def main():
opt = parser.parse_args()
print(opt)
# Creating log directory
try:
os.makedirs(opt.outf)
except OSError:
pass
try:
os.makedirs(os.path.join(opt.outf, 'models'))
except OSError:
pass
# Setting random seed
random.seed(opt.seed)
torch.manual_seed(opt.seed)
if opt.gpu>=0:
torch.cuda.manual_seed_all(opt.seed)
# GPU/CPU flags
cudnn.benchmark = True
if torch.cuda.is_available() and opt.gpu == -1:
print("WARNING: You have a CUDA device, so you should probably run with --gpu [gpu id]")
if opt.gpu>=0:
os.environ['CUDA_VISIBLE_DEVICES'] = str(opt.gpu)
source_dataset, sourceval_dataset, target_dataset = datasetLoad(person=args.person)
nclasses = 4
# Training
GTA_trainer = trainer.GTA(opt, nclasses, source_dataset, sourceval_dataset, target_dataset)
acc_list = GTA_trainer.train()
jd = {"test_acc": acc_list}
with open(str(args.seed)+'/acc'+str(args.person)+'.json', 'w') as f:
json.dump(jd, f)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--dataselect',
type=int,
required=True,
help='1. svhn->mnist 2. mnist->svhn 3. cifar10->stl10 4. stl10->cifar10'
)
parser.add_argument('--class_balance', type=float, required=True)
parser.add_argument('--augmentation', type=int, required=True)
parser.add_argument('--auxLoss',
default=True,
type=lambda x: (str(x).lower() == 'true'))
parser.add_argument('--vae',
default=False,
type=lambda x: (str(x).lower() == 'true'))
parser.add_argument('--kl_weight',
type=float,
default=0.0000001,
help='Weight of KL penalty')
parser.add_argument('--dataroot',
required=True,
help='path to source dataset')
parser.add_argument('--workers',
type=int,
help='number of data loading workers',
default=2)
parser.add_argument('--batchSize',
type=int,
default=100,
help='input batch size')
parser.add_argument(
'--imageSize',
type=int,
default=32,
help='the height / width of the input image to network')
parser.add_argument('--nz',
type=int,
default=512,
help='size of the latent z vector')
parser.add_argument(
'--ngf',
type=int,
default=64,
help='Number of filters to use in the generator network')
parser.add_argument(
'--ndf',
type=int,
default=64,
help='Number of filters to use in the discriminator network')
parser.add_argument('--nepochs',
type=int,
default=100,
help='number of epochs to train for')
parser.add_argument('--lr',
type=float,
default=0.0005,
help='learning rate, default=0.0002')
parser.add_argument('--beta1',
type=float,
default=0.8,
help='beta1 for adam. default=0.5')
parser.add_argument('--gpu',
type=int,
default=1,
help='GPU to use, -1 for CPU training')
parser.add_argument('--outf',
default='results',
help='folder to output images and model checkpoints')
parser.add_argument('--method',
default='GTA',
help='Method to train| GTA, sourceonly')
parser.add_argument('--manualSeed', type=int, help='manual seed')
parser.add_argument('--adv_weight',
type=float,
default=0.1,
help='weight for adv loss')
parser.add_argument('--lrd',
type=float,
default=0.0001,
help='learning rate decay, default=0.0002')
parser.add_argument('--alpha',
type=float,
default=0.3,
help='multiplicative factor for target adv. loss')
opt = parser.parse_args()
print(opt)
if opt.dataselect not in [1, 2, 3, 4]:
exit(
'Please select the target and source dataset! \n 1. svhn->mnist 2. mnist->svhn 3. cifar10->stl10 4. stl10->cifar10'
)
# Creating log directory
try:
os.makedirs(opt.outf)
except OSError:
pass
try:
os.makedirs(os.path.join(opt.outf, 'visualization'))
except OSError:
pass
try:
os.makedirs(os.path.join(opt.outf, 'models'))
except OSError:
pass
# Setting random seed
if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
if opt.gpu >= 0:
torch.cuda.manual_seed_all(opt.manualSeed)
# GPU/CPU flags
cudnn.benchmark = True
if torch.cuda.is_available() and opt.gpu == -1:
print(
"WARNING: You have a CUDA device, so you should probably run with --gpu [gpu id]"
)
if opt.gpu >= 0:
