def train(args, model, ad_net, random_layer, train_loader, train_loader1,
optimizer, optimizer_ad, epoch, start_epoch, method):
model.train()
len_source = len(train_loader)
len_target = len(train_loader1)
if len_source > len_target:
num_iter = len_source
else:
num_iter = len_target
for batch_idx in range(num_iter):
if batch_idx % len_source == 0:
iter_source = iter(train_loader)
if batch_idx % len_target == 0:
iter_target = iter(train_loader1)
data_source, label_source = iter_source.next()
data_source, label_source = data_source.cuda(), label_source.cuda()
data_target, label_target = iter_target.next()
data_target = data_target.cuda()
print('data_source:', data_source.shape, data_target.shape)
optimizer.zero_grad()
optimizer_ad.zero_grad()
feature, output = model(torch.cat((data_source, data_target), 0))
loss = nn.CrossEntropyLoss()(output.narrow(0, 0, data_source.size(0)),
label_source)
softmax_output = nn.Softmax(dim=1)(output)
if epoch > start_epoch:
if method == 'CDAN-E':
entropy = loss_func.Entropy(softmax_output)
loss += loss_func.CDAN(
[feature, softmax_output], ad_net, entropy,
network.calc_coeff(num_iter * (epoch - start_epoch) +
batch_idx), random_layer)
elif method == 'CDAN':
loss += loss_func.CDAN([feature, softmax_output], ad_net, None,
None, random_layer)
elif method == 'DANN':
loss += loss_func.DANN(feature, ad_net)
else:
raise ValueError('Method cannot be recognized.')
loss.backward()
optimizer.step()
if epoch > start_epoch:
optimizer_ad.step()
if (batch_idx + epoch * num_iter) % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * args.batch_size, num_iter * args.batch_size,
100. * batch_idx / num_iter, loss.item()))
def train(config):
## set pre-process
prep_dict = {}
prep_config = config["prep"]
prep_dict["source"] = prep.image_train(**config["prep"]['params'])
prep_dict["target"] = prep.image_train(**config["prep"]['params'])
if prep_config["test_10crop"]:
prep_dict["test"] = prep.image_test_10crop(**config["prep"]['params'])
else:
prep_dict["test"] = prep.image_test(**config["prep"]['params'])
## prepare data
dsets = {}
dset_loaders = {}
data_config = config["data"]
train_bs = data_config["source"]["batch_size"]
test_bs = data_config["test"]["batch_size"]
dsets["source"] = ImageList(open(data_config["source"]["list_path"]).readlines(), \
transform=prep_dict["source"])
dset_loaders["source"] = DataLoader(dsets["source"], batch_size=train_bs, \
shuffle=True, num_workers=0, drop_last=True)
dsets["target"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["target"])
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, \
shuffle=True, num_workers=0, drop_last=True)
if prep_config["test_10crop"]:
for i in range(10):
dsets["test"] = [ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"][i]) for i in range(10)]
dset_loaders["test"] = [DataLoader(dset, batch_size=test_bs, \
shuffle=False, num_workers=0) for dset in dsets['test']]
else:
dsets["test"] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"])
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=test_bs, \
shuffle=False, num_workers=0)
class_num = config["network"]["params"]["class_num"]
## set base network
net_config = config["network"]
base_network = net_config["name"](**net_config["params"])
# base_network = base_network.cuda()
## 添加判别器D_s,D_t,生成器G_s2t,G_t2s
z_dimension = 256
D_s = network.models["Discriminator"]()
# D_s = D_s.cuda()
G_s2t = network.models["Generator"](z_dimension, 1024)
# G_s2t = G_s2t.cuda()
D_t = network.models["Discriminator"]()
# D_t = D_t.cuda()
G_t2s = network.models["Generator"](z_dimension, 1024)
# G_t2s = G_t2s.cuda()
criterion_GAN = torch.nn.MSELoss()
criterion_cycle = torch.nn.L1Loss()
criterion_identity = torch.nn.L1Loss()
criterion_Sem = torch.nn.L1Loss()
optimizer_G = torch.optim.Adam(itertools.chain(G_s2t.parameters(), G_t2s.parameters()), lr=0.0003)
optimizer_D_s = torch.optim.Adam(D_s.parameters(), lr=0.0003)
optimizer_D_t = torch.optim.Adam(D_t.parameters(), lr=0.0003)
fake_S_buffer = ReplayBuffer()
fake_T_buffer = ReplayBuffer()
classifier_optimizer = torch.optim.Adam(base_network.parameters(), lr=0.0003)
## 添加分类器
classifier1 = net.Net(256,class_num)
# classifier1 = classifier1.cuda()
classifier1_optim = optim.Adam(classifier1.parameters(), lr=0.0003)
## add additional network for some methods
if config["loss"]["random"]:
random_layer = network.RandomLayer([base_network.output_num(), class_num], config["loss"]["random_dim"])
ad_net = network.AdversarialNetwork(config["loss"]["random_dim"], 1024)
else:
random_layer = None
ad_net = network.AdversarialNetwork(base_network.output_num() * class_num, 1024)
if config["loss"]["random"]:
random_layer.cuda()
# ad_net = ad_net.cuda()
parameter_list = base_network.get_parameters() + ad_net.get_parameters()
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optimizer_config["type"](parameter_list, \
**(optimizer_config["optim_params"]))
param_lr = []
for param_group in optimizer.param_groups:
param_lr.append(param_group["lr"])
schedule_param = optimizer_config["lr_param"]
lr_scheduler = lr_schedule.schedule_dict[optimizer_config["lr_type"]]
gpus = config['gpu'].split(',')
if len(gpus) > 1:
ad_net = nn.DataParallel(ad_net, device_ids=[int(i) for i in gpus])
base_network = nn.DataParallel(base_network, device_ids=[int(i) for i in gpus])
## train
len_train_source = len(dset_loaders["source"])
len_train_target = len(dset_loaders["target"])
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
best_acc = 0.0
for i in range(config["num_iterations"]):
if i % config["test_interval"] == config["test_interval"] - 1:
base_network.train(False)
temp_acc = image_classification_test(dset_loaders, \
base_network, test_10crop=prep_config["test_10crop"])
temp_model = nn.Sequential(base_network)
if temp_acc > best_acc:
best_acc = temp_acc
best_model = temp_model
now = datetime.datetime.now()
d = str(now.month) + '-' + str(now.day) + ' ' + str(now.hour) + ':' + str(now.minute) + ":" + str(
now.second)
torch.save(best_model, osp.join(config["output_path"],
"{}_to_{}_best_model_acc-{}_{}.pth.tar".format(args.source, args.target,
best_acc, d)))
log_str = "iter: {:05d}, precision: {:.5f}".format(i, temp_acc)
config["out_file"].write(log_str + "\n")
config["out_file"].flush()
print(log_str)
if i % config["snapshot_interval"] == 0:
torch.save(nn.Sequential(base_network), osp.join(config["output_path"], \
"{}_to_{}_iter_{:05d}_model_{}.pth.tar".format(args.source,
args.target,
i, str(
datetime.datetime.utcnow()))))
print("it_train: {:05d} / {:05d} start".format(i, config["num_iterations"]))
loss_params = config["loss"]
## train one iter
classifier1.train(True)
base_network.train(True)
ad_net.train(True)
optimizer = lr_scheduler(optimizer, i, **schedule_param)
optimizer.zero_grad()
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()
# 提取特征
features_source, outputs_source = base_network(inputs_source)
features_target, outputs_target = base_network(inputs_target)
features = torch.cat((features_source, features_target), dim=0)
outputs = torch.cat((outputs_source, outputs_target), dim=0)
softmax_out = nn.Softmax(dim=1)(outputs)
outputs_source1 = classifier1(features_source.detach())
outputs_target1 = classifier1(features_target.detach())
outputs1 = torch.cat((outputs_source1,outputs_target1),dim=0)
softmax_out1 = nn.Softmax(dim=1)(outputs1)
softmax_out = (1-args.cla_plus_weight)*softmax_out + args.cla_plus_weight*softmax_out1
if config['method'] == 'CDAN+E':
entropy = loss.Entropy(softmax_out)
transfer_loss = loss.CDAN([features, softmax_out], ad_net, entropy, network.calc_coeff(i), random_layer)
elif config['method'] == 'CDAN':
transfer_loss = loss.CDAN([features, softmax_out], ad_net, None, None, random_layer)
elif config['method'] == 'DANN':
transfer_loss = loss.DANN(features, ad_net)
else:
raise ValueError('Method cannot be recognized.')
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
# Cycle
num_feature = features_source.size(0)
# =================train discriminator T
real_label = Variable(torch.ones(num_feature))
# real_label = Variable(torch.ones(num_feature)).cuda()
fake_label = Variable(torch.zeros(num_feature))
# fake_label = Variable(torch.zeros(num_feature)).cuda()
# 训练生成器
optimizer_G.zero_grad()
# Identity loss
same_t = G_s2t(features_target.detach())
loss_identity_t = criterion_identity(same_t, features_target)
same_s = G_t2s(features_source.detach())
loss_identity_s = criterion_identity(same_s, features_source)
# Gan loss
fake_t = G_s2t(features_source.detach())
pred_fake = D_t(fake_t)
loss_G_s2t = criterion_GAN(pred_fake, labels_source.float())
fake_s = G_t2s(features_target.detach())
pred_fake = D_s(fake_s)
loss_G_t2s = criterion_GAN(pred_fake, labels_source.float())
# cycle loss
recovered_s = G_t2s(fake_t)
loss_cycle_sts = criterion_cycle(recovered_s, features_source)
recovered_t = G_s2t(fake_s)
loss_cycle_tst = criterion_cycle(recovered_t, features_target)
# sem loss
pred_recovered_s = base_network.fc(recovered_s)
pred_fake_t = base_network.fc(fake_t)
loss_sem_t2s = criterion_Sem(pred_recovered_s, pred_fake_t)
pred_recovered_t = base_network.fc(recovered_t)
pred_fake_s = base_network.fc(fake_s)
loss_sem_s2t = criterion_Sem(pred_recovered_t, pred_fake_s)
loss_cycle = loss_cycle_tst + loss_cycle_sts
weights = args.weight_in_lossG.split(',')
loss_G = float(weights[0]) * (loss_identity_s + loss_identity_t) + \
float(weights[1]) * (loss_G_s2t + loss_G_t2s) + \
float(weights[2]) * loss_cycle + \
float(weights[3]) * (loss_sem_s2t + loss_sem_t2s)
# 训练softmax分类器
outputs_fake = classifier1(fake_t.detach())
# 分类器优化
classifier_loss1 = nn.CrossEntropyLoss()(outputs_fake, labels_source)
classifier1_optim.zero_grad()
classifier_loss1.backward()
classifier1_optim.step()
total_loss = loss_params["trade_off"] * transfer_loss + classifier_loss + args.cyc_loss_weight*loss_G
total_loss.backward()
optimizer.step()
optimizer_G.step()
###### Discriminator S ######
optimizer_D_s.zero_grad()
# Real loss
pred_real = D_s(features_source.detach())
loss_D_real = criterion_GAN(pred_real, real_label)
# Fake loss
fake_s = fake_S_buffer.push_and_pop(fake_s)
pred_fake = D_s(fake_s.detach())
loss_D_fake = criterion_GAN(pred_fake, fake_label)
# Total loss
loss_D_s = loss_D_real + loss_D_fake
loss_D_s.backward()
optimizer_D_s.step()
###################################
###### Discriminator t ######
optimizer_D_t.zero_grad()
# Real loss
pred_real = D_t(features_target.detach())
loss_D_real = criterion_GAN(pred_real, real_label)
# Fake loss
fake_t = fake_T_buffer.push_and_pop(fake_t)
pred_fake = D_t(fake_t.detach())
loss_D_fake = criterion_GAN(pred_fake, fake_label)
# Total loss
loss_D_t = loss_D_real + loss_D_fake
loss_D_t.backward()
optimizer_D_t.step()
print("it_train: {:05d} / {:05d} over".format(i, config["num_iterations"]))
now = datetime.datetime.now()
d = str(now.month)+'-'+str(now.day)+' '+str(now.hour)+':'+str(now.minute)+":"+str(now.second)
torch.save(best_model, osp.join(config["output_path"],
"{}_to_{}_best_model_acc-{}_{}.pth.tar".format(args.source, args.target,
best_acc,d)))
return best_acc
def train(config):
# set pre-process
prep_config = config["prep"]
prep_dict = {}
prep_dict["source"] = prep.image_train(**config["prep"]['params'])
prep_dict["target"] = prep.image_train(**config["prep"]['params'])
if prep_config["test_10crop"]:
prep_dict["test"] = prep.image_test_10crop(**config["prep"]['params'])
else:
prep_dict["test"] = prep.image_test(**config["prep"]['params'])
# prepare data
dsets = {}
dset_loaders = {}
data_config = config["data"]
train_bs = data_config["source"]["batch_size"]
test_bs = data_config["test"]["batch_size"]
dsets["source"] = datasets.ImageFolder(data_config['source']['list_path'], transform=prep_dict["source"])
dset_loaders['source'] = getdataloader(dsets['source'], batchsize=train_bs, num_workers=4, drop_last=True, weightsampler=True)
dsets["target"] = datasets.ImageFolder(data_config['target']['list_path'], transform=prep_dict["target"])
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs,
shuffle=True, num_workers=4, drop_last=True)
if prep_config["test_10crop"]:
for i in range(10):
dsets["test"] = [datasets.ImageFolder(data_config['test']['list_path'],
transform=prep_dict["test"][i]) for i in range(10)]
dset_loaders["test"] = [DataLoader(dset, batch_size=test_bs,
shuffle=False, num_workers=4) for dset in dsets['test']]
else:
dsets["test"] = datasets.ImageFolder(data_config['test']['list_path'],
transform=prep_dict["test"])
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=test_bs,
shuffle=False, num_workers=4)
class_num = config["network"]["params"]["class_num"]
# set base network
net_config = config["network"]
base_network = net_config["name"](**net_config["params"])
base_network = base_network.cuda()
# set test_ad_net
test_ad_net = network.AdversarialNetwork(base_network.output_num(), 1024, test_ad_net=True)
test_ad_net = test_ad_net.cuda()
# add additional network for some methods
if config['method'] == 'DANN':
random_layer = None
ad_net = network.AdversarialNetwork(base_network.output_num(), 1024)
elif config['method'] == 'MADA':
random_layer = None
ad_net = network.AdversarialNetworkClassGroup(base_network.output_num(), 1024, class_num)
elif config['method'] == 'proposed':
if config['loss']['random']:
random_layer = network.RandomLayer([base_network.output_num(), class_num], config['loss']['random_dim'])
ad_net = network.AdversarialNetwork(config['loss']['random_dim'], 1024)
ad_net_group = network.AdversarialNetworkGroup(config['loss']['random_dim'], 256, class_num, config['center_threshold'])
else:
random_layer = None
ad_net = network.AdversarialNetwork(base_network.output_num(), 1024)
ad_net_group = network.AdversarialNetworkGroup(base_network.output_num(), 1024, class_num, config['center_threshold'])
elif config['method'] == 'base':
pass
else:
if config["loss"]["random"]:
random_layer = network.RandomLayer([base_network.output_num(), class_num], config["loss"]["random_dim"])
