def predict(config):
## set pre-process
prep_dict = {}
prep_config = config["prep"]
if prep_config["test_10crop"]:
prep_dict["database"] = prep.image_test_10crop( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
prep_dict["test"] = prep.image_test_10crop( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
else:
prep_dict["database"] = prep.image_test( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
prep_dict["test"] = prep.image_test( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
## prepare data
dsets = {}
dset_loaders = {}
data_config = config["data"]
if prep_config["test_10crop"]:
for i in range(10):
dsets["database"+str(i)] = ImageList(open(data_config["database"]["list_path"]).readlines(), \
transform=prep_dict["database"]["val"+str(i)])
dset_loaders["database"+str(i)] = util_data.DataLoader(dsets["database"+str(i)], \
batch_size=data_config["database"]["batch_size"], \
shuffle=False, num_workers=4)
dsets["test"+str(i)] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"]["val"+str(i)])
dset_loaders["test"+str(i)] = util_data.DataLoader(dsets["test"+str(i)], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=4)
else:
dsets["database"] = ImageList(open(data_config["database"]["list_path"]).readlines(), \
transform=prep_dict["database"])
dset_loaders["database"] = util_data.DataLoader(dsets["database"], \
batch_size=data_config["database"]["batch_size"], \
shuffle=False, num_workers=4)
dsets["test"] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"])
dset_loaders["test"] = util_data.DataLoader(dsets["test"], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=4)
## set base network
base_network = torch.load(config["snapshot_path"])
use_gpu = torch.cuda.is_available()
if use_gpu:
base_network = base_network.cuda()
database_codes, database_labels = code_predict(dset_loaders, base_network, "database", test_10crop=prep_config["test_10crop"], gpu=use_gpu)
test_codes, test_labels = code_predict(dset_loaders, base_network, "test", test_10crop=prep_config["test_10crop"], gpu=use_gpu)
return {"database_code":database_codes.numpy(), "database_labels":database_labels.numpy(), \
"test_code":test_codes.numpy(), "test_labels":test_labels.numpy()}
def run(config,path):
## 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 = 34
test_bs = 34
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)
dset_loaders["test1"] = DataLoader(dsets["target"], batch_size=train_bs, \
shuffle=False, num_workers=1, drop_last=False)
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)
n_class = config["network"]["params"]["class_num"]
# for p in [499,999,1499,1999,2499,2999]:
# PATH = path + '/'+ str(p) + '_model.pth.tar'
#
# model = load_model(PATH)
# base_network = model.cuda()
# fun1(dset_loaders, base_network, n_class)
PATH = path+'/2999_model.pth.tar'
model = load_model(PATH)
base_network = model.cuda()
# homo_cl = train_homo_cl(dset_loaders, base_network)
fun1(dset_loaders, base_network, n_class, dsets)
def run(config, model):
## 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 = 34
test_bs = 34
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=False)
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=False)
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)
n_class = config["network"]["params"]["class_num"]
model.train(False)
temp_acc = image_classification_test(dset_loaders, model, n_class=n_class)
log_str = "precision: {:.5f}".format(temp_acc)
print(log_str)
def save(config, model, save_name):
## 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 = 34
test_bs = 34
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=False)
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=False)
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)
n_class = config["network"]["params"]["class_num"]
model.train(False)
save_feature(dset_loaders, model, './snapshot/model/', save_name)
def train(config):
# set pre-process
prep_dict = {'source': prep.image_train(**config['prep']['params']),
'target': prep.image_train(**config['prep']['params'])}
if config['prep']['test_10crop']:
prep_dict['test'] = prep.image_test_10crop(**config['prep']['params'])
else:
prep_dict['test'] = prep.image_test(**config['prep']['params'])
# prepare data
data_set = {}
data_set_loader = {}
data_config = config['data']
data_set['source'] = ImageList(open(data_config['source']['list_path']).readlines(),
transform=prep_dict['source'])
data_set_loader['source'] = torch.utils.data.DataLoader(data_set['source'],
batch_size=data_config['source']['batch_size'],
shuffle=True, num_workers=4, drop_last=True)
data_set['target'] = ImageList(open(data_config['target']['list_path']).readlines(),
transform=prep_dict['target'])
data_set_loader['target'] = torch.utils.data.DataLoader(data_set['target'],
batch_size=data_config['target']['batch_size'],
shuffle=True, num_workers=4, drop_last=True)
if config['prep']['test_10crop']:
data_set['test'] = [ImageList(open(data_config['test']['list_path']).readlines(),
transform=prep_dict['test'][i])
for i in range(10)]
data_set_loader['test'] = [torch.utils.data.DataLoader(dset, batch_size=data_config['test']['batch_size'],
shuffle=False, num_workers=4)
for dset in data_set['test']]
else:
data_set['test'] = ImageList(open(data_config['test']['list_path']).readlines(),
transform=prep_dict['test'])
data_set_loader['test'] = torch.utils.data.DataLoader(data_set['test'],
batch_size=data_config['test']['batch_size'],
shuffle=False, num_workers=4)
# set base network
net_config = config['network']
base_net = net_config['name']() # res50
base_net = base_net.cuda()
# add domain, classifier network
class_num = config['network']['params']['class_num']
ad_net = network.Domain(in_features=base_net.output_num(), hidden_size=1024)
f1_net = network.Classifier(in_features=base_net.output_num(), hidden_size=128, class_num=class_num)
f2_net = network.Classifier(in_features=base_net.output_num(), hidden_size=128, class_num=class_num)
ad_net = ad_net.cuda()
f1_net = f1_net.cuda()
f2_net = f2_net.cuda()
parameter_list = base_net.get_parameters() + ad_net.get_parameters() \
+ f1_net.get_parameters() + f2_net.get_parameters()
# multi gpus
gpus = config['gpu'].split(',')
if len(gpus) > 1:
base_net = nn.DataParallel(base_net, device_ids=[int(i) for i in gpus])
f1_net = nn.DataParallel(f1_net, device_ids=[int(i) for i in gpus])
f2_net = nn.DataParallel(f2_net, device_ids=[int(i) for i in gpus])
ad_net = nn.DataParallel(ad_net, device_ids=[int(i) for i in gpus])
# set optimizer
optimizer_config = config['optimizer']
optimizer = optimizer_config['type'](parameter_list, **(optimizer_config['optim_params']))
schedule_param = optimizer_config['lr_param']
lr_scheduler = lr_schedule.schedule_dict[optimizer_config['lr_type']]
# set loss
class_criterion = nn.CrossEntropyLoss()
transfer_criterion = nn.BCELoss()
loss_params = config['loss']
# train
len_train_source = len(data_set_loader['source'])
len_train_taget = len(data_set_loader['target'])
best_acc = 0.0
since = time.time()
for num_iter in tqdm(range(config['max_iter'])):
if num_iter % config['val_iter'] == 0:
base_net.train(False)
f1_net.train(False)
f2_net.train(False)
temp_acc = val(data_set_loader, base_net, f1_net, f2_net,
test_10crop=config['prep']['test_10crop'],
config=config, num_iter=num_iter)
if temp_acc > best_acc:
best_acc = temp_acc
log_str = 'iter: {:d}, accu: {:.4f}\ntime: {:.4f}'.format(num_iter, temp_acc, time.time() - since)
config['logger'].logger.debug(log_str)
config['results'][num_iter].append(temp_acc)
if num_iter % config['snapshot_iter'] == 0:
# TODO
pass
base_net.train(True)
f1_net.train(True)
f2_net.train(True)
ad_net.train(True)
optimizer = lr_scheduler(optimizer, num_iter, **schedule_param)
optimizer.zero_grad()
if num_iter % len_train_source == 0:
iter_source = iter(data_set_loader['source'])
if num_iter % len_train_taget == 0:
iter_target = iter(data_set_loader['target'])
input_source, label_source = iter_source.next()
input_target, _ = iter_target.next()
input_source, label_source, input_target = input_source.cuda(), label_source.cuda(), input_target.cuda()
inputs = torch.cat((input_source, input_target), 0)
batch_size = len(label_source)
# class-wise adaptation
features = base_net(inputs)
alpha = 2. / (1. + np.exp(-10 * float(num_iter / config['max_iter']))) - 1
output1 = f1_net(features, alpha)
output2 = f2_net(features, alpha)
output_s1 = output1[:batch_size, :]
output_s2 = output2[:batch_size, :]
output_t1 = output1[batch_size:, :]
output_t2 = output2[batch_size:, :]
output_t1 = F.softmax(output_t1)
output_t2 = F.softmax(output_t2)
inconsistency_loss = torch.mean(torch.abs(output_t1 - output_t2))
classifier_loss1 = class_criterion(output_s1, label_source)
classifier_loss2 = class_criterion(output_s2, label_source)
classifier_loss = classifier_loss1 + classifier_loss2
# domain-wise adaptation
domain_labels = torch.from_numpy(np.array([[1]] * batch_size + [[0]] * batch_size)).float().cuda()
domain_output = ad_net(features, alpha)
transfer_loss = transfer_criterion(domain_output, domain_labels)
total_loss = classifier_loss + loss_params['domain_off'] * transfer_loss \
- loss_params['dis_off'] * inconsistency_loss
total_loss.backward()
optimizer.step()
if num_iter % config['val_iter'] == 0:
print('class:', classifier_loss.item(),
'domain:', transfer_loss.item() * loss_params['domain_off'],
'inconsistency:', inconsistency_loss.item() * loss_params['dis_off'],
'total:', total_loss.item())
if config['is_writer']:
config['writer'].add_scalars('train', {'total': total_loss.item(), 'class': classifier_loss.item(),
'domain': transfer_loss.item() * loss_params['domain_off'],
'inconsistency': inconsistency_loss.item() * loss_params[
'dis_off']},
num_iter)
if config['is_writer']:
config['writer'].close()
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() #加载基础网络结构
parameter_list = base_network.get_parameters()
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optimizer_config["type"](parameter_list, \
**(optimizer_config["optim_params"]))
crit = LabelSmoothingLoss(smoothing=0.1, classes=31) #标签平滑操作
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:
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"])
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=False)
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)))
## train one iter
base_network.train(True) # 训练模式
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)
#目标域的熵正则化操作
t_logit = outputs_target
t_prob = F.softmax(t_logit)
t_entropy_loss = get_entropy_loss(t_prob) # 计算目标域的熵的损失
entropy_loss = config['ENT_w'] * (t_entropy_loss)
# classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source) # 源域的分类损失
classifier_loss = crit(outputs_source, labels_source)
total_loss = classifier_loss + entropy_loss
if i % config["test_interval"] == config["test_interval"] - 1:
print('total loss: {:.4f}, classifier loss: {:.4f}'.format(
total_loss.item(), classifier_loss.item()))
total_loss.backward()
optimizer.step()
if (i + 1) % 1000 == 0:
model_path = os.path.join(
os.path.dirname(config['save_model_name']), 'temp_model.pth')
save_model(base_network, model_path)
model_path = config['save_model_name']
best_model_path = os.path.join(os.path.dirname(model_path),
'best_model.pth')
save_model(base_network, model_path)
save_model(best_model, best_model_path)
torch.save(best_model, osp.join(config["output_path"],
"best_model.pth.tar"))
return best_acc
def transfer_classification(config):
# ---set log writer ---
writer = SummaryWriter(log_dir=config["log_dir"])
# set pre-process
prep_dict = {}
for prep_config in config["prep"]:
name = prep_config["name"]
prep_dict[name] = {}
if prep_config["type"] == "image":
prep_dict[name]["test_10crop"] = prep_config["test_10crop"]
prep_dict[name]["train"] = prep.image_train(resize_size=prep_config["resize_size"], crop_size=prep_config["crop_size"])
if prep_config["test_10crop"]:
prep_dict[name]["test"] = prep.image_test_10crop(resize_size=prep_config["resize_size"], crop_size=prep_config["crop_size"])
else:
prep_dict[name]["test"] = prep.image_test(resize_size=prep_config["resize_size"], crop_size=prep_config["crop_size"])
# print(prep_dict)
## set loss
class_criterion = nn.CrossEntropyLoss()
loss_config = config["loss"]
# transfer_criterion = loss.loss_dict[loss_config["name"]]
if "params" not in loss_config:
loss_config["params"] = {}
# print(transfer_criterion)
transfer_criterion = DANLoss(**loss_config["params"])
## prepare data
dsets = {}
dset_loaders = {}
for data_config in config["data"]:
name = data_config['name']
dsets[name] = {}
dset_loaders[name] = {}
## image data
if data_config["type"] == "image":
dsets[name]["train"] = ImageList(open(data_config["list_path"]["train"]).readlines(),
transform=prep_dict[name]["train"])
dset_loaders[name]["train"] = util_data.DataLoader(dsets[name]["train"],
batch_size=data_config["batch_size"]["train"],
shuffle=True, num_workers=4)
if "test" in data_config["list_path"]:
if prep_dict[name]["test_10crop"]:
for i in range(10):
dsets[name]["test" + str(i)] = ImageList(
open(data_config["list_path"]["test"]).readlines(),
transform=prep_dict[name]["test"]["val" + str(i)])
dset_loaders[name]["test" + str(i)] = util_data.DataLoader(
dsets[name]["test" + str(i)], batch_size=data_config["batch_size"]["test"],
shuffle=False, num_workers=4)
else:
dsets[name]["test"] = ImageList(
open(data_config["list_path"]["test"]).readlines(),
transform=prep_dict[name]["test"])
dset_loaders[name]["test"] = util_data.DataLoader(
dsets[name]["test"], batch_size = data_config["batch_size"]["test"],
shuffle=False, num_workers=4)
else:
if prep_dict[name]["test_10crop"]:
for i in range(10):
dsets[name]["test" + str(i)] = ImageList(
open(data_config["list_path"]["train"]).readlines(),
transform=prep_dict[name]["test"]["val" + str(i)])
dset_loaders[name]["test" + str(i)] = util_data.DataLoader(
dsets[name]["test" + str(i)], batch_size=data_config["batch_size"]["test"],
shuffle=False, num_workers=4)
else:
dsets[name]["test"] = ImageList(open(data_config["list_path"]["train"]).readlines(),
transform=prep_dict[name]["test"])
dset_loaders[data_config["name"]]["test"] = util_data.DataLoader(
dsets[data_config["name"]]["test"], batch_size=data_config["batch_size"]["test"],
shuffle=False, num_workers=4)
class_num = 31
## set base network
net_config = config["network"]
base_network = network.network_dict[net_config["name"]]()
if net_config["use_bottleneck"]:
bottleneck_layer = nn.Linear(base_network.output_num(), net_config["bottleneck_dim"])
classifier_layer = nn.Linear(bottleneck_layer.out_features, class_num)
else:
classifier_layer = nn.Linear(base_network.output_num(), class_num)
## initialization
if net_config["use_bottleneck"]:
bottleneck_layer.weight.data.normal_(0, 0.005)
bottleneck_layer.bias.data.fill_(0.1)
bottleneck_layer = nn.Sequential(bottleneck_layer, nn.ReLU(), nn.Dropout(0.5))
classifier_layer.weight.data.normal_(0, 0.01)
classifier_layer.bias.data.fill_(0.0)
# print(base_network.state_dict())
# print(classifier_layer.state_dict())
use_gpu = torch.cuda.is_available()
if use_gpu:
if net_config["use_bottleneck"]:
bottleneck_layer = bottleneck_layer.to(device)
classifier_layer = classifier_layer.to(device)
base_network = base_network.to(device)
## collect parameters
if net_config["use_bottleneck"]:
parameter_list = [{"params":base_network.parameters(), "lr":1},
{"params":bottleneck_layer.parameters(), "lr":10},
{"params":classifier_layer.parameters(), "lr":10}]
else:
parameter_list = [{"params":base_network.parameters(), "lr":1},
{"params":classifier_layer.parameters(), "lr":10}]
## add additional network for some methods
if loss_config["name"] == "JAN":
softmax_layer = nn.Softmax()
if use_gpu:
softmax_layer = softmax_layer.to(device)
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optim_dict[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"]]