os.environ['CUDA_VISIBLE_DEVICES'] = str(opt.gpu)
# Creating data loaders
if opt.dataselect in [1, 2]:
mean = np.array([0.44, 0.44, 0.44])
std = np.array([0.19, 0.19, 0.19])
else:
mean = np.array(
[0.4913997551666284, 0.48215855929893703, 0.4465309133731618])
std = np.array(
[0.24703225141799082, 0.24348516474564, 0.26158783926049628])
if opt.dataselect == 1:
source_train_root = os.path.join(opt.dataroot, 'svhn/trainset')
source_val_root = os.path.join(opt.dataroot, 'svhn/testset')
target_root = os.path.join(opt.dataroot, 'mnist/trainset')
elif opt.dataselect == 2:
source_train_root = os.path.join(opt.dataroot, 'mnist/trainset')
source_val_root = os.path.join(opt.dataroot, 'mnist/testset')
target_root = os.path.join(opt.dataroot, 'svhn/trainset')
elif opt.dataselect == 3:
source_train_root = os.path.join(opt.dataroot, 'cifar10/trainset')
source_val_root = os.path.join(opt.dataroot, 'cifar10/testset')
target_root = os.path.join(opt.dataroot, 'stl10/trainset')
elif opt.dataselect == 4:
source_train_root = os.path.join(opt.dataroot, 'stl10/trainset')
source_val_root = os.path.join(opt.dataroot, 'stl10/testset')
target_root = os.path.join(opt.dataroot, 'cifar10/trainset')
transform_source = transforms.Compose([
transforms.Resize(opt.imageSize),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
transform_target = transforms.Compose([
transforms.Resize(opt.imageSize),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
source_train = dset.ImageFolder(root=source_train_root,
transform=transform_source)
source_val = dset.ImageFolder(root=source_val_root,
transform=transform_source)
target_train = dset.ImageFolder(root=target_root,
transform=transform_target)
source_trainloader = torch.utils.data.DataLoader(source_train,
batch_size=opt.batchSize,
shuffle=True,
num_workers=2,
drop_last=True)
source_valloader = torch.utils.data.DataLoader(source_val,
batch_size=opt.batchSize,
shuffle=False,
num_workers=2,
drop_last=False)
targetloader = torch.utils.data.DataLoader(target_train,
batch_size=opt.batchSize,
shuffle=True,
num_workers=2,
drop_last=True)
nclasses = len(source_train.classes)
# Training
if opt.method == 'GTA':
GTA_trainer = trainer.GTA(opt, nclasses, mean, std, source_trainloader,
source_valloader, targetloader,
opt.class_balance, opt.augmentation)
GTA_trainer.train()
elif opt.method == 'sourceonly':
sourceonly_trainer = trainer.Sourceonly(opt, nclasses,
source_trainloader,
source_valloader)
sourceonly_trainer.train()
else:
raise ValueError('method argument should be GTA or sourceonly')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--dataroot', required=False, help='path to source dataset', default='/data/')
parser.add_argument('--workers', type=int, help='number of data loading workers', default=2)
parser.add_argument('--batchSize', type=int, default=100, help='input batch size')
parser.add_argument('--imageSize', type=int, default=32, help='the height / width of the input image to network')
parser.add_argument('--nz', type=int, default=512, help='size of the latent z vector')
parser.add_argument('--ngf', type=int, default=64, help='Number of filters to use in the generator network')
parser.add_argument('--ndf', type=int, default=64, help='Number of filters to use in the discriminator network')
parser.add_argument('--nepochs', type=int, default=100, help='number of epochs to train for')
parser.add_argument('--lr', type=float, default=0.0005, help='learning rate, default=0.0002')
parser.add_argument('--beta1', type=float, default=0.8, help='beta1 for adam. default=0.5')
parser.add_argument('--gpu', type=int, default=-1, help='GPU to use, -1 for CPU training')
parser.add_argument('--outf', default='results', help='folder to output images and model checkpoints')
parser.add_argument('--method', default='GTA', help='Method to train| GTA, sourceonly')
parser.add_argument('--manualSeed', type=int, help='manual seed')
