_var.data.clone()
source_num = float((torch.sum(source_weight)))
return Ly, source_weight, source_num
def cal_Lt(target_y_softmax):
# 这是entropy loss吧？
Gt_var = target_y_softmax
Gt_en = -torch.sum((Gt_var * torch.log(Gt_var + 1e-8)), 1)
Lt = torch.mean(Gt_en)
return Lt
def train(extractor, classifier, ad_net, config, epoch):
start_epoch = 0
extractor.train()
classifier.train()
ad_net.train()
iter_source = iter(config['source_train_loader'])
iter_target = iter(config['target_train_loader'])
len_source_loader = len(config['source_train_loader'])
len_target_loader = len(config['target_train_loader'])
num_iter = len_source_loader
if config['slim'] > 0:
iter_target_semi = iter(config['target_train_semi_loader'])
len_target_semi_loader = len(config['target_train_semi_loader'])
for step in range(1, num_iter + 1):
data_source, label_source = iter_source.next()
data_target, _ = iter_target.next()
if config['slim'] > 0:
data_target_semi, label_target_semi = iter_target_semi.next()
if step % len_target_semi_loader == 0:
iter_target_semi = iter(config['target_train_semi_loader'])
if step % len_target_loader == 0:
iter_target = iter(config['target_train_loader'])
if torch.cuda.is_available():
data_source, label_source = data_source.cuda(
), label_source.cuda()
data_target = data_target.cuda()
if config['slim'] > 0:
data_target_semi, label_target_semi = data_target_semi.cuda(
), label_target_semi.cuda()
source_domain_label = torch.FloatTensor(config['batch_size'], 1)
target_domain_label = torch.FloatTensor(config['batch_size'], 1)
source_domain_label.fill_(1)
target_domain_label.fill_(0)
domain_label = torch.cat(
[source_domain_label, target_domain_label], 0)
domain_label = domain_label.cuda()
inputs = torch.cat([data_source, data_target], 0)
features = extractor(inputs)
gamma = 2 / (1 + math.exp(-10 * (epoch) / config['n_epochs'])) - 1
y_var = classifier(features)
features = features.view(features.size(0), -1)
d_var = ad_net(features, gamma)
y_softmax_var = nn.Softmax(dim=1)(y_var)
source_y, target_y = y_var.chunk(2, 0)
source_y_softmax, target_y_softmax = y_softmax_var.chunk(2, 0)
source_d, target_d = d_var.chunk(2, 0)
# h_s = extractor(data_source)
# h_s = h_s.view(h_s.size(0), -1)
# h_t = extractor(data_target)
# h_t = h_t.view(h_t.size(0), -1)
# source_preds = classifier(h_s)
# softmax_output_s = nn.Softmax(dim=1)(source_preds)
# target_preds = classifier(h_t)
# softmax_output_t = nn.Softmax(dim=1)(target_preds)
# source_d, d_loss_source = loss_func.DANN_logits(h_s, ad_net, gamma)
# target_d, d_loss_target = loss_func.DANN_logits(h_t, ad_net, gamma)
# source_d = ad_net(h_s, gamma)
# target_d = ad_net(h_t, gamma)
#calculate Ly
if epoch < config['startiter']:
#也就是cls_loss，不考虑权重
Ly = nn.CrossEntropyLoss()(source_y, label_source)
else:
Ly, source_weight, source_num = cal_Ly(source_y_softmax,
source_d, label_source)
# print('source_num {}'.format(source_num))
target_weight = torch.ones(source_weight.size()).cuda()
#calculate Lt
# 计算target category的熵
Lt = cal_Lt(target_y_softmax)
#calculate Ld
if epoch < config['startiter']:
Ld = nn.BCELoss()(d_var, domain_label)
else:
domain_weight = torch.cat([source_weight, target_weight], 0)
