def digit_load(args):
train_bs = args.batch_size
if args.dset == 's':
train_source = svhn.SVHN('./data/svhn/', split='train', download=True,
transform=transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
test_source = svhn.SVHN('./data/svhn/', split='test', download=True,
transform=transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
elif args.dset == 'u':
train_source = usps.USPS('./data/usps/', train=True, download=True,
transform=transforms.Compose([
transforms.RandomCrop(28, padding=4),
transforms.RandomRotation(10),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
test_source = usps.USPS('./data/usps/', train=False, download=True,
transform=transforms.Compose([
transforms.RandomCrop(28, padding=4),
transforms.RandomRotation(10),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
elif args.dset == 'm':
train_source = mnist.MNIST('./data/mnist/', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
test_source = mnist.MNIST('./data/mnist/', train=False, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
dset_loaders = {}
dset_loaders["train"] = DataLoader(train_source, batch_size=train_bs, shuffle=True,
num_workers=args.worker, drop_last=False)
dset_loaders["test"] = DataLoader(test_source, batch_size=train_bs*2, shuffle=False,
num_workers=args.worker, drop_last=False)
return dset_loaders
def split_target(args):
train_bs = args.batch_size
if args.dset == 's2m':
train_target = mnist.MNIST(
'./data/mnist/',
train=True,
download=True,
transform=transforms.Compose([
transforms.Resize(32),
transforms.Lambda(lambda x: x.convert("RGB")),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
train_target2 = mnist.MNIST_twice(
'./data/mnist/',
train=True,
download=True,
transform=transforms.Compose([
transforms.Resize(32),
transforms.Lambda(lambda x: x.convert("RGB")),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
test_target = mnist.MNIST(
'./data/mnist/',
train=False,
download=True,
transform=transforms.Compose([
transforms.Resize(32),
transforms.Lambda(lambda x: x.convert("RGB")),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
elif args.dset == 'u2m':
train_target = mnist.MNIST('./data/mnist/',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))
]))
train_target2 = mnist.MNIST_twice('./data/mnist/',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, ),
(0.5, ))
]))
test_target = mnist.MNIST('./data/mnist/',
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))
]))
elif args.dset == 'm2u':
train_target = usps.USPS(
'./data/usps/',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
# transforms.Lambda(lambda x: _gaussian_blur(x, sigma=0.1)),
transforms.Normalize((0.5, ), (0.5, ))
]))
train_target2 = usps.USPS_twice(
'./data/usps/',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
# transforms.Lambda(lambda x: _gaussian_blur(x, sigma=0.1)),
transforms.Normalize((0.5, ), (0.5, ))
]))
test_target = usps.USPS(
'./data/usps/',
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
# transforms.Lambda(lambda x: _gaussian_blur(x, sigma=0.1)),
transforms.Normalize((0.5, ), (0.5, ))
]))
dset_loaders = {}
dset_loaders["target_te"] = DataLoader(test_target,
batch_size=train_bs,
shuffle=False,
num_workers=args.worker,
drop_last=False)
dset_loaders["target"] = DataLoader(train_target,
batch_size=train_bs,
shuffle=False,
num_workers=args.worker,
drop_last=False)
dset_loaders["target2"] = DataLoader(train_target2,
batch_size=train_bs,
shuffle=False,
num_workers=args.worker,
drop_last=False)
if args.dset == 'u2m':
netF = network.LeNetBase().cuda()
elif args.dset == 'm2u':
netF = network.LeNetBase().cuda()
elif args.dset == 's2m':
netF = network.DTNBase().cuda()
netB = network.feat_bootleneck(type=args.classifier,
feature_dim=netF.in_features,
bottleneck_dim=args.bottleneck).cuda()
netC = network.feat_classifier(type=args.layer,
class_num=args.class_num,
bottleneck_dim=args.bottleneck).cuda()
if args.model == 'source':
modelpath = args.output_dir + "/source_F.pt"
netF.load_state_dict(torch.load(modelpath))
modelpath = args.output_dir + "/source_B.pt"
netB.load_state_dict(torch.load(modelpath))
modelpath = args.output_dir + "/source_C.pt"
netC.load_state_dict(torch.load(modelpath))
pass
else:
modelpath = args.output_dir + "/target_F_" + args.savename + ".pt"
netF.load_state_dict(torch.load(modelpath))
modelpath = args.output_dir + "/target_B_" + args.savename + ".pt"
netB.load_state_dict(torch.load(modelpath))
modelpath = args.output_dir + "/target_C_" + args.savename + ".pt"
netC.load_state_dict(torch.load(modelpath))
netF.eval()
netB.eval()
netC.eval()
start_test = True
with torch.no_grad():
iter_test = iter(dset_loaders['target_te'])
for i in range(len(dset_loaders['target_te'])):
data = iter_test.next()
# pdb.set_trace()
inputs = data[0]
labels = data[1]
inputs = inputs.cuda()