parser.add_argument('--adv_weight', type=float, default = 0.1, help='weight for adv loss')
parser.add_argument('--lrd', type=float, default=0.0001, help='learning rate decay, default=0.0002')
parser.add_argument('--alpha', type=float, default = 0.3, help='multiplicative factor for target adv. loss')
opt = parser.parse_args()
print(opt)
# Creating log directory
try:
os.makedirs(opt.outf)
except OSError:
pass
try:
os.makedirs(os.path.join(opt.outf, 'visualization'))
except OSError:
pass
try:
os.makedirs(os.path.join(opt.outf, 'models'))
except OSError:
pass
# Setting random seed
if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
if opt.gpu>=0:
torch.cuda.manual_seed_all(opt.manualSeed)
# GPU/CPU flags
cudnn.benchmark = True
if torch.cuda.is_available() and opt.gpu == -1:
print("WARNING: You have a CUDA device, so you should probably run with --gpu [gpu id]")
if opt.gpu>=0:
os.environ['CUDA_VISIBLE_DEVICES'] = str(opt.gpu)
# Creating data loaders
# mean = np.array([0.44, 0.44, 0.44])
# std = np.array([0.19, 0.19, 0.19])
mean = np.array((0.5,))
std = np.array((0.5,))
transform_source = transforms.Compose([transforms.Resize(opt.imageSize), transforms.ToTensor(), transforms.Normalize(mean,std)])
transform_target = transforms.Compose([transforms.Resize(opt.imageSize), transforms.ToTensor(), transforms.Normalize(mean,std)])
# source_train_root = os.path.join(opt.dataroot, 'mnist')
# source_val_root = os.path.join(opt.dataroot, 'mnist')
# target_root = os.path.join(opt.dataroot, 'usps')
# source_train = dset.ImageFolder(root=source_train_root, transform=transform_source)
# source_val = dset.ImageFolder(root=source_val_root, transform=transform_source)
# target_train = dset.ImageFolder(root=target_root, transform=transform_target)
source_train_list = '/data/mnist/mnist_train.txt'
source_val_list = '/data/mnist/mnist_test.txt'
target_train_list = '/data/usps/usps_train.txt'
source_train = ImageList(open(source_train_list).readlines(),transform=transform_source)
source_val = ImageList(open(source_val_list).readlines(),transform=transform_source)
target_train = ImageList(open(target_train_list).readlines(),transform=transform_target)
source_trainloader = torch.utils.data.DataLoader(source_train, batch_size=opt.batchSize, shuffle=True, num_workers=0, drop_last=True)
source_valloader = torch.utils.data.DataLoader(source_val, batch_size=opt.batchSize, shuffle=False, num_workers=0, drop_last=False)
targetloader = torch.utils.data.DataLoader(target_train, batch_size=opt.batchSize, shuffle=True, num_workers=0, drop_last=True)
nclasses = 10
# Training
if opt.method == 'GTA':
GTA_trainer = trainer.GTA(opt, nclasses, mean, std, source_trainloader, source_valloader, targetloader)
GTA_trainer.train()
elif opt.method == 'sourceonly':
sourceonly_trainer = trainer.Sourceonly(opt, nclasses, source_trainloader, source_valloader)
sourceonly_trainer.train()
else:
raise ValueError('method argument should be GTA or sourceonly')
target_val = GetLoader(img_root=os.path.join(target_image_root, "test"),
label_path=os.path.join(target_image_root, 'test.csv'),
transform=transform)
target_train = GetLoader(img_root=os.path.join(target_image_root, "train"),
label_path=os.path.join(target_image_root,
'train.csv'),
transform=transform)
source_trainloader = torch.utils.data.DataLoader(source_train,
batch_size=consts.batch_size,
shuffle=True,
num_workers=consts.workers,
drop_last=True)
target_valloader = torch.utils.data.DataLoader(target_val,
batch_size=consts.batch_size,
shuffle=False,
num_workers=consts.workers,
drop_last=False)
targetloader = torch.utils.data.DataLoader(target_train,
batch_size=consts.batch_size,
shuffle=True,
num_workers=consts.workers,
drop_last=True)
# Training
GTA_trainer = trainer.GTA(mean, std, source_trainloader, target_valloader,
targetloader)
GTA_trainer.train()