domain_weight = domain_weight.view(-1, 1)
# print('domain_weight {}'.format(domain_weight.shape))
# print('domain_weight {}'.format(domain_weight))
# print('d_var {}'.format(d_var))
domain_criterion = nn.BCELoss(weight=domain_weight).cuda()
# domain_criterion = nn.BCELoss().cuda()
# print('max {}'.format(torch.max(d_var)))
# print('min {}'.format(torch.min(d_var)))
# print(d_var)
Ld = domain_criterion(d_var, domain_label)
loss = Ly + config['traded'] * Ld + config['tradet'] * Lt
if config['slim'] > 0:
feature_target_semi = extractor(data_target_semi)
feature_target_semi = feature_target_semi.view(
feature_target_semi.size(0), -1)
preds_target_semi = classifier(feature_target_semi)
loss += nn.CrossEntropyLoss()(preds_target_semi,
label_target_semi)
optimizer.zero_grad()
optimizer_ad.zero_grad()
# net.zero_grad()
loss.backward()
optimizer.step()
optimizer_ad.step()
# if (step) % 20 == 0:
# print('Train Epoch {} closs {:.6f}, dloss {:.6f}, coral_loss {:.6f}, Loss {:.6f}'.format(epoch, cls_loss.item(), d_loss.item(), coral_loss.item(), loss.item()))
# print('Train Epoch {} closs {:.6f}, dloss {:.6f}, Loss {:.6f}'.format(epoch, cls_loss.item(), d_loss.item(), loss.item()))
best_accuracy = 0
best_model_index = -1
for epoch in range(1, config['n_epochs'] + 1):
train(extractor, classifier, ad_net, config, epoch)
if epoch % config['TEST_INTERVAL'] == 0:
# print('test on source_test_loader')
# test(extractor, classifier, config['source_test_loader'], epoch)
print('test on target_test_loader')
accuracy = test(extractor, classifier,
config['target_test_loader'], epoch)
if accuracy > best_accuracy:
best_accuracy = accuracy
best_model_index = epoch
torch.save(extractor.state_dict(), extractor_path)
torch.save(classifier.state_dict(), classifier_path)
torch.save(ad_net.state_dict(), adnet_path)
print(
'epoch {} accuracy: {:.6f}, best accuracy {:.6f} on epoch {}'.
format(epoch, accuracy, best_accuracy, best_model_index))
if epoch % config['VIS_INTERVAL'] == 0:
title = config['models']
draw_confusion_matrix(extractor, classifier,
config['target_test_loader'], res_dir, epoch,
title)
draw_tsne(extractor,
classifier,
config['source_train_loader'],
config['target_test_loader'],
res_dir,
epoch,
title,
separate=True)
draw_tsne(extractor,
classifier,
config['source_train_loader'],
config['target_test_loader'],
res_dir,
epoch,
title,
separate=False)
def train_wasserstein(config):
extractor = Extractor(n_flattens=config['n_flattens'],
n_hiddens=config['n_hiddens'])
# extractor = InceptionV1(num_classes=32)
classifier = Classifier2(n_flattens=config['n_flattens'],
n_hiddens=config['n_hiddens'],
n_class=config['n_class'])
critic = Critic(n_flattens=config['n_flattens'],
n_hiddens=config['n_hiddens'])
if torch.cuda.is_available():
extractor = extractor.cuda()
classifier = classifier.cuda()
critic = critic.cuda()
triplet_type = config['triplet_type']
gamma = config['w_gamma']
weight_wd = config['w_weight']
weight_triplet = config['t_weight']
t_margin = config['t_margin']
t_confidence = config['t_confidence']
k_critic = 3
k_clf = 1
TRIPLET_START_INDEX = 95
if triplet_type == 'none':
res_dir = os.path.join(
config['res_dir'],