ad_net = network.AdversarialNetwork(config["loss"]["random_dim"], 1024)
else:
random_layer = None
ad_net = network.AdversarialNetwork(base_network.output_num() * class_num, 1024)
if config["loss"]["random"] and config['method'] != 'base' and config['method'] != 'DANN' and config['method'] != 'MADA':
random_layer.cuda()
if config['method'] != 'base':
ad_net = ad_net.cuda()
if config['method'] == 'proposed':
ad_net_group = ad_net_group.cuda()
# set parameters
if config['method'] == 'proposed':
parameter_list = base_network.get_parameters() + test_ad_net.get_parameters() + ad_net.get_parameters() + ad_net_group.get_parameters()
elif config['method'] == 'base':
parameter_list = base_network.get_parameters() + test_ad_net.get_parameters()
elif config['method'] == 'MADA':
parameter_list = base_network.get_parameters() + test_ad_net.get_parameters() + ad_net.get_parameters()
else:
parameter_list = base_network.get_parameters() + test_ad_net.get_parameters() + ad_net.get_parameters()
# set optimizer
optimizer_config = config["optimizer"]
optimizer = optimizer_config["type"](parameter_list, **(optimizer_config["optim_params"]))
param_lr = []
for param_group in optimizer.param_groups:
param_lr.append(param_group["lr"])
schedule_param = optimizer_config["lr_param"]
lr_scheduler = lr_schedule.schedule_dict[optimizer_config["lr_type"]]
# parallel
gpus = config['gpu'].split(',')
if len(gpus) > 1:
base_network = nn.DataParallel(base_network)
test_ad_net = nn.DataParallel(test_ad_net)
if config['method'] == 'DANN':
ad_net = nn.DataParallel(ad_net)
elif config['method'] == 'proposed':
if config['loss']['random']:
random_layer = nn.DataParallel(random_layer)
ad_net = nn.DataParallel(ad_net)
#将ad_net_group设置成并行将会引发error，原因可能是由于ad_net_group的输出不是tensor类型，parallel还不能支持。
#ad_net_group = nn.DataParallel(ad_net_group)
else:
ad_net = nn.DataParallel(ad_net)
#ad_net_group = nn.DataParallel(ad_net_group)
elif config['method'] == 'base':
pass
else:
# CDAN+E
if config["loss"]["random"]:
random_layer = nn.DataParallel(random_layer)
ad_net = nn.DataParallel(ad_net)
# CDAN
else:
ad_net = nn.DataParallel(ad_net)
## train
len_train_source = len(dset_loaders["source"])
len_train_target = len(dset_loaders["target"])
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
best_acc = 0.0
for i in range(config["num_iterations"]):
if i % config["test_interval"] == config["test_interval"] - 1:
base_network.train(False) # eval() == train(False) is True
temp_acc = image_classification_test(dset_loaders, base_network, test_10crop=prep_config["test_10crop"])
temp_model = nn.Sequential(base_network)
if temp_acc > best_acc:
best_acc = temp_acc
best_model = temp_model
log_str = "iter: {:05d}, precision: {:.5f}".format(i, temp_acc)
config["out_file"].write(log_str + "\n")
config["out_file"].flush()
print(log_str)
# if i % config["snapshot_interval"] == 0:
# torch.save(nn.Sequential(base_network), osp.join(config["output_path"],
# "iter_{:05d}_model.pth.tar".format(i)))
loss_params = config["loss"]
# train one iter
base_network.train(True)
if config['method'] != 'base':
ad_net.train(True)
if config['method'] == 'proposed':
ad_net_group.train(True)
# lr_scheduler
optimizer = lr_scheduler(optimizer, i, **schedule_param)
optimizer.zero_grad()
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 = base_network(inputs_source)
features_target, outputs_target = base_network(inputs_target)
if config['tsne']:
# feature visualization by using T-SNE
if i == int(0.98*config['num_iterations']):
features_source_total = features_source.cpu().detach().numpy()
features_target_total = features_target.cpu().detach().numpy()
elif i > int(0.98*config['num_iterations']) and i < int(0.98*config['num_iterations'])+10:
features_source_total = np.concatenate((features_source_total, features_source.cpu().detach().numpy()))
features_target_total = np.concatenate((features_target_total, features_target.cpu().detach().numpy()))
elif i == int(0.98*config['num_iterations'])+10:
for index in range(config['tsne_num']):
features_embeded = TSNE(perplexity=10,n_iter=5000).fit_transform(np.concatenate((features_source_total, features_target_total)))
fig = plt.figure()
plt.scatter(features_embeded[:len(features_embeded)//2, 0], features_embeded[:len(features_embeded)//2, 1], c='r', s=1)
plt.scatter(features_embeded[len(features_embeded)//2:, 0], features_embeded[len(features_embeded)//2:, 1], c='b', s=1)
plt.savefig(osp.join(config["output_path"], config['method']+'-'+str(index)+'.png'))
plt.close()
else:
pass
assert features_source.size(0) == features_target.size(0), 'The batchsize must be same'
assert outputs_source.size(0) == outputs_target.size(0), 'The batchsize must be same'
# source first, target second
features = torch.cat((features_source, features_target), dim=0)
outputs = torch.cat((outputs_source, outputs_target), dim=0)
# output the A_distance
if i % config["test_interval"] == config["test_interval"] - 1:
A_distance = cal_A_distance(test_ad_net, features)
config['A_distance_file'].write(str(A_distance)+'\n')
config['A_distance_file'].flush()
softmax_out = nn.Softmax(dim=1)(outputs)
if config['method'] == 'CDAN+E':
entropy = loss.Entropy(softmax_out)
transfer_loss = loss.CDAN([features, softmax_out], ad_net, entropy, network.calc_coeff(i), random_layer)
elif config['method'] == 'CDAN':
transfer_loss = loss.CDAN([features, softmax_out], ad_net, None, None, random_layer)
elif config['method'] == 'DANN':
transfer_loss = loss.DANN(features, ad_net)
elif config['method'] == 'MADA':
transfer_loss = loss.MADA(features, softmax_out, ad_net)
elif config['method'] == 'proposed':
entropy = loss.Entropy(softmax_out)
transfer_loss = loss.proposed([features, outputs], labels_source, ad_net, ad_net_group, entropy,
network.calc_coeff(i), i, random_layer, config['loss']['trade_off23'])
elif config['method'] == 'base':
pass
else:
raise ValueError('Method cannot be recognized.')
test_domain_loss = loss.DANN(features.clone().detach(), test_ad_net)
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
if config['method'] == 'base':
total_loss = classifier_loss + test_domain_loss
else:
total_loss = loss_params["trade_off"] * transfer_loss + classifier_loss + test_domain_loss
total_loss.backward()
optimizer.step()
# torch.save(best_model, osp.join(config["output_path"], "best_model.pth.tar"))
return best_acc
def train(args, model, ad_net, train_loader, train_loader1, optimizer,
optimizer_ad, epoch, start_epoch, method):
model.train()
len_source = len(train_loader)
len_target = len(train_loader1)
if len_source > len_target:
num_iter = len_source
else:
num_iter = len_target
args.log_interval = num_iter
high = args.trade_off
loss_value = 0
loss_target_value = 0
for batch_idx in tqdm(range(num_iter), total=num_iter):
if batch_idx % len_source == 0:
iter_source = iter(train_loader)
if batch_idx % len_target == 0:
iter_target = iter(train_loader1)
data_source, label_source = iter_source.next()
data_source, label_source = data_source.to(
network.dev), label_source.to(network.dev)
data_target, label_target = iter_target.next()
data_target = data_target.to(network.dev)
optimizer.zero_grad()
optimizer_ad.zero_grad()
features_source, outputs_source = model(data_source)
features_target, outputs_target = model(data_target)
feature = torch.cat((features_source, features_target), dim=0)
output = torch.cat((outputs_source, outputs_target), dim=0)
classifier_loss = nn.CrossEntropyLoss()(output.narrow(
0, 0, data_source.size(0)), label_source)
softmax_output = nn.Softmax(dim=1)(output)
if epoch > start_epoch:
if method == 'DANN':
transfer_loss = loss_func.DANN(feature, ad_net)
elif method == "ALDA":
ad_out = ad_net(feature)
if label_source.size(0) != ad_out.size(0) // 2:
continue
adv_loss, reg_loss, correct_loss = loss_func.ALDA_loss(
ad_out,
label_source,
softmax_output,
weight_type=1,
threshold=args.threshold)
# whether add the corrected self-training loss
if "nocorrect" in args.loss_type:
transfer_loss = adv_loss
else:
transfer_loss = adv_loss + correct_loss
# reg_loss is only backward to the discriminator
if "noreg" not in args.loss_type:
for param in model.parameters():
param.requires_grad = False
reg_loss.backward(retain_graph=True)
for param in model.parameters():
param.requires_grad = True
else:
raise ValueError('Method cannot be recognized.')
loss_target_value += transfer_loss.item() / args.log_interval
else:
transfer_loss = 0
loss = classifier_loss + transfer_loss #loss_func.Square(softmax_output) + transfer_loss
if math.isnan(loss.item()):
raise AssertionError
loss.backward()
optimizer.step()
if epoch > start_epoch:
optimizer_ad.step()
if batch_idx % args.log_interval == args.log_interval - 1:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * args.batch_size, num_iter * args.batch_size,
100. * batch_idx / num_iter, loss.item()))
print("transfer_loss: {:.3f} classifier_loss: {:.3f}".format(
loss_target_value, loss_value))
loss_value = 0
loss_target_value = 0
def train(config):
## set pre-process
prep_dict = {}
prep_config = config["prep"]
prep_dict["source"] = prep.image_train(**config["prep"]['params'])
prep_dict["target"] = prep.image_train(**config["prep"]['params'])
if prep_config["test_10crop"]:
prep_dict["test"] = prep.image_test_10crop(**config["prep"]['params'])
else:
prep_dict["test"] = prep.image_test(**config["prep"]['params'])
## prepare data
dsets = {}
dset_loaders = {}
data_config = config["data"]
train_bs = data_config["source"]["batch_size"]
test_bs = data_config["test"]["batch_size"]
source_list = [
'.' + i for i in open(data_config["source"]["list_path"]).readlines()
]
target_list = [
'.' + i for i in open(data_config["target"]["list_path"]).readlines()
]
dsets["source"] = ImageList(source_list, \
transform=prep_dict["source"])
dset_loaders["source"] = DataLoader(dsets["source"], batch_size=train_bs, \
shuffle=True, num_workers=config['args'].num_worker, drop_last=True)
dsets["target"] = ImageList(target_list, \
transform=prep_dict["target"])
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, \
shuffle=True, num_workers=config['args'].num_worker, drop_last=True)
print("source dataset len:", len(dsets["source"]))
print("target dataset len:", len(dsets["target"]))
if prep_config["test_10crop"]:
for i in range(10):
test_list = [
'.' + i
for i in open(data_config["test"]["list_path"]).readlines()
]
dsets["test"] = [ImageList(test_list, \
transform=prep_dict["test"][i]) for i in range(10)]
dset_loaders["test"] = [DataLoader(dset, batch_size=test_bs, \
shuffle=False, num_workers=config['args'].num_worker) for dset in dsets['test']]
else:
test_list = [
'.' + i
for i in open(data_config["test"]["list_path"]).readlines()
]
dsets["test"] = ImageList(test_list, \
transform=prep_dict["test"])
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=test_bs, \
shuffle=False, num_workers=config['args'].num_worker)
dsets["target_label"] = ImageList_label(target_list, \
transform=prep_dict["target"])
dset_loaders["target_label"] = DataLoader(dsets["target_label"], batch_size=test_bs, \
shuffle=False, num_workers=config['args'].num_worker, drop_last=False)
class_num = config["network"]["params"]["class_num"]
## set base network
net_config = config["network"]
base_network = net_config["name"](**net_config["params"])
base_network = base_network.to(network.dev)
if config["restore_path"]:
checkpoint = torch.load(
osp.join(config["restore_path"], "best_model.pth"))["base_network"]
ckp = {}
for k, v in checkpoint.items():
if "module" in k:
ckp[k.split("module.")[-1]] = v
else:
ckp[k] = v
base_network.load_state_dict(ckp)
log_str = "successfully restore from {}".format(
osp.join(config["restore_path"], "best_model.pth"))
config["out_file"].write(log_str + "\n")
config["out_file"].flush()
print(log_str)
## add additional network for some methods
if "ALDA" in args.method:
ad_net = network.Multi_AdversarialNetwork(base_network.output_num(),
1024, class_num)
else:
ad_net = network.AdversarialNetwork(base_network.output_num(), 1024)
ad_net = ad_net.to(network.dev)
parameter_list = base_network.get_parameters() + ad_net.get_parameters()
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optimizer_config["type"](parameter_list, \
**(optimizer_config["optim_params"]))
param_lr = []
for param_group in optimizer.param_groups:
param_lr.append(param_group["lr"])
schedule_param = optimizer_config["lr_param"]
lr_scheduler = lr_schedule.schedule_dict[optimizer_config["lr_type"]]
gpus = config['gpu'].split(',')
if len(gpus) > 1:
ad_net = nn.DataParallel(ad_net,
device_ids=[int(i) for i in range(len(gpus))])
base_network = nn.DataParallel(
base_network, device_ids=[int(i) for i in range(len(gpus))])
loss_params = config["loss"]
high = loss_params["trade_off"]
begin_label = False
writer = SummaryWriter(config["output_path"])
## train
len_train_source = len(dset_loaders["source"])
len_train_target = len(dset_loaders["target"])
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
best_acc = 0.0
loss_value = 0
loss_adv_value = 0
loss_correct_value = 0
for i in tqdm(range(config["num_iterations"]),
total=config["num_iterations"]):
if i % config["test_interval"] == config["test_interval"] - 1:
base_network.train(False)
temp_acc = image_classification_test(dset_loaders, \
base_network, test_10crop=prep_config["test_10crop"])
temp_model = base_network #nn.Sequential(base_network)
if temp_acc > best_acc:
best_step = i
best_acc = temp_acc
best_model = temp_model
checkpoint = {
"base_network": best_model.state_dict(),
"ad_net": ad_net.state_dict()
}
torch.save(checkpoint,
osp.join(config["output_path"], "best_model.pth"))
print(
"\n########## save the best model. #############\n")
log_str = "iter: {:05d}, precision: {:.5f}".format(i, temp_acc)
config["out_file"].write(log_str + "\n")
config["out_file"].flush()
writer.add_scalar('precision', temp_acc, i)
print(log_str)
print("adv_loss: {:.3f} correct_loss: {:.3f} class_loss: {:.3f}".
