def main():
# init random seed
init_random_seed(params.manual_seed)
# Load dataset
svhn_data_loader = get_svhn(split='train', download=True)
svhn_data_loader_eval = get_svhn(split='test', download=True)
mnist_data_loader = get_mnist(train=True, download=True)
mnist_data_loader_eval = get_mnist(train=False, download=True)
# Model init DANN
tgt_encoder = model_init(Encoder(), params.tgt_encoder_dann_rb_path)
critic = model_init(
Discriminator(in_dims=params.d_in_dims,
h_dims=params.d_h_dims,
out_dims=params.d_out_dims), params.disc_dann_rb_path)
clf = model_init(Classifier(), params.clf_dann_rb_path)
# Train models
print(
"====== Training source encoder and classifier in SVHN and MNIST domains ======"
)
if not (tgt_encoder.pretrained and clf.pretrained and critic.pretrained
and params.model_trained):
tgt_encoder, clf, critic = train_dann(tgt_encoder,
clf,
critic,
svhn_data_loader,
mnist_data_loader,
mnist_data_loader_eval,
robust=True)
# Eval target encoder on test set of target dataset
print(
"====== Evaluating classifier for encoded SVHN and MNIST domains ======"
)
print("-------- SVHN domain --------")
eval_tgt_robust(tgt_encoder, clf, svhn_data_loader_eval)
print("-------- MNIST adaption --------")
eval_tgt_robust(tgt_encoder, clf, mnist_data_loader_eval)
def main():
# init random seed
init_random_seed(params.manual_seed)
# Load dataset
mnist_data_loader = get_mnist(train=True, download=True)
mnist_data_loader_eval = get_mnist(train=False, download=True)
usps_data_loader = get_usps(train=True, download=True)
usps_data_loader_eval = get_usps(train=False, download=True)
# Model init DANN
tgt_encoder = model_init(Encoder(), params.tgt_encoder_dann_path)
critic = model_init(Discriminator(), params.disc_dann_path)
clf = model_init(Classifier(), params.clf_dann_path)
# Train models
print(
"====== Training source encoder and classifier in MNIST and USPS domains ======"
)
if not (tgt_encoder.pretrained and clf.pretrained and critic.pretrained
and params.model_trained):
tgt_encoder, clf, critic = train_dann(tgt_encoder,
clf,
critic,
mnist_data_loader,
usps_data_loader,
usps_data_loader_eval,
robust=False)
# Eval target encoder on test set of target dataset
print(
"====== Evaluating classifier for encoded MNIST and USPS domain ======"
)
print("-------- MNIST domain --------")
eval_tgt_robust(tgt_encoder, clf, mnist_data_loader_eval)
print("-------- USPS adaption --------")
eval_tgt_robust(tgt_encoder, clf, usps_data_loader_eval)
manual_seed = 8888
alpha = 0
# params for optimizing models
lr = 2e-4
params = Config()
# init random seed
init_random_seed(params.manual_seed)
# load dataset
src_data_loader = get_data_loader(params.src_dataset, params.dataset_root,
params.batch_size)
tgt_data_loader = get_data_loader(params.tgt_dataset, params.dataset_root,
params.batch_size)
# load dann model
dann = init_model(net=AlexModel(), restore=None)
# train dann model
print("Start training dann model.")
if not (dann.restored and params.dann_restore):
dann = train_dann(dann, params, src_data_loader, tgt_data_loader,
tgt_data_loader)
print('done')
# params for optimizing models
lr = 2e-4
params = Config()
currentDir = os.path.dirname(os.path.realpath(__file__))
logFile = os.path.join(currentDir+'/../', 'dann-{}-{}.log'.format(params.src_dataset, params.tgt_dataset))
loggi = setLogger(logFile)
# init random seed
init_random_seed(params.manual_seed)
# init device
device = torch.device("cuda:" + params.gpu_id if torch.cuda.is_available() else "cpu")
# load dataset
src_data_loader = get_data_loader(params.src_dataset, params.dataset_root, params.batch_size)
tgt_data_loader = get_data_loader(params.tgt_dataset, params.dataset_root, params.batch_size)
# load dann model
# dann = init_model(net=AlexModel(), restore=None)
dann = init_model(net=ResNet50(), restore=None)
# train dann model
print("Start training dann model.")