'bs{}-lr{}-w{}-gamma{}'.format(config['batch_size'], config['lr'],
weight_wd, gamma))
if not os.path.exists(res_dir):
os.makedirs(res_dir)
extractor_path = os.path.join(res_dir, "extractor.pth")
classifier_path = os.path.join(res_dir, "classifier.pth")
critic_path = os.path.join(res_dir, "critic.pth")
EPOCH_START = 1
TEST_INTERVAL = 10
else:
TEST_INTERVAL = 1
w_dir = os.path.join(
config['res_dir'],
'bs{}-lr{}-w{}-gamma{}'.format(config['batch_size'], config['lr'],
weight_wd, gamma))
if not os.path.exists(w_dir):
os.makedirs(w_dir)
res_dir = os.path.join(
w_dir, '{}_t_weight{}_margin{}_confidence{}'.format(
triplet_type, weight_triplet, t_margin, t_confidence))
if not os.path.exists(res_dir):
os.makedirs(res_dir)
extractor_path = os.path.join(w_dir, "extractor.pth")
classifier_path = os.path.join(w_dir, "classifier.pth")
critic_path = os.path.join(w_dir, "critic.pth")
if os.path.exists(extractor_path):
extractor.load_state_dict(torch.load(extractor_path))
classifier.load_state_dict(torch.load(classifier_path))
critic.load_state_dict(torch.load(critic_path))
print('load models')
EPOCH_START = TRIPLET_START_INDEX
else:
EPOCH_START = 1
set_log_config(res_dir)
print('start epoch {}'.format(EPOCH_START))
print('triplet type {}'.format(triplet_type))
print(config)
logging.debug('train_wt')
logging.debug(extractor)
logging.debug(classifier)
logging.debug(critic)
logging.debug(config)
criterion = torch.nn.CrossEntropyLoss()
softmax_layer = nn.Softmax(dim=1)
critic_opt = torch.optim.Adam(critic.parameters(), lr=config['lr'])
classifier_opt = torch.optim.Adam(classifier.parameters(), lr=config['lr'])
feature_opt = torch.optim.Adam(extractor.parameters(),
lr=config['lr'] / 10)
def train(extractor, classifier, critic, config, epoch):
extractor.train()
classifier.train()
critic.train()
iter_source = iter(config['source_train_loader'])
iter_target = iter(config['target_train_loader'])
len_source_loader = len(config['source_train_loader'])
len_target_loader = len(config['target_train_loader'])
num_iter = len_source_loader
for step in range(1, num_iter):
data_source, label_source = iter_source.next()
data_target, _ = iter_target.next()
if step % len_target_loader == 0:
iter_target = iter(config['target_train_loader'])
if torch.cuda.is_available():
data_source, label_source = data_source.cuda(
), label_source.cuda()
data_target = data_target.cuda()
# 1. train critic
set_requires_grad(extractor, requires_grad=False)
set_requires_grad(classifier, requires_grad=False)
set_requires_grad(critic, requires_grad=True)
with torch.no_grad():
h_s = extractor(data_source)
h_s = h_s.view(h_s.size(0), -1)
h_t = extractor(data_target)
h_t = h_t.view(h_t.size(0), -1)
for j in range(k_critic):
gp = gradient_penalty(critic, h_s, h_t)
critic_s = critic(h_s)
critic_t = critic(h_t)
wasserstein_distance = critic_s.mean() - critic_t.mean()
critic_cost = -wasserstein_distance + gamma * gp
critic_opt.zero_grad()
critic_cost.backward()
critic_opt.step()
if step == 10 and j == 0:
print('EPOCH {}, DISCRIMINATOR: wd {}, gp {}, loss {}'.
format(epoch, wasserstein_distance.item(),
(gamma * gp).item(), critic_cost.item()))
logging.debug(
'EPOCH {}, DISCRIMINATOR: wd {}, gp {}, loss {}'.