format(loss_adv_value, loss_correct_value, loss_value))
loss_value = 0
loss_adv_value = 0
loss_correct_value = 0
#show val result on tensorboard
images_inv = prep.inv_preprocess(inputs_source.clone().cpu(), 3)
for index, img in enumerate(images_inv):
writer.add_image(str(index) + '/Images', img, i)
# save the pseudo_label
if 'PseudoLabel' in config['method'] and (
i % config["label_interval"] == config["label_interval"] - 1):
base_network.train(False)
pseudo_label_list = image_label(dset_loaders, base_network, threshold=config['threshold'], \
out_dir=config["output_path"])
dsets["target"] = ImageList(open(pseudo_label_list).readlines(), \
transform=prep_dict["target"])
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, \
shuffle=True, num_workers=config['args'].num_worker, drop_last=True)
iter_target = iter(
dset_loaders["target"]
) # replace the target dataloader with Pseudo_Label dataloader
begin_label = True
if i > config["stop_step"]:
log_str = "method {}, iter: {:05d}, precision: {:.5f}".format(
config["output_path"], best_step, best_acc)
config["final_log"].write(log_str + "\n")
config["final_log"].flush()
break
## train one iter
base_network.train(True)
ad_net.train(True)
optimizer = lr_scheduler(optimizer, i, **schedule_param)
optimizer.zero_grad()
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 = Variable(
inputs_source).to(network.dev), Variable(inputs_target).to(
network.dev), Variable(labels_source).to(network.dev)
features_source, outputs_source = base_network(inputs_source)
if args.source_detach:
features_source = features_source.detach()
features_target, outputs_target = base_network(inputs_target)
features = torch.cat((features_source, features_target), dim=0)
outputs = torch.cat((outputs_source, outputs_target), dim=0)
softmax_out = nn.Softmax(dim=1)(outputs)
loss_params["trade_off"] = network.calc_coeff(
i, high=high) #if i > 500 else 0.0
transfer_loss = 0.0
if 'DANN' in config['method']:
transfer_loss = loss.DANN(features, ad_net)
elif "ALDA" in config['method']:
ad_out = ad_net(features)
adv_loss, reg_loss, correct_loss = loss.ALDA_loss(
ad_out,
labels_source,
softmax_out,
weight_type=config['args'].weight_type,
threshold=config['threshold'])
# whether add the corrected self-training loss
if "nocorrect" in config['args'].loss_type:
transfer_loss = adv_loss
else:
transfer_loss = config['args'].adv_weight * adv_loss + config[
'args'].adv_weight * loss_params["trade_off"] * correct_loss
# reg_loss is only backward to the discriminator
if "noreg" not in config['args'].loss_type:
for param in base_network.parameters():
param.requires_grad = False
reg_loss.backward(retain_graph=True)
for param in base_network.parameters():
param.requires_grad = True
# on-line self-training
elif 'SelfTraining' in config['method']:
transfer_loss += loss_params["trade_off"] * loss.SelfTraining_loss(
outputs, softmax_out, config['threshold'])
# off-line self-training
elif 'PseudoLabel' in config['method']:
labels_target = labels_target.to(network.dev)
if begin_label:
transfer_loss += loss_params["trade_off"] * nn.CrossEntropyLoss(
ignore_index=-1)(outputs_target, labels_target)
else:
transfer_loss += 0.0 * nn.CrossEntropyLoss(ignore_index=-1)(
outputs_target, labels_target)
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
loss_value += classifier_loss.item() / config["test_interval"]
loss_adv_value += adv_loss.item() / config["test_interval"]
loss_correct_value += correct_loss.item() / config["test_interval"]
total_loss = classifier_loss + transfer_loss
total_loss.backward()
optimizer.step()
checkpoint = {
"base_network": temp_model.state_dict(),
"ad_net": ad_net.state_dict()
}
torch.save(checkpoint, osp.join(config["output_path"], "final_model.pth"))
return best_acc
def train(config):
base_network = network.ResNetFc('ResNet50', use_bottleneck=True, bottleneck_dim=config["bottleneck_dim"], new_cls=True, class_num=config["class_num"])
ad_net = network.AdversarialNetwork(config["bottleneck_dim"], config["hidden_dim"])
base_network = base_network.cuda()
ad_net = ad_net.cuda()
parameter_list = base_network.get_parameters() + ad_net.get_parameters()
source_path = ImageList(open(config["s_path"]).readlines(), transform=preprocess.image_train(resize_size=256, crop_size=224))
target_path = ImageList(open(config["t_path"]).readlines(), transform=preprocess.image_train(resize_size=256, crop_size=224))
test_path = ImageList(open(config["t_path"]).readlines(), transform=preprocess.image_test(resize_size=256, crop_size=224))
source_loader = DataLoader(source_path, batch_size=config["train_bs"], shuffle=True, num_workers=0, drop_last=True)
target_loader = DataLoader(target_path, batch_size=config["train_bs"], shuffle=True, num_workers=0, drop_last=True)
test_loader = DataLoader(test_path, batch_size=config["test_bs"], shuffle=True, num_workers=0, drop_last=True)
optimizer_config = config["optimizer"]
optimizer = optimizer_config["type"](parameter_list, \
**(optimizer_config["optim_params"]))
param_lr = []
for param_group in optimizer.param_groups:
param_lr.append(param_group["lr"])
schedule_param = optimizer_config["lr_param"]
lr_scheduler = lr_schedule.schedule_dict[optimizer_config["lr_type"]]
gpus = config["gpus"].split(',')
if len(gpus) > 1:
ad_net = nn.DataParallel(ad_net, device_ids=[int(i) for i in gpus])
base_network = nn.DataParallel(base_network, device_ids=[int(i) for i in gpus])
len_train_source = len(source_loader)
len_train_target = len(target_loader)
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
best_acc = 0.0
best_model_path = None
for i in trange(config["iterations"], leave=False):
if i % config["test_interval"] == config["test_interval"] - 1:
base_network.train(False)
temp_acc = image_classification_test(test_loader, base_network)
temp_model = nn.Sequential(base_network)
if temp_acc > best_acc:
best_acc = temp_acc
best_model = copy.deepcopy(temp_model)
best_iter = i
if best_model_path and osp.exists(best_model_path):
try:
os.remove(best_model_path)
except:
pass
best_model_path = osp.join(config["output_path"], "iter_{:05d}.pth.tar".format(best_iter))
torch.save(best_model, best_model_path)
log_str = "iter: {:05d}, precision: {:.5f}".format(i, temp_acc)
config["out_file"].write(log_str+"\n")
config["out_file"].flush()
# print("cut_loss: ", cut_loss.item())
print("mix_loss: ", mix_loss.item())
print(log_str)
base_network.train(True)
ad_net.train(True)
optimizer = lr_scheduler(optimizer, i, **schedule_param)
optimizer.zero_grad()
if i % len_train_source == 0:
iter_source = iter(source_loader)
if i % len_train_target == 0:
iter_target = iter(target_loader)
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()
labels_src_one_hot = torch.nn.functional.one_hot(labels_source, config["class_num"]).float()
# inputs_cut, labels_cut = cutmix(base_network, inputs_source, labels_src_one_hot, inputs_target, config["alpha"], config["class_num"])
inputs_mix, labels_mix = mixup(base_network, inputs_source, labels_src_one_hot, inputs_target, config["alpha"], config["class_num"], config["temperature"])
features_source, outputs_source = base_network(inputs_source)
features_target, outputs_target = base_network(inputs_target)
# features_cut, outputs_cut = base_network(inputs_cut)
features_mix, outputs_mix = base_network(inputs_mix)
features = torch.cat((features_source, features_target), dim=0)
outputs = torch.cat((outputs_source, outputs_target), dim=0)
softmax_out = nn.Softmax(dim=1)(outputs)
if config["method"] == 'DANN':
transfer_loss = loss.DANN(features, ad_net)
# cut_loss = utils.kl_loss(outputs_cut, labels_cut.detach())
mix_loss = utils.kl_loss(outputs_mix, labels_mix.detach())
else:
raise ValueError('Method cannot be recognized.')
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
total_loss = transfer_loss + classifier_loss + (5*mix_loss)
total_loss.backward()
optimizer.step()
torch.save(best_model, osp.join(config["output_path"], "best_model.pth.tar"))
print("Training Finished! Best Accuracy: ", best_acc)
return best_acc
def train(args, validate=False, label=None):
## set pre-process
if validate:
dset_loaders = data_load_y(args, label)
else:
dset_loaders = data_load(args)
class_num = args.class_num
class_weight_src = torch.ones(class_num, ).cuda()
##################################################################################################
## set base network
if args.net == 'resnet101':
netG = utils.ResBase101().cuda()
elif args.net == 'resnet50':
netG = utils.ResBase50().cuda()
netF = utils.ResClassifier(class_num=class_num,
feature_dim=netG.in_features,
bottleneck_dim=args.bottleneck_dim).cuda()
max_len = max(len(dset_loaders["source"]), len(dset_loaders["target"]))
args.max_iter = args.max_epoch * max_len
ad_flag = False
if args.method in {'DANN', 'DANNE'}:
ad_net = utils.AdversarialNetwork(args.bottleneck_dim,
1024,
max_iter=args.max_iter).cuda()
ad_flag = True
if args.method in {'CDAN', 'CDANE'}:
ad_net = utils.AdversarialNetwork(args.bottleneck_dim * class_num,
1024,
max_iter=args.max_iter).cuda()
random_layer = None
ad_flag = True
optimizer_g = optim.SGD(netG.parameters(), lr=args.lr * 0.1)
optimizer_f = optim.SGD(netF.parameters(), lr=args.lr)
if ad_flag:
optimizer_d = optim.SGD(ad_net.parameters(), lr=args.lr)
base_network = nn.Sequential(netG, netF)
if args.pl.startswith('atdoc_na'):
mem_fea = torch.rand(len(dset_loaders["target"].dataset),
args.bottleneck_dim).cuda()
mem_fea = mem_fea / torch.norm(mem_fea, p=2, dim=1, keepdim=True)
mem_cls = torch.ones(len(dset_loaders["target"].dataset),
class_num).cuda() / class_num
if args.pl == 'atdoc_nc':
mem_fea = torch.rand(args.class_num, args.bottleneck_dim).cuda()
mem_fea = mem_fea / torch.norm(mem_fea, p=2, dim=1, keepdim=True)
source_loader_iter = iter(dset_loaders["source"])
target_loader_iter = iter(dset_loaders["target"])
####
list_acc = []
best_ent = 100
for iter_num in range(1, args.max_iter + 1):
base_network.train()
lr_scheduler(optimizer_g,
init_lr=args.lr * 0.1,
iter_num=iter_num,
max_iter=args.max_iter)
lr_scheduler(optimizer_f,
init_lr=args.lr,
iter_num=iter_num,
max_iter=args.max_iter)
if ad_flag:
lr_scheduler(optimizer_d,
init_lr=args.lr,
iter_num=iter_num,
max_iter=args.max_iter)
try:
inputs_source, labels_source = source_loader_iter.next()
except:
source_loader_iter = iter(dset_loaders["source"])
inputs_source, labels_source = source_loader_iter.next()
try:
inputs_target, _, idx = target_loader_iter.next()
except:
target_loader_iter = iter(dset_loaders["target"])
inputs_target, _, idx = target_loader_iter.next()
inputs_source, inputs_target, labels_source = inputs_source.cuda(
), inputs_target.cuda(), labels_source.cuda()
if args.method == 'srconly' and args.pl == 'none':
features_source, outputs_source = base_network(inputs_source)
else:
features_source, outputs_source = base_network(inputs_source)
features_target, outputs_target = base_network(inputs_target)
features = torch.cat((features_source, features_target), dim=0)
outputs = torch.cat((outputs_source, outputs_target), dim=0)
softmax_out = nn.Softmax(dim=1)(outputs)
eff = utils.calc_coeff(iter_num, max_iter=args.max_iter)
if args.method[-1] == 'E':
entropy = loss.Entropy(softmax_out)
else:
entropy = None
if args.method in {'CDAN', 'CDANE'}:
transfer_loss = loss.CDAN([features, softmax_out], ad_net, entropy,
eff, random_layer)
elif args.method in {'DANN', 'DANNE'}:
transfer_loss = loss.DANN(features, ad_net, entropy, eff)
elif args.method == 'DAN':
transfer_loss = eff * loss.DAN(features_source, features_target)
elif args.method == 'DAN_Linear':
transfer_loss = eff * loss.DAN_Linear(features_source,
features_target)
elif args.method == 'JAN':
transfer_loss = eff * loss.JAN(
[features_source, softmax_out[0:args.batch_size, :]],
[features_target, softmax_out[args.batch_size::, :]])
elif args.method == 'JAN_Linear':
transfer_loss = eff * loss.JAN_Linear(
[features_source, softmax_out[0:args.batch_size, :]],
[features_target, softmax_out[args.batch_size::, :]])
elif args.method == 'CORAL':
transfer_loss = eff * loss.CORAL(features_source, features_target)
elif args.method == 'DDC':
transfer_loss = loss.MMD_loss()(features_source, features_target)
elif args.method == 'srconly':
transfer_loss = torch.tensor(0.0).cuda()
else:
raise ValueError('Method cannot be recognized.')
src_ = loss.CrossEntropyLabelSmooth(reduction='none',
num_classes=class_num,
epsilon=args.smooth)(
outputs_source, labels_source)
weight_src = class_weight_src[labels_source].unsqueeze(0)
classifier_loss = torch.sum(
weight_src * src_) / (torch.sum(weight_src).item())
total_loss = transfer_loss + classifier_loss
eff = iter_num / args.max_iter
if args.pl == 'none':
pass
elif args.pl == 'square':
softmax_out = nn.Softmax(dim=1)(outputs_target)
square_loss = -torch.sqrt((softmax_out**2).sum(dim=1)).mean()
total_loss += args.tar_par * eff * square_loss
elif args.pl == 'bsp':
sigma_loss = bsp_loss(features)
total_loss += args.tar_par * sigma_loss
elif args.pl == 'bnm':
softmax_out = nn.Softmax(dim=1)(outputs_target)
bnm_loss = -torch.norm(softmax_out, 'nuc')
cof = torch.tensor(
np.sqrt(np.min(softmax_out.size())) / softmax_out.size(0))
bnm_loss *= cof
total_loss += args.tar_par * eff * bnm_loss
elif args.pl == "mcc":
softmax_out = nn.Softmax(dim=1)(outputs_target)
ent_weight = 1 + torch.exp(-loss.Entropy(softmax_out)).detach()
ent_weight /= ent_weight.sum()
cov_tar = softmax_out.t().mm(
torch.diag(softmax_out.size(0) * ent_weight)).mm(softmax_out)
mcc_loss = (torch.diag(cov_tar) / cov_tar.sum(dim=1)).mean()
total_loss -= args.tar_par * eff * mcc_loss
elif args.pl == 'ent':
softmax_out = nn.Softmax(dim=1)(outputs_target)
ent_loss = torch.mean(loss.Entropy(softmax_out))
ent_loss /= torch.log(torch.tensor(class_num + 0.0))
total_loss += args.tar_par * eff * ent_loss
elif args.pl[0:3] == 'npl':
softmax_out = nn.Softmax(dim=1)(outputs_target)
softmax_out = softmax_out**2 / ((softmax_out**2).sum(dim=0))
weight_, pred = torch.max(softmax_out, 1)
loss_ = nn.CrossEntropyLoss(reduction='none')(outputs_target, pred)
classifier_loss = torch.sum(
weight_ * loss_) / (torch.sum(weight_).item())
total_loss += args.tar_par * eff * classifier_loss
elif args.pl == 'atdoc_nc':
mem_fea_norm = mem_fea / torch.norm(
mem_fea, p=2, dim=1, keepdim=True)
dis = torch.mm(features_target.detach(), mem_fea_norm.t())
_, pred = torch.max(dis, dim=1)
classifier_loss = nn.CrossEntropyLoss()(outputs_target, pred)
total_loss += args.tar_par * eff * classifier_loss
elif args.pl.startswith('atdoc_na'):
dis = -torch.mm(features_target.detach(), mem_fea.t())
for di in range(dis.size(0)):
dis[di, idx[di]] = torch.max(dis)
_, p1 = torch.sort(dis, dim=1)
w = torch.zeros(features_target.size(0), mem_fea.size(0)).cuda()
for wi in range(w.size(0)):
for wj in range(args.K):
w[wi][p1[wi, wj]] = 1 / args.K
weight_, pred = torch.max(w.mm(mem_cls), 1)
if args.pl == 'atdoc_na_now':
classifier_loss = nn.CrossEntropyLoss()(outputs_target, pred)
else:
loss_ = nn.CrossEntropyLoss(reduction='none')(outputs_target,
pred)
classifier_loss = torch.sum(
weight_ * loss_) / (torch.sum(weight_).item())
total_loss += args.tar_par * eff * classifier_loss
optimizer_g.zero_grad()
optimizer_f.zero_grad()
if ad_flag:
optimizer_d.zero_grad()
total_loss.backward()
optimizer_g.step()
optimizer_f.step()
if ad_flag:
optimizer_d.step()
if args.pl.startswith('atdoc_na'):
base_network.eval()
with torch.no_grad():
features_target, outputs_target = base_network(inputs_target)
features_target = features_target / torch.norm(
features_target, p=2, dim=1, keepdim=True)
softmax_out = nn.Softmax(dim=1)(outputs_target)
if args.pl == 'atdoc_na_nos':
outputs_target = softmax_out
else:
outputs_target = softmax_out**2 / (
(softmax_out**2).sum(dim=0))
mem_fea[idx] = (1.0 - args.momentum) * mem_fea[
idx] + args.momentum * features_target.clone()
mem_cls[idx] = (1.0 - args.momentum) * mem_cls[
idx] + args.momentum * outputs_target.clone()
if args.pl == 'atdoc_nc':
base_network.eval()
with torch.no_grad():
features_target, outputs_target = base_network(inputs_target)
softmax_t = nn.Softmax(dim=1)(outputs_target)
_, pred_t = torch.max(softmax_t, 1)
onehot_t = torch.eye(args.class_num)[pred_t].cuda()
center_t = torch.mm(features_target.t(),
onehot_t) / (onehot_t.sum(dim=0) + 1e-8)
mem_fea = (1.0 - args.momentum
) * mem_fea + args.momentum * center_t.t().clone()
if iter_num % int(args.eval_epoch * max_len) == 0:
base_network.eval()
if args.dset == 'VISDA-C':
acc, py, score, y, tacc = utils.cal_acc_visda(
dset_loaders["test"], base_network)
args.out_file.write(tacc + '\n')
args.out_file.flush()
_ent = loss.Entropy(score)
mean_ent = 0
for ci in range(args.class_num):
mean_ent += _ent[py == ci].mean()