## train
len_train_source = len(dset_loaders["source"]["train"]) - 1
len_train_target = len(dset_loaders["target"]["train"]) - 1
# transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
for i in range(config["num_iterations"]):
# test in the train
if i % config["test_interval"] == 0:
base_network.train(False)
classifier_layer.train(False)
if net_config["use_bottleneck"]:
bottleneck_layer.train(False)
test_model = nn.Sequential(base_network, bottleneck_layer, classifier_layer)
test_acc = image_classification_test(dset_loaders["target"],
test_model,
test_10crop=prep_dict["target"]["test_10crop"],
gpu=use_gpu)
print("iteration: %d, test accuracy: %f" % (i, test_acc))
else:
test_model = nn.Sequential(base_network, classifier_layer)
test_acc = image_classification_test(dset_loaders["target"],
test_model,
test_10crop=prep_dict["target"]["test_10crop"],
gpu=use_gpu)
print("iteration: %d, test accuracy: %f" % (i, test_acc))
# save model parameters
if i % config["checkpoint_iterval"] == 0:
torch.save(test_model.state_dict(), config["checkpoint_dir"]+"checkpoint_"+str(i).zfill(5)+".pth")
# loss_test = nn.BCELoss()
# train one iter
base_network.train(True)
if net_config["use_bottleneck"]:
bottleneck_layer.train(True)
classifier_layer.train(True)
optimizer = lr_scheduler(param_lr, optimizer, i, **schedule_param)
optimizer.zero_grad()
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"]["train"])
if i % len_train_target == 0:
iter_target = iter(dset_loaders["target"]["train"])
inputs_source, labels_source = iter_source.next()
inputs_target, labels_target = iter_target.next()
inputs_source = inputs_source.to(device)
inputs_target = inputs_target.to(device)
labels_source = labels_source.to(device)
inputs = torch.cat((inputs_source, inputs_target), dim=0) ### cat the source and target
features = base_network(inputs)
if net_config["use_bottleneck"]:
features = bottleneck_layer(features)
outputs = classifier_layer(features)
# split the output for different loss
num = inputs.size(0)//2
# the premise is the batch size of source and target is the same
classifier_loss = class_criterion(outputs.narrow(0, 0, num), labels_source)
## switch between different transfer loss
if loss_config["name"] == "DAN":
# transfer_loss = transfer_criterion(features.narrow(0, 0, num),
# features.narrow(0, num, num),
# **loss_config["params"])
transfer_loss = transfer_criterion(features.narrow(0, 0, num),
features.narrow(0, num, num))
elif loss_config["name"] == "RTN":
## RTN is still under developing
transfer_loss = 0
elif loss_config["name"] == "JAN":
softmax_out = softmax_layer(outputs)
transfer_loss = transfer_criterion([features.narrow(0, 0, num), softmax_out.narrow(0, 0, num)],
[features.narrow(0, num, num), softmax_out.narrow(0, num, num)],
**loss_config["params"])
total_loss = loss_config["trade_off"] * transfer_loss + classifier_loss
if i % 100 == 0:
print("iteration: ", str(i), "\t transfer loss: ", str(transfer_loss.item()),
"\t classification loss: ", str(classifier_loss.item()),
"\t total loss: ", str(total_loss.item()))
for param_group in optimizer.param_groups:
print("lr: ", param_group["lr"])
# log for losses
writer.add_scalar('Train/loss_classification', classifier_loss.item(), i//100)
writer.add_scalar('Train/loss_transfer', transfer_loss.item(), i//100)
writer.add_scalar('Train/loss_total', total_loss.item(), i//100)
total_loss.backward()
optimizer.step()
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()
## 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"])
best_acc = 0.0
best_model = nn.Sequential(base_network)
each_log = ""
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}, transfer_loss:{:.4f}, classifier_loss:{:.4f}, total_loss:{:.4f}" \
.format(i, temp_acc, transfer_loss.item(), classifier_loss.item(), total_loss.item())
config["out_file"].write(log_str + "\n")
config["out_file"].flush()
print(log_str)
config["out_file"].write(each_log)
config["out_file"].flush()
each_log = ""
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)
labels_target_fake = torch.max(nn.Softmax(dim=1)(outputs_target), 1)[1]
labels = torch.cat((labels_source, labels_target_fake))
entropy = loss.Entropy(softmax_out)
transfer_loss = loss.CDAN([features, softmax_out], ad_net, entropy,
network.calc_coeff(i), random_layer)
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
mdd_loss = loss.mdd_loss(features=features,
labels=labels,
left_weight=args.left_weight,
right_weight=args.right_weight)
max_entropy_loss = loss.EntropicConfusion(features)
total_loss = loss_params["trade_off"] * transfer_loss \
+ args.cls_weight * classifier_loss \
+ args.mdd_weight * mdd_loss \
+ args.entropic_weight * max_entropy_loss
total_loss.backward()
optimizer.step()
log_str = "iter: {:05d},transfer_loss:{:.4f}, classifier_loss:{:.4f}, mdd_loss:{:4f}," \
"max_entropy_loss:{:.4f},total_loss:{:.4f}" \
.format(i, transfer_loss.item(), classifier_loss.item(), mdd_loss.item(),
max_entropy_loss.item(), total_loss.item())
each_log += log_str + "\n"
torch.save(
best_model, config['model_output_path'] + "{}_{}_p-{}_e-{}".format(
config['log_name'], str(best_acc), str(config["mdd_weight"]),
str(config["entropic_weight"])))
return best_acc
def train(config):
## set up summary writer
writer = SummaryWriter(config['output_path'])
# set up early stop
early_stop_engine = EarlyStopping(config["early_stop_patience"])
## set pre-process
prep_dict = {}
prep_config = config["prep"]
prep_dict["source"] = prep.image_train( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
prep_dict["target"] = prep.image_train( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
if prep_config["test_10crop"]:
prep_dict["test"] = prep.image_test_10crop( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
else:
prep_dict["test"] = prep.image_test( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
## set loss
class_num = config["network"]["params"]["class_num"]
loss_params = config["loss"]
class_criterion = nn.CrossEntropyLoss()
transfer_criterion = loss.PADA
center_criterion = loss_params["loss_type"](num_classes=class_num,
feat_dim=config["network"]["params"]["bottleneck_dim"])
## prepare data
dsets = {}
dset_loaders = {}
data_config = config["data"]
dsets["source"] = ImageList(stratify_sampling(open(data_config["source"]["list_path"]).readlines(), prep_config["source_size"]), \
transform=prep_dict["source"])
dset_loaders["source"] = util_data.DataLoader(dsets["source"], \
batch_size=data_config["source"]["batch_size"], \
shuffle=True, num_workers=1)
dsets["target"] = ImageList(stratify_sampling(open(data_config["target"]["list_path"]).readlines(), prep_config["target_size"]), \
transform=prep_dict["target"])
dset_loaders["target"] = util_data.DataLoader(dsets["target"], \
batch_size=data_config["target"]["batch_size"], \
shuffle=True, num_workers=1)
if prep_config["test_10crop"]:
for i in range(10):
dsets["test"+str(i)] = ImageList(stratify_sampling(open(data_config["test"]["list_path"]).readlines(), ratio=prep_config['target_size']), \
transform=prep_dict["test"]["val"+str(i)])
dset_loaders["test"+str(i)] = util_data.DataLoader(dsets["test"+str(i)], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=1)
dsets["target"+str(i)] = ImageList(stratify_sampling(open(data_config["target"]["list_path"]).readlines(), ratio=prep_config['target_size']), \
transform=prep_dict["test"]["val"+str(i)])
dset_loaders["target"+str(i)] = util_data.DataLoader(dsets["target"+str(i)], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=1)
else:
dsets["test"] = ImageList(stratify_sampling(open(data_config["test"]["list_path"]).readlines(), ratio=prep_config['target_size']), \
transform=prep_dict["test"])
dset_loaders["test"] = util_data.DataLoader(dsets["test"], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=1)
dsets["target_test"] = ImageList(stratify_sampling(open(data_config["target"]["list_path"]).readlines(), ratio=prep_config['target_size']), \
transform=prep_dict["test"])
dset_loaders["target_test"] = MyDataLoader(dsets["target_test"], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=1)
config['out_file'].write("dataset sizes: source={}, target={}\n".format(
len(dsets["source"]), len(dsets["target"])))
## set base network
net_config = config["network"]
base_network = net_config["name"](**net_config["params"])
use_gpu = torch.cuda.is_available()
if use_gpu:
base_network = base_network.cuda()
## collect parameters
if net_config["params"]["new_cls"]:
if net_config["params"]["use_bottleneck"]:
parameter_list = [{"params":base_network.feature_layers.parameters(), "lr_mult":1, 'decay_mult':2}, \
{"params":base_network.bottleneck.parameters(), "lr_mult":10, 'decay_mult':2}, \
{"params":base_network.fc.parameters(), "lr_mult":10, 'decay_mult':2}]
else:
parameter_list = [{"params":base_network.feature_layers.parameters(), "lr_mult":1, 'decay_mult':2}, \
{"params":base_network.fc.parameters(), "lr_mult":10, 'decay_mult':2}]
else:
parameter_list = [{"params":base_network.parameters(), "lr_mult":1, 'decay_mult':2}]
## add additional network for some methods
ad_net = network.AdversarialNetwork(base_network.output_num())
gradient_reverse_layer = network.AdversarialLayer(high_value=config["high"]) #,
#max_iter_value=config["num_iterations"])
if use_gpu:
ad_net = ad_net.cuda()
parameter_list.append({"params":ad_net.parameters(), "lr_mult":10, 'decay_mult':2})
parameter_list.append({"params":center_criterion.parameters(), "lr_mult": 10, 'decay_mult':1})
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optim_dict[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"]]
## train
len_train_source = len(dset_loaders["source"]) - 1
len_train_target = len(dset_loaders["target"]) - 1
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"] == 0:
base_network.train(False)
if config['loss']['ly_type'] == "cosine":
temp_acc = image_classification_test(dset_loaders, \
base_network, test_10crop=prep_config["test_10crop"], \
gpu=use_gpu)
elif config['loss']['ly_type'] == "euclidean":
temp_acc, _ = distance_classification_test(dset_loaders, \
base_network, center_criterion.centers.detach(), test_10crop=prep_config["test_10crop"], \
gpu=use_gpu)
else:
raise ValueError("no test method for cls loss: {}".format(config['loss']['ly_type']))
snapshot_obj = {'step': i,
"base_network": base_network.state_dict(),
'precision': temp_acc,
}
if config["loss"]["loss_name"] != "laplacian" and config["loss"]["ly_type"] == "euclidean":
snapshot_obj['center_criterion'] = center_criterion.state_dict()
if temp_acc > best_acc:
best_acc = temp_acc
# save best model
torch.save(snapshot_obj,
osp.join(config["output_path"], "best_model.pth.tar"))
log_str = "iter: {:05d}, {} precision: {:.5f}\n".format(i, config['loss']['ly_type'], temp_acc)
config["out_file"].write(log_str)
config["out_file"].flush()
writer.add_scalar("precision", temp_acc, i)
if early_stop_engine.is_stop_training(temp_acc):
config["out_file"].write("no improvement after {}, stop training at step {}\n".format(
config["early_stop_patience"], i))
# config["out_file"].write("finish training! \n")
break
if (i+1) % config["snapshot_interval"] == 0:
torch.save(snapshot_obj,
osp.join(config["output_path"], "iter_{:05d}_model.pth.tar".format(i)))
## train one iter
base_network.train(True)
optimizer = lr_scheduler(param_lr, 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()
if use_gpu:
inputs_source, inputs_target, labels_source = \
Variable(inputs_source).cuda(), Variable(inputs_target).cuda(), \
Variable(labels_source).cuda()
else:
inputs_source, inputs_target, labels_source = Variable(inputs_source), \
Variable(inputs_target), Variable(labels_source)
inputs = torch.cat((inputs_source, inputs_target), dim=0)
source_batch_size = inputs_source.size(0)
if config['loss']['ly_type'] == 'cosine':
features, logits = base_network(inputs)
source_logits = logits.narrow(0, 0, source_batch_size)
elif config['loss']['ly_type'] == 'euclidean':
features, _ = base_network(inputs)
logits = -1.0 * loss.distance_to_centroids(features, center_criterion.centers.detach())
source_logits = logits.narrow(0, 0, source_batch_size)
ad_net.train(True)
weight_ad = torch.ones(inputs.size(0))
transfer_loss = transfer_criterion(features, ad_net, gradient_reverse_layer, \
weight_ad, use_gpu)
ad_out, _ = ad_net(features.detach())
ad_acc, source_acc_ad, target_acc_ad = domain_cls_accuracy(ad_out)
# source domain classification task loss
classifier_loss = class_criterion(source_logits, labels_source)
# fisher loss on labeled source domain
fisher_loss, fisher_intra_loss, fisher_inter_loss, center_grad = center_criterion(features.narrow(0, 0, int(inputs.size(0)/2)), labels_source, inter_class=config["loss"]["inter_type"],
intra_loss_weight=loss_params["intra_loss_coef"], inter_loss_weight=loss_params["inter_loss_coef"])
# entropy minimization loss
em_loss = loss.EntropyLoss(nn.Softmax(dim=1)(logits))
# final loss
total_loss = loss_params["trade_off"] * transfer_loss \
+ fisher_loss \
+ loss_params["em_loss_coef"] * em_loss \
+ classifier_loss
total_loss.backward()
if center_grad is not None:
# clear mmc_loss
center_criterion.centers.grad.zero_()
# Manually assign centers gradients other than using autograd
center_criterion.centers.backward(center_grad)
optimizer.step()
if i % config["log_iter"] == 0:
config['out_file'].write('iter {}: total loss={:0.4f}, transfer loss={:0.4f}, cls loss={:0.4f}, '
'em loss={:0.4f}, '
'mmc loss={:0.4f}, intra loss={:0.4f}, inter loss={:0.4f}, '
'ad acc={:0.4f}, source_acc={:0.4f}, target_acc={:0.4f}\n'.format(
i, total_loss.data.cpu().float().item(), transfer_loss.data.cpu().float().item(), classifier_loss.data.cpu().float().item(),
em_loss.data.cpu().float().item(),
fisher_loss.cpu().float().item(), fisher_intra_loss.cpu().float().item(), fisher_inter_loss.cpu().float().item(),
ad_acc, source_acc_ad, target_acc_ad,
))
config['out_file'].flush()
writer.add_scalar("total_loss", total_loss.data.cpu().float().item(), i)
writer.add_scalar("cls_loss", classifier_loss.data.cpu().float().item(), i)
writer.add_scalar("transfer_loss", transfer_loss.data.cpu().float().item(), i)
writer.add_scalar("ad_acc", ad_acc, i)
writer.add_scalar("d_loss/total", fisher_loss.data.cpu().float().item(), i)
writer.add_scalar("d_loss/intra", fisher_intra_loss.data.cpu().float().item(), i)
writer.add_scalar("d_loss/inter", fisher_inter_loss.data.cpu().float().item(), i)
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=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"]
n_class = config["network"]["params"]["class_num"]
## set base network
net_config = config["network"]
base_network_stu = net_config["name"](**net_config["params"]).cuda()
base_network_tea = net_config["name"](**net_config["params"]).cuda()
## add additional network for some methods
if config["loss"]["random"]:
random_layer = network.RandomLayer([base_network_stu.output_num(), n_class], config["loss"]["random_dim"])
ad_net = network.AdversarialNetwork(config["loss"]["random_dim"], 1024)
else:
random_layer = None
if config['method'] == 'DANN':
ad_net = network.AdversarialNetwork(base_network_stu.output_num(), 1024)#DANN
else:
ad_net = network.AdversarialNetwork(base_network_stu.output_num() * n_class, 1024)
if config["loss"]["random"]:
random_layer.cuda()
ad_net = ad_net.cuda()
ad_net2 = network.AdversarialNetwork(n_class, n_class*4)
ad_net2.cuda()
parameter_list = base_network_stu.get_parameters() + ad_net.get_parameters()
teacher_params = list(base_network_tea.parameters())
for param in teacher_params:
param.requires_grad = False
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optimizer_config["type"](parameter_list, \
**(optimizer_config["optim_params"]))
teacher_optimizer = EMAWeightOptimizer(base_network_tea, base_network_stu, alpha=0.99)
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"]]
## train
len_train_source = len(dset_loaders["source"])