format(epoch, wasserstein_distance.item(),
(gamma * gp).item(), critic_cost.item()))
# 2. train feature and class_classifier
set_requires_grad(extractor, requires_grad=True)
set_requires_grad(classifier, requires_grad=True)
set_requires_grad(critic, requires_grad=False)
for _ in range(k_clf):
h_s = extractor(data_source)
h_s = h_s.view(h_s.size(0), -1)
h_t = extractor(data_target)
h_t = h_t.view(h_t.size(0), -1)
source_preds, _ = classifier(h_s)
clf_loss = criterion(source_preds, label_source)
wasserstein_distance = critic(h_s).mean() - critic(h_t).mean()
if triplet_type != 'none' and epoch >= TRIPLET_START_INDEX:
target_preds, _ = classifier(h_t)
target_labels = target_preds.data.max(1)[1]
target_logits = softmax_layer(target_preds)
if triplet_type == 'all':
triplet_index = np.where(
target_logits.data.max(1)[0].cpu().numpy() >
t_margin)[0]
images = torch.cat((h_s, h_t[triplet_index]), 0)
labels = torch.cat(
(label_source, target_labels[triplet_index]), 0)
elif triplet_type == 'src':
images = h_s
labels = label_source
elif triplet_type == 'tgt':
triplet_index = np.where(
target_logits.data.max(1)[0].cpu().numpy() >
t_confidence)[0]
images = h_t[triplet_index]
labels = target_labels[triplet_index]
elif triplet_type == 'sep':
triplet_index = np.where(
target_logits.data.max(1)[0].cpu().numpy() >
t_confidence)[0]
images = h_t[triplet_index]
labels = target_labels[triplet_index]
t_loss_sep, _ = triplet_loss(extractor, {
"X": images,
"y": labels
}, t_confidence)
images = h_s
labels = label_source
t_loss, _ = triplet_loss(extractor, {
"X": images,
"y": labels
}, t_margin)
loss = clf_loss + \
weight_wd * wasserstein_distance + \
weight_triplet * t_loss
if triplet_type == 'sep':
loss += t_loss_sep
feature_opt.zero_grad()
classifier_opt.zero_grad()
loss.backward()
feature_opt.step()
classifier_opt.step()
if step == 10:
print(
'EPOCH {}, CLASSIFIER: clf_loss {}, wd {}, t_loss {}, total loss {}'
.format(epoch, clf_loss.item(),
weight_wd * wasserstein_distance.item(),
weight_triplet * t_loss.item(),
loss.item()))
logging.debug(
'EPOCH {}, CLASSIFIER: clf_loss {}, wd {}, t_loss {}, total loss {}'
.format(epoch, clf_loss.item(),
weight_wd * wasserstein_distance.item(),
weight_triplet * t_loss.item(),
loss.item()))
else:
loss = clf_loss + weight_wd * wasserstein_distance
feature_opt.zero_grad()
classifier_opt.zero_grad()
loss.backward()
feature_opt.step()
classifier_opt.step()
if step == 10:
print(
'EPOCH {}, CLASSIFIER: clf_loss {}, wd {}, loss {}'
.format(epoch, clf_loss.item(),
weight_wd * wasserstein_distance.item(),
loss.item()))
logging.debug(
'EPOCH {}, CLASSIFIER: clf_loss {}, wd {}, loss {}'
.format(epoch, clf_loss.item(),
weight_wd * wasserstein_distance.item(),
loss.item()))
# pretrain(model, config, pretrain_epochs=20)
for epoch in range(EPOCH_START, config['n_epochs'] + 1):
train(extractor, classifier, critic, config, epoch)
if epoch % TEST_INTERVAL == 0:
# print('test on source_test_loader')
# test(extractor, classifier, config['source_test_loader'], epoch)
# print('test on target_train_loader')
# test(model, config['target_train_loader'], epoch)
print('test on target_test_loader')
test(extractor, classifier, config['target_test_loader'], epoch)
if epoch % config['VIS_INTERVAL'] == 0:
if triplet_type == 'none':
title = '(a) WDGRL'
else:
title = '(b) TLADA'
draw_confusion_matrix(extractor, classifier,
config['target_test_loader'], res_dir, epoch,
title)
draw_tsne(extractor,
classifier,
config['source_train_loader'],
config['target_test_loader'],
res_dir,
epoch,
title,
separate=True)