mean_ent /= args.class_num
else:
acc, py, score, y = utils.cal_acc(dset_loaders["test"],
base_network)
mean_ent = torch.mean(loss.Entropy(score))
list_acc.append(acc * 100)
if best_ent > mean_ent:
best_ent = mean_ent
val_acc = acc * 100
best_y = y
best_py = py
best_score = score
log_str = 'Task: {}, Iter:{}/{}; Accuracy = {:.2f}%; Mean Ent = {:.4f}'.format(
args.name, iter_num, args.max_iter, acc * 100, mean_ent)
args.out_file.write(log_str + '\n')
args.out_file.flush()
print(log_str + '\n')
idx = np.argmax(np.array(list_acc))
max_acc = list_acc[idx]
final_acc = list_acc[-1]
log_str = '\n==========================================\n'
log_str += '\nVal Acc = {:.2f}\nMax Acc = {:.2f}\nFin Acc = {:.2f}\n'.format(
val_acc, max_acc, final_acc)
args.out_file.write(log_str + '\n')
args.out_file.flush()
# torch.save(base_network.state_dict(), osp.join(args.output_dir, args.log + ".pt"))
# sio.savemat(osp.join(args.output_dir, args.log + ".mat"), {'y':best_y.cpu().numpy(),
# 'py':best_py.cpu().numpy(), 'score':best_score.cpu().numpy()})
return best_y.cpu().numpy().astype(np.int64)
def train(config):
print("Deep copy of model with margin as 1.0")
## set pre-process
prep_dict = {}
prep_config = config["prep"]
prep_dict["source"] = prep.image_train(**config["prep"]['params'])
prep_dict["target"] = prep.image_train(**config["prep"]['params'])
if prep_config["test_10crop"]:
prep_dict["test"] = prep.image_test_10crop(**config["prep"]['params'])
else:
prep_dict["test"] = prep.image_test(**config["prep"]['params'])
## prepare data
dsets = {}
dset_loaders = {}
data_config = config["data"]
train_bs = data_config["source"]["batch_size"]
test_bs = data_config["test"]["batch_size"]
dsets["source"] = ImageList(open(data_config["source"]["list_path"]).readlines(), \
transform=prep_dict["source"])
dset_loaders["source"] = DataLoader(dsets["source"], batch_size=train_bs, \
shuffle=True, num_workers=4, drop_last=True)
dsets["target"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["target"])
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, \
shuffle=True, num_workers=4, drop_last=True)
if prep_config["test_10crop"]:
for i in range(10):
dsets["test"] = [ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"][i]) for i in range(10)]
dset_loaders["test"] = [DataLoader(dset, batch_size=test_bs, \
shuffle=False, num_workers=4) for dset in dsets['test']]
else:
dsets["test"] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"])
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=test_bs, \
shuffle=False, num_workers=4)
class_num = config["network"]["params"]["class_num"]
## set base network
net_config = config["network"]
base_network = net_config["call"](net_config["name"],
**net_config["params"])
base_network_teacher = net_config["call"](net_config["name"],
**net_config["params_teacher"])
base_network = base_network.cuda()
base_network_teacher = copy.deepcopy(base_network).cuda()
for param in base_network_teacher.parameters():
param.detach_()
# base_network_teacher = base_network_teacher.cuda()
# print("check init: ", torch.equal(base_network.fc.weight, base_network_teacher.fc.weight))
base_network.layer1[-1].relu = nn.ReLU()
base_network.layer2[-1].relu = nn.ReLU()
base_network.layer3[-1].relu = nn.ReLU()
base_network.layer4[-1].relu = nn.ReLU()
base_network_teacher.layer1[-1].relu = nn.ReLU()
base_network_teacher.layer2[-1].relu = nn.ReLU()
base_network_teacher.layer3[-1].relu = nn.ReLU()
base_network_teacher.layer4[-1].relu = nn.ReLU()
# print(base_network)
for n, m in base_network.named_modules():
if n == 'layer1.2.bn3' or 'layer2.3.bn3' or 'layer3.5.bn3' or 'layer4.2.bn3':
m.register_forward_hook(get_activation_student(n))
if config["loss"]["random"]:
random_layer = network.RandomLayer(
[base_network.output_num(), class_num],
config["loss"]["random_dim"])
ad_net = network.AdversarialNetwork(config["loss"]["random_dim"], 1024)
else:
random_layer = None
ad_net = network.AdversarialNetwork(
base_network.output_num() * class_num, 1024)
if config["loss"]["random"]:
random_layer.cuda()
ad_net = ad_net.cuda()
parameter_list = base_network.get_parameters() + ad_net.get_parameters()
Hloss = loss.Entropy()
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optimizer_config["type"](parameter_list, \
**(optimizer_config["optim_params"]))
param_lr = []
for param_group in optimizer.param_groups:
param_lr.append(param_group["lr"])
schedule_param = optimizer_config["lr_param"]
lr_scheduler = lr_schedule.schedule_dict[optimizer_config["lr_type"]]
gpus = config['gpu'].split(',')
if len(gpus) > 1:
ad_net = nn.DataParallel(ad_net, device_ids=[int(i) for i in gpus])
base_network = nn.DataParallel(base_network,
device_ids=[int(i) for i in gpus])
## train
len_train_source = len(dset_loaders["source"])
len_train_target = len(dset_loaders["target"])
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
best_acc = 0.0
temperature = config["temperature"]
for i in trange(config["num_iterations"], leave=False):
global activation_student
if i % config["test_interval"] == config["test_interval"] - 1:
base_network.eval()
base_network_teacher.eval()
temp_acc, temp_acc_teacher = image_classification_test(dset_loaders, \
base_network, base_network_teacher, test_10crop=prep_config["test_10crop"])
temp_model = nn.Sequential(base_network_teacher)
if temp_acc > best_acc:
best_acc = temp_acc
best_model = temp_model
log_str = "iter: {:05d}, precision: {:.5f}".format(i, temp_acc)
log_str1 = "precision: {:.5f}".format(temp_acc_teacher)
config["out_file"].write(log_str + "\t" + log_str1 + "\t" +
str(classifier_loss.item()) + "\t" +
str(dann_loss.item()) + "\t" +
str(ent_loss.item()) + "\t" + "\n")
config["out_file"].flush()
print("ent Loss: ", ent_loss.item())
print("Dann loss: ", dann_loss.item())
print("Classification Loss: ", classifier_loss.item())
print(log_str)
print(log_str1)
# if i % config["snapshot_interval"] == 0:
# torch.save(nn.Sequential(base_network), osp.join(config["output_path"], \
# "iter_{:05d}_model.pth.tar".format(i)))
loss_params = config["loss"]
## train one iter
base_network.train(True)
base_network_teacher.train(True)
ad_net.train(True)
optimizer = lr_scheduler(optimizer, i, **schedule_param)
optimizer.zero_grad()
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_source1, inputs_source2, inputs_target1, inputs_target2, labels_source = utils.get_copies(
inputs_source, inputs_target, labels_source)
margin = 1
loss_alter = 0
#### For source data
features_source, outputs_source = base_network(inputs_source1)
# features_source2, outputs_source2 = base_network(inputs_source2)
feature1 = base_network_teacher.features1(inputs_source2)
feature2 = base_network_teacher.features2(feature1)
feature3 = base_network_teacher.features3(feature2)
feature4 = base_network_teacher.features4(feature3)
feature4_avg = base_network_teacher.avgpool(feature4)
feature4_res = feature4_avg.view(feature4_avg.size(0), -1)
features_source2 = base_network_teacher.bottleneck(feature4_res)
outputs_source2 = base_network_teacher.fc(features_source2)
loss_alter += loss.decision_boundary_transfer(
activation_student['layer1.2.bn3'], feature1.detach(), margin) / (
train_bs * activation_student['layer1.2.bn3'].size(1) * 8)
loss_alter += loss.decision_boundary_transfer(
activation_student['layer2.3.bn3'], feature2.detach(), margin) / (
train_bs * activation_student['layer2.3.bn3'].size(1) * 4)
loss_alter += loss.decision_boundary_transfer(
activation_student['layer3.5.bn3'], feature3.detach(), margin) / (
train_bs * activation_student['layer3.5.bn3'].size(1) * 2)
loss_alter += loss.decision_boundary_transfer(
activation_student['layer4.2.bn3'], feature4.detach(),
margin) / (train_bs * activation_student['layer4.2.bn3'].size(1))
## For Target data
ramp = utils.sigmoid_rampup(i, 100004)
ramp_confidence = utils.sigmoid_rampup(5 * i, 100004)
features_target, outputs_target = base_network(inputs_target1)
sample_selection_indices = get_confident_idx.confident_samples(
base_network, inputs_target1, ramp_confidence, class_num, train_bs)
confident_targets = utils.subsample(outputs_target,
sample_selection_indices)
feature1_teacher = base_network_teacher.features1(inputs_target2)
feature2_teacher = base_network_teacher.features2(feature1_teacher)
feature3_teacher = base_network_teacher.features3(feature2_teacher)
feature4_teacher = base_network_teacher.features4(feature3_teacher)
feature4_teacher_avg = base_network_teacher.avgpool(feature4_teacher)
feature4_teacher_res = feature4_teacher_avg.view(
feature4_teacher_avg.size(0), -1)
features_target2 = base_network_teacher.bottleneck(
feature4_teacher_res)
outputs_target2 = base_network_teacher.fc(features_target2)
loss_alter += loss.decision_boundary_transfer(
activation_student['layer1.2.bn3'], feature1_teacher.detach(),
margin) / (train_bs * activation_student['layer1.2.bn3'].size(1) *
8)
loss_alter += loss.decision_boundary_transfer(
activation_student['layer2.3.bn3'], feature2_teacher.detach(),
margin) / (train_bs * activation_student['layer2.3.bn3'].size(1) *
4)
loss_alter += loss.decision_boundary_transfer(
activation_student['layer3.5.bn3'], feature3_teacher.detach(),
margin) / (train_bs * activation_student['layer3.5.bn3'].size(1) *
2)
loss_alter += loss.decision_boundary_transfer(
activation_student['layer4.2.bn3'], feature4_teacher.detach(),
margin) / (train_bs * activation_student['layer4.2.bn3'].size(1))
loss_alter = loss_alter / 1000 ## May be multiply with 4 later in tests
loss_alter = loss_alter.unsqueeze(0).unsqueeze(1)
features = torch.cat((features_source, features_target), dim=0)
outputs = torch.cat((outputs_source, outputs_target), dim=0)
softmax_out_src = nn.Softmax(dim=1)(outputs_source)
softmax_out_tar = nn.Softmax(dim=1)(outputs_target)
softmax_out = nn.Softmax(dim=1)(outputs)
features_teacher = torch.cat((features_source2, features_target2),
dim=0)
outputs_teacher = torch.cat((outputs_source2, outputs_target2), dim=0)
softmax_out_src_teacher = nn.Softmax(dim=1)(outputs_source2)
softmax_out_tar_teacher = nn.Softmax(dim=1)(outputs_target2)
softmax_out_teacher = nn.Softmax(dim=1)(outputs_teacher)
if config['method'] == 'DANN+E':
ent_loss = Hloss(confident_targets)
dann_loss = loss.DANN(features, ad_net)
elif config['method'] == 'DANN':
dann_loss = loss.DANN(features, ad_net)
# dann_loss = 0
else:
raise ValueError('Method cannot be recognized.')
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
# loss_KD = -(F.softmax(outputs_teacher/ temperature, 1).detach() *
# (F.log_softmax(outputs/temperature, 1) - F.log_softmax(outputs_teacher/temperature, 1).detach())).sum() / train_bs
# print(loss_KD)
# total_loss = loss_alter #+ (config["ent_loss"] * ent_loss)
total_loss = dann_loss + classifier_loss + (
ramp * ent_loss) #+ (config["ent_loss"] * ent_loss)
total_loss.backward(retain_graph=True)
optimizer.step()
loss.update_ema_variables(base_network, base_network_teacher,
config["teacher_alpha"], i)
torch.save(best_model, osp.join(config["output_path"],
"best_model.pth.tar"))
return best_acc
def train(args, model, ad_net, random_layer, train_loader, train_loader1,
optimizer, optimizer_ad, epoch, start_epoch, method, ccp):
cl_method = 'ga' #choices=['ga', 'nn', 'free', 'pc', 'forward']
meta_method = 'free' if cl_method == 'ga' else cl_method
K = 10
model.train()
len_source = len(train_loader)
len_target = len(train_loader1)
if len_source > len_target:
num_iter = len_source
else:
num_iter = len_target
for batch_idx in range(num_iter):
if batch_idx % len_source == 0:
iter_source = iter(train_loader)
if batch_idx % len_target == 0:
iter_target = iter(train_loader1)
data_source, label_source = iter_source.next()
data_source, label_source = data_source.cuda(), label_source.cuda()
data_target, label_target = iter_target.next()
data_target = data_target.cuda()
optimizer.zero_grad()
optimizer_ad.zero_grad()
feature, output = model(torch.cat((data_source, data_target), 0))
#err_s_label, loss_vector = non_negative_loss (f=output.narrow(0, 0, data_source.size(0)), K=10, labels=label_source, ccp=ccp,beta=0)
loss, loss_vector = chosen_loss_c(f=output.narrow(
0, 0, data_source.size(0)),
K=K,
labels=label_source,
ccp=ccp,
meta_method=meta_method)
#loss = nn.CrossEntropyLoss()(output.narrow(0, 0, data_source.size(0)), label_source)
softmax_output = nn.Softmax(dim=1)(output)
if cl_method == 'ga':
if torch.min(loss_vector).item() < 0:
loss_vector_with_zeros = torch.cat(
(loss_vector.view(-1, 1), torch.zeros(
K, requires_grad=True).view(-1, 1).to(device)), 1)
min_loss_vector, _ = torch.min(loss_vector_with_zeros, dim=1)
loss = torch.sum(min_loss_vector)
loss.backward(retain_graph=True)
for group in optimizer.param_groups:
for p in group['params']:
p.grad = -1 * p.grad
else:
loss.backward(retain_graph=True)
else:
loss.backward(retain_graph=True)
optimizer.step()
optimizer.zero_grad()
if epoch > start_epoch:
if method == 'CDAN-E':
softmax_output = Tsharpen(softmax_output)
entropy = loss_func.Entropy(softmax_output)
loss2 = loss_func.CDAN(
[feature, softmax_output], ad_net, entropy,
network.calc_coeff(num_iter * (epoch - start_epoch) +
batch_idx), random_layer)
elif method == 'CDAN':
loss2 = loss_func.CDAN([feature, softmax_output], ad_net, None,
None, random_layer)
elif method == 'DANN':
loss2 = loss_func.DANN(feature, ad_net)
else:
raise ValueError('Method cannot be recognized.')
if epoch > start_epoch:
loss2.backward()
optimizer.step()
optimizer_ad.step()
if (batch_idx + epoch * num_iter) % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss1: {:.6f}'.format(
epoch, batch_idx * args.batch_size, num_iter * args.batch_size,
100. * batch_idx / num_iter, loss.item()))
def train(args, model, ad_net, source_samples, source_labels, target_samples,
target_labels, optimizer, optimizer_ad, epoch, start_epoch, method,
source_label_distribution, out_wei_file, cov_mat,
pseudo_target_label, class_weights, true_weights):
model.train()
cov_mat[:] = 0.0
pseudo_target_label[:] = 0.0
len_source = source_labels.shape[0]
len_target = target_labels.shape[0]
size = max(len_source, len_target)
num_iter = int(size / args.batch_size)
for batch_idx in range(num_iter):
t = time.time()
source_idx = np.random.choice(len_source, args.batch_size)
target_idx = np.random.choice(len_target, args.batch_size)
data_source, label_source = source_samples[source_idx], source_labels[
source_idx]
data_target, _ = target_samples[target_idx], target_labels[target_idx]
optimizer.zero_grad()
optimizer_ad.zero_grad()
feature, output = model(torch.cat((data_source, data_target), 0))
if 'IW' in method:
ys_onehot = torch.zeros(args.batch_size, 10).to(args.device)
ys_onehot.scatter_(1, label_source.view(-1, 1), 1)
# Compute weights on source data.
if 'ORACLE' in method:
weights = torch.mm(ys_onehot, true_weights)
else:
weights = torch.mm(ys_onehot, model.im_weights)
source_preds, target_preds = output[:args.batch_size], output[
args.batch_size:]
# Compute the aggregated distribution of pseudo-label on the target domain.
pseudo_target_label += torch.sum(F.softmax(target_preds, dim=1),
dim=0).view(-1, 1).detach()
# Update the covariance matrix on the source domain as well.
cov_mat += torch.mm(
F.softmax(source_preds, dim=1).transpose(1, 0),
ys_onehot).detach()
loss = torch.mean(
nn.CrossEntropyLoss(weight=class_weights, reduction='none')
(output.narrow(0, 0, data_source.size(0)), label_source) *
weights) / 10.0
else:
loss = nn.CrossEntropyLoss()(output.narrow(0, 0,
data_source.size(0)),
label_source)
if epoch > start_epoch:
if method == 'CDAN-E':
softmax_output = nn.Softmax(dim=1)(output)
entropy = loss_func.Entropy(softmax_output)
loss += loss_func.CDAN(
[feature, softmax_output],
ad_net,
entropy,
network.calc_coeff(num_iter * (epoch - start_epoch) +
batch_idx),
None,
device=args.device)
elif 'IWCDAN-E' in method:
softmax_output = nn.Softmax(dim=1)(output)
entropy = loss_func.Entropy(softmax_output)
loss += loss_func.CDAN(
[feature, softmax_output],
ad_net,
entropy,
network.calc_coeff(num_iter * (epoch - start_epoch) +
batch_idx),
None,
weights=weights,
device=args.device)
elif method == 'CDAN':
softmax_output = nn.Softmax(dim=1)(output)
loss += loss_func.CDAN([feature, softmax_output],
ad_net,
None,
None,
None,
device=args.device)
elif 'IWCDAN' in method:
softmax_output = nn.Softmax(dim=1)(output)
loss += loss_func.CDAN([feature, softmax_output],
ad_net,
None,
None,
None,
weights=weights,
device=args.device)
elif method == 'DANN':
loss += loss_func.DANN(feature, ad_net, args.device)
elif 'IWDAN' in method:
dloss = loss_func.IWDAN(feature, ad_net, weights)
loss += args.mu * dloss
elif method == 'NANN':
pass
else:
raise ValueError('Method cannot be recognized.')