len_train_target = len(dset_loaders["target"])
best_acc = 0.0
output1(log_name)
loss1, loss2, loss3, loss4, loss5,loss6 = 0, 0, 0, 0, 0,0
output1(' ======= DA TRAINING ======= ')
best1 = 0
f_t_result = []
max_iter = config["num_iterations"]
for i in range(max_iter+1):
if i % config["test_interval"] == config["test_interval"] - 1 and i > 1500:
base_network_tea.train(False)
base_network_stu.train(False)
# print("test")
if 'MT' in config['method']:
temp_acc = image_classification_test(dset_loaders,
base_network_tea, test_10crop=prep_config["test_10crop"])
if temp_acc > best_acc:
best_acc = temp_acc
log_str = "iter: {:05d}, tea_precision: {:.5f}".format(i, temp_acc)
output1(log_str)
#
# if i > 20001 and best_acc < 0.69:
# break
#
# if temp_acc < 0.67:
# break
#
# if i > 30001 and best_acc < 0.71:
# break
# torch.save(base_network_tea, osp.join(path, "_model.pth.tar"))
# temp_acc = image_classification_test(dset_loaders,
# base_network_stu, test_10crop=prep_config["test_10crop"])
if temp_acc > best_acc:
best_acc = temp_acc
torch.save(base_network_stu, osp.join(path, "_model.pth.tar"))
# log_str = "iter: {:05d}, stu_precision: {:.5f}".format(i, temp_acc)
# output1(log_str)
else:
temp_acc = image_classification_test(dset_loaders,
base_network_stu, test_10crop=prep_config["test_10crop"])
if temp_acc > best_acc:
best_acc = temp_acc
torch.save(base_network_stu, osp.join(path,"_model.pth.tar"))
log_str = "iter: {:05d}, precision: {:.5f}".format(i, temp_acc)
output1(log_str)
loss_params = config["loss"]
## train one iter
base_network_stu.train(True)
base_network_tea.train(True)
ad_net.train(True)
optimizer = lr_scheduler(optimizer, i, **schedule_param)
optimizer.zero_grad()
if i % len_train_target == 0:
iter_target = iter(dset_loaders["target"])
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"])
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_stu(inputs_source)
features_target_stu, outputs_target_stu = base_network_stu(inputs_target)
features_target_tea, outputs_target_tea = base_network_tea(inputs_target)
softmax_out_source = nn.Softmax(dim=1)(outputs_source)
softmax_out_target_stu = nn.Softmax(dim=1)(outputs_target_stu)
softmax_out_target_tea = nn.Softmax(dim=1)(outputs_target_tea)
features = torch.cat((features_source, features_target_stu), dim=0)
if 'MT' in config['method']:
softmax_out = torch.cat((softmax_out_source, softmax_out_target_tea), dim=0)
else:
softmax_out = torch.cat((softmax_out_source, softmax_out_target_stu), dim=0)
vat_loss = VAT(base_network_stu).cuda()
n, d = features_source.shape
decay = cal_decay(start=1,end=0.6,i = i)
# image number in each class
s_labels = labels_source
t_max, t_labels = torch.max(softmax_out_target_tea, 1)
t_max, t_labels = t_max.cuda(), t_labels.cuda()
if config['method'] == 'DANN+dis' or config['method'] == 'CDRL':
pass
elif config['method'] == 'RESNET':
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
total_loss = classifier_loss
elif config['method'] == 'CDAN+E':
entropy = losssntg.Entropy(softmax_out)
ad_loss = losssntg.CDANori([features, softmax_out], ad_net, entropy, network.calc_coeff(i),
random_layer)
transfer_loss = loss_params["trade_off"] * ad_loss
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
total_loss = transfer_loss + classifier_loss
elif config['method'] == 'CDAN':
ad_loss = losssntg.CDANori([features, softmax_out], ad_net, None, None, random_layer)
transfer_loss = loss_params["trade_off"] * ad_loss
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
total_loss = transfer_loss + classifier_loss
elif config['method'] == 'DANN':
ad_loss = losssntg.DANN(features, ad_net)
transfer_loss = loss_params["trade_off"] * ad_loss
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
total_loss = transfer_loss + classifier_loss
elif config['method'] == 'CDAN+MT':
th = config['th']
ad_loss = losssntg.CDANori([features, softmax_out], ad_net, None, None, random_layer)
unsup_loss = compute_aug_loss(softmax_out_target_stu, softmax_out_target_tea, n_class,confidence_thresh=th)
unsup_loss = compute_aug_loss2(softmax_out_target_stu, softmax_out_target_tea, n_class,confidence_thresh=th)
transfer_loss = loss_params["trade_off"] * ad_loss \
+ 0.01 * unsup_loss
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
total_loss = transfer_loss + classifier_loss
elif config['method'] == 'CDAN+MT+VAT':
cent = ConditionalEntropyLoss().cuda()
ad_loss = losssntg.CDANori([features, softmax_out], ad_net, None, None, random_layer)
unsup_loss = compute_aug_loss(softmax_out_target_stu, softmax_out_target_tea, n_class)
loss_trg_cent = 1e-2 * cent(outputs_target_stu)
loss_trg_vat = 1e-2 * vat_loss(inputs_target, outputs_target_stu)
transfer_loss = loss_params["trade_off"] * ad_loss \
+ 0.001*(unsup_loss + loss_trg_cent + loss_trg_vat)
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
total_loss = transfer_loss + classifier_loss
elif config['method'] == 'CDAN+MT+cent+VAT+temp':
th = 0.7
ad_loss = losssntg.CDANori([features, softmax_out], ad_net, None, None, random_layer)
unsup_loss = compute_aug_loss(softmax_out_target_stu, softmax_out_target_tea, n_class,confidence_thresh=th)
cent = ConditionalEntropyLoss().cuda()
# loss_src_vat = vat_loss(inputs_source, outputs_source)
loss_trg_cent = 1e-2 * cent(outputs_target_stu)
loss_trg_vat = 1e-2 * vat_loss(inputs_target, outputs_target_stu)
transfer_loss = loss_params["trade_off"] * ad_loss \
+ unsup_loss + loss_trg_cent + loss_trg_vat
# temperature
classifier_loss = nn.NLLLoss()(nn.LogSoftmax(1)(outputs_source / 1.05), labels_source)
total_loss = transfer_loss + classifier_loss
elif config['method'] == 'CDAN+MT+cent+VAT+weightCross+T':
if i % len_train_target == 0:
if i != 0:
# print(cnt)
cnt = torch.tensor(cnt).float()
weight = cnt.sum() - cnt
weight = weight.cuda()
else:
weight = torch.ones(n_class).cuda()
cnt = [0] * n_class
for j in t_labels:
cnt[j.item()] += 1
a = config['a']
b = config['b']
th = config['th']
temp = config['temp']
ad_loss = losssntg.CDANori([features, softmax_out], ad_net, None, None, random_layer)
unsup_loss = compute_aug_loss2(softmax_out_target_stu, softmax_out_target_tea, n_class,confidence_thresh=th)
# cbloss = compute_cbloss(softmax_out_target_stu, n_class, cls_balance=0.05)
# unsup_loss = compute_aug_loss(softmax_out_target_stu, softmax_out_target_tea, n_class,confidence_thresh=th)
cent = ConditionalEntropyLoss().cuda()
# loss_src_vat = vat_loss(inputs_source, outputs_source)
loss_trg_cent = 1e-2 * cent(outputs_target_stu)
loss_trg_vat = 1e-2 * vat_loss(inputs_target, outputs_target_stu)
classifier_loss = nn.CrossEntropyLoss(weight=weight)(outputs_source/temp, labels_source)
# classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
transfer_loss = loss_params["trade_off"] * ad_loss \
+ a*unsup_loss + b*(loss_trg_vat+loss_trg_cent)
total_loss = transfer_loss + classifier_loss
elif config['method'] == 'CDAN+MT+E+VAT+weightCross+T':
entropy = losssntg.Entropy(softmax_out)
if i % len_train_target == 0:
if i != 0:
# print(cnt)
cnt = torch.tensor(cnt).float()
weight = cnt.sum() - cnt
weight = weight.cuda()
else:
weight = torch.ones(n_class).cuda()
cnt = [0] * n_class
for j in t_labels:
cnt[j.item()] += 1
a = config['a']
b = config['b']
th = config['th']
temp = config['temp']
ad_loss = losssntg.CDANori([features, softmax_out], ad_net, entropy, network.calc_coeff(i),
random_layer) # unsup_loss = compute_aug_loss2(softmax_out_target_stu, softmax_out_target_tea, n_class,confidence_thresh=th)
# cbloss = compute_cbloss(softmax_out_target_stu, n_class, cls_balance=0.05)
unsup_loss = compute_aug_loss(softmax_out_target_stu, softmax_out_target_tea, n_class, confidence_thresh=th)
cent = ConditionalEntropyLoss().cuda()
# loss_src_vat = vat_loss(inputs_source, outputs_source)
loss_trg_cent = 1e-2 * cent(outputs_target_stu)
loss_trg_vat = 1e-2 * vat_loss(inputs_target, outputs_target_stu)
classifier_loss = nn.CrossEntropyLoss(weight=weight)(outputs_source / temp, labels_source)
# classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
transfer_loss = loss_params["trade_off"] * ad_loss \
+ a * unsup_loss + b * (loss_trg_vat + loss_trg_cent)
total_loss = transfer_loss + classifier_loss
# adout
# ad_out1 = ad_net(features_source)
# w = 1-ad_out1
# c = w * nn.CrossEntropyLoss(reduction='none')(outputs_source, labels_source)
# classifier_loss = c.mean()
# total_loss = transfer_loss + classifier_loss
total_loss.backward()
optimizer.step()
teacher_optimizer.step()
loss1 += ad_loss.item()
loss2 += classifier_loss.item()
# loss3 += unsup_loss.item()
# loss4 += loss_trg_cent.item()
# loss5 += loss_trg_vat.item()
# loss6 += cbloss.item()
# dis_sloss_l += sloss_l.item()
if i % 50 == 0 and i != 0:
output1('iter:{:d}, ad_loss_D:{:.2f}, closs:{:.2f}, unsup_loss:{:.2f}, loss_trg_cent:{:.2f}, loss_trg_vatcd:{:.2f}, cbloss:{:.2f}'
.format(i, loss1, loss2, loss3, loss4, loss5,loss6))
loss1, loss2, loss3, loss4, loss5, loss6 = 0, 0, 0, 0, 0, 0
# torch.save(best_model, osp.join(path, "best_model.pth.tar"))
return best_acc
dataset_loaders["val"] = torch.utils.data.DataLoader(
dataset_list,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers)
dataset_list = ImageList(open(dataset_test).readlines(),
transform=prep.image_train(resize_size=256,
crop_size=224))
dataset_loaders["test"] = torch.utils.data.DataLoader(
dataset_list,
batch_size=4,
shuffle=False,
num_workers=args.num_workers)
prep_dict_test = prep.image_test_10crop(resize_size=256, crop_size=224)
for i in range(10):
dataset_list = ImageList(open(dataset_test).readlines(),
transform=prep_dict_test["val" + str(i)])
dataset_loaders["val" + str(i)] = torch.utils.data.DataLoader(
dataset_list, batch_size=4, shuffle=False, num_workers=6)
# network construction
feature_len = 2048
# fine-grained feature extractor + fine-grained label predictor
devices = list(range(torch.cuda.device_count()))
model_fc = model_no.Resnet50Fc()
model_fc = model_fc.to(device)
model_fc = nn.DataParallel(model_fc, device_ids=devices)
my_fine_net = fine_net(feature_len)
my_fine_net = my_fine_net.to(device)
def transfer_classification(config):
## set pre-process
prep_dict = {}
for prep_config in config["prep"]:
prep_dict[prep_config["name"]] = {}
if prep_config["type"] == "image":
prep_dict[prep_config["name"]]["test_10crop"] = prep_config[
"test_10crop"]
prep_dict[prep_config["name"]]["train"] = prep.image_train(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
if prep_config["test_10crop"]:
prep_dict[
prep_config["name"]]["test"] = prep.image_test_10crop(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
else:
prep_dict[prep_config["name"]]["test"] = prep.image_test(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
## set loss
class_criterion = nn.CrossEntropyLoss()
loss_config = config["loss"]
transfer_criterion = loss.loss_dict[loss_config["name"]]
if "params" not in loss_config:
loss_config["params"] = {}
## prepare data
dsets = {}
dset_loaders = {}
for data_config in config["data"]:
dsets[data_config["name"]] = {}
dset_loaders[data_config["name"]] = {}
## image data
if data_config["type"] == "image":
dsets[data_config["name"]]["train"] = ImageList(
open(data_config["list_path"]["train"]).readlines(),
transform=prep_dict[data_config["name"]]["train"])
dset_loaders[data_config["name"]]["train"] = util_data.DataLoader(
dsets[data_config["name"]]["train"],
batch_size=data_config["batch_size"]["train"],
shuffle=True,
num_workers=4)
if "test" in data_config["list_path"]:
if prep_dict[data_config["name"]]["test_10crop"]:
for i in range(10):
dsets[data_config["name"]][
"test" + str(i)] = ImageList(
open(data_config["list_path"]
["test"]).readlines(),
transform=prep_dict[data_config["name"]]
["test"]["val" + str(i)])
dset_loaders[data_config["name"]][
"test" + str(i)] = util_data.DataLoader(
dsets[data_config["name"]]["test" + str(i)],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
dsets[data_config["name"]]["test"] = ImageList(
open(data_config["list_path"]["test"]).readlines(),
transform=prep_dict[data_config["name"]]["test"])
dset_loaders[
data_config["name"]]["test"] = util_data.DataLoader(
dsets[data_config["name"]]["test"],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
if prep_dict[data_config["name"]]["test_10crop"]:
for i in range(10):
dsets[data_config["name"]][
"test" + str(i)] = ImageList(
open(data_config["list_path"]
["train"]).readlines(),
transform=prep_dict[data_config["name"]]
["test"]["val" + str(i)])
dset_loaders[data_config["name"]][
"test" + str(i)] = util_data.DataLoader(
dsets[data_config["name"]]["test" + str(i)],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
dsets[data_config["name"]]["test"] = ImageList(
open(data_config["list_path"]["train"]).readlines(),
transform=prep_dict[data_config["name"]]["test"])
dset_loaders[
data_config["name"]]["test"] = util_data.DataLoader(
dsets[data_config["name"]]["test"],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
class_num = 2
## set base network
net_config = config["network"]
base_network = network.network_dict[net_config["name"]]()
if net_config["use_bottleneck"]:
bottleneck_layer = nn.Linear(base_network.output_num(),
net_config["bottleneck_dim"])
classifier_layer = nn.Linear(bottleneck_layer.out_features, class_num)
else:
classifier_layer = nn.Linear(base_network.output_num(), class_num)
for param in base_network.parameters():
param.requires_grad = True
## initialization
if net_config["use_bottleneck"]:
bottleneck_layer.weight.data.normal_(0, 0.005)
bottleneck_layer.bias.data.fill_(0.1)
bottleneck_layer = nn.Sequential(bottleneck_layer, nn.ReLU(),
nn.Dropout(0.5))
classifier_layer.weight.data.normal_(0, 0.01)
classifier_layer.bias.data.fill_(0.0)
use_gpu = torch.cuda.is_available()
if use_gpu:
if net_config["use_bottleneck"]:
bottleneck_layer = bottleneck_layer.cuda()
classifier_layer = classifier_layer.cuda()
base_network = base_network.cuda()
## collect parameters
if net_config["use_bottleneck"]:
parameter_list = [{
"params": base_network.parameters(),
"lr": 10
}, {
"params": bottleneck_layer.parameters(),
"lr": 10
}, {
"params": classifier_layer.parameters(),
"lr": 10
}]
else:
parameter_list = [{
"params": base_network.parameters(),
"lr": 10
}, {
"params": classifier_layer.parameters(),
"lr": 10
}]
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optim_dict[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"]]
## train
len_train_source = len(dset_loaders["source"]["train"]) - 1
len_train_target = len(dset_loaders["target"]["train"]) - 1
F_best = 0
mmd_meter = AverageMeter()
for i in range(config["num_iterations"]):
## test in the train
if i % config["test_interval"] == 0:
base_network.train(False)
classifier_layer.train(False)
if net_config["use_bottleneck"]:
bottleneck_layer.train(False)
test_acc, F = image_classification_test(
dset_loaders["target"],
nn.Sequential(base_network, bottleneck_layer,
classifier_layer),
test_10crop=prep_dict["target"]["test_10crop"],
gpu=use_gpu)
else:
test_acc, F = image_classification_test(
dset_loaders["target"],
nn.Sequential(base_network, classifier_layer),
test_10crop=prep_dict["target"]["test_10crop"],
gpu=use_gpu)
print(args.source + '->' + args.target)
print('Iter: %d, mmd = %.4f, test_acc = %.3f, f1 score = %.4f' %
(i, mmd_meter.avg, test_acc, F))
mmd_meter.reset()
if F_best < F:
F_best = F
## train one iter
if net_config["use_bottleneck"]:
bottleneck_layer.train(True)
classifier_layer.train(True)
optimizer = lr_scheduler(param_lr, optimizer, i, **schedule_param)
# optimzer4center = optim.SGD(lmcl_loss.parameters(), lr=0.05)
# sheduler_4center = lr_scheduler_torch.StepLR(optimizer, 20, gamma=0.5)
# optimizer.zero_grad()
# optimzer4center.zero_grad()
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"]["train"])
if i % len_train_target == 0:
iter_target = iter(dset_loaders["target"]["train"])
inputs_source, labels_source = iter_source.next()