# draw_tsne(extractor, classifier, config['source_test_loader'], config['target_test_loader'], res_dir, epoch, title, separate=False)
if triplet_type == 'none':
torch.save(extractor.state_dict(), extractor_path)
torch.save(classifier.state_dict(), classifier_path)
torch.save(critic.state_dict(), critic_path)
def train_cdan_iw(config):
if config['network'] == 'inceptionv1':
extractor = InceptionV1(num_classes=32)
elif config['network'] == 'inceptionv1s':
extractor = InceptionV1s(num_classes=32)
else:
extractor = Extractor(n_flattens=config['n_flattens'],
n_hiddens=config['n_hiddens'])
classifier = Classifier(n_flattens=config['n_flattens'],
n_hiddens=config['n_hiddens'],
n_class=config['n_class'])
if torch.cuda.is_available():
extractor = extractor.cuda()
classifier = classifier.cuda()
#summary(extractor, (1, 5120))
cdan_random = config['random_layer']
res_dir = os.path.join(
config['res_dir'],
'normal{}-{}-dilation{}-iw{}-lr{}'.format(config['normal'],
config['network'],
config['dilation'],
config['iw'], config['lr']))
if not os.path.exists(res_dir):
os.makedirs(res_dir)
print('train_cdan_iw')
#print(extractor)
#print(classifier)
print(config)
set_log_config(res_dir)
logging.debug('train_cdan')
logging.debug(extractor)
logging.debug(classifier)
logging.debug(config)
if config['models'] == 'DANN_IW':
random_layer = None
ad_net = AdversarialNetwork(config['n_flattens'], config['n_hiddens'])
elif cdan_random:
random_layer = RandomLayer([config['n_flattens'], config['n_class']],
config['n_hiddens'])
ad_net = AdversarialNetwork(config['n_hiddens'], config['n_hiddens'])
random_layer.cuda()
else:
random_layer = None
ad_net = AdversarialNetwork(config['n_flattens'] * config['n_class'],
config['n_hiddens'])
ad_net = ad_net.cuda()
optimizer = torch.optim.Adam([{
'params': extractor.parameters(),
'lr': config['lr']
}, {
'params': classifier.parameters(),
'lr': config['lr']
}],
weight_decay=0.0001)
optimizer_ad = torch.optim.Adam(ad_net.parameters(),
lr=config['lr'],
weight_decay=0.0001)
print(ad_net)
extractor_path = os.path.join(res_dir, "extractor.pth")
classifier_path = os.path.join(res_dir, "classifier.pth")
adnet_path = os.path.join(res_dir, "adnet.pth")
def train(extractor, classifier, ad_net, config, epoch):
start_epoch = 0
extractor.train()
classifier.train()
ad_net.train()
iter_source = iter(config['source_train_loader'])
iter_target = iter(config['target_train_loader'])
len_source_loader = len(config['source_train_loader'])
len_target_loader = len(config['target_train_loader'])
num_iter = len_source_loader
for step in range(1, num_iter + 1):
data_source, label_source = iter_source.next()
data_target, _ = iter_target.next()
if step % len_target_loader == 0:
iter_target = iter(config['target_train_loader'])
if torch.cuda.is_available():
data_source, label_source = data_source.cuda(
), label_source.cuda()
data_target = data_target.cuda()
"""
add code start
"""
with torch.no_grad():
if config['models'] == 'CDAN_IW':
h_s = extractor(data_source)
h_s = h_s.view(h_s.size(0), -1)
h_t = extractor(data_target)
h_t = h_t.view(h_t.size(0), -1)
source_preds = classifier(h_s)
softmax_output_s = nn.Softmax(dim=1)(source_preds)
# print(softmax_output_s.shape)
# print(softmax_output_s.unsqueeze(2).shape)
# print(softmax_output_s)
# target_preds = classifier(h_t)
# softmax_output_t = nn.Softmax(dim=1)(target_preds)
# feature = torch.cat((h_s, h_t), 0)
# softmax_output = torch.cat((softmax_output_s, softmax_output_t), 0)
weights = torch.ones(softmax_output_s.shape).cuda()
weights = 1.0 * weights
weights = weights.unsqueeze(2)