# if not (dann.restored and params.dann_restore):
dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader, device, loggi)
print('done')
def main():
# init random seed
init_random_seed(params.manual_seed)
# Load dataset
mnist_data_loader = get_mnist(train=True, download=True)
mnist_data_loader_eval = get_mnist(train=False, download=True)
usps_data_loader = get_usps(train=True, download=True)
usps_data_loader_eval = get_usps(train=False, download=True)
# Model init DANN
tgt_encoder = model_init(Encoder(), params.tgt_encoder_dann_rb_path)
critic = model_init(Discriminator(), params.disc_dann_rb_path)
clf = model_init(Classifier(), params.clf_dann_rb_path)
# Train models
print(
"====== Robust Training source encoder and classifier in MNIST and USPS domains ======"
)
if not (tgt_encoder.pretrained and clf.pretrained and critic.pretrained
and params.model_trained):
tgt_encoder, clf, critic = train_dann(tgt_encoder,
clf,
critic,
mnist_data_loader,
usps_data_loader,
usps_data_loader_eval,
robust=False)
# Eval target encoder on test set of target dataset
print(
"====== Evaluating classifier for encoded MNIST and USPS domains ======"
)
print("-------- MNIST domain --------")
eval_tgt_robust(tgt_encoder, clf, critic, mnist_data_loader_eval)
print("-------- USPS adaption --------")
eval_tgt_robust(tgt_encoder, clf, critic, usps_data_loader_eval)
print("====== Pseudo labeling on USPS domain ======")
pseudo_label(tgt_encoder, clf, "usps_train_pseudo", usps_data_loader)
# Init a new model
tgt_encoder = model_init(Encoder(), params.tgt_encoder_path)
clf = model_init(Classifier(), params.clf_path)
# Load pseudo labeled dataset
usps_pseudo_loader = get_usps(train=True, download=True, get_pseudo=True)
print("====== Standard training on USPS domain with pseudo labels ======")
if not (tgt_encoder.pretrained and clf.pretrained):
train_src_adda(tgt_encoder, clf, usps_pseudo_loader, mode='ADV')
print("====== Evaluating on USPS domain with real labels ======")
eval_tgt(tgt_encoder, clf, usps_data_loader_eval)
tgt_encoder = model_init(Encoder(), params.tgt_encoder_rb_path)
clf = model_init(Classifier(), params.clf_rb_path)
print("====== Robust training on USPS domain with pseudo labels ======")
if not (tgt_encoder.pretrained and clf.pretrained):
train_src_robust(tgt_encoder, clf, usps_pseudo_loader, mode='ADV')
print("====== Evaluating on USPS domain with real labels ======")
eval_tgt(tgt_encoder, clf, usps_data_loader_eval)
# eval_step = 5 # epochs
manual_seed = 8888
alpha = 0
# params for optimizing models
lr = 2e-4
params = Config()
# init random seed
init_random_seed(params.manual_seed)
# init device
device = torch.device("cuda:" + params.gpu_id if torch.cuda.is_available() else "cpu")
# load dataset
src_data_loader = get_data_loader(params.src_dataset, params.dataset_root, params.batch_size, train=True)
src_data_loader_eval = get_data_loader(params.src_dataset, params.dataset_root, params.batch_size, train=False)
tgt_data_loader = get_data_loader(params.tgt_dataset, params.dataset_root, params.batch_size, train=True)
tgt_data_loader_eval = get_data_loader(params.tgt_dataset, params.dataset_root, params.batch_size, train=False)
# load dann model
dann = init_model(net=MNISTmodel_plain(), restore=None)
# train dann model
print("Training dann model")
if not (dann.restored and params.dann_restore):
dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device)