inputs_target, labels_target = iter_target.next()
if use_gpu:
inputs_source, inputs_target, labels_source = Variable(
inputs_source).cuda(), Variable(
inputs_target).cuda(), Variable(labels_source).cuda()
else:
inputs_source, inputs_target, labels_source = Variable(
inputs_source), Variable(inputs_target), Variable(
labels_source)
inputs = torch.cat((inputs_source, inputs_target), dim=0)
features = base_network(inputs)
if net_config["use_bottleneck"]:
features = bottleneck_layer(features)
outputs = classifier_layer(features)
#
# logits, mlogits = lmcl_loss(outputs.narrow(0, 0, int(inputs.size(0)/2)), labels_source)
# classifier_loss = class_criterion(mlogits, labels_source)
classifier_loss = class_criterion(
outputs.narrow(0, 0, int(inputs.size(0) / 2)), labels_source)
## switch between different transfer loss
if loss_config["name"] == "DAN" or loss_config["name"] == "DAN_Linear":
transfer_loss = transfer_criterion(
features.narrow(0, 0, int(features.size(0) / 2)),
features.narrow(0, int(features.size(0) / 2),
int(features.size(0) / 2)),
**loss_config["params"])
mmd_meter.update(transfer_loss.item(), inputs_source.size(0))
# mmd_meter.update(transfer_loss.data[0], inputs_source.size(0))
total_loss = loss_config["trade_off"] * transfer_loss + classifier_loss
# total_loss = classifier_loss
total_loss.backward()
optimizer.step()
# optimzer4center.step()
# sheduler_4center.step()
print(args.source + '->' + args.target)
print(F_best)
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 transfer_classification(config):
# ---set log writer ---
writer = SummaryWriter(log_dir=config["log_dir"])
# set pre-process
prep_dict = {}
for prep_config in config["prep"]:
name = prep_config["name"]
prep_dict[name] = {}
if prep_config["type"] == "image":
prep_dict[name]["test_10crop"] = prep_config["test_10crop"]
prep_dict[name]["train"] = prep.image_train(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
if prep_config["test_10crop"]:
prep_dict[name]["test"] = prep.image_test_10crop(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
else:
prep_dict[name]["test"] = prep.image_test(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
# print(prep_dict)
## set loss
class_criterion = nn.CrossEntropyLoss()
loss_config = config["loss"]
# transfer_criterion = loss.loss_dict[loss_config["name"]]
if "params" not in loss_config:
loss_config["params"] = {}
# print(transfer_criterion)
transfer_criterion = DANLoss(**loss_config["params"])
## prepare data
dsets = {}
dset_loaders = {}
for data_config in config["data"]:
name = data_config['name']
dsets[name] = {}
dset_loaders[name] = {}
## image data
if data_config["type"] == "image":
dsets[name]["train"] = ImageList(
open(data_config["list_path"]["train"]).readlines(),
transform=prep_dict[name]["train"])
dset_loaders[name]["train"] = util_data.DataLoader(
dsets[name]["train"],
batch_size=data_config["batch_size"]["train"],
shuffle=True,
num_workers=4)
if "test" in data_config["list_path"]:
if prep_dict[name]["test_10crop"]:
for i in range(10):
dsets[name]["test" + str(i)] = ImageList(
open(data_config["list_path"]["test"]).readlines(),
transform=prep_dict[name]["test"]["val" + str(i)])
dset_loaders[name][
"test" + str(i)] = util_data.DataLoader(
dsets[name]["test" + str(i)],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
dsets[name]["test"] = ImageList(
open(data_config["list_path"]["test"]).readlines(),
transform=prep_dict[name]["test"])
dset_loaders[name]["test"] = util_data.DataLoader(
dsets[name]["test"],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
if prep_dict[name]["test_10crop"]:
for i in range(10):
dsets[name]["test" + str(i)] = ImageList(
open(
data_config["list_path"]["train"]).readlines(),
transform=prep_dict[name]["test"]["val" + str(i)])
dset_loaders[name][
"test" + str(i)] = util_data.DataLoader(
dsets[name]["test" + str(i)],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
dsets[name]["test"] = ImageList(
open(data_config["list_path"]["train"]).readlines(),
transform=prep_dict[name]["test"])
dset_loaders[
data_config["name"]]["test"] = util_data.DataLoader(
dsets[data_config["name"]]["test"],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
class_num = 31
## set base network
net_config = config["network"]
base_network = network.network_dict[net_config["name"]]()
if net_config["use_bottleneck"]:
bottleneck_layer = nn.Linear(base_network.output_num(),
net_config["bottleneck_dim"])
classifier_layer = nn.Linear(bottleneck_layer.out_features, class_num)
else:
classifier_layer = nn.Linear(base_network.output_num(), class_num)
## initialization
if net_config["use_bottleneck"]:
bottleneck_layer.weight.data.normal_(0, 0.005)
bottleneck_layer.bias.data.fill_(0.1)
bottleneck_layer = nn.Sequential(bottleneck_layer, nn.ReLU(),
nn.Dropout(0.5))
classifier_layer.weight.data.normal_(0, 0.01)
classifier_layer.bias.data.fill_(0.0)
# print(base_network.state_dict())
# print(classifier_layer.state_dict())
use_gpu = torch.cuda.is_available()
if use_gpu:
if net_config["use_bottleneck"]:
bottleneck_layer = bottleneck_layer.to(device)
classifier_layer = classifier_layer.to(device)
base_network = base_network.to(device)
## collect parameters
if net_config["use_bottleneck"]:
parameter_list = [{
"params": base_network.parameters(),
"lr": 1
}, {
"params": bottleneck_layer.parameters(),
"lr": 10
}, {
"params": classifier_layer.parameters(),
"lr": 10
}]
else:
parameter_list = [{
"params": base_network.parameters(),
"lr": 1
}, {
"params": classifier_layer.parameters(),
"lr": 10
}]
## add additional network for some methods
if loss_config["name"] == "JAN":
softmax_layer = nn.Softmax()
if use_gpu:
softmax_layer = softmax_layer.to(device)
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optim_dict[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"]]
## train
len_train_source = len(dset_loaders["source"]["train"]) - 1
len_train_target = len(dset_loaders["target"]["train"]) - 1
# transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
for i in range(config["num_iterations"]):
# test in the train
if i % config["test_interval"] == 0:
base_network.train(False)
classifier_layer.train(False)
if net_config["use_bottleneck"]:
bottleneck_layer.train(False)
test_model = nn.Sequential(base_network, bottleneck_layer,
classifier_layer)
test_acc = image_classification_test(
dset_loaders["target"],
test_model,
test_10crop=prep_dict["target"]["test_10crop"],
gpu=use_gpu)
print("iteration: %d, test accuracy: %f" % (i, test_acc))
else:
test_model = nn.Sequential(base_network, classifier_layer)
test_acc = image_classification_test(
dset_loaders["target"],
test_model,
test_10crop=prep_dict["target"]["test_10crop"],
gpu=use_gpu)
print("iteration: %d, test accuracy: %f" % (i, test_acc))
# save model parameters
if i % config["checkpoint_iterval"] == 0:
torch.save(
test_model.state_dict(), config["checkpoint_dir"] +
"checkpoint_" + str(i).zfill(5) + ".pth")
# loss_test = nn.BCELoss()
# train one iter
base_network.train(True)
if net_config["use_bottleneck"]:
bottleneck_layer.train(True)
classifier_layer.train(True)
optimizer = lr_scheduler(param_lr, optimizer, i, **schedule_param)
optimizer.zero_grad()
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"]["train"])
if i % len_train_target == 0:
iter_target = iter(dset_loaders["target"]["train"])
inputs_source, labels_source = iter_source.next()
inputs_target, labels_target = iter_target.next()
inputs_source = inputs_source.to(device)
inputs_target = inputs_target.to(device)
labels_source = labels_source.to(device)
inputs = torch.cat((inputs_source, inputs_target),
dim=0) ### cat the source and target
features = base_network(inputs)
if net_config["use_bottleneck"]:
features = bottleneck_layer(features)
outputs = classifier_layer(features)
# split the output for different loss
num = inputs.size(0) // 2
# the premise is the batch size of source and target is the same
classifier_loss = class_criterion(outputs.narrow(0, 0, num),
labels_source)
## switch between different transfer loss
if loss_config["name"] == "DAN":
# transfer_loss = transfer_criterion(features.narrow(0, 0, num),
# features.narrow(0, num, num),
# **loss_config["params"])
transfer_loss = transfer_criterion(features.narrow(0, 0, num),
features.narrow(0, num, num))
elif loss_config["name"] == "RTN":
## RTN is still under developing
transfer_loss = 0
elif loss_config["name"] == "JAN":
softmax_out = softmax_layer(outputs)
transfer_loss = transfer_criterion([
features.narrow(0, 0, num),
softmax_out.narrow(0, 0, num)
], [features.narrow(0, num, num),
softmax_out.narrow(0, num, num)], **loss_config["params"])
total_loss = loss_config["trade_off"] * transfer_loss + classifier_loss
if i % 100 == 0:
print("iteration: ", str(i), "\t transfer loss: ",
str(transfer_loss.item()), "\t classification loss: ",
str(classifier_loss.item()), "\t total loss: ",
str(total_loss.item()))
for param_group in optimizer.param_groups:
print("lr: ", param_group["lr"])
# log for losses
writer.add_scalar('Train/loss_classification',
classifier_loss.item(), i // 100)
writer.add_scalar('Train/loss_transfer', transfer_loss.item(),
i // 100)
writer.add_scalar('Train/loss_total', total_loss.item(), i // 100)
total_loss.backward()
optimizer.step()
def train(config):
## set pre-process
prep_dict = {}
prep_config = config["prep"]
prep_dict["source"] = prep.image_train( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
prep_dict["target"] = prep.image_train( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
if prep_config["test_10crop"]:
prep_dict["test"] = prep.image_test_10crop( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
else:
prep_dict["test"] = prep.image_test( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
## set loss
class_criterion = nn.CrossEntropyLoss()
transfer_criterion = loss.PADA
loss_params = config["loss"]
## prepare data
dsets = {}
dset_loaders = {}
data_config = config["data"]
dsets["source"] = ImageList(open(data_config["source"]["list_path"]).readlines(), \
transform=prep_dict["source"])
dset_loaders["source"] = util_data.DataLoader(dsets["source"], \
batch_size=data_config["source"]["batch_size"], \
shuffle=True, num_workers=4)
dsets["target"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["target"])
dset_loaders["target"] = util_data.DataLoader(dsets["target"], \
batch_size=data_config["target"]["batch_size"], \
shuffle=True, num_workers=4)
if prep_config["test_10crop"]:
for i in range(10):
dsets["test"+str(i)] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"]["val"+str(i)])
dset_loaders["test"+str(i)] = util_data.DataLoader(dsets["test"+str(i)], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=4)
dsets["target"+str(i)] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["test"]["val"+str(i)])
dset_loaders["target"+str(i)] = util_data.DataLoader(dsets["target"+str(i)], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=4)
else:
dsets["test"] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"])
dset_loaders["test"] = util_data.DataLoader(dsets["test"], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=4)
dsets["target_test"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["test"])
dset_loaders["target_test"] = MyDataLoader(dsets["target_test"], \
batch_size=data_config["test"]["batch_size"], \
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"])
use_gpu = torch.cuda.is_available()
if use_gpu:
base_network = base_network.cuda()
## collect parameters
if net_config["params"]["new_cls"]:
if net_config["params"]["use_bottleneck"]:
parameter_list = [{"params":base_network.feature_layers.parameters(), "lr":1}, \
{"params":base_network.bottleneck.parameters(), "lr":10}, \
{"params":base_network.fc.parameters(), "lr":10}]
else:
parameter_list = [{"params":base_network.feature_layers.parameters(), "lr":1}, \
{"params":base_network.fc.parameters(), "lr":10}]
else:
parameter_list = [{"params": base_network.parameters(), "lr": 1}]
## add additional network for some methods
class_weight = torch.from_numpy(np.array([1.0] * class_num))
if use_gpu:
class_weight = class_weight.cuda()
ad_net = network.AdversarialNetwork(base_network.output_num())
gradient_reverse_layer = network.AdversarialLayer(
high_value=config["high"])
if use_gpu:
ad_net = ad_net.cuda()
parameter_list.append({"params": ad_net.parameters(), "lr": 10})
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optim_dict[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"]]
## train
len_train_source = len(dset_loaders["source"]) - 1
len_train_target = len(dset_loaders["target"]) - 1
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"] == 0:
base_network.train(False)
temp_acc = image_classification_test(dset_loaders, \
base_network, test_10crop=prep_config["test_10crop"], \
gpu=use_gpu)
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)
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)))
## train one iter
base_network.train(True)
optimizer = lr_scheduler(param_lr, 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()
if use_gpu:
inputs_source, inputs_target, labels_source = \
Variable(inputs_source).cuda(), Variable(inputs_target).cuda(), \
Variable(labels_source).cuda()
else:
inputs_source, inputs_target, labels_source = Variable(inputs_source), \
Variable(inputs_target), Variable(labels_source)
inputs = torch.cat((inputs_source, inputs_target), dim=0)
features, outputs = base_network(inputs)
softmax_out = nn.Softmax(dim=1)(outputs).detach()
ad_net.train(True)
transfer_loss = transfer_criterion(features, ad_net,
gradient_reverse_layer, use_gpu)
classifier_loss = class_criterion(
outputs.narrow(0, 0,
inputs.size(0) / 2), labels_source)
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 transfer_classification(config, name):
## set pre-process
prep_dict = {}
for prep_config in config["prep"]:
prep_dict[prep_config["name"]] = {}
if prep_config["type"] == "image":
prep_dict[prep_config["name"]]["test_10crop"] = prep_config[
"test_10crop"]
prep_dict[prep_config["name"]]["train"] = prep.image_train(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
if prep_config["test_10crop"]:
prep_dict[
prep_config["name"]]["test"] = prep.image_test_10crop(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
else:
prep_dict[prep_config["name"]]["test"] = prep.image_test(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
## set loss
class_criterion = nn.CrossEntropyLoss()
loss_config = config["loss"]
transfer_criterion = loss.loss_dict[loss_config["name"]]
if "params" not in loss_config:
loss_config["params"] = {}
## prepare data
dsets = {}
dset_loaders = {}
for data_config in config["data"]:
dsets[data_config["name"]] = {}
dset_loaders[data_config["name"]] = {}
## image data
if data_config["type"] == "image":
# dsets[data_config["name"]]["train"]为ImageList类，其中含有source中数据及标签和预处理过程
dsets[data_config["name"]]["train"] = ImageList(
open(data_config["list_path"]["train"]).readlines(),
transform=prep_dict[data_config["name"]]["train"])
# dset_loaders含有数据batchsize信息等
dset_loaders[data_config["name"]]["train"] = util_data.DataLoader(
dsets[data_config["name"]]["train"],
batch_size=data_config["batch_size"]["train"],
shuffle=True,
num_workers=4)
if "test" in data_config["list_path"]:
if prep_dict[data_config["name"]]["test_10crop"]:
for i in range(10):
dsets[data_config["name"]][
"test" + str(i)] = ImageList(
open(data_config["list_path"]
["test"]).readlines(),
transform=prep_dict[data_config["name"]]
["test"]["val" + str(i)])
dset_loaders[data_config["name"]][
"test" + str(i)] = util_data.DataLoader(
dsets[data_config["name"]]["test" + str(i)],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
dsets[data_config["name"]]["test"] = ImageList(
open(data_config["list_path"]["test"]).readlines(),
transform=prep_dict[data_config["name"]]["test"])
dset_loaders[
data_config["name"]]["test"] = util_data.DataLoader(