# op_out = torch.bmm(softmax_output_s.unsqueeze(2), h_s.unsqueeze(1))
op_out = torch.bmm(weights, h_s.unsqueeze(1))
# gamma = 2 / (1 + math.exp(-10 * (epoch) / config['n_epochs'])) - 1
gamma = 1
ad_out = ad_net(op_out.view(
-1,
softmax_output_s.size(1) * h_s.size(1)),
gamma,
training=False)
# dom_entropy = loss_func.Entropy(ad_out)
dom_entropy = 1 + (torch.abs(0.5 - ad_out))**config['iw']
# dom_weight = dom_entropy / torch.sum(dom_entropy)
dom_weight = dom_entropy
elif config['models'] == 'DANN_IW':
h_s = extractor(data_source)
h_s = h_s.view(h_s.size(0), -1)
# gamma = 2 / (1 + math.exp(-10 * (epoch) / config['n_epochs'])) - 1
gamma = 1
ad_out = ad_net(h_s, gamma, training=False)
# dom_entropy = 1-((torch.abs(0.5-ad_out))**config['iw'])
# dom_weight = dom_entropy
dom_weight = torch.ones(ad_out.shape).cuda()
#dom_entropy = loss_func.Entropy(dom_entropy)
# dom_weight = dom_entropy / torch.sum(dom_entropy)
"""
add code end
"""
optimizer.zero_grad()
optimizer_ad.zero_grad()
h_s = extractor(data_source)
h_s = h_s.view(h_s.size(0), -1)
h_t = extractor(data_target)
h_t = h_t.view(h_t.size(0), -1)
source_preds = classifier(h_s)
softmax_output_s = nn.Softmax(dim=1)(source_preds)
target_preds = classifier(h_t)
softmax_output_t = nn.Softmax(dim=1)(target_preds)
feature = torch.cat((h_s, h_t), 0)
softmax_output = torch.cat((softmax_output_s, softmax_output_t), 0)
cls_loss = nn.CrossEntropyLoss(reduction='none')(source_preds,
label_source)
cls_loss = torch.mean(dom_weight * cls_loss)
if epoch > start_epoch:
gamma = 2 / (1 + math.exp(-10 *
(epoch) / config['n_epochs'])) - 1
if config['models'] == 'CDAN_EIW':
entropy = loss_func.Entropy(softmax_output)
# print('softmax_output {}, entropy {}'.format(softmax_output.size(), entropy.size()))
d_loss = loss_func.CDAN(
[feature, softmax_output], ad_net, gamma, entropy,
loss_func.calc_coeff(num_iter * (epoch - start_epoch) +
step), random_layer)
elif config['models'] == 'CDAN_IW':
d_loss = loss_func.CDAN([feature, softmax_output], ad_net,
gamma, None, None, random_layer)
elif config['models'] == 'DANN_IW':
d_loss = loss_func.DANN(feature, ad_net, gamma)
else:
raise ValueError('Method cannot be recognized.')
else:
d_loss = 0
loss = cls_loss + d_loss
loss.backward()
optimizer.step()
if epoch > start_epoch:
optimizer_ad.step()
if (step) % 20 == 0:
print('Train Epoch {} closs {:.6f}, dloss {:.6f}, Loss {:.6f}'.
format(epoch, cls_loss.item(), d_loss.item(),
loss.item()))
best_accuracy = 0
best_model_index = -1
for epoch in range(1, config['n_epochs'] + 1):
train(extractor, classifier, ad_net, config, epoch)
if epoch % config['TEST_INTERVAL'] == 0:
# print('test on source_test_loader')
# test(extractor, classifier, config['source_test_loader'], epoch)
print('test on target_test_loader')
accuracy = test(extractor, classifier,
config['target_test_loader'], epoch)
if accuracy > best_accuracy:
best_accuracy = accuracy
best_model_index = epoch
torch.save(extractor.state_dict(), extractor_path)
torch.save(classifier.state_dict(), classifier_path)
torch.save(ad_net.state_dict(), adnet_path)
print(
'epoch {} accuracy: {:.6f}, best accuracy {:.6f} on epoch {}'.
format(epoch, accuracy, best_accuracy, best_model_index))
if epoch % config['VIS_INTERVAL'] == 0:
title = config['models']
draw_confusion_matrix(extractor, classifier,
config['target_test_loader'], res_dir, epoch,
title)
draw_tsne(extractor,
classifier,
config['source_train_loader'],
config['target_test_loader'],
res_dir,
epoch,
title,
separate=True)
draw_tsne(extractor,
classifier,
config['source_train_loader'],
config['target_test_loader'],
res_dir,
epoch,
title,
separate=False)