loss.backward()
optimizer.step()
if epoch > start_epoch and method != 'NANN':
optimizer_ad.step()
if 'IW' in method and epoch > start_epoch:
pseudo_target_label /= args.batch_size * num_iter
cov_mat /= args.batch_size * num_iter
# Recompute the importance weight by solving a QP.
model.im_weights_update(source_label_distribution,
pseudo_target_label.cpu().detach().numpy(),
cov_mat.cpu().detach().numpy(), args.device)
current_weights = [
round(x, 4) for x in model.im_weights.data.cpu().numpy().flatten()
]
write_list(out_wei_file, [
np.linalg.norm(current_weights -
true_weights.cpu().numpy().flatten())
] + current_weights)
print(
np.linalg.norm(current_weights -
true_weights.cpu().numpy().flatten()),
current_weights)
def train(args):
## set pre-process
dset_loaders = data_load(args)
class_num = args.class_num
class_weight_src = torch.ones(class_num, ).cuda()
##################################################################################################
## set base network
if args.net == 'resnet34':
netG = utils.ResBase34().cuda()
elif args.net == 'vgg16':
netG = utils.VGG16Base().cuda()
netF = utils.ResClassifier(class_num=class_num,
feature_dim=netG.in_features,
bottleneck_dim=args.bottleneck_dim).cuda()
max_len = max(len(dset_loaders["source"]), len(dset_loaders["target"]))
args.max_iter = args.max_epoch * max_len
ad_flag = False
if args.method == 'DANN':
ad_net = utils.AdversarialNetwork(args.bottleneck_dim,
1024,
max_iter=args.max_iter).cuda()
ad_flag = True
if args.method == 'CDANE':
ad_net = utils.AdversarialNetwork(args.bottleneck_dim * class_num,
1024,
max_iter=args.max_iter).cuda()
random_layer = None
ad_flag = True
optimizer_g = optim.SGD(netG.parameters(), lr=args.lr * 0.1)
optimizer_f = optim.SGD(netF.parameters(), lr=args.lr)
if ad_flag:
optimizer_d = optim.SGD(ad_net.parameters(), lr=args.lr)
base_network = nn.Sequential(netG, netF)
if args.pl.startswith('atdoc_na'):
mem_fea = torch.rand(
len(dset_loaders["target"].dataset) +
len(dset_loaders["ltarget"].dataset), args.bottleneck_dim).cuda()
mem_fea = mem_fea / torch.norm(mem_fea, p=2, dim=1, keepdim=True)
mem_cls = torch.ones(
len(dset_loaders["target"].dataset) +
len(dset_loaders["ltarget"].dataset), class_num).cuda() / class_num
if args.pl == 'atdoc_nc':
mem_fea = torch.rand(args.class_num, args.bottleneck_dim).cuda()
mem_fea = mem_fea / torch.norm(mem_fea, p=2, dim=1, keepdim=True)
source_loader_iter = iter(dset_loaders["source"])
target_loader_iter = iter(dset_loaders["target"])
ltarget_loader_iter = iter(dset_loaders["ltarget"])
# ###
list_acc = []
best_val_acc = 0
for iter_num in range(1, args.max_iter + 1):
# print(iter_num)
base_network.train()
lr_scheduler(optimizer_g,
init_lr=args.lr * 0.1,
iter_num=iter_num,
max_iter=args.max_iter)
lr_scheduler(optimizer_f,
init_lr=args.lr,
iter_num=iter_num,
max_iter=args.max_iter)
if ad_flag:
lr_scheduler(optimizer_d,
init_lr=args.lr,
iter_num=iter_num,
max_iter=args.max_iter)
try:
inputs_source, labels_source = source_loader_iter.next()
except:
source_loader_iter = iter(dset_loaders["source"])
inputs_source, labels_source = source_loader_iter.next()
try:
inputs_target, _, idx = target_loader_iter.next()
except:
target_loader_iter = iter(dset_loaders["target"])
inputs_target, _, idx = target_loader_iter.next()
try:
inputs_ltarget, labels_ltarget, lidx = ltarget_loader_iter.next()
except:
ltarget_loader_iter = iter(dset_loaders["ltarget"])
inputs_ltarget, labels_ltarget, lidx = ltarget_loader_iter.next()
inputs_ltarget, labels_ltarget = inputs_ltarget.cuda(
), labels_ltarget.cuda()
inputs_source, inputs_target, labels_source = inputs_source.cuda(
), inputs_target.cuda(), labels_source.cuda()
if args.method == 'srconly' and args.pl == 'none':
features_source, outputs_source = base_network(inputs_source)
features_ltarget, outputs_ltarget = base_network(inputs_ltarget)
else:
features_ltarget, outputs_ltarget = base_network(inputs_ltarget)
features_source, outputs_source = base_network(inputs_source)
features_target, outputs_target = base_network(inputs_target)
features_target = torch.cat((features_ltarget, features_target),
dim=0)
outputs_target = torch.cat((outputs_ltarget, outputs_target),
dim=0)
features = torch.cat((features_source, features_target), dim=0)
outputs = torch.cat((outputs_source, outputs_target), dim=0)
softmax_out = nn.Softmax(dim=1)(outputs)
eff = utils.calc_coeff(iter_num, max_iter=args.max_iter)
if args.method[-1] == 'E':
entropy = loss.Entropy(softmax_out)
else:
entropy = None
if args.method == 'CDANE':
transfer_loss = loss.CDAN([features, softmax_out], ad_net, entropy,
eff, random_layer)
elif args.method == 'DANN':
transfer_loss = loss.DANN(features, ad_net, entropy, eff)
elif args.method == 'srconly':
transfer_loss = torch.tensor(0.0).cuda()
else:
raise ValueError('Method cannot be recognized.')
src_ = loss.CrossEntropyLabelSmooth(reduction='none',
num_classes=class_num,
epsilon=args.smooth)(
outputs_source, labels_source)
weight_src = class_weight_src[labels_source].unsqueeze(0)
classifier_loss = torch.sum(
weight_src * src_) / (torch.sum(weight_src).item())
total_loss = transfer_loss + classifier_loss
ltar_ = loss.CrossEntropyLabelSmooth(reduction='none',
num_classes=class_num,
epsilon=args.smooth)(
outputs_ltarget,
labels_ltarget)
weight_src = class_weight_src[labels_ltarget].unsqueeze(0)
ltar_classifier_loss = torch.sum(
weight_src * ltar_) / (torch.sum(weight_src).item())
total_loss += ltar_classifier_loss
eff = iter_num / args.max_iter
if not args.pl == 'none':
outputs_target = outputs_target[-args.batch_size // 3:, :]
features_target = features_target[-args.batch_size // 3:, :]
if args.pl == 'none':
pass
elif args.pl == 'square':
softmax_out = nn.Softmax(dim=1)(outputs_target)
square_loss = -torch.sqrt((softmax_out**2).sum(dim=1)).mean()
total_loss += args.tar_par * eff * square_loss
elif args.pl == 'bsp':
sigma_loss = bsp_loss(features)
total_loss += args.tar_par * sigma_loss
elif args.pl == 'ent':
softmax_out = nn.Softmax(dim=1)(outputs_target)
ent_loss = torch.mean(loss.Entropy(softmax_out))
ent_loss /= torch.log(torch.tensor(class_num + 0.0))
total_loss += args.tar_par * eff * ent_loss
elif args.pl == 'bnm':
softmax_out = nn.Softmax(dim=1)(outputs_target)
bnm_loss = -torch.norm(softmax_out, 'nuc')
cof = torch.tensor(
np.sqrt(np.min(softmax_out.size())) / softmax_out.size(0))
bnm_loss *= cof
total_loss += args.tar_par * eff * bnm_loss
elif args.pl == 'mcc':
softmax_out = nn.Softmax(dim=1)(outputs_target)
ent_weight = 1 + torch.exp(-loss.Entropy(softmax_out)).detach()
ent_weight /= ent_weight.sum()
cov_tar = softmax_out.t().mm(
torch.diag(softmax_out.size(0) * ent_weight)).mm(softmax_out)
mcc_loss = (torch.diag(cov_tar) / cov_tar.sum(dim=1)).mean()
total_loss -= args.tar_par * eff * mcc_loss
elif args.pl == 'npl':
softmax_out = nn.Softmax(dim=1)(outputs_target)
softmax_out = softmax_out**2 / ((softmax_out**2).sum(dim=0))
weight_, pred = torch.max(softmax_out, 1)
loss_ = nn.CrossEntropyLoss(reduction='none')(outputs_target, pred)
classifier_loss = torch.sum(
weight_ * loss_) / (torch.sum(weight_).item())
total_loss += args.tar_par * eff * classifier_loss
elif args.pl == 'atdoc_nc':
mem_fea_norm = mem_fea / torch.norm(
mem_fea, p=2, dim=1, keepdim=True)
dis = torch.mm(features_target.detach(), mem_fea_norm.t())
_, pred = torch.max(dis, dim=1)
classifier_loss = nn.CrossEntropyLoss()(outputs_target, pred)
total_loss += args.tar_par * eff * classifier_loss
elif args.pl.startswith('atdoc_na'):
dis = -torch.mm(features_target.detach(), mem_fea.t())
for di in range(dis.size(0)):
dis[di, idx[di]] = torch.max(dis)
_, p1 = torch.sort(dis, dim=1)
w = torch.zeros(features_target.size(0), mem_fea.size(0)).cuda()
for wi in range(w.size(0)):
for wj in range(args.K):
w[wi][p1[wi, wj]] = 1 / args.K
weight_, pred = torch.max(w.mm(mem_cls), 1)
if args.pl.startswith('atdoc_na_now'):
classifier_loss = nn.CrossEntropyLoss()(outputs_target, pred)
else:
loss_ = nn.CrossEntropyLoss(reduction='none')(outputs_target,
pred)
classifier_loss = torch.sum(
weight_ * loss_) / (torch.sum(weight_).item())
total_loss += args.tar_par * eff * classifier_loss
optimizer_g.zero_grad()
optimizer_f.zero_grad()
if ad_flag:
optimizer_d.zero_grad()
total_loss.backward()
optimizer_g.step()
optimizer_f.step()
if ad_flag:
optimizer_d.step()
if args.pl.startswith('atdoc_na'):
base_network.eval()
with torch.no_grad():
features_target, outputs_target = base_network(inputs_target)
features_target = features_target / torch.norm(
features_target, p=2, dim=1, keepdim=True)
softmax_out = nn.Softmax(dim=1)(outputs_target)
if args.pl.startswith('atdoc_na_nos'):
outputs_target = softmax_out
else:
outputs_target = softmax_out**2 / (
(softmax_out**2).sum(dim=0))
mem_fea[idx] = (1.0 - args.momentum) * mem_fea[
idx] + args.momentum * features_target.clone()
mem_cls[idx] = (1.0 - args.momentum) * mem_cls[
idx] + args.momentum * outputs_target.clone()
with torch.no_grad():
features_ltarget, outputs_ltarget = base_network(
inputs_ltarget)
features_ltarget = features_ltarget / torch.norm(
features_ltarget, p=2, dim=1, keepdim=True)
softmax_out = nn.Softmax(dim=1)(outputs_ltarget)
if args.pl.startswith('atdoc_na_nos'):
outputs_ltarget = softmax_out
else:
outputs_ltarget = softmax_out**2 / (
(softmax_out**2).sum(dim=0))
mem_fea[lidx + len(dset_loaders["target"].dataset)] = (1.0 - args.momentum) * \
mem_fea[lidx + len(dset_loaders["target"].dataset)] + args.momentum * features_ltarget.clone()
mem_cls[lidx + len(dset_loaders["target"].dataset)] = (1.0 - args.momentum) * \
mem_cls[lidx + len(dset_loaders["target"].dataset)] + args.momentum * outputs_ltarget.clone()
if args.pl == 'atdoc_nc':
base_network.eval()
with torch.no_grad():
feat_u, outputs_target = base_network(inputs_target)
softmax_t = nn.Softmax(dim=1)(outputs_target)
_, pred_t = torch.max(softmax_t, 1)
onehot_tu = torch.eye(args.class_num)[pred_t].cuda()
feat_l, outputs_target = base_network(inputs_ltarget)
softmax_t = nn.Softmax(dim=1)(outputs_target)
_, pred_t = torch.max(softmax_t, 1)
onehot_tl = torch.eye(args.class_num)[pred_t].cuda()
center_t = ((torch.mm(feat_u.t(), onehot_tu) + torch.mm(
feat_l.t(), onehot_tl))) / (onehot_tu.sum(dim=0) +
onehot_tl.sum(dim=0) + 1e-8)
mem_fea = (1.0 - args.momentum
) * mem_fea + args.momentum * center_t.t().clone()
if iter_num % int(args.eval_epoch * max_len) == 0:
base_network.eval()
acc, py, score, y = utils.cal_acc(dset_loaders["test"],
base_network)
val_acc, _, _, _ = utils.cal_acc(dset_loaders["val"], base_network)
list_acc.append(acc * 100)
if best_val_acc <= val_acc:
best_val_acc = val_acc
best_acc = acc
best_y = y
best_py = py
best_score = score
log_str = 'Task: {}, Iter:{}/{}; Accuracy = {:.2f}%; Val Acc = {:.2f}%'.format(
args.name, iter_num, args.max_iter, acc * 100, val_acc * 100)
args.out_file.write(log_str + '\n')
args.out_file.flush()
print(log_str + '\n')
val_acc = best_acc * 100
idx = np.argmax(np.array(list_acc))
max_acc = list_acc[idx]
final_acc = list_acc[-1]
log_str = '\n==========================================\n'
log_str += '\nVal Acc = {:.2f}\nMax Acc = {:.2f}\nFin Acc = {:.2f}\n'.format(
val_acc, max_acc, final_acc)
args.out_file.write(log_str + '\n')
args.out_file.flush()
def train(source_loader, target_loader, model, ad_net, criterion, optimizer,
epoch, log, num_class):
batch_time = AverageMeter()
data_time = AverageMeter()
losses_a = AverageMeter() #adversarial loss
losses_c = AverageMeter() #classification loss2
losses = AverageMeter() #final loss
top1 = AverageMeter()
top5 = AverageMeter()
if args.no_partialbn:
model.module.partialBN(False)
else:
model.module.partialBN(True)
# switch to train mode
model.train()
ad_net.train()
end = time.time()
data_loader = enumerate(zip(source_loader, target_loader))
for i, ((source_data, source_label), (target_data,
target_label)) in data_loader:
# measure data loading time
data_time.update(time.time() - end)
source_label = source_label.cuda(async=True)
target_label = target_label.cuda(async=True)
source_input_var = torch.autograd.Variable(source_data)
source_target_var = torch.autograd.Variable(source_label)
output_s, feature_s = model(source_input_var)
target_input_var = torch.autograd.Variable(target_data)
target_target_var = torch.autograd.Variable(target_label)
output_t, feature_t = model(target_input_var)
features = torch.cat((feature_s, feature_t), dim=0)
#print('features.size() is:', features.size())
#======calculate loss function=====#
#1. calculate classification loss losses_c
loss_c = criterion(output_s, source_target_var)
losses_c.update(loss_c.data[0], source_data.size(0))
loss = loss_c
#2. calculate the adversarial loss loss_a
#features = features.view(features.size(0), -1)
loss_a = los.DANN(features, ad_net)
loss = loss_c + loss_a
# measure accuracy and record loss
#prec1, prec5 = accuracy(output_s.data, source_label, topk=(1,5))
prec1, prec5 = accuracy(output_s.data, source_label, topk=(1, 5))
top1.update(prec1[0], output_s.size(0))
top5.update(prec5[0], output_s.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
losses.update(loss.data[0])
loss.backward()
#loss_c.backward()
if args.clip_gradient is not None:
total_norm = clip_grad_norm(model.parameters(), args.clip_gradient)
# if total_norm > args.clip_gradient:
# print("clipping gradient: {} with coef {}".format(total_norm, args.clip_gradient / total_norm))
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
output = ('Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch,
i,
len(source_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1,
top5=top5,
lr=optimizer.param_groups[-1]['lr']))
print(output)
log.write(output + '\n')
log.flush()
def train(config):
## set pre-process
prep_dict = {}
prep_config = config["prep"]
prep_dict["source"] = prep.image_train(**config["prep"]['params'])
prep_dict["target"] = prep.image_train(**config["prep"]['params'])
if prep_config["test_10crop"]:
prep_dict["test"] = prep.image_test_10crop(**config["prep"]['params'])
else:
prep_dict["test"] = prep.image_test(**config["prep"]['params'])
## prepare data
dsets = {}
dset_loaders = {}
data_config = config["data"]
train_bs = data_config["source"]["batch_size"]
test_bs = data_config["test"]["batch_size"]
dsets["source"] = ImageList(open(data_config["source"]["list_path"]).readlines(), \
transform=prep_dict["source"])
dset_loaders["source"] = DataLoader(dsets["source"], batch_size=train_bs, \
shuffle=True, num_workers=4, drop_last=True)
dsets["target"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["target"])
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, \
shuffle=True, num_workers=4, drop_last=True)
if prep_config["test_10crop"]:
for i in range(10):
dsets["test"] = [ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"][i]) for i in range(10)]
dset_loaders["test"] = [DataLoader(dset, batch_size=test_bs, \
shuffle=False, num_workers=4) for dset in dsets['test']]
else:
dsets["test"] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"])
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=test_bs, \
shuffle=False, num_workers=4)
class_num = config["network"]["params"]["class_num"]
## set base network
net_config = config["network"]
base_network = net_config["name"](**net_config["params"])
base_network = base_network.cuda()
with torch.no_grad():
cluster_data_loader = {}
cluster_data_loader["source"] = DataLoader(dsets["source"], batch_size=100, \
shuffle=True, num_workers=0, drop_last=True)
cluster_data_loader["target"] = DataLoader(dsets["source"], batch_size=100, \
shuffle=True, num_workers=0, drop_last=True)
## add additional network for some methods
if config["loss"]["random"]:
random_layer = network.RandomLayer([base_network.output_num(), class_num], config["loss"]["random_dim"])
ad_net = network.AdversarialNetwork(config["loss"]["random_dim"], 1024)
else:
random_layer = None
ad_net = network.AdversarialNetwork(base_network.output_num() * class_num, 1024)
if config["loss"]["random"]:
random_layer.cuda()
ad_net = ad_net.cuda()
parameter_list = base_network.get_parameters() + ad_net.get_parameters()
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optimizer_config["type"](parameter_list, \
**(optimizer_config["optim_params"]))
param_lr = []
for param_group in optimizer.param_groups:
param_lr.append(param_group["lr"])
schedule_param = optimizer_config["lr_param"]
lr_scheduler = lr_schedule.schedule_dict[optimizer_config["lr_type"]]
gpus = config['gpu'].split(',')
if len(gpus) > 1:
ad_net = nn.DataParallel(ad_net, device_ids=[int(i) for i in gpus])
base_network = nn.DataParallel(base_network, device_ids=[int(i) for i in gpus])
# dset_loaders["ps_target"]=[]
## train
len_train_source = len(dset_loaders["source"])
# len_train_target = len(dset_loaders["ps_target"])
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
best_acc = 0.0
for i in range(config["num_iterations"]):
lamb = adaptation_factor((i+1)/10000)
cls_lamb = adaptation_factor(5*(i+1)/10000)
epoch = int(i / len_train_source)
if i% len_train_source ==0:
testing = True
pl_update=True
print_loss =True
# print("epoch: {} ".format(int(i / len_train_source)))
if epoch % 5 ==0 and pl_update:
pl_update= False
# del dset_loaders["ps_target"]
pseudo_labeled_targets,target_g_ctr, source_g_ctr = pseudo_labeling(base_network, cluster_data_loader, class_num)
global_source_ctr = source_g_ctr.detach_()
global_target_ctr = target_g_ctr.detach_()
if len(pseudo_labeled_targets["label_list"]) !=0:
print("new pl at epoch {}".format(epoch))
pseudo_dataset = PS_ImageList(pseudo_labeled_targets, transform=prep_dict["target"])
dset_loaders["ps_target"] = DataLoader(pseudo_dataset, batch_size=train_bs, \
shuffle=False, num_workers=0, drop_last=True)
len_train_target = len(dset_loaders["ps_target"])
else:
print("no pl at epoch {}".format(epoch))
# print("pseudo labeling done")
# print(i)
# if i % config["test_interval"] == config["test_interval"] - 1:
if epoch % 5 ==0 and testing and i>0:
base_network.train(False)
temp_acc,v_loss = image_classification_test(dset_loaders, \
base_network, test_10crop=prep_config["test_10crop"])
temp_model = nn.Sequential(base_network)
if temp_acc > best_acc:
best_acc = temp_acc
best_model = temp_model
log_str = "iter: {:05d}, precision: {:.5f}".format(i, temp_acc)
config["out_file"].write(log_str + "\n")
config["out_file"].flush()
print(log_str)
testing=False
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print("epoch: {} ".format(int(i / len_train_source)))
print("time: {} ".format(current_time))
print("best acc: {} ".format(best_acc))
print("loss: {} ".format(v_loss))
print("adaptation rate : {}".format(lamb))
print("learning rare : {} {} {} {}".format(optimizer.param_groups[0]["lr"],optimizer.param_groups[1]["lr"],optimizer.param_groups[2]["lr"],optimizer.param_groups[3]["lr"]))
print("------------")
if i % config["snapshot_interval"] == 0:
torch.save(nn.Sequential(base_network), osp.join(config["output_path"], \
"iter_{:05d}_model.pth.tar".format(i)))
loss_params = config["loss"]
## train one iter
base_network.train(True)
ad_net.train(True)
optimizer = lr_scheduler(optimizer, i, **schedule_param)
optimizer.zero_grad()
###
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"])
if i % len_train_target == 0:
# print(i,len_train_target)
iter_target = iter(dset_loaders["ps_target"])
try:
inputs_source, labels_source, _ = iter_source.next()
inputs_target, labels_target = iter_target.next()
except StopIteration:
iter_target = iter(dset_loaders["ps_target"])
inputs_target, labels_target = iter_target.next()
inputs_source, inputs_target, labels_source, labels_target = inputs_source.cuda(), inputs_target.cuda(), labels_source.cuda(), labels_target.cuda()
features_source, outputs_source = base_network(inputs_source)
features_target, outputs_target = base_network(inputs_target)
##class_aware
batch_source_centroids = utils.get_batch_centers(features_source, labels_source, class_num)
batch_target_centroids = utils.get_batch_centers(features_target,labels_target, class_num)
# if i==0:
# global_source_ctr = batch_source_centroids
# global_target_ctr = batch_target_centroids
# if i>0:
batch_source_centroids = ctr_adapt_factor* global_source_ctr + (1- ctr_adapt_factor) * batch_source_centroids
batch_target_centroids = ctr_adapt_factor * global_target_ctr + (1 - ctr_adapt_factor) * batch_target_centroids
global_source_ctr = batch_source_centroids.clone().detach_()
global_target_ctr = batch_target_centroids.clone().detach_()
#
# global_source_ctr = global_source_ctr.cpu().data.numpy()
# global_target_ctr.detach_()
# ctr_alignment_loss = utils.cosine_distance(global_source_ctr,global_target_ctr,cross=False)
# source_p2c_Distances = 0 - utils.cosine_distance(features_source, global_source_ctr, cross=True)
#
# target_p2c_Distances = 0 - utils.cosine_distance(features_target, global_target_ctr, cross=True)
#
#
#
# zero_ctrs_s = torch.unique(torch.where(global_source_ctr==0)[0])
# zero_ctrs_t = torch.unique(torch.where(global_target_ctr == 0)[0])
alignment_index = []
identity = np.eye(class_num)
ctr_alignment_count =0
pos = []
post = []
neg =[]
negt =[]
index_s = np.empty([0,1])
index_t = np.empty([0,1])
itt=0
triplets ={}
# with torch.no_grad():
labels = labels_source.cpu().data.numpy()
labelt = labels_target.cpu().data.numpy()
# zero_ctrs_s = zero_ctrs_s.cpu().data.numpy()
# zero_ctrs_t = zero_ctrs_t.cpu().data.numpy()
#####npair
# labels = labels.cpu().data.numpy()
n_pairs = []
for label in set(labels):
label_mask = (labels == label)
label_indices = np.where(label_mask)[0]
if len(label_indices) < 1:
continue
anchor = np.random.choice(label_indices, 1, replace=False)
n_pairs.append([anchor, np.array([label])])
n_pairs = np.array(n_pairs)
n_negatives = []
for i in range(len(n_pairs)):
negative = np.concatenate([n_pairs[:i, 1], n_pairs[i + 1:, 1]])
n_negatives.append(negative)
n_negatives = np.array(n_negatives)
n_pairs_s = torch.LongTensor(n_pairs)
n_neg_s = torch.LongTensor(n_negatives)
n_pairs = []
for label in set(labelt):
label_mask = (labelt == label)
label_indices = np.where(label_mask)[0]
if len(label_indices) < 1:
continue
anchor = np.random.choice(label_indices, 1, replace=False)
n_pairs.append([anchor, np.array([label])])
n_pairs = np.array(n_pairs)
n_negatives = []
for i in range(len(n_pairs)):
negative = np.concatenate([n_pairs[:i, 1], n_pairs[i + 1:, 1]])
n_negatives.append(negative)
n_negatives = np.array(n_negatives)
n_pairs_t = torch.LongTensor(n_pairs)
n_neg_t = torch.LongTensor(n_negatives)
# return torch.LongTensor(n_pairs), torch.LongTensor(n_negatives)
#####
for it in range(class_num):
label_mask = (labels == it)
label_maskt = (labelt == it)
idx = np.where(label_mask)[0]
idxt = np.where(label_maskt)[0]
# idx = torch.flatten(torch.nonzero(labels_source== torch.tensor(it).cuda()))
if len(idx) !=0:
index_s =np.append(index_s,idx)
pos += [it for cc in range(len(idx))]
mask = 1- identity[it,:]
neg_id = np.nonzero(mask.flatten())[0].flatten()
# neg_idx = np.where(np.in1d(neg_id,zero_ctrs_s)!=True)[0]
neg += [[neg_id] for cc in range(len(idx))]
if len(idxt) !=0:
index_t = np.append(index_t, idxt)
post += [it for cc in range(len(idxt))]
maskt = 1- identity[it,:]
neg_idt = np.nonzero(maskt.flatten())[0].flatten()
# neg_idxt = np.where(np.in1d(neg_idt, zero_ctrs_t))[0]
negt += [[neg_idt] for cc in range(len(idxt))]
# negt += [[neg_idt] for cc in range(len(idxt))]
# alignment_ctr_idx =idx[torch.nonzero(torch.where(idx ==idxt, idx,0))]
if len(idx) != 0 and len(idxt) !=0:
ctr_alignment_count +=1
alignment_index +=[it]
# alignment_loss +=[utils.cosine_distance(batch_source_centroids[it], batch_source_centroids[it], cross=False)]
# tempp = torch.cat(source_loss,0)
# posetives_s = torch.cat(pos, dim=0)
# negatives_s = torch.cat(neg, dim=0)
# posetives_t = torch.cat(post, dim=0)
# negatives_t = torch.cat(negt, dim=0)
# a_i = torch.LongTensor(index_s.flatten()).cuda()
# a_p = torch.LongTensor(pos).cuda()
# a_n = torch.LongTensor(neg).cuda()
ctr_alignment_loss =0
anchors_s = features_source[index_s.flatten(),:]
positive_s = global_source_ctr[pos,:]
negative_s = global_source_ctr[neg].squeeze(1)
# n_pairs_s = n_pairs_s.cuda().squeeze(2)
# n_neg_s = n_neg_s.cuda().squeeze(2)
# anchors_s = features_source[n_pairs_s[:, 0]]
# positive_s = global_source_ctr[n_pairs_s[:, 1]]
# negative_s = global_source_ctr[n_neg_s]
#
# n_pairs_t = n_pairs_t.cuda().squeeze(2)
#
# n_neg_t = n_neg_t.cuda().squeeze(2)
# anchors_t = features_source[n_pairs_t[:, 0]]
# positive_t = global_source_ctr[n_pairs_t[:, 1]]
# negative_t = global_source_ctr[n_neg_t]
# anchors_s.retain_graph=True
# positive_s.retain_graph=True
# negative_s.retain_graph=True
anchors_t = features_target[index_t.flatten(), :]
positive_t = global_target_ctr[post, :]
negative_t = global_target_ctr[negt].squeeze(1)
# FAT_loss = torch.empty([],requires_grad=True)
# FAT_loss.requires_grad = True
# FAT_loss.retain_grad()
# nfat_s = Variable(n_pair_loss(anchors_s,positive_s, negative_s,class_num,train_bs))
# nfat_t = Variable(n_pair_loss(anchors_t,positive_t, negative_t,class_num,train_bs))
# FAT_loss.requires_grad = True
# FAT_loss.retain_grad()
FAT_loss = n_pair_loss(anchors_s,positive_s, negative_s,class_num,train_bs) + n_pair_loss(anchors_t,positive_t, negative_t,class_num,train_bs)/2
if len(alignment_index) != 0:
ctr_alignment_loss = torch.sum(utils.cosine_distance(batch_source_centroids[alignment_index], batch_target_centroids[alignment_index], cross=False))#/ctr_alignment_count
# source_batch_FAT_Loss = torch.mean(torch.cat(source_loss,0), 0)/class_num
# target_batch_FAT_Loss = torch.mean(torch.cat(target_loss,0),0)/class_num
#
# FAT_loss = source_batch_FAT_Loss.add(target_batch_FAT_Loss)
##
# print("train loss: ", FAT_loss)
# ctr_alignment_loss.grad_required =True
# ctr_alignment_loss.retain_grad()
features = torch.cat((features_source, features_target), dim=0)
outputs = torch.cat((outputs_source, outputs_target), dim=0)
softmax_out = nn.Softmax(dim=1)(outputs)
if config['method'] == 'CDAN+E':
entropy = loss.Entropy(softmax_out)
transfer_loss = loss.CDAN([features, softmax_out], ad_net, entropy, network.calc_coeff(i), random_layer)
elif config['method'] == 'CDAN':
transfer_loss = loss.CDAN([features, softmax_out], ad_net, None, None, random_layer)
elif config['method'] == 'DANN':
transfer_loss = loss.DANN(features, ad_net)
else:
raise ValueError('Method cannot be recognized.')