dsets[data_config["name"]]["test"],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
if prep_dict[data_config["name"]]["test_10crop"]:
for i in range(10):
dsets[data_config["name"]][
"test" + str(i)] = ImageList(
open(data_config["list_path"]
["train"]).readlines(),
transform=prep_dict[data_config["name"]]
["test"]["val" + str(i)])
dset_loaders[data_config["name"]][
"test" + str(i)] = util_data.DataLoader(
dsets[data_config["name"]]["test" + str(i)],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
dsets[data_config["name"]]["test"] = ImageList(
open(data_config["list_path"]["train"]).readlines(),
transform=prep_dict[data_config["name"]]["test"])
dset_loaders[
data_config["name"]]["test"] = util_data.DataLoader(
dsets[data_config["name"]]["test"],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
class_num = 12
## set base network
net_config = config["network"]
base_network = network.network_dict[net_config["name"]]()
if net_config["use_bottleneck"]:
bottleneck_layer = nn.Linear(base_network.output_num(),
net_config["bottleneck_dim"])
classifier_layer = nn.Linear(bottleneck_layer.out_features, class_num)
else:
print 'outputnum:', base_network.output_num()
classifier_layer = nn.Linear(base_network.output_num(), class_num)
## initialization
if net_config["use_bottleneck"]:
# 初始化bottlenect层weight
bottleneck_layer.weight.data.normal_(0, 0.005)
bottleneck_layer.bias.data.fill_(0.1)
# nn.sequential按次序封装层
bottleneck_layer = nn.Sequential(bottleneck_layer, nn.ReLU(),
nn.Dropout(0.5))
classifier_layer.weight.data.normal_(0, 0.01)
classifier_layer.bias.data.fill_(0.0)
use_gpu = torch.cuda.is_available()
if use_gpu:
if net_config["use_bottleneck"]:
bottleneck_layer = bottleneck_layer.cuda()
classifier_layer = classifier_layer.cuda()
base_network = base_network.cuda()
## collect parameters
if net_config["use_bottleneck"]:
parameter_list = [{
"params": base_network.parameters(),
"lr": 1
}, {
"params": bottleneck_layer.parameters(),
"lr": 10
}, {
"params": classifier_layer.parameters(),
"lr": 10
}]
else:
parameter_list = [{
"params": base_network.parameters(),
"lr": 1
}, {
"params": classifier_layer.parameters(),
"lr": 10
}]
## add additional network for some methods
if loss_config["name"] == "JAN":
softmax_layer = nn.Softmax()
if use_gpu:
softmax_layer = softmax_layer.cuda()
## set optimizer
optimizer_config = config["optimizer"]
# optimizer初始化了所有权重
optimizer = optim_dict[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"]]
## train
len_train_source = len(dset_loaders["source"]["train"]) - 1
len_train_target = len(dset_loaders["target"]["train"]) - 1
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
max_accuracy = 0
for i in range(config["num_iterations"] + 1):
## test in the train
if i % config["test_interval"] == 0 and i != 0:
base_network.train(False)
classifier_layer.train(False)
if net_config["use_bottleneck"]:
bottleneck_layer.train(False)
accuracy = image_classification_test(
dset_loaders["target"],
nn.Sequential(base_network, bottleneck_layer,
classifier_layer),
test_10crop=prep_dict["target"]["test_10crop"],
gpu=use_gpu)
torch.save(base_network,
'../save/nobottle_base_network_' + name + '.pkl')
torch.save(
classifier_layer,
'../save/nobottle_classifier_layer_' + name + '.pkl')
print 'save', i
if (accuracy > max_accuracy):
max_accuracy = accuracy
print 'iter{0}accuracy:'.format(
i), accuracy, 'max_accuracy:', max_accuracy
else:
torch.save(base_network,
'../save/nobottle_base_network_' + name + '.pkl')
torch.save(
classifier_layer,
'../save/nobottle_classifier_layer_' + name + '.pkl')
print 'save', i
accuracy = image_classification_test(
dset_loaders["target"],
nn.Sequential(base_network, classifier_layer),
test_10crop=prep_dict["target"]["test_10crop"],
gpu=use_gpu)
if (accuracy > max_accuracy):
max_accuracy = accuracy
print 'iter{0}accuracy:'.format(
i), accuracy, 'max_accuracy:', max_accuracy
loss_test = nn.BCELoss()
## train one iter
base_network.train(True)
if net_config["use_bottleneck"]:
bottleneck_layer.train(True)
classifier_layer.train(True)
optimizer = lr_scheduler(param_lr, optimizer, i, **schedule_param)
optimizer.zero_grad()
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"]["train"])
if i % len_train_target == 0:
iter_target = iter(dset_loaders["target"]["train"])
inputs_source, labels_source = iter_source.next()
inputs_target, labels_target = iter_target.next()
if use_gpu:
inputs_source, inputs_target, labels_source = Variable(
inputs_source).cuda(), Variable(
inputs_target).cuda(), Variable(labels_source).cuda()
else:
inputs_source, inputs_target, labels_source = Variable(
inputs_source), Variable(inputs_target), Variable(
labels_source)
inputs = torch.cat((inputs_source, inputs_target), dim=0)
features = base_network(inputs)
if net_config["use_bottleneck"]:
features = bottleneck_layer(features)
outputs = classifier_layer(features)
classifier_loss = class_criterion(
outputs.narrow(0, 0,
inputs.size(0) / 2), labels_source)
total_loss = classifier_loss
total_loss.backward()
optimizer.step()
def transfer_classification(config):
## set pre-process
prep_dict = {}
for prep_config in config["prep"]:
prep_dict[prep_config["name"]] = {}
if prep_config["type"] == "image":
prep_dict[prep_config["name"]]["test_10crop"] = prep_config[
"test_10crop"]
prep_dict[prep_config["name"]]["train"] = prep.image_train(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
if prep_config["test_10crop"]:
prep_dict[
prep_config["name"]]["test"] = prep.image_test_10crop(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
else:
prep_dict[prep_config["name"]]["test"] = prep.image_test(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
## set loss
class_criterion = nn.CrossEntropyLoss()
loss_config = config["loss"]
transfer_criterion = loss.loss_dict[loss_config["name"]]
if "params" not in loss_config:
loss_config["params"] = {}
## prepare data
dsets = {}
dset_loaders = {}
for data_config in config["data"]:
dsets[data_config["name"]] = {}
dset_loaders[data_config["name"]] = {}
## image data
if data_config["type"] == "image":
dsets[data_config["name"]]["train"] = ImageList(
open(data_config["list_path"]["train"]).readlines(),
transform=prep_dict[data_config["name"]]["train"])
dset_loaders[data_config["name"]]["train"] = util_data.DataLoader(
dsets[data_config["name"]]["train"],
batch_size=data_config["batch_size"]["train"],
shuffle=True,
num_workers=4)
if "test" in data_config["list_path"]:
if prep_dict[data_config["name"]]["test_10crop"]:
for i in range(10):
dsets[data_config["name"]][
"test" + str(i)] = ImageList(
open(data_config["list_path"]
["test"]).readlines(),
transform=prep_dict[data_config["name"]]
["test"]["val" + str(i)])
dset_loaders[data_config["name"]][
"test" + str(i)] = util_data.DataLoader(
dsets[data_config["name"]]["test" + str(i)],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
dsets[data_config["name"]]["test"] = ImageList(
open(data_config["list_path"]["test"]).readlines(),
transform=prep_dict[data_config["name"]]["test"])
dset_loaders[
data_config["name"]]["test"] = util_data.DataLoader(
dsets[data_config["name"]]["test"],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
if prep_dict[data_config["name"]]["test_10crop"]:
for i in range(10):
dsets[data_config["name"]][
"test" + str(i)] = ImageList(
open(data_config["list_path"]
["train"]).readlines(),
transform=prep_dict[data_config["name"]]
["test"]["val" + str(i)])
dset_loaders[data_config["name"]][
"test" + str(i)] = util_data.DataLoader(
dsets[data_config["name"]]["test" + str(i)],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
dsets[data_config["name"]]["test"] = ImageList(
open(data_config["list_path"]["train"]).readlines(),
transform=prep_dict[data_config["name"]]["test"])
dset_loaders[
data_config["name"]]["test"] = util_data.DataLoader(
dsets[data_config["name"]]["test"],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
class_num = 31
## set base network
net_config = config["network"]
base_network = network.network_dict[net_config["name"]]()
if net_config["use_bottleneck"]:
bottleneck_layer = nn.Linear(base_network.output_num(),
net_config["bottleneck_dim"])
classifier_layer = nn.Linear(bottleneck_layer.out_features, class_num)
else:
classifier_layer = nn.Linear(base_network.output_num(), class_num)
for param in base_network.parameters():
param.requires_grad = False
## initialization
if net_config["use_bottleneck"]:
bottleneck_layer.weight.data.normal_(0, 0.005)
bottleneck_layer.bias.data.fill_(0.1)
bottleneck_layer = nn.Sequential(bottleneck_layer, nn.ReLU(),
nn.Dropout(0.5))
classifier_layer.weight.data.normal_(0, 0.01)
classifier_layer.bias.data.fill_(0.0)
use_gpu = torch.cuda.is_available()
if use_gpu:
if net_config["use_bottleneck"]:
bottleneck_layer = bottleneck_layer.cuda()
classifier_layer = classifier_layer.cuda()
base_network = base_network.cuda()
## collect parameters
if net_config["use_bottleneck"]:
parameter_list = [{
"params": bottleneck_layer.parameters(),
"lr": 10
}, {
"params": classifier_layer.parameters(),
"lr": 10
}]
else:
parameter_list = [{"params": classifier_layer.parameters(), "lr": 10}]
## add additional network for some methods
if loss_config["name"] == "JAN":
softmax_layer = nn.Softmax()
if use_gpu:
softmax_layer = softmax_layer.cuda()
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optim_dict[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"]]
## train
len_train_source = len(dset_loaders["source"]["train"]) - 1
len_train_target = len(dset_loaders["target"]["train"]) - 1
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
for i in range(config["num_iterations"]):
## test in the train
if i % config["test_interval"] == 0:
base_network.train(False)
classifier_layer.train(False)
if net_config["use_bottleneck"]:
bottleneck_layer.train(False)
print image_classification_test(
dset_loaders["target"],
nn.Sequential(base_network, bottleneck_layer,
classifier_layer),
test_10crop=prep_dict["target"]["test_10crop"],
gpu=use_gpu)
else:
print image_classification_test(
dset_loaders["target"],
nn.Sequential(base_network, classifier_layer),
test_10crop=prep_dict["target"]["test_10crop"],
gpu=use_gpu)
loss_test = nn.BCELoss()
## train one iter
if net_config["use_bottleneck"]:
bottleneck_layer.train(True)
classifier_layer.train(True)
optimizer = lr_scheduler(param_lr, optimizer, i, **schedule_param)
optimizer.zero_grad()
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"]["train"])
if i % len_train_target == 0:
iter_target = iter(dset_loaders["target"]["train"])
inputs_source, labels_source = iter_source.next()
inputs_target, labels_target = iter_target.next()
if use_gpu:
inputs_source, inputs_target, labels_source = Variable(
inputs_source).cuda(), Variable(
inputs_target).cuda(), Variable(labels_source).cuda()
else:
inputs_source, inputs_target, labels_source = Variable(
inputs_source), Variable(inputs_target), Variable(
labels_source)
inputs = torch.cat((inputs_source, inputs_target), dim=0)
features = base_network(inputs)
if net_config["use_bottleneck"]:
features = bottleneck_layer(features)
outputs = classifier_layer(features)
classifier_loss = class_criterion(
outputs.narrow(0, 0,
inputs.size(0) / 2), labels_source)
## switch between different transfer loss
if loss_config["name"] == "DAN":
transfer_loss = transfer_criterion(
features.narrow(0, 0,
features.size(0) / 2),
features.narrow(0,
features.size(0) / 2,
features.size(0) / 2), **loss_config["params"])
elif loss_config["name"] == "RTN":
## RTN is still under developing
transfer_loss = 0
elif loss_config["name"] == "JAN":
softmax_out = softmax_layer(outputs)
transfer_loss = transfer_criterion([
features.narrow(0, 0,
features.size(0) / 2),
softmax_out.narrow(0, 0,
softmax_out.size(0) / 2)
], [
features.narrow(0,
features.size(0) / 2,
features.size(0) / 2),
softmax_out.narrow(0,
softmax_out.size(0) / 2,
softmax_out.size(0) / 2)
], **loss_config["params"])
total_loss = loss_config["trade_off"] * transfer_loss + classifier_loss
total_loss.backward()
optimizer.step()
def transfer_classification(config):
## set pre-process
prep_dict = {}
for prep_config in config["prep"]:
prep_dict[prep_config["name"]] = {}
if prep_config["type"] == "image":
prep_dict[prep_config["name"]]["test_10crop"] = prep_config["test_10crop"]
prep_dict[prep_config["name"]]["train"] = prep.image_train(resize_size=prep_config["resize_size"], crop_size=prep_config["crop_size"])
if prep_config["test_10crop"]:
prep_dict[prep_config["name"]]["validation"] = prep.image_test_10crop(resize_size=prep_config["resize_size"], crop_size=prep_config["crop_size"])
else:
prep_dict[prep_config["name"]]["validation"] = prep.image_test(resize_size=prep_config["resize_size"], crop_size=prep_config["crop_size"])
print("Data preprocessing done..........::")
## set loss
class_criterion = nn.CrossEntropyLoss()
loss_config = config["loss"]
transfer_criterion = loss.loss_dict[loss_config["name"]]
if "params" not in loss_config:
loss_config["params"] = {}
print("Setting loss done...............::")
print("Data Preparation progressing................::")
## prepare data
dsets = {}
dset_loaders = {}
for data_config in config["data"]:
dsets[data_config["name"]] = {}
dset_loaders[data_config["name"]] = {}
## image data
if data_config["type"] == "image":
dsets[data_config["name"]]["train"] = ImageList(open(data_config["list_path"]["train"]).readlines(), transform=prep_dict[data_config["name"]]["train"])
dset_loaders[data_config["name"]]["train"] = util_data.DataLoader(dsets[data_config["name"]]["train"], batch_size=data_config["batch_size"]["train"], shuffle=True, num_workers=2)
if "validation" in data_config["list_path"]:
if prep_dict[data_config["name"]]["test_10crop"]:
for i in range(10):
dsets[data_config["name"]]["validation"+str(i)] = ImageList(open(data_config["list_path"]["validation"]).readlines(), transform=prep_dict[data_config["name"]]["validation"]["val"+str(i)])
dset_loaders[data_config["name"]]["validation"+str(i)] = util_data.DataLoader(dsets[data_config["name"]]["validation"+str(i)], batch_size=data_config["batch_size"]["validation"], shuffle=False, num_workers=2)
else:
dsets[data_config["name"]]["validation"] = ImageList(open(data_config["list_path"]["validation"]).readlines(), transform=prep_dict[data_config["name"]]["validation"])
dset_loaders[data_config["name"]]["validation"] = util_data.DataLoader(dsets[data_config["name"]]["validation"], batch_size=data_config["batch_size"]["validation"], shuffle=False, num_workers=4)
class_num = 2
print("Data prepration done.....::")
print("Setting base network......::")
## set base network
net_config = config["network"]
base_network = network.network_dict[net_config["name"]]()
if net_config["use_bottleneck"]:
bottleneck_layer = nn.Linear(base_network.output_num(), 256)
# bottleneck_layer_1 = nn.Linear(256, 64)
classifier_layer = nn.Linear(256, class_num)
else:
classifier_layer = nn.Linear(base_network.output_num(), class_num)
for param in base_network.parameters():
param.requires_grad = True
print("Base network set......::")
print("Intilializing bottleneck layer....::")
## initialization
if net_config["use_bottleneck"]:
bottleneck_layer.weight.data.normal_(0, 0.1)
bottleneck_layer.bias.data.fill_(0.1)
bottleneck_layer = nn.Sequential(bottleneck_layer, nn.ReLU(), nn.Dropout(0.2))
# bottleneck_layer_1.weight.data.normal_(0, 0.1)
# bottleneck_layer_1.bias.data.fill_(0.1)
# bottleneck_layer_1 = nn.Sequential(bottleneck_layer_1, nn.ReLU(), nn.Dropout(0.2))
classifier_layer.weight.data.normal_(0, 0.01)
classifier_layer.bias.data.fill_(0.0)
use_gpu = torch.cuda.is_available()
# use_gpu = False
if use_gpu:
if net_config["use_bottleneck"]:
bottleneck_layer = bottleneck_layer.cuda()
# bottleneck_layer_1 = bottleneck_layer_1.cuda()
classifier_layer = classifier_layer.cuda()
base_network = base_network.cuda()
print("Collect parameters.......")