outputs = netC(netB(netF(inputs)))
if start_test:
all_output = outputs.float().cpu()
all_label = labels.float()
start_test = False
else:
all_output = torch.cat((all_output, outputs.float().cpu()), 0)
all_label = torch.cat((all_label, labels.float()), 0)
top_pred, predict = torch.max(all_output, 1)
acc = torch.sum(
torch.squeeze(predict).float() == all_label).item() / float(
all_label.size()[0]) * 100
mean_ent = loss.Entropy(nn.Softmax(dim=1)(all_output))
log_str = 'Task: {}, Iter:{}/{}; Accuracy = {:.2f}%; Mean Ent = {:.4f}'.format(
args.dset + '_test', 0, 0, acc, mean_ent.mean())
args.out_file.write(log_str + '\n')
args.out_file.flush()
print(log_str + '\n')
start_test = True
with torch.no_grad():
iter_test = iter(dset_loaders['target'])
for i in range(len(dset_loaders['target'])):
data = iter_test.next()
# pdb.set_trace()
inputs = data[0]
labels = data[1]
inputs = inputs.cuda()
outputs = netC(netB(netF(inputs)))
if start_test:
all_output = outputs.float().cpu()
all_label = labels.float()
start_test = False
else:
all_output = torch.cat((all_output, outputs.float().cpu()), 0)
all_label = torch.cat((all_label, labels.float()), 0)
top_pred, predict = torch.max(all_output, 1)
acc = torch.sum(
torch.squeeze(predict).float() == all_label).item() / float(
all_label.size()[0]) * 100
mean_ent = loss.Entropy(nn.Softmax(dim=1)(all_output))
log_str = 'Task: {}, Iter:{}/{}; Accuracy = {:.2f}%; Mean Ent = {:.4f}'.format(
args.dset + '_train', 0, 0, acc, mean_ent.mean())
args.out_file.write(log_str + '\n')
args.out_file.flush()
print(log_str + '\n')
if args.ps == 0:
est_p = (mean_ent < mean_ent.mean()).sum().item() / mean_ent.size(0)
log_str = 'Task: {:.2f}'.format(est_p)
print(log_str + '\n')
args.out_file.write(log_str + '\n')
args.out_file.flush()
PS = est_p
else:
PS = args.ps
if args.choice == "ent":
value = mean_ent
elif args.choice == "maxp":
value = -top_pred
elif args.choice == "marginp":
pred, _ = torch.sort(all_output, 1)
value = pred[:, 1] - pred[:, 0]
else:
value = torch.rand(len(mean_ent))
predict = predict.numpy()
train_idx = np.zeros(predict.shape)
cls_k = args.class_num
for c in range(cls_k):
c_idx = np.where(predict == c)
c_idx = c_idx[0]
c_value = value[c_idx]
_, idx_ = torch.sort(c_value)
c_num = len(idx_)
c_num_s = int(c_num * PS)
# print(c, c_num, c_num_s)
for ei in range(0, c_num_s):
ee = c_idx[idx_[ei]]
train_idx[ee] = 1
train_target.targets = predict
new_src = copy.deepcopy(train_target)
new_tar = copy.deepcopy(train_target2)
# pdb.set_trace()
if args.dset == 'm2u':
new_src.train_data = np.delete(new_src.train_data,
np.where(train_idx == 0)[0],
axis=0)
new_src.train_labels = np.delete(new_src.train_labels,
np.where(train_idx == 0)[0],
axis=0)
new_tar.train_data = np.delete(new_tar.train_data,
np.where(train_idx == 1)[0],
axis=0)
new_tar.train_labels = np.delete(new_tar.train_labels,
np.where(train_idx == 1)[0],
axis=0)
else:
new_src.data = np.delete(new_src.data,
np.where(train_idx == 0)[0],
axis=0)
new_src.targets = np.delete(new_src.targets,
np.where(train_idx == 0)[0],
axis=0)
new_tar.data = np.delete(new_tar.data,
np.where(train_idx == 1)[0],
axis=0)
new_tar.targets = np.delete(new_tar.targets,
np.where(train_idx == 1)[0],
axis=0)
# pdb.set_trace()
return new_src, new_tar
def data_load(args, txt_src, txt_tgt):
if args.dset == 's2m':
train_target = mnist.MNIST(
'./data/mnist/',
train=True,
download=True,
transform=transforms.Compose([
transforms.Resize(32),
transforms.Lambda(lambda x: x.convert("RGB")),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
test_target = mnist.MNIST(
'./data/mnist/',
train=False,
download=True,
transform=transforms.Compose([
transforms.Resize(32),
transforms.Lambda(lambda x: x.convert("RGB")),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
elif args.dset == 'u2m':
train_target = mnist.MNIST('./data/mnist/',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))
]))
test_target = mnist.MNIST('./data/mnist/',
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))
]))
elif args.dset == 'm2u':
train_target = usps.USPS('./data/usps/',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))
]))
test_target = usps.USPS('./data/usps/',
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))
]))
dset_loaders = {}
dset_loaders["train"] = DataLoader(train_target,
batch_size=args.batch_size * 2,
shuffle=False,
num_workers=args.worker,
drop_last=False)
dset_loaders["test"] = DataLoader(test_target,
batch_size=args.batch_size * 2,
shuffle=False,
num_workers=args.worker,
drop_last=False)
dset_loaders["source"] = DataLoader(txt_src,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.worker,
drop_last=True)
dset_loaders["target"] = DataLoader(txt_tgt,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.worker,
drop_last=True)
return dset_loaders