classifier_loss = nn.CrossEntropyLoss()(outputs_source/(2), labels_source)
total_loss = loss_params["trade_off"] * (transfer_loss) + classifier_loss
if lamb >.1:
cls_lamb = 1.0
else:
cls_lamb = 10*lamb
# total_loss = lamb * ( FAT_loss + 10*ctr_alignment_loss) + (transfer_loss) + cls_lamb*classifier_loss
# total_loss =transfer_loss + lamb * (FAT_loss + ctr_alignment_loss) + classifier_loss
# FAT_loss.backward(retain_graph=True)
# optimizer.zero_grad()
total_loss.backward()
optimizer.step()
# my_lr_scheduler.step()
if epoch % 5 ==0 and print_loss:
print("fat loss ", FAT_loss)#.grad_fn, FAT_loss.requires_grad)
print("ctr align: ", ctr_alignment_loss)
print("tot: ", total_loss)
print("clss: ",classifier_loss)
print("trs: ", transfer_loss)
print("++++++++++++++++++++++++end of epoch++++++++++++++++++++")
print_loss =False
def train(config):
## Define start time
start_time = time.time()
## set pre-process
prep_dict = {}
prep_config = config["prep"]
prep_dict["source"] = prep.image_train(**config["prep"]['params'])
prep_dict["target"] = prep.image_train(**config["prep"]['params'])
prep_dict["test"] = prep.image_test(**config["prep"]['params'])
## prepare data
print("Preparing data", flush=True)
dsets = {}
dset_loaders = {}
data_config = config["data"]
train_bs = data_config["source"]["batch_size"]
test_bs = data_config["test"]["batch_size"]
root_folder = data_config["root_folder"]
dsets["source"] = ImageList(open(osp.join(root_folder, data_config["source"]["list_path"])).readlines(), \
transform=prep_dict["source"], root_folder=root_folder, ratios=config["ratios_source"])
dset_loaders["source"] = DataLoader(dsets["source"], batch_size=train_bs, \
shuffle=True, num_workers=4, drop_last=True)
dsets["target"] = ImageList(open(osp.join(root_folder, data_config["target"]["list_path"])).readlines(), \
transform=prep_dict["target"], root_folder=root_folder, ratios=config["ratios_target"])
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, \
shuffle=True, num_workers=4, drop_last=True)
dsets["test"] = ImageList(open(
osp.join(root_folder, data_config["test"]["list_path"])).readlines(),
transform=prep_dict["test"],
root_folder=root_folder,
ratios=config["ratios_test"])
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=test_bs, \
shuffle=False, num_workers=4)
test_path = os.path.join(root_folder, data_config["test"]["dataset_path"])
if os.path.exists(test_path):
print('Found existing dataset for test', flush=True)
with open(test_path, 'rb') as f:
[test_samples, test_labels] = pickle.load(f)
test_labels = torch.LongTensor(test_labels).to(config["device"])
else:
print('Missing test dataset', flush=True)
print('Building dataset for test and writing to {}'.format(test_path),
flush=True)
dset_test = ImageList(open(
osp.join(root_folder,
data_config["test"]["list_path"])).readlines(),
transform=prep_dict["test"],
root_folder=root_folder,
ratios=config['ratios_test'])
loaded_dset_test = LoadedImageList(dset_test)
test_samples, test_labels = loaded_dset_test.samples.numpy(
), loaded_dset_test.targets.numpy()
with open(test_path, 'wb') as f:
pickle.dump([test_samples, test_labels], f)
class_num = config["network"]["params"]["class_num"]
test_samples, test_labels = sample_ratios(test_samples, test_labels,
config['ratios_test'])
# compute labels distribution on the source and target domain
source_label_distribution = np.zeros((class_num))
for img in dsets["source"].imgs:
source_label_distribution[img[1]] += 1
print("Total source samples: {}".format(np.sum(source_label_distribution)),
flush=True)
print("Source samples per class: {}".format(source_label_distribution),
flush=True)
source_label_distribution /= np.sum(source_label_distribution)
print("Source label distribution: {}".format(source_label_distribution),
flush=True)
target_label_distribution = np.zeros((class_num))
for img in dsets["target"].imgs:
target_label_distribution[img[1]] += 1
print("Total target samples: {}".format(np.sum(target_label_distribution)),
flush=True)
print("Target samples per class: {}".format(target_label_distribution),
flush=True)
target_label_distribution /= np.sum(target_label_distribution)
print("Target label distribution: {}".format(target_label_distribution),
flush=True)
mixture = (source_label_distribution + target_label_distribution) / 2
jsd = (scipy.stats.entropy(source_label_distribution, qk=mixture) \
+ scipy.stats.entropy(target_label_distribution, qk=mixture)) / 2
print("JSD : {}".format(jsd), flush=True)
test_label_distribution = np.zeros((class_num))
for img in test_labels:
test_label_distribution[int(img.item())] += 1
print("Test samples per class: {}".format(test_label_distribution),
flush=True)
test_label_distribution /= np.sum(test_label_distribution)
print("Test label distribution: {}".format(test_label_distribution),
flush=True)
write_list(config["out_wei_file"],
[round(x, 4) for x in test_label_distribution])
write_list(config["out_wei_file"],
[round(x, 4) for x in source_label_distribution])
write_list(config["out_wei_file"],
[round(x, 4) for x in target_label_distribution])
true_weights = torch.tensor(
target_label_distribution / source_label_distribution,
dtype=torch.float,
requires_grad=False)[:, None].to(config["device"])
print("True weights : {}".format(true_weights[:, 0].cpu().numpy()))
config["out_wei_file"].write(str(jsd) + "\n")
## set base network
net_config = config["network"]
base_network = net_config["name"](**net_config["params"])
base_network = base_network.to(config["device"])
## add additional network for some methods
if config["loss"]["random"]:
random_layer = network.RandomLayer(
[base_network.output_num(), class_num],
config["loss"]["random_dim"])
ad_net = network.AdversarialNetwork(config["loss"]["random_dim"], 1024)
else:
random_layer = None
if 'CDAN' in config['method']:
ad_net = network.AdversarialNetwork(
base_network.output_num() * class_num, 1024)
else:
ad_net = network.AdversarialNetwork(base_network.output_num(),
1024)
if config["loss"]["random"]:
random_layer.to(config["device"])
ad_net = ad_net.to(config["device"])
parameter_list = ad_net.get_parameters() + base_network.get_parameters()
parameter_list[-1]["lr_mult"] = config["lr_mult_im"]
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optimizer_config["type"](parameter_list, \
**(optimizer_config["optim_params"]))
param_lr = []
for param_group in optimizer.param_groups:
param_lr.append(param_group["lr"])
schedule_param = optimizer_config["lr_param"]
lr_scheduler = lr_schedule.schedule_dict[optimizer_config["lr_type"]]
# Maintain two quantities for the QP.
cov_mat = torch.tensor(np.zeros((class_num, class_num), dtype=np.float32),
requires_grad=False).to(config["device"])
pseudo_target_label = torch.tensor(np.zeros((class_num, 1),
dtype=np.float32),
requires_grad=False).to(
config["device"])
# Maintain one weight vector for BER.
class_weights = torch.tensor(1.0 / source_label_distribution,
dtype=torch.float,
requires_grad=False).to(config["device"])
gpus = config['gpu'].split(',')
if len(gpus) > 1:
ad_net = nn.DataParallel(ad_net, device_ids=[int(i) for i in gpus])
base_network = nn.DataParallel(base_network,
device_ids=[int(i) for i in gpus])
## train
len_train_source = len(dset_loaders["source"])
len_train_target = len(dset_loaders["target"])
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
best_acc = 0.0
print("Preparations done in {:.0f} seconds".format(time.time() -
start_time),
flush=True)
print("Starting training for {} iterations using method {}".format(
config["num_iterations"], config['method']),
flush=True)
start_time_test = start_time = time.time()
for i in range(config["num_iterations"]):
if i % config["test_interval"] == config["test_interval"] - 1:
base_network.train(False)
temp_acc = image_classification_test_loaded(
test_samples, test_labels, base_network)
temp_model = nn.Sequential(base_network)
if temp_acc > best_acc:
best_acc = temp_acc
log_str = " iter: {:05d}, sec: {:.0f}, class: {:.5f}, da: {:.5f}, precision: {:.5f}".format(
i,
time.time() - start_time_test, classifier_loss_value,
transfer_loss_value, temp_acc)
config["out_log_file"].write(log_str + "\n")
config["out_log_file"].flush()
print(log_str, flush=True)
if 'IW' in config['method']:
current_weights = [
round(x, 4) for x in
base_network.im_weights.data.cpu().numpy().flatten()
]
# write_list(config["out_wei_file"], current_weights)
print(current_weights, flush=True)
start_time_test = time.time()
if i % 500 == -1:
print("{} iterations in {} seconds".format(
i,
time.time() - start_time),
flush=True)
loss_params = config["loss"]
## train one iter
base_network.train(True)
ad_net.train(True)
optimizer = lr_scheduler(optimizer, i, **schedule_param)
optimizer.zero_grad()
t = time.time()
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, label_source = iter_source.next()
inputs_target, _ = iter_target.next()
inputs_source, inputs_target, label_source = inputs_source.to(
config["device"]), inputs_target.to(
config["device"]), label_source.to(config["device"])
features_source, outputs_source = base_network(inputs_source)
features_target, outputs_target = base_network(inputs_target)
features = torch.cat((features_source, features_target), dim=0)
outputs = torch.cat((outputs_source, outputs_target), dim=0)
softmax_out = nn.Softmax(dim=1)(outputs)
if 'IW' in config['method']:
ys_onehot = torch.zeros(train_bs, class_num).to(config["device"])
ys_onehot.scatter_(1, label_source.view(-1, 1), 1)
# Compute weights on source data.
if 'ORACLE' in config['method']:
weights = torch.mm(ys_onehot, true_weights)
else:
weights = torch.mm(ys_onehot, base_network.im_weights)
source_preds, target_preds = outputs[:train_bs], outputs[train_bs:]
# Compute the aggregated distribution of pseudo-label on the target domain.
pseudo_target_label += torch.sum(F.softmax(target_preds, dim=1),
dim=0).view(-1, 1).detach()
# Update the covariance matrix on the source domain as well.
cov_mat += torch.mm(
F.softmax(source_preds, dim=1).transpose(1, 0),
ys_onehot).detach()
if config['method'] == 'CDAN-E':
classifier_loss = nn.CrossEntropyLoss()(outputs_source,
label_source)
entropy = loss.Entropy(softmax_out)
transfer_loss = loss.CDAN([features, softmax_out], ad_net, entropy,
network.calc_coeff(i), random_layer)
total_loss = loss_params["trade_off"] * \
transfer_loss + classifier_loss
elif 'IWCDAN-E' in config['method']:
classifier_loss = torch.mean(
nn.CrossEntropyLoss(weight=class_weights, reduction='none')
(outputs_source, label_source) * weights) / class_num
entropy = loss.Entropy(softmax_out)
transfer_loss = loss.CDAN([features, softmax_out],
ad_net,
entropy,
network.calc_coeff(i),
random_layer,
weights=weights,
device=config["device"])
total_loss = loss_params["trade_off"] * \
transfer_loss + classifier_loss
elif config['method'] == 'CDAN':
classifier_loss = nn.CrossEntropyLoss()(outputs_source,
label_source)
transfer_loss = loss.CDAN([features, softmax_out], ad_net, None,
None, random_layer)
total_loss = loss_params[
"trade_off"] * transfer_loss + classifier_loss
elif 'IWCDAN' in config['method']:
classifier_loss = torch.mean(
nn.CrossEntropyLoss(weight=class_weights, reduction='none')
(outputs_source, label_source) * weights) / class_num
transfer_loss = loss.CDAN([features, softmax_out],
ad_net,
None,
None,
random_layer,
weights=weights)
total_loss = loss_params["trade_off"] * \
transfer_loss + classifier_loss
elif config['method'] == 'DANN':
classifier_loss = nn.CrossEntropyLoss()(outputs_source,
label_source)
transfer_loss = loss.DANN(features, ad_net, config["device"])
total_loss = loss_params["trade_off"] * \
transfer_loss + classifier_loss
elif 'IWDAN' in config['method']:
classifier_loss = torch.mean(
nn.CrossEntropyLoss(weight=class_weights, reduction='none')
(outputs_source, label_source) * weights) / class_num
transfer_loss = loss.IWDAN(features, ad_net, weights)
total_loss = loss_params["trade_off"] * \
transfer_loss + classifier_loss
elif config['method'] == 'NANN':
classifier_loss = nn.CrossEntropyLoss()(outputs_source,
label_source)
total_loss = classifier_loss
else:
raise ValueError('Method cannot be recognized.')
total_loss.backward()
optimizer.step()
transfer_loss_value = 0 if config[
'method'] == 'NANN' else transfer_loss.item()
classifier_loss_value = classifier_loss.item()
total_loss_value = transfer_loss_value + classifier_loss_value
if ('IW' in config['method']
) and i % (config["dataset_mult_iw"] * len_train_source
) == config["dataset_mult_iw"] * len_train_source - 1:
pseudo_target_label /= train_bs * \
len_train_source * config["dataset_mult_iw"]
cov_mat /= train_bs * len_train_source * config["dataset_mult_iw"]
print(i, np.sum(cov_mat.cpu().detach().numpy()),
train_bs * len_train_source)
# Recompute the importance weight by solving a QP.
base_network.im_weights_update(
source_label_distribution,
pseudo_target_label.cpu().detach().numpy(),
cov_mat.cpu().detach().numpy(), config["device"])
current_weights = [
round(x, 4)
for x in base_network.im_weights.data.cpu().numpy().flatten()
]
write_list(config["out_wei_file"], [
np.linalg.norm(current_weights -
true_weights.cpu().numpy().flatten())
] + current_weights)
print(
np.linalg.norm(current_weights -
true_weights.cpu().numpy().flatten()),
current_weights)
cov_mat[:] = 0.0
pseudo_target_label[:] = 0.0
return best_acc
def train(args, model, ad_net, random_layer, train_loader, train_loader1, optimizer, optimizer_ad, epoch, start_epoch, method,
D_s, D_t, G_s2t, G_t2s, criterion_Sem, criterion_GAN, criterion_cycle, criterion_identity, optimizer_G,
optimizer_D_t, optimizer_D_s,
classifier1, classifier1_optim, fake_S_buffer, fake_T_buffer):
model.train()
len_source = len(train_loader)
len_target = len(train_loader1)
if len_source > len_target:
num_iter = len_source
else:
num_iter = len_target
for batch_idx in range(num_iter):
if batch_idx % len_source == 0:
iter_source = iter(train_loader)
if batch_idx % len_target == 0:
iter_target = iter(train_loader1)
data_source, label_source = iter_source.next()
# data_source, label_source = data_source.cuda(), label_source.cuda()
data_target, label_target = iter_target.next()
# data_target = data_target.cuda()
optimizer.zero_grad()
optimizer_ad.zero_grad()
features_source, outputs_source = model(data_source)
features_target, outputs_target = model(data_target)
features = torch.cat((features_source, features_target), dim=0)
outputs = torch.cat((outputs_source, outputs_target), dim=0)
loss = nn.CrossEntropyLoss()(outputs.narrow(0, 0, data_source.size(0)), label_source)
softmax_output = nn.Softmax(dim=1)(outputs)
output1 = classifier1(features)
softmax_output1 = nn.Softmax(dim=1)(output1)
softmax_output = (1 - args.cla_plus_weight) * softmax_output + args.cla_plus_weight * softmax_output1
if epoch > start_epoch:
if method == 'CDAN-E':
entropy = loss_func.Entropy(softmax_output)
loss += loss_func.CDAN([features, softmax_output], ad_net, entropy, network.calc_coeff(num_iter*(epoch-start_epoch)+batch_idx), random_layer)
elif method == 'CDAN':
loss += loss_func.CDAN([features, softmax_output], ad_net, None, None, random_layer)
elif method == 'DANN':
loss += loss_func.DANN(features, ad_net)
else:
raise ValueError('Method cannot be recognized.')