## collect parameters
if net_config["use_bottleneck"]:
parameter_list = [{"params":bottleneck_layer.parameters(), "lr":0.1}, {"params":classifier_layer.parameters(), "lr":0.1}]
else:
parameter_list = [{"params":classifier_layer.parameters(), "lr":1.0}]
## add additional network for some methods
if loss_config["name"] == "JAN":
softmax_layer = nn.Softmax()
if use_gpu:
softmax_layer = softmax_layer.cuda()
print("set optimizer.....::")
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optim_dict[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"]]
print("Training progress......::")
## train =
len_train_source = len(dset_loaders["source"]["train"]) - 1
len_train_target = len(dset_loaders["target"]["train"]) - 1
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
running_loss = 0.0
for i in range(config["num_iterations"]):
## test in the train
# print("iteration::", i)
if i % config["test_interval"] == 0 or i < 10:
base_network.train(False)
classifier_layer.train(False)
if net_config["use_bottleneck"]:
bottleneck_layer.train(False)
# bottleneck_layer_1.train(False)
print("Validation Accuracy:::", image_classification_test(dset_loaders["target"], nn.Sequential(base_network, bottleneck_layer, classifier_layer), test_10crop=prep_dict["target"]["test_10crop"], gpu=use_gpu))
else:
print ("Validation Accuracy:::", image_classification_test(dset_loaders["target"], nn.Sequential(base_network, classifier_layer), test_10crop=prep_dict["target"]["test_10crop"], gpu=use_gpu))
loss_test = nn.BCELoss()
## train one iter
# base_network.train(True)
if net_config["use_bottleneck"]:
bottleneck_layer.train(True)
# bottleneck_layer_1.train(True)
classifier_layer.train(True)
optimizer = lr_scheduler(param_lr, optimizer, i, **schedule_param)
optimizer.zero_grad()
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"]["train"])
if i % len_train_target == 0:
iter_target = iter(dset_loaders["target"]["train"])
inputs_source, labels_source = iter_source.next()
inputs_target, labels_target = iter_target.next()
if use_gpu:
inputs_source, inputs_target, labels_source = Variable(inputs_source).cuda(), Variable(inputs_target).cuda(), Variable(labels_source).cuda()
else:
inputs_source, inputs_target, labels_source = Variable(inputs_source), Variable(inputs_target), Variable(labels_source)
# print("source::",inputs_source)
# print("target::", inputs_target)
# print("label source::", labels_source)
# print("labels target::", labels_target)
inputs = torch.cat((inputs_source, inputs_target), dim=0)
features_ = base_network(inputs)
# print("********Base network features*******::", features_)
# sys.exit(0)
if net_config["use_bottleneck"]:
features = bottleneck_layer(features_)
# features = bottleneck_layer_1(features_1)
# print("*****features from bottleneck layer*****", features)
outputs = classifier_layer(features)
# print("***output****", outputs)
#print("Inputs size::",inputs.size(0)/2)
classifier_loss = class_criterion(outputs.narrow(0, 0, int(inputs.size(0)/2)), labels_source)
## switch between different transfer loss
if loss_config["name"] == "DAN":
transfer_loss = transfer_criterion(features.narrow(0, 0, int(features.size(0)/2)), features.narrow(0, int(features.size(0)/2), int(features.size(0)/2)), **loss_config["params"])
elif loss_config["name"] == "RTN":
## RTN is still under developing
transfer_loss = 0
elif loss_config["name"] == "JAN":
softmax_out = softmax_layer(outputs)
transfer_loss = transfer_criterion([features.narrow(0, 0, int(features.size(0)/2)), softmax_out.narrow(0, 0, int(softmax_out.size(0)/2))], [features.narrow(0, int(features.size(0)/2), int(features.size(0)/2)), softmax_out.narrow(0, int(softmax_out.size(0)/2), int(softmax_out.size(0)/2))], **loss_config["params"])
total_loss = loss_config["trade_off"] * transfer_loss + classifier_loss
running_loss += total_loss.data.cpu().numpy()
total_loss.backward()
optimizer.step()
if i % config["test_interval"] == 0 or i<10:
if i == 0:
print("Training loss at iteration {} is {}".format(i, (running_loss)))
else:
print("Training loss at iteration {} is {}".format(i, (running_loss/i)))
if net_config["use_bottleneck"]:
model = nn.Sequential(base_network, bottleneck_layer, classifier_layer)
else:
model = nn.Sequential(base_network, classifier_layer)
torch.save(model.state_dict(), "./weights_sp/r18_epoch_{}.pt".format(i))
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'])
tensor_writer = SummaryWriter(config["tensorboard_path"])
## 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()
parameter_list = base_network.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:
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"])
best_acc = 0.0
for i in range(config["num_iterations"]):
if i % config["test_interval"] == config["test_interval"] - 1 or i==0:
base_network.train(False)
temp_acc, output, prediction, label, feature = 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)
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)
outputs_target_temp = outputs_target / config['temperature']
target_softmax_out_temp = nn.Softmax(dim=1)(outputs_target_temp)
target_entropy_weight = loss.Entropy(target_softmax_out_temp).detach()
target_entropy_weight = 1 + torch.exp(-target_entropy_weight)
target_entropy_weight = train_bs * target_entropy_weight / torch.sum(target_entropy_weight)
cov_matrix_t = target_softmax_out_temp.mul(target_entropy_weight.view(-1,1)).transpose(1,0).mm(target_softmax_out_temp)
cov_matrix_t = cov_matrix_t / torch.sum(cov_matrix_t, dim=1)
mcc_loss = (torch.sum(cov_matrix_t) - torch.trace(cov_matrix_t)) / class_num
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
total_loss = classifier_loss + mcc_loss
total_loss.backward()
optimizer.step()
tensor_writer.add_scalar('total_loss', total_loss, i)
tensor_writer.add_scalar('classifier_loss', classifier_loss, i)
tensor_writer.add_scalar('cov_matrix_penalty', mcc_loss, i)
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"]
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 transfer_classification(config):
## set pre-process
prep_dict = {}
for prep_config in config["prep"]:
prep_dict[prep_config["name"]] = {}
if prep_config["type"] == "image":
prep_dict[prep_config["name"]]["test_10crop"] = prep_config[
"test_10crop"]
prep_dict[prep_config["name"]]["train"] = prep.image_train(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
if prep_config["test_10crop"]:
prep_dict[
prep_config["name"]]["test"] = prep.image_test_10crop(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
else:
prep_dict[prep_config["name"]]["test"] = prep.image_test(
resize_size=prep_config["resize_size"],
crop_size=prep_config["crop_size"])
## set loss
class_criterion = nn.CrossEntropyLoss()
loss_config = config["loss"]
transfer_criterion = loss.loss_dict[loss_config["name"]]
if "params" not in loss_config:
loss_config["params"] = {}
## prepare data
dsets = {}
dset_loaders = {}
for data_config in config["data"]:
dsets[data_config["name"]] = {}
dset_loaders[data_config["name"]] = {}
## image data
if data_config["type"] == "image":
dsets[data_config["name"]]["train"] = ImageList(
open(data_config["list_path"]["train"]).readlines(),
transform=prep_dict[data_config["name"]]["train"])
dset_loaders[data_config["name"]]["train"] = util_data.DataLoader(
dsets[data_config["name"]]["train"],
batch_size=data_config["batch_size"]["train"],
shuffle=True,
num_workers=4)
if "test" in data_config["list_path"]:
if prep_dict[data_config["name"]]["test_10crop"]:
for i in range(10):
dsets[data_config["name"]][
"test" + str(i)] = ImageList(
open(data_config["list_path"]
["test"]).readlines(),
transform=prep_dict[data_config["name"]]
["test"]["val" + str(i)])
dset_loaders[data_config["name"]][
"test" + str(i)] = util_data.DataLoader(
dsets[data_config["name"]]["test" + str(i)],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
dsets[data_config["name"]]["test"] = ImageList(
open(data_config["list_path"]["test"]).readlines(),
transform=prep_dict[data_config["name"]]["test"])
dset_loaders[
data_config["name"]]["test"] = util_data.DataLoader(
dsets[data_config["name"]]["test"],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
if prep_dict[data_config["name"]]["test_10crop"]:
for i in range(10):
dsets[data_config["name"]][
"test" + str(i)] = ImageList(
open(data_config["list_path"]
["train"]).readlines(),
transform=prep_dict[data_config["name"]]
["test"]["val" + str(i)])
dset_loaders[data_config["name"]][
"test" + str(i)] = util_data.DataLoader(
dsets[data_config["name"]]["test" + str(i)],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
else:
dsets[data_config["name"]]["test"] = ImageList(
open(data_config["list_path"]["train"]).readlines(),
transform=prep_dict[data_config["name"]]["test"])
dset_loaders[
data_config["name"]]["test"] = util_data.DataLoader(
dsets[data_config["name"]]["test"],
batch_size=data_config["batch_size"]["test"],
shuffle=False,
num_workers=4)
class_num = config["classnum"]
## set base network
net_config = config["network"]
base_network = network.network_dict[net_config["name"]]()
if net_config["DAN2"]:
fc7 = nn.Sequential(nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096))
fc7_relu = nn.Sequential(nn.ReLU(True), nn.Dropout())
if net_config["use_bottleneck"]:
bottleneck_layer = nn.Linear(base_network.output_num(),
net_config["bottleneck_dim"])
classifier_layer = nn.Linear(bottleneck_layer.out_features, class_num)
else:
classifier_layer = nn.Linear(base_network.output_num(), class_num)
for param in base_network.parameters():
param.requires_grad = False
## initialization
if net_config["use_bottleneck"]:
bottleneck_layer.weight.data.normal_(0, 0.005)
bottleneck_layer.bias.data.fill_(0.1)
bottleneck_layer = nn.Sequential(bottleneck_layer, nn.ReLU(),
nn.Dropout(0.5))
classifier_layer.weight.data.normal_(0, 0.01)
classifier_layer.bias.data.fill_(0.0)
use_gpu = config["use_gpu"]
if use_gpu:
if net_config["use_bottleneck"]:
bottleneck_layer = bottleneck_layer.cuda()
if net_config["DAN2"]:
fc7 = fc7.cuda()
fc7_relu = fc7_relu.cuda()
classifier_layer = classifier_layer.cuda()
base_network = base_network.cuda()
## collect parameters
if net_config["use_bottleneck"]:
parameter_list = [{
"params": bottleneck_layer.parameters(),
"lr": 10
}, {
"params": classifier_layer.parameters(),
"lr": 10
}]
else:
parameter_list = [{"params": classifier_layer.parameters(), "lr": 10}]
## add additional network for some methods
if loss_config["name"] == "JAN":
softmax_layer = nn.Softmax()
if use_gpu:
softmax_layer = softmax_layer.cuda()
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optim_dict[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"]]
## train
len_train_source = len(dset_loaders["source"]["train"]) - 1
len_train_target = len(dset_loaders["target"]["train"]) - 1
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
## Quyan::HERE needs to add mini-batch------------------------------------------------------------------------------------------------------------------------
for i in range(config["num_iterations"]):
## train one iter
if net_config["use_bottleneck"]:
bottleneck_layer.train(True)
classifier_layer.train(True)
optimizer = lr_scheduler(param_lr, optimizer, i, **schedule_param)
optimizer.zero_grad()
if i % len_train_source == 0:
iter_source = iter(dset_loaders["source"]["train"])
if i % len_train_target == 0:
iter_target = iter(dset_loaders["target"]["train"])
inputs_source, labels_source = iter_source.next()
inputs_target, labels_target = iter_target.next()
if use_gpu:
inputs_source, inputs_target, labels_source = Variable(
inputs_source).cuda(), Variable(
inputs_target).cuda(), Variable(labels_source).cuda()
else:
inputs_source, inputs_target, labels_source = Variable(
inputs_source), Variable(inputs_target), Variable(
labels_source)
inputs = torch.cat((inputs_source, inputs_target), dim=0)
features = base_network(inputs)
if net_config["use_bottleneck"]:
features = bottleneck_layer(features)
if net_config["DAN2"]:
features2 = fc7(features)
outputs = classifier_layer(fc7_relu(features2))
else:
outputs = classifier_layer(features)
classifier_loss = class_criterion(
outputs.narrow(0, 0, int((inputs.size(0) / 2))), labels_source)
## switch between different transfer loss
if loss_config["name"] == "DAN":
transfer_loss = transfer_criterion(
features.narrow(0, 0, int(features.size(0) / 2)),
features.narrow(0, int(features.size(0) / 2),
int(features.size(0) / 2)),
**loss_config["params"])
elif loss_config["name"] == "RTN":
## RTN is still under developing
transfer_loss = 0
elif loss_config["name"] == "JAN":
softmax_out = softmax_layer(outputs)
transfer_loss = transfer_criterion([
features.narrow(0, 0, int(features.size(0) / 2)),
softmax_out.narrow(0, 0, int(softmax_out.size(0) / 2))
], [
features.narrow(0, int(features.size(0) / 2),
int(features.size(0) / 2)),
softmax_out.narrow(0, int(softmax_out.size(0) / 2),
int(softmax_out.size(0) / 2))
], **loss_config["params"])
if net_config["DAN2"]:
transfer_loss2 = transfer_criterion(
features2.narrow(0, 0, int(features2.size(0) / 2)),
features2.narrow(0, int(features2.size(0) / 2),
int(features2.size(0) / 2)),
**loss_config["params"])
total_loss = loss_config["trade_off"] * transfer_loss + classifier_loss
if net_config["DAN2"]:
total_loss = total_loss + loss_config["trade_off"] * transfer_loss2
total_loss.backward()
optimizer.step()
## show loss every show_interval
if (i + 1) % config["show_interval"] == 0 and not (
i + 1) % config["test_interval"] == 0:
print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
"----------------------------------iter=", i + 1,
"\nclassifier_loss=", round(float(classifier_loss),
3), " | transfer_loss=",
round(float(transfer_loss), 3))
## test in the train
if (i + 1) % config["test_interval"] == 0:
print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
"----------------------------------iter=", i + 1,
"\nclassifier_loss=", round(float(classifier_loss),
3), " | transfer_loss=",
round(float(transfer_loss), 3))
print("optimizer_param=", optimizer.param_groups)
base_network.train(False)
classifier_layer.train(False)
if net_config["use_bottleneck"]:
bottleneck_layer.train(False)
print(
"accuracy=",
round(
image_classification_test(
dset_loaders["target"],
nn.Sequential(base_network, bottleneck_layer,
classifier_layer),
test_10crop=prep_dict["target"]["test_10crop"],
gpu=use_gpu), 3))
else:
if net_config["DAN2"]:
print(
"Domain S[" + args.source + "] accuracy=",
round(
image_classification_test(
dset_loaders["source"],
nn.Sequential(base_network, fc7, fc7_relu,
classifier_layer),
test_10crop=prep_dict["source"]["test_10crop"],
gpu=use_gpu), 3))
print(
"Domain T[" + args.target + "] accuracy=",
round(
image_classification_test(
dset_loaders["target"],
nn.Sequential(base_network, fc7, fc7_relu,
classifier_layer),
test_10crop=prep_dict["target"]["test_10crop"],
gpu=use_gpu), 3))
else:
print(
"accuracy=",
round(
image_classification_test(
dset_loaders["target"],
nn.Sequential(base_network, classifier_layer),
test_10crop=prep_dict["target"]["test_10crop"],
gpu=use_gpu), 3))
loss_test = nn.BCELoss()
## Quyan::snap-------------------------------------------------------------------------------------------------------------------------------------------
if config["snap"]["snap"] and (i + 1) % config["snap"]["step"] == 0:
# save model
torch.save(
base_network, "../model/office/" + config["loss"]["name"] +
"_" + config["network"]["name"] + "_iter" +
str(config["num_iterations"]) + ".pkl")
# torch.save(bottleneck_layer, "../model/office/" + config["loss"]["name"] + "_" + config["network"]["name"] + "_bottleneck_iter" + str(config["num_iterations"]) + ".pkl")
torch.save(
classifier_layer, "../model/office/" + config["loss"]["name"] +
"_" + config["network"]["name"] + "_classifier_iter" +
str(config["num_iterations"]) + ".pkl")
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(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"]
print(config, data_config)
# 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)
print('load data finished')
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()
#print(base_network)
base_network = torch.load('snapshot/iter_90000_model.pth.tar')
base_network = list(base_network.children())[0]
base_network = base_network.module
# for key, value in base_network.state_dict().items():
# print(key)
base_network = list(base_network.children())[9].cuda()
# base_network= torch.nn.Sequential(*list(base_network.children())[:-1]).cuda()
base_network = nn.DataParallel(base_network)
print(base_network)
#base_network.load_state_dict(checkpoint, strict=False)
# ## 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
base_network.train(False)
image_classification_test(dset_loaders, \
base_network, test_10crop=prep_config["test_10crop"])
return
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_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'])
tensor_writer = SummaryWriter(config["output_path"])
## 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)
dsets["source"] = ImageFolder(root=data_config["source"]["list_path"], \
transform=prep_dict["source"])
dset_loaders["source"] = DataLoader(dsets["source"], batch_size=train_bs, \
shuffle=True, num_workers=4, drop_last=True)
dsets["target"] = ImageFolder(root=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["val"] = [ImageFolder(root=data_config["source"]["list_path"], \
transform=prep_dict["test"][i]) for i in range(10)]
dset_loaders["val"] = [DataLoader(dset, batch_size=test_bs, \
shuffle=False, num_workers=4) for dset in dsets['val']]
dsets["test"] = [ImageFolder(root=data_config["target"]["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"] = ImageFolder(root=data_config["target"]["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 = torch.load(
"./snapshot/finetune_pet/iter_01000_model.pth.tar")[0]
base_network = base_network.cuda()
classifier_network = network.Classifier(
class_num=config["network"]["params"]["class_num"])
classifier_network = classifier_network.cuda()
parameter_list = classifier_network.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:
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"]):
print(i)
if i % config["test_interval"] == config["test_interval"] - 1:
base_network.train(False)
temp_acc, output, label, feature, logit = image_classification_val(dset_loaders, \
base_network, classifier_network, test_10crop=prep_config["test_10crop"])
np.save(
os.path.join(config["output_path"],
str(i) + '_output_src.npy'), output)
np.save(
os.path.join(config["output_path"],
str(i) + '_label_src.npy'), label)
np.save(
os.path.join(config["output_path"],
str(i) + '_feature_src.npy'), feature)
np.save(
os.path.join(config["output_path"],
str(i) + '_logit_src.npy'), logit)
temp_acc, output, label, feature, logit = image_classification_test(dset_loaders, \
base_network, classifier_network, test_10crop=prep_config["test_10crop"])
np.save(
os.path.join(config["output_path"],
str(i) + '_output.npy'), output)
np.save(os.path.join(config["output_path"],
str(i) + '_label.npy'), label)
np.save(
os.path.join(config["output_path"],
str(i) + '_feature.npy'), feature)
np.save(os.path.join(config["output_path"],
str(i) + '_logit.npy'), logit)
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(classifier_network), osp.join(config["output_path"], \
"iter_{:05d}_classifier.pth.tar".format(i)))
loss_params = config["loss"]
## train one iter
base_network.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, _ = base_network(inputs_source)
embed_source, outputs_source = classifier_network(features_source)
classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source)
total_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(config):
# pre-process training and test data
prep_dict = {
'source': prep.image_train(**config['prep']['params']),
'target': prep.image_train(**config['prep']['params'])
}
if config['prep']['test_10crop']:
prep_dict['test'] = prep.image_test_10crop(**config['prep']['params'])
else:
prep_dict['test'] = prep.image_test(**config['prep']['params'])
data_set = {}
dset_loaders = {}
data_config = config['data']
data_set['source'] = ImageList(open(
data_config['source']['list_path']).readlines(),
transform=prep_dict['source'])
dset_loaders['source'] = torch.utils.data.DataLoader(
data_set['source'],
batch_size=data_config['source']['batch_size'],
shuffle=True,
num_workers=4,
drop_last=True)
data_set['target'] = ImageList(open(
data_config['target']['list_path']).readlines(),
transform=prep_dict['target'])
dset_loaders['target'] = torch.utils.data.DataLoader(
data_set['target'],
batch_size=data_config['target']['batch_size'],
shuffle=True,
num_workers=4,
drop_last=True)
if config['prep']['test_10crop']:
data_set['test'] = [
ImageList(open(data_config['test']['list_path']).readlines(),
transform=prep_dict['test'][i]) for i in range(10)
]
dset_loaders['test'] = [
torch.utils.data.DataLoader(
dset,
batch_size=data_config['test']['batch_size'],
shuffle=False,
num_workers=4) for dset in data_set['test']
]
else:
data_set['test'] = ImageList(open(
data_config['test']['list_path']).readlines(),
transform=prep_dict['test'])
dset_loaders['test'] = torch.utils.data.DataLoader(
data_set['test'],
batch_size=data_config['test']['batch_size'],
shuffle=False,
num_workers=4)
# set base network, classifier network, residual net
class_num = config['network']['params']['class_num']
net_config = config['network']
if net_config['name'] == '50':
base_network = dcca_resnet50()
elif net_config['name'] == '101':
base_network = dcca_resnet101()
else:
raise ValueError('base network %s not found!' % (net_config['name']))
classifier_layer = nn.Linear(2048, class_num)
# feature residual layer: two fully connected layers
residual_fc1 = nn.Linear(2048, 2048)
residual_bn1 = nn.BatchNorm1d(2048)
residual_fc2 = nn.Linear(2048, 128)
residual_bn2 = nn.BatchNorm1d(128)
residual_fc3 = nn.Linear(128, 2048)
classifier_layer.weight.data.normal_(0, 0.01)
classifier_layer.bias.data.fill_(0.0)
residual_fc1.weight.data.normal_(0, 0.005)
residual_fc1.bias.data.fill_(0.1)
residual_fc2.weight.data.normal_(0, 0.005)
residual_fc2.bias.data.fill_(0.1)
residual_fc3.weight.data.normal_(0, 0.005)
residual_fc3.bias.data.fill_(0.1)
feature_residual_layer = nn.Sequential(residual_fc2, nn.ReLU(),
residual_fc3)
# class residual layer: two fully connected layers
residual_fc22 = nn.Linear(classifier_layer.out_features,
classifier_layer.out_features)
residual_bn22 = nn.BatchNorm1d(classifier_layer.out_features)
residual_fc23 = nn.Linear(classifier_layer.out_features,
classifier_layer.out_features)
residual_fc22.weight.data.normal_(0, 0.005)
residual_fc22.bias.data.fill_(0.1)
residual_fc23.weight.data.normal_(0, 0.005)
residual_fc23.bias.data.fill_(0.1)
class_residual_layer = nn.Sequential(residual_fc22, nn.ReLU(),
residual_fc23)
base_network = base_network.cuda()
feature_residual_layer = feature_residual_layer.cuda()
classifier_layer = classifier_layer.cuda()
class_residual_layer = class_residual_layer.cuda()
softmax_layer = nn.Softmax().cuda()
# set optimizer
parameter_list = [{
'params': base_network.parameters(),
'lr_mult': 1,
'decay_mult': 2
}, {
'params': classifier_layer.parameters(),
'lr_mult': 10,
'decay_mult': 2
}, {
'params': feature_residual_layer.parameters(),
'lr_mult': 0.01,
'decay_mult': 2
}, {
'params': class_residual_layer.parameters(),
'lr_mult': 0.01,
'decay_mult': 2
}]
optimizer_config = config['optimizer']
optimizer = optimizer_config['type'](parameter_list,
**(optimizer_config['optim_params']))
schedule_param = optimizer_config['lr_param']
lr_scheduler = lr_schedule.schedule_dict[optimizer_config['lr_type']]
# set loss
class_criterion = nn.CrossEntropyLoss().cuda()
loss_config = config['loss']
if 'params' not in loss_config:
loss_config['params'] = {}
# train
len_train_source = len(dset_loaders['source'])
len_train_target = len(dset_loaders['target'])
best_acc = 0.0
since = time.time()
for num_iter in tqdm(range(config['max_iter'])):
if num_iter % config['val_iter'] == 0 and num_iter != 0:
base_network.train(False)
classifier_layer.train(False)
feature_residual_layer.train(False)
class_residual_layer.train(False)
base_network = nn.Sequential(base_network)
classifier_layer = nn.Sequential(classifier_layer)
feature_residual_layer = nn.Sequential(feature_residual_layer)
class_residual_layer = nn.Sequential(class_residual_layer)
temp_acc = image_classification_test(
loader=dset_loaders,
base_net=base_network,
classifier_layer=classifier_layer,
residual_layer1=feature_residual_layer,
residual_layer2=class_residual_layer,
test_10crop=config['prep']['test_10crop'],
config=config,
num_iter=num_iter)
if temp_acc > best_acc:
best_acc = temp_acc
best_model = {
'base': base_network.state_dict(),
'classifier': classifier_layer.state_dict(),
'feature_residual': feature_residual_layer.state_dict(),
'class_residual': class_residual_layer.state_dict()
}
log_str = 'iter: {:d}, all_accu: {:.4f},\ttime: {:.4f}'.format(
num_iter, temp_acc,
time.time() - since)
config['logger'].logger.debug(log_str)
config['results'][num_iter].append(temp_acc)
# This has any effect only on modules such as Dropout or BatchNorm.
base_network.train(True)
classifier_layer.train(True)
feature_residual_layer.train(True)
class_residual_layer.train(True)
# freeze BN layers
for m in base_network.modules():
if isinstance(m, nn.BatchNorm2d):
m.training = False
m.weight.requires_grad = False
m.bias.requires_grad = False
# load data
if num_iter % len_train_source == 0:
iter_source = iter(dset_loaders['source'])
if num_iter % len_train_target == 0:
iter_target = iter(dset_loaders['target'])
inputs_source, labels_source = iter_source.next()
inputs_target, _ = iter_target.next()
batch_size = len(labels_source)
inputs_source, inputs_target, labels_source = inputs_source.cuda(
), inputs_target.cuda(), labels_source.cuda()
optimizer = lr_scheduler(optimizer, num_iter / config['max_iter'],
**schedule_param)
optimizer.zero_grad()
inputs = torch.cat((inputs_source, inputs_target), dim=0)
features_base = base_network(inputs)
features_residual = feature_residual_layer(features_base)
total_feature_residual = features_base + features_residual
# source classification loss with original features
output_base = classifier_layer(features_base)
classifier_loss = class_criterion(output_base[:batch_size, :],
labels_source)
# target residual feature entropy loss
residual_output_base = classifier_layer(total_feature_residual)
output_residual = class_residual_layer(residual_output_base)
total_output_residual = residual_output_base + output_residual
softmax_output_base = softmax_layer(output_base)
total_softmax_residual = softmax_layer(total_output_residual)
entropy_loss = EntropyLoss(total_softmax_residual[batch_size:, :])
# alignment of L task-specific feature layers (Here, we have one layer)
transfer_loss = MMD(features_base[:batch_size, :],
total_feature_residual[batch_size:, :])
# alignment of softmax layer
transfer_loss += MMD(softmax_output_base[:batch_size, :],
total_softmax_residual[batch_size:, :],
kernel_num=1,
fix_sigma=1.68)
source_labels_data = labels_source.data.float()
sum_reg_loss = 0
for k in range(class_num):
source_k_index = []
for index, source_k in enumerate(source_labels_data):
# find all indexes of k-th class source samples
if source_k == k:
source_k_index.append(index)
fea_reg_loss = 0
out_reg_loss = 0
if len(source_k_index) > 0:
# random subset indexes of source samples
source_rand_index = []
index = 0
for z in range(batch_size):
prob = random.random()
if prob < config['random_prob'] / class_num:
source_rand_index.append(index)
index += 1
if len(source_rand_index) > 0:
# source feature of k-th class
source_k_fea = features_base.index_select(
0,
torch.tensor(source_k_index, dtype=torch.long).cuda())
source_k_out = output_base.index_select(
0,
torch.tensor(source_k_index, dtype=torch.long).cuda())
# random selected source feature
source_rand_fea = total_feature_residual.index_select(
0,
torch.tensor(source_rand_index,
dtype=torch.long).cuda())
source_rand_out = total_output_residual.index_select(
0,
torch.tensor(source_rand_index,
dtype=torch.long).cuda())
fea_reg_loss = MMD_reg(source_k_fea, source_rand_fea)
out_reg_loss = MMD_reg(source_k_out,
source_rand_out,
kernel_num=1,
fix_sigma=1.68)
sum_reg_loss += (fea_reg_loss + out_reg_loss)
total_loss = classifier_loss + \
config['loss']['alpha_off'] * (transfer_loss +
config['loss']['constant_off'] * sum_reg_loss) + \
config['loss']['beta_off'] * entropy_loss
total_loss.backward()
optimizer.step()
if num_iter % config['val_iter'] == 0:
config['logger'].logger.debug(
'class: {:.4f}\tmmd: {:.4f}\tmmd_seg: {:.4f}\tentropy: {:.4f}'.