# Cycle
num_feature = features.size(0)
# =================train discriminator T
real_label = Variable(torch.ones(num_feature))
# real_label = Variable(torch.ones(num_feature)).cuda()
fake_label = Variable(torch.zeros(num_feature))
# fake_label = Variable(torch.zeros(num_feature)).cuda()
# 训练生成器
optimizer_G.zero_grad()
# Identity loss
same_t = G_s2t(features_target)
loss_identity_t = criterion_identity(same_t, features_target)
same_s = G_t2s(features_source)
loss_identity_s = criterion_identity(same_s, features_source)
# Gan loss
fake_t = G_s2t(features_source)
pred_fake = D_t(fake_t)
loss_G_s2t = criterion_GAN(pred_fake, label_source.float())
fake_s = G_t2s(features_target)
pred_fake = D_s(fake_s)
loss_G_t2s = criterion_GAN(pred_fake, label_source.float())
# cycle loss
recovered_s = G_t2s(fake_t)
loss_cycle_sts = criterion_cycle(recovered_s, features_source)
recovered_t = G_s2t(fake_s)
loss_cycle_tst = criterion_cycle(recovered_t, features_target)
# sem loss
pred_recovered_s = model.classifier(recovered_s)
pred_fake_t = model.classifier(fake_t)
loss_sem_t2s = criterion_Sem(pred_recovered_s, pred_fake_t)
pred_recovered_t = model.classifier(recovered_t)
pred_fake_s = model.classifier(fake_s)
loss_sem_s2t = criterion_Sem(pred_recovered_t, pred_fake_s)
loss_cycle = loss_cycle_tst + loss_cycle_sts
weight_in_loss_g = args.weight_in_loss_g.split(',')
loss_G = float(weight_in_loss_g[0]) * (loss_identity_s + loss_identity_t) + \
float(weight_in_loss_g[1]) * (loss_G_s2t + loss_G_t2s) + \
float(weight_in_loss_g[2]) * loss_cycle + \
float(weight_in_loss_g[3]) * (loss_sem_s2t + loss_sem_t2s)
# 训练softmax分类器
outputs_fake = classifier1(fake_t.detach())
# 分类器优化
classifier_loss1 = nn.CrossEntropyLoss()(outputs_fake, label_source)
classifier1_optim.zero_grad()
classifier_loss1.backward()
classifier1_optim.step()
total_loss = loss + args.cyc_loss_weight * loss_G
total_loss.backward()
optimizer.step()
optimizer_G.step()
###### Discriminator S ######
optimizer_D_s.zero_grad()
# Real loss
pred_real = D_s(features_source.detach())
loss_D_real = criterion_GAN(pred_real, real_label)
# Fake loss
fake_s = fake_S_buffer.push_and_pop(fake_s)
pred_fake = D_s(fake_s.detach())
loss_D_fake = criterion_GAN(pred_fake, fake_label)
# Total loss
loss_D_s = loss_D_real + loss_D_fake
loss_D_s.backward()
optimizer_D_s.step()
###################################
###### Discriminator t ######
optimizer_D_t.zero_grad()
# Real loss
pred_real = D_t(features_target.detach())
loss_D_real = criterion_GAN(pred_real, real_label)
# Fake loss
fake_t = fake_T_buffer.push_and_pop(fake_t)
pred_fake = D_t(fake_t.detach())
loss_D_fake = criterion_GAN(pred_fake, fake_label)
# Total loss
loss_D_t = loss_D_real + loss_D_fake
loss_D_t.backward()
optimizer_D_t.step()
if epoch > start_epoch:
optimizer_ad.step()
if (batch_idx + epoch * num_iter) % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tLoss+G: {:.6f}'.format(
epoch, batch_idx * args.batch_size, num_iter * args.batch_size,
100. * batch_idx / num_iter, loss.item(), total_loss.item()))
def train(config):
## set pre-process
prep_dict = {}
prep_config = config["prep"]
prep_dict["source"] = prep.image_train(**config["prep"]['params'])
prep_dict["target"] = prep.image_train(**config["prep"]['params'])
if prep_config["test_10crop"]:
prep_dict["test"] = prep.image_test_10crop(**config["prep"]['params'])
else:
prep_dict["test"] = prep.image_test(**config["prep"]['params'])
## prepare data
dsets = {}
dset_loaders = {}
data_config = config["data"]
train_bs = data_config["source"]["batch_size"]
test_bs = data_config["test"]["batch_size"]
dsets["source"] = ImageList(open(data_config["source"]["list_path"]).readlines(), \
transform=prep_dict["source"])
dset_loaders["source"] = DataLoader(dsets["source"], batch_size=train_bs, \
shuffle=True, num_workers=4, drop_last=True)
dsets["target"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["target"])
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, \
shuffle=True, num_workers=4, drop_last=True)
if prep_config["test_10crop"]:
for i in range(10):
dsets["test"] = [ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"][i]) for i in range(10)]
dset_loaders["test"] = [DataLoader(dset, batch_size=test_bs, \
shuffle=False, num_workers=4) for dset in dsets['test']]
else:
dsets["test"] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"])
dset_loaders["test"] = DataLoader(dsets["test"], batch_size=test_bs, \
shuffle=False, num_workers=4)
class_num = config["network"]["params"]["class_num"]
crit = LabelSmoothingLoss(smoothing=0.05, classes=class_num)#标签平滑操作
## set base network
net_config = config["network"]
base_network = net_config["name"](**net_config["params"])
base_network = base_network.cuda() # 加载基础网络结构
## add additional network for some methods
if config["loss"]["random"]:
random_layer = network.RandomLayer([base_network.output_num(), class_num], config["loss"]["random_dim"])
ad_net = network.AdversarialNetwork(config["loss"]["random_dim"], 1024)
else:
random_layer = None
ad_net = network.AdversarialNetwork(base_network.output_num() * class_num, 1024) # 对抗网络结构
if config["loss"]["random"]:
random_layer.cuda()
ad_net = ad_net.cuda()
parameter_list = base_network.get_parameters() + ad_net.get_parameters()
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optimizer_config["type"](parameter_list, \
**(optimizer_config["optim_params"]))
#中心损失函数
criterion_centor=CenterLoss(num_classes=class_num,feat_dim=256,use_gpu=True)
optimizer_centerloss=torch.optim.SGD(criterion_centor.parameters(),lr=config['lr'])
param_lr = []
for param_group in optimizer.param_groups:
param_lr.append(param_group["lr"])
schedule_param = optimizer_config["lr_param"]
lr_scheduler = lr_schedule.schedule_dict[optimizer_config["lr_type"]]
gpus = config['gpu'].split(',')
if len(gpus) > 1:
ad_net = nn.DataParallel(ad_net, device_ids=[int(i) for i in gpus])
base_network = nn.DataParallel(base_network, device_ids=[int(i) for i in gpus])
## train
len_train_source = len(dset_loaders["source"])
len_train_target = len(dset_loaders["target"])
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
best_acc = 0.0
start_time = time.time()
for i in range(config["num_iterations"]):
if i % config["test_interval"] == config["test_interval"] - 1:
# 在这里进行测试的工作
base_network.train(False)
temp_acc = image_classification_test(dset_loaders, \
base_network, test_10crop=prep_config["test_10crop"])
temp_model = nn.Sequential(base_network)
if temp_acc > best_acc:
best_acc = temp_acc
best_model = temp_model
log_str = "iter: {:05d}, precision: {:.5f}".format(i, temp_acc)
config["out_file"].write(log_str + "\n")
config["out_file"].flush()
print(log_str)
end_time = time.time()
print('iter {} cost time {:.4f} sec.'.format(i, end_time - start_time)) # 打印时间间隔
start_time = time.time()
if i % config["snapshot_interval"] == 0:
torch.save(nn.Sequential(base_network), osp.join(config["output_path"], \
"iter_{:05d}_model.pth.tar".format(i)))
loss_params = config["loss"]
## train one iter
base_network.train(True) # 训练模式
ad_net.train(True)
optimizer = lr_scheduler(optimizer, i, **schedule_param)
# optimizer_centerloss=lr_scheduler(optimizer_centerloss, i, **schedule_param)
optimizer.zero_grad()
optimizer_centerloss.zero_grad()
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 = base_network(inputs_source)
features_target, outputs_target = base_network(inputs_target)
features = torch.cat((features_source, features_target), dim=0)
outputs = torch.cat((outputs_source, outputs_target), dim=0)
softmax_out = nn.Softmax(dim=1)(outputs)
if config['method'] == 'CDAN+E':
entropy = loss.Entropy(softmax_out)
transfer_loss = loss.CDAN([features, softmax_out], ad_net, entropy, network.calc_coeff(i), random_layer)
elif config['method'] == 'CDAN':
transfer_loss = loss.CDAN([features, softmax_out], ad_net, None, None, random_layer)
elif config['method'] == 'DANN':
transfer_loss = loss.DANN(features, ad_net)
else:
raise ValueError('Method cannot be recognized.')
# classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source) # 源域的分类损失
classifier_loss = crit(outputs_source, labels_source) # 源域的分类损失，标签平滑操作
# 目标域的熵正则化操作
outputs_target=outputs[len(inputs_source):,:]
t_logit = outputs_target
t_prob = torch.softmax(t_logit,dim=1)
t_entropy_loss = get_entropy_loss(t_prob) # 计算目标域的熵的损失
entropy_loss = 0.05 * (t_entropy_loss)
# 计算中心损失函数
loss_centor = criterion_centor(features_source, labels_source) # 中心损失计算
total_loss = loss_params["trade_off"] * transfer_loss + classifier_loss + config['centor_w']*loss_centor
if i % config["test_interval"] == config["test_interval"] - 1:
print('total loss: {:.4f}, transfer loss: {:.4f}, classifier loss: {:.4f}, centor loss: {:.4f}'.format(
total_loss.item(),transfer_loss.item(),classifier_loss.item(),config['centor_w']*loss_centor.item()
))
total_loss.backward()
optimizer.step()
# by doing so, weight_cent would not impact on the learning of centers
for param in criterion_centor.parameters():
param.grad.data *= (1. / config['centor_w'])
optimizer_centerloss.step()
torch.save(best_model, osp.join(config["output_path"], "best_model.pth.tar"))
return best_acc
def train(config):
## set pre-process
prep_dict = {}
prep_config = config["prep"]
prep_dict["source"] = prep.image_train(**config["prep"]['params'])
prep_dict["target"] = prep.image_train(**config["prep"]['params'])
if prep_config["test_10crop"]:
prep_dict["test"] = prep.image_test_10crop(**config["prep"]['params'])
else:
prep_dict["test"] = prep.image_test(**config["prep"]['params'])
## prepare data
dsets = {}
dset_loaders = {}
data_config = config["data"]
train_bs = data_config["source"]["batch_size"]
test_bs = data_config["test"]["batch_size"]
dsets["source"] = ImageList(open(data_config["source"]["list_path"]).readlines(), \
transform=prep_dict["source"])
dset_loaders["source"] = DataLoader(dsets["source"], batch_size=train_bs, \
shuffle=True, num_workers=4, drop_last=True)
dsets["target"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["target"])
dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, \
shuffle=True, num_workers=4, drop_last=True)
# if prep_config["test_10crop"]:
# for i in range(10):
# dsets["test"] = [ImageList(open(data_config["test"]["list_path"]).readlines(), \
# transform=prep_dict["test"][i]) for i in range(10)]
# dset_loaders["test"] = [DataLoader(dset, batch_size=test_bs, \
# shuffle=False, num_workers=4) for dset in dsets['test']]
# else:
# dsets["test"] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
# transform=prep_dict["test"])
# dset_loaders["test"] = DataLoader(dsets["test"], batch_size=test_bs, \
# shuffle=False, num_workers=4)
class_num = config["network"]["params"]["class_num"]
## set base network
net_config = config["network"]
base_network = net_config["name"](**net_config["params"])
base_network = base_network.cuda()
## add additional network for some methods
if config["loss"]["random"]:
random_layer = network.RandomLayer(
[base_network.output_num(), class_num],
config["loss"]["random_dim"])
ad_net = network.AdversarialNetwork(config["loss"]["random_dim"], 1024)
else:
random_layer = None
ad_net = network.AdversarialNetwork(
base_network.output_num() * class_num, 1024)
if config["loss"]["random"]:
random_layer.cuda()
ad_net = ad_net.cuda()
parameter_list = base_network.get_parameters() + ad_net.get_parameters()
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optimizer_config["type"](parameter_list, \
**(optimizer_config["optim_params"]))
param_lr = []
for param_group in optimizer.param_groups:
param_lr.append(param_group["lr"])
schedule_param = optimizer_config["lr_param"]
lr_scheduler = lr_schedule.schedule_dict[optimizer_config["lr_type"]]
gpus = config['gpu'].split(',')
if len(gpus) > 1:
ad_net = nn.DataParallel(ad_net, device_ids=[int(i) for i in gpus])
base_network = nn.DataParallel(base_network,
device_ids=[int(i) for i in gpus])
## train
len_train_source = len(dset_loaders["source"])
len_train_target = len(dset_loaders["target"])
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
best_acc = 0.0
for i in range(config["num_iterations"]):
# if i % config["test_interval"] == config["test_interval"] - 1:
# base_network.train(False)
# temp_acc = image_classification_test(dset_loaders, \
# base_network, test_10crop=prep_config["test_10crop"])
# temp_model = nn.Sequential(base_network)
# if temp_acc > best_acc:
# best_acc = temp_acc
# best_model = temp_model
# log_str = "iter: {:05d}, precision: {:.5f}".format(i, temp_acc)
# config["out_file"].write(log_str+"\n")
# config["out_file"].flush()
# print(log_str)
if i % config["snapshot_interval"] == 0:
torch.save(nn.Sequential(base_network), osp.join(config["output_path"], \
"iter_{:05d}_model.pth.tar".format(i)))
loss_params = config["loss"]
## train one iter
base_network.train(True)
ad_net.train(True)
optimizer = lr_scheduler(optimizer, i, **schedule_param)
optimizer.zero_grad()
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 = base_network(inputs_source)
features_target, outputs_target = base_network(inputs_target)
features = torch.cat((features_source, features_target), dim=0)
outputs = torch.cat((outputs_source, outputs_target), dim=0)
softmax_out = nn.Softmax(dim=1)(outputs)
if config['method'] == 'CDAN+E':
entropy = loss.Entropy(softmax_out)
transfer_loss = loss.CDAN([features, softmax_out], ad_net, entropy,
network.calc_coeff(i), random_layer)
elif config['method'] == 'CDAN':
transfer_loss = loss.CDAN([features, softmax_out], ad_net, None,
None, random_layer)
elif config['method'] == 'DANN':
transfer_loss = loss.DANN(features, ad_net)
else:
raise ValueError('Method cannot be recognized.')
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
if i % 10 == 0:
print('iter: ', i, 'classifier_loss: ', classifier_loss.data,
'transfer_loss: ', transfer_loss.data)
total_loss = loss_params["trade_off"] * transfer_loss + classifier_loss
total_loss.backward()
optimizer.step()
torch.save(best_model, osp.join(config["output_path"],
"best_model.pth.tar"))
return best_acc
def train(args,
i,
model,
ad_net,
ad_w_net,
train_loader,
train_loader1,
optimizer,
optimizer_ad,
optimizer_ad_w,
epoch,
start_epoch,
method,
random_layer=None):
model.train()
len_source = len(train_loader)
len_target = len(train_loader1)
if len_source > len_target:
num_iter = len_source
else:
num_iter = len_target
for batch_idx in range(num_iter):
if batch_idx % len_source == 0:
iter_source = iter(train_loader)
if batch_idx % len_target == 0:
iter_target = iter(train_loader1)
data_source, label_source = iter_source.next()
data_source, label_source = data_source.cuda(), label_source.cuda()
data_target, label_target = iter_target.next()
data_target = data_target.cuda()
optimizer.zero_grad()
optimizer_ad.zero_grad()
optimizer_ad_w.zero_grad()
feature, output = model(torch.cat((data_source, data_target), 0))
train_progress = (epoch - 1 + 1. *
(batch_idx + 1) / num_iter) / args.epochs
temp = loss_func.calc_temp(train_progress,
alpha=args.alpha,
max_iter=1.,
temp_max=args.temp_max)
w_s, w_t = loss_func.w_from_ad(feature,
ad_w_net,
temp=temp,
weight=(args.weight == 1))
w = torch.cat([w_s, w_t], dim=0)
loss = (w_s.detach() * nn.CrossEntropyLoss(reduction='none')(
output.narrow(0, 0, data_source.size(0)), label_source)).mean()
softmax_output = nn.Softmax(dim=1)(output)
if epoch > start_epoch:
if method == 'CDAN':
loss += loss_func.CDAN([feature, softmax_output],
ad_net,
w_s=w_s,
w_t=w_t,
random_layer=random_layer)
elif method == 'DANN':
loss += loss_func.DANN(feature,
ad_net,
w_s=w_s,
w_t=w_t,
hook=True)
elif method == 'Y_DAN':
loss += loss_func.Y_DAN([feature, softmax_output],
ad_net,
w_s=w_s,
w_t=w_t)
else:
raise ValueError('Method cannot be recognized.')
loss.backward(retain_graph=True)
ad_w_net.zero_grad()
optimizer.step()
if epoch > start_epoch:
optimizer_ad.step()
invariance_loss = loss_func.DANN(feature.detach(),
ad_w_net,
hook=False)
invariance_loss.backward()
optimizer_ad_w.step()
if (batch_idx + epoch * num_iter) % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * args.batch_size, num_iter * args.batch_size,
100. * batch_idx / num_iter, loss.item()))