format(classifier_loss.item(), transfer_loss.item(),
config['loss']['constant_off'] * sum_reg_loss,
entropy_loss.item() * config['loss']['beta_off']))
if config['is_writer']:
config['writer'].add_scalars(
'train', {
'class':
classifier_loss.item(),
'mmd':
transfer_loss.item(),
'mmd_seg':
config['loss']['constant_off'] * sum_reg_loss.item(),
'entropy':
config['loss']['beta_off'] * entropy_loss.item()
}, num_iter)
if config['is_writer']:
config['writer'].close()
torch.save(
best_model,
osp.join(config['path']['model'], config['task'] + '_best_model.pth'))
return best_acc
def evaluate(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 = open(data_config["source"]["list_path"]).readlines()
target_list = 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=4, 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=4, drop_last=True)
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=4) 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=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.to(network.dev)
if config["restore_path"]:
checkpoint = torch.load(
osp.join(config["restore_path"], "best_model.pth"))
base_network.load_state_dict(checkpoint)
print("successfully restore from ",
osp.join(config["restore_path"], "best_model.pth"))
gpus = config['gpu'].split(',')
if len(gpus) > 1:
base_network = nn.DataParallel(
base_network, device_ids=[int(i) for i in range(len(gpus))])
evaluater = Eval(class_num)
base_network.train(False)
temp_acc, predict, all_label = image_classification_test(dset_loaders, \
base_network, test_10crop=prep_config["test_10crop"])
print("arg acc:", temp_acc)
evaluater.add_batch(predict, all_label)
evaluater.Print_Every_class_Eval()
evaluater.reset()
return
def test(config):
## set pre-process
prep_dict = {}
prep_config = config["prep"]
prep_dict["source"] = prep.image_train( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
prep_dict["target"] = prep.image_train( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
if prep_config["test_10crop"]:
prep_dict["test"] = prep.image_test_10crop( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
else:
prep_dict["test"] = prep.image_test( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
## prepare data
dsets = {}
dset_loaders = {}
data_config = config["data"]
dsets["source"] = ImageList(open(data_config["source"]["list_path"]).readlines(), \
transform=prep_dict["source"])
dset_loaders["source"] = util_data.DataLoader(dsets["source"], \
batch_size=data_config["source"]["batch_size"], \
shuffle=True, num_workers=2)
dsets["target"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["target"])
dset_loaders["target"] = util_data.DataLoader(dsets["target"], \
batch_size=data_config["target"]["batch_size"], \
shuffle=True, num_workers=2)
if prep_config["test_10crop"]:
for i in range(10):
dsets["test"+str(i)] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"]["val"+str(i)])
dset_loaders["test"+str(i)] = util_data.DataLoader(dsets["test"+str(i)], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=2)
dsets["target"+str(i)] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["test"]["val"+str(i)])
dset_loaders["target"+str(i)] = util_data.DataLoader(dsets["target"+str(i)], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=2)
else:
dsets["test"] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"])
dset_loaders["test"] = util_data.DataLoader(dsets["test"], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=2)
dsets["target_test"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["test"])
dset_loaders["target_test"] = util_data.DataLoader(dsets["target_test"], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=2)
class_num = config["network"]["params"]["class_num"]
# load checkpoint
print('load model from {}'.format(config['ckpt_path']))
# load in an old way
# base_network = torch.load(config["ckpt_path"])[0]
# recommended practice
ckpt = torch.load(config['ckpt_path'])
print('recorded best precision: {:0.4f} at step {}'.format(
ckpt["precision"], ckpt["step"]))
train_accuracy = ckpt["precision"]
## set base network
net_config = config["network"]
base_network = net_config["name"](**net_config["params"])
base_network.load_state_dict(ckpt['base_network'])
centroids = None
if 'center_criterion' in ckpt.keys():
centroids = ckpt['center_criterion']['centers'].cpu()
target_centroids = None
if 'target_center_criterion' in ckpt.keys():
target_centroids = ckpt['target_center_criterion']['centers'].cpu()
use_gpu = torch.cuda.is_available()
if use_gpu:
base_network = base_network.cuda()
## test
print("start test: ")
base_network.train(False)
if config["ly_type"] == 'cosine':
test_acc, test_confusion_matrix = image_classification_test(dset_loaders, \
base_network, test_10crop=prep_config["test_10crop"], \
gpu=use_gpu)
elif config["ly_type"] == "euclidean":
eval_centroids = None
if centroids is not None:
eval_centroids = centroids
if target_centroids is not None:
eval_centroids = target_centroids
test_acc, test_confusion_matrix = utils.distance_classification_test(dset_loaders, \
base_network, eval_centroids, test_10crop=prep_config["test_10crop"], \
gpu=use_gpu)
# save train/test accuracy as pkl file
with open(os.path.join(config["output_path"], 'accuracy.pkl'),
'wb') as pkl_file:
pkl.dump({'train': train_accuracy, 'test': test_acc}, pkl_file)
## top k confusion classes for each class
#base_data_path = os.path.split(data_config["target"]["list_path"])[0]
#with open(os.path.join(base_data_path, 'label2class.json'), "r") as f:
# obj = json.load(f)[0]
# label_to_class = {int(k):v for k, v in obj.items()} #convert key to int
np.set_printoptions(precision=2)
log_str = "train precision: {:.5f}\ttest precision: {:.5f}\nconfusion matrix:\n{}\n".format(
train_accuracy, test_acc, test_confusion_matrix)
config["out_file"].write(log_str)
config["out_file"].flush()
print(log_str)
return test_acc
def train(config):
## set pre-process
prep_dict = {}
prep_config = config["prep"]
prep_dict["source"] = prep.image_train( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
prep_dict["target"] = prep.image_train( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
if prep_config["test_10crop"]:
prep_dict["test"] = prep.image_test_10crop( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
else:
prep_dict["test"] = prep.image_test( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
## set loss
class_criterion = nn.CrossEntropyLoss()
transfer_criterion = loss.PADA
loss_params = config["loss"]
## prepare data
dsets = {}
dset_loaders = {}
data_config = config["data"]
# seperate the source and validation set
cls_source_list, cls_validation_list = sep.split_set(
data_config["source"]["list_path"],
config["network"]["params"]["class_num"])
source_list = sep.dimension_rd(cls_source_list)
dsets["source"] = ImageList(source_list, \
transform=prep_dict["source"])
dset_loaders["source"] = util_data.DataLoader(dsets["source"], \
batch_size=data_config["source"]["batch_size"], \
shuffle=True, num_workers=4)
dsets["target"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["target"])
dset_loaders["target"] = util_data.DataLoader(dsets["target"], \
batch_size=data_config["target"]["batch_size"], \
shuffle=True, num_workers=4)
if prep_config["test_10crop"]:
for i in range(10):
dsets["test"+str(i)] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"]["val"+str(i)])
dset_loaders["test"+str(i)] = util_data.DataLoader(dsets["test"+str(i)], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=4)
dsets["target"+str(i)] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["test"]["val"+str(i)])
dset_loaders["target"+str(i)] = util_data.DataLoader(dsets["target"+str(i)], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=4)
else:
dsets["test"] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"])
dset_loaders["test"] = util_data.DataLoader(dsets["test"], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=4)
dsets["target_test"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["test"])
dset_loaders["target_test"] = MyDataLoader(dsets["target_test"], \
batch_size=data_config["test"]["batch_size"], \
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"])
use_gpu = torch.cuda.is_available()
if use_gpu:
base_network = base_network.cuda()
## collect parameters
if net_config["params"]["new_cls"]:
if net_config["params"]["use_bottleneck"]:
parameter_list = [{"params":base_network.feature_layers.parameters(), "lr":1}, \
{"params":base_network.bottleneck.parameters(), "lr":10}, \
{"params":base_network.fc.parameters(), "lr":10}]
else:
parameter_list = [{"params":base_network.feature_layers.parameters(), "lr":1}, \
{"params":base_network.fc.parameters(), "lr":10}]
else:
parameter_list = [{"params": base_network.parameters(), "lr": 1}]
## add additional network for some methods
class_weight = torch.from_numpy(np.array([1.0] * class_num))
if use_gpu:
class_weight = class_weight.cuda()
ad_net = network.AdversarialNetwork(base_network.output_num())
gradient_reverse_layer = network.AdversarialLayer(
high_value=config["high"])
if use_gpu:
ad_net = ad_net.cuda()
parameter_list.append({"params": ad_net.parameters(), "lr": 10})
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optim_dict[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"]]
## train
len_train_source = len(dset_loaders["source"]) - 1
len_train_target = len(dset_loaders["target"]) - 1
transfer_loss_value = classifier_loss_value = total_loss_value = 0.0
best_acc = 0.0
best_model = 0
for i in range(config["num_iterations"]):
if (i + 1) % config["test_interval"] == 0:
base_network.train(False)
temp_acc = image_classification_test(dset_loaders, \
base_network, test_10crop=prep_config["test_10crop"], \
gpu=use_gpu)
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)
config["out_file"].flush()
print(log_str)
if (i + 1) % config["snapshot_interval"] == 0:
torch.save(nn.Sequential(base_network), osp.join(config["output_path"], \
"iter_{:05d}_model.pth.tar".format(i)))
# if i % loss_params["update_iter"] == loss_params["update_iter"] - 1:
# base_network.train(False)
# target_fc8_out = image_classification_predict(dset_loaders, base_network, softmax_param=config["softmax_param"])
# class_weight = torch.mean(target_fc8_out, 0)
# class_weight = (class_weight / torch.mean(class_weight)).cuda().view(-1)
# class_criterion = nn.CrossEntropyLoss(weight = class_weight)
## train one iter
base_network.train(True)
optimizer = lr_scheduler(param_lr, 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()
if use_gpu:
inputs_source, inputs_target, labels_source = \
Variable(inputs_source).cuda(), Variable(inputs_target).cuda(), \
Variable(labels_source).cuda()
else:
inputs_source, inputs_target, labels_source = Variable(inputs_source), \
Variable(inputs_target), Variable(labels_source)
inputs = torch.cat((inputs_source, inputs_target), dim=0)
features, outputs = base_network(inputs)
# #
# if i % 100 == 0:
# check = dev.get_label_list(open(data_config["source"]["list_path"]).readlines(),
# base_network,
# prep_config["resize_size"],
# prep_config["crop_size"],
# data_config["target"]["batch_size"],
# use_gpu)
# f = open("Class_result.txt", "a+")
# f.close()
# for cls in range(class_num):
# count = 0
# for j in check:
# if int(j.split(" ")[1].replace("\n", "")) == cls:
# count = count + 1
# f = open("Source_result.txt", "a+")
# f.write("Source_Class: " + str(cls) + "\n" + "Number of images: " + str(count) + "\n")
# f.close()
#
# check = dev.get_label_list(open(data_config["target"]["list_path"]).readlines(),
# base_network,
# prep_config["resize_size"],
# prep_config["crop_size"],
# data_config["target"]["batch_size"],
# use_gpu)
# f = open("Class_result.txt", "a+")
# f.write("Iteration: " + str(i) + "\n")
# f.close()
# for cls in range(class_num):
# count = 0
# for j in check:
# if int(j.split(" ")[1].replace("\n", "")) == cls:
# count = count + 1
# f = open("Class_result.txt", "a+")
# f.write("Target_Class: " + str(cls) + "\n" + "Number of images: " + str(count) + "\n")
# f.close()
#
# #
# print("Training test:")
# print(features)
# print(features.shape)
# print(outputs)
# print(outputs.shape)
softmax_out = nn.Softmax(dim=1)(outputs).detach()
ad_net.train(True)
weight_ad = torch.ones(inputs.size(0))
# label_numpy = labels_source.data.cpu().numpy()
# for j in range(int(inputs.size(0) / 2)):
# weight_ad[j] = class_weight[int(label_numpy[j])]
# weight_ad = weight_ad / torch.max(weight_ad[0:int(inputs.size(0)/2)])
# for j in range(int(inputs.size(0) / 2), inputs.size(0)):
# weight_ad[j] = 1.0
transfer_loss = transfer_criterion(features, ad_net, gradient_reverse_layer, \
weight_ad, use_gpu)
classifier_loss = class_criterion(
outputs.narrow(0, 0, int(inputs.size(0) / 2)), labels_source)
total_loss = loss_params["trade_off"] * transfer_loss + classifier_loss
total_loss.backward()
optimizer.step()
cv_loss = dev.cross_validation_loss(
base_network, base_network, cls_source_list,
data_config["target"]["list_path"], cls_validation_list, class_num,
prep_config["resize_size"], prep_config["crop_size"],
data_config["target"]["batch_size"], use_gpu)
print(cv_loss)
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(config):
tie = 1.0
## set pre-process
prep_dict = {}
prep_config = config["prep"]
prep_dict["source"] = prep.image_train( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
prep_dict["target"] = prep.image_train( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
if prep_config["test_10crop"]:
prep_dict["test"] = prep.image_test_10crop( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
else:
prep_dict["test"] = prep.image_test( \
resize_size=prep_config["resize_size"], \
crop_size=prep_config["crop_size"])
## set loss
class_criterion = nn.CrossEntropyLoss() # 分类损失
transfer_criterion1 = loss.PADA # 迁移学习损失(带权重）,BCEloss
balance_criterion = loss.balance_loss()
loss_params = config["loss"]
## prepare data
dsets = {}
dset_loaders = {}
data_config = config["data"]
dsets["source"] = ImageList(open(data_config["source"]["list_path"]).readlines(), \
transform=prep_dict["source"])
dset_loaders["source"] = util_data.DataLoader(dsets["source"], \
batch_size=data_config["source"]["batch_size"], \
shuffle=True, num_workers=4)
dsets["target"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["target"])
dset_loaders["target"] = util_data.DataLoader(dsets["target"], \
batch_size=data_config["target"]["batch_size"], \
shuffle=True, num_workers=4)
if prep_config["test_10crop"]:
for i in range(10):
dsets["test" + str(i)] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"]["val" + str(i)])
dset_loaders["test" + str(i)] = util_data.DataLoader(dsets["test" + str(i)], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=4)
dsets["target" + str(i)] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["test"]["val" + str(i)])
dset_loaders["target" + str(i)] = util_data.DataLoader(dsets["target" + str(i)], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=4)
else:
dsets["test"] = ImageList(open(data_config["test"]["list_path"]).readlines(), \
transform=prep_dict["test"])
dset_loaders["test"] = util_data.DataLoader(dsets["test"], \
batch_size=data_config["test"]["batch_size"], \
shuffle=False, num_workers=4)
dsets["target_test"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \
transform=prep_dict["test"])
dset_loaders["target_test"] = MyDataLoader(dsets["target_test"], \
batch_size=data_config["test"]["batch_size"], \
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"])
use_gpu = torch.cuda.is_available()
if use_gpu:
base_network = base_network.cuda()
## collect parameters
if net_config["params"]["new_cls"]:
if net_config["params"]["use_bottleneck"]:
parameter_list = [{"params": base_network.feature_layers.parameters(), "lr": 1}, \
{"params": base_network.bottleneck.parameters(), "lr": 10}, \
{"params": base_network.fc.parameters(), "lr": 10}]
else:
parameter_list = [{"params": base_network.feature_layers.parameters(), "lr": 1}, \
{"params": base_network.fc.parameters(), "lr": 10}]
else:
parameter_list = [{"params": base_network.parameters(), "lr": 1}]
## add additional network for some methods
class_weight = torch.from_numpy(np.array(
[1.0] * class_num)) # 权重初始化为class_num维向量，初始值为1
if use_gpu:
class_weight = class_weight.cuda()
ad_net = network.AdversarialNetwork(
base_network.output_num()) # 鉴别器设置，输入为特征提取器的维数，输出为属于共域的可能性
nad_net = network.NAdversarialNetwork(base_network.output_num())
gradient_reverse_layer = network.AdversarialLayer(
high_value=config["high"])
if use_gpu:
ad_net = ad_net.cuda()
nad_net = nad_net.cuda()
parameter_list.append({"params": ad_net.parameters(), "lr": 10})
## set optimizer
optimizer_config = config["optimizer"]
optimizer = optim_dict[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"]]
## train
len_train_source = len(dset_loaders["source"]) - 1
len_train_target = len(dset_loaders["target"]) - 1
transfer_loss_value = classifier_loss_value = total_loss_value = attention_loss_value = 0.0
best_acc = 0.0
for i in range(config["num_iterations"]):
if i % config["test_interval"] == 0: # 测试
base_network.train(False) # 先对特征提取器进行训练
temp_acc = image_classification_test(dset_loaders, \
base_network, test_10crop=prep_config["test_10crop"], \
gpu=use_gpu) # 对第一个有监督训练器进行分类训练，提取特征
temp_model = nn.Sequential(base_network)
if temp_acc > best_acc:
best_acc = temp_acc
best_model = temp_model
log_str = "iter: {:05d}, alpha: {:03f}, tradeoff: {:03f} ,precision: {:.5f}".format(
i, loss_params["para_alpha"], loss_params["trade_off"],
temp_acc)
config["out_file"].write(log_str + '\n')
config["out_file"].flush()
print(log_str)
if i % config["snapshot_interval"] == 0: # 输出
torch.save(nn.Sequential(base_network), osp.join(config["output_path"], \
"iter_{:05d}_model.pth.tar".format(i)))
if i % loss_params["update_iter"] == loss_params["update_iter"] - 1:
base_network.train(False)
target_fc8_out = image_classification_predict(
dset_loaders,
base_network,
softmax_param=config["softmax_param"]
) # 在对有监督学习进行了训练后，喂进去目标域数据，判断权重
class_weight = torch.mean(
target_fc8_out, 0) # predict模型输出的预测向量取均值，为每一个体权重，将个体权重转化为类别权重
class_weight = (class_weight /
torch.mean(class_weight)).cuda().view(-1) # 归一化
class_criterion = nn.CrossEntropyLoss(
weight=class_weight) # 这里的交叉熵损失函数就是带权重的
## train one iter
base_network.train(True)
optimizer = lr_scheduler(param_lr, 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()
if use_gpu:
inputs_source, inputs_target, labels_source = \
Variable(inputs_source).cuda(), Variable(inputs_target).cuda(), \
Variable(labels_source).cuda()
else:
inputs_source, inputs_target, labels_source = Variable(inputs_source), \
Variable(inputs_target), Variable(labels_source)
inputs = torch.cat((inputs_source, inputs_target),
dim=0) # 这里输入后一半目标域，前一半源域
features, outputs = base_network(inputs) # 从有分类网络获得特征与输出，到这里是特征提取器
softmax_out = nn.Softmax(dim=1)(outputs).detach()
ad_net.train(True)
nad_net.train(True)
weight_ad = torch.zeros(inputs.size(0))
label_numpy = labels_source.data.cpu().numpy()
for j in range(inputs.size(0) / 2):
weight_ad[j] = class_weight[int(label_numpy[j])] # 计算实际样例权重
# print(label_numpy)
weight_ad = weight_ad / torch.max(
weight_ad[0:inputs.size(0) / 2]) # 权重归一化
for j in range(inputs.size(0) / 2,
inputs.size(0)): # 前一半源域，所以权重是计算的，后一半目标域，权重全为1
weight_ad[j] = 1.0
classifier_loss = class_criterion(
outputs.narrow(0, 0,
inputs.size(0) / 2), labels_source) # 分类损失
total_loss = classifier_loss
total_loss.backward()
optimizer.step()
torch.save(best_model, osp.join(config["output_path"],
"best_model.pth.tar"))
return best_acc
