def main(args):
total_step = 100//args.EF
# set random seed
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
random.seed(args.seed)
# prepare checkpoints and log folders
if not os.path.exists(args.checkpoints_dir):
os.makedirs(args.checkpoints_dir)
if not os.path.exists(args.logs_dir):
os.makedirs(args.logs_dir)
# initialize dataset
if args.dataset == 'visda':
args.data_dir = os.path.join(args.data_dir, 'visda')
data = Visda_Dataset(root=args.data_dir, partition='train', label_flag=None)
elif args.dataset == 'office':
args.data_dir = os.path.join(args.data_dir, 'Office')
data = Office_Dataset(root=args.data_dir, partition='train', label_flag=None, source=args.source_name,
target=args.target_name)
elif args.dataset == 'home':
args.data_dir = os.path.join(args.data_dir, 'OfficeHome')
data = Home_Dataset(root=args.data_dir, partition='train', label_flag=None, source=args.source_name,
target=args.target_name)
elif args.dataset == 'visda18':
args.data_dir = os.path.join(args.data_dir, 'visda18')
data = Visda18_Dataset(root=args.data_dir, partition='train', label_flag=None)
else:
print('Unknown dataset!')
args.class_name = data.class_name
args.num_class = data.num_class
args.alpha = data.alpha
# setting experiment name
label_flag = None
selected_idx = None
args.experiment = set_exp_name(args)
logger = Logger(args)
if not args.visualization:
for step in range(total_step):
print("This is {}-th step with EF={}%".format(step, args.EF))
trainer = ModelTrainer(args=args, data=data, step=step, label_flag=label_flag, v=selected_idx, logger=logger)
# train the model
args.log_epoch = 4 + step//2
trainer.train(step, epochs= 4 + (step) * 2, step_size=args.log_epoch)
# pseudo_label
pred_y, pred_score, pred_acc = trainer.estimate_label()
# select data from target to source
selected_idx = trainer.select_top_data(pred_score)
# add new data
label_flag, data = trainer.generate_new_train_data(selected_idx, pred_y, pred_acc)
else:
# load trained weights
trainer = ModelTrainer(args=args, data=data)
trainer.load_model_weight(args.checkpoint_path)
vgg_feat, node_feat, target_labels, split = trainer.extract_feature()
visualize_TSNE(node_feat, target_labels, args.num_class, args, split)
plt.savefig('./node_tsne.png', dpi=300)
def main(args):
# Modified here
total_step = 100 // args.EF
# set random seed
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
random.seed(args.seed)
# prepare checkpoints and log folders
if not os.path.exists(args.checkpoints_dir):
os.makedirs(args.checkpoints_dir)
if not os.path.exists(args.logs_dir):
os.makedirs(args.logs_dir)
# initialize dataset
if args.dataset == 'nusimg':
args.data_dir = os.path.join(args.data_dir, 'visda')
data = NUSIMG_Dataset(root=args.data_dir,
partition='train',
label_flag=None,
source=args.source_path,
target=args.target_path)
elif args.dataset == 'office':
args.data_dir = os.path.join(args.data_dir, 'Office')
data = Office_Dataset(root=args.data_dir,
partition='train',
label_flag=None,
source=args.source_path,
target=args.target_path)
elif args.dataset == 'mrc':
data = MRC_Dataset(root=args.data_dir,
partition='train',
label_flag=None,
source=args.source_path,
target=args.target_path)
else:
print('Unknown dataset!')
args.class_name = data.class_name
args.num_class = data.num_class
args.alpha = data.alpha
# setting experiment name
label_flag = None
selected_idx = None
args.experiment = set_exp_name(args)
logger = Logger(args)
trainer = ModelTrainer(args=args,
data=data,
label_flag=label_flag,
v=selected_idx,
logger=logger)
for step in range(total_step):
print("This is {}-th step with EF={}%".format(step, args.EF))
# train the model
args.log_epoch = 5
trainer.train(epochs=24, step=step) #24
# psedo_label
pred_y, pred_score, pred_acc = trainer.estimate_label()
# select data from target to source
selected_idx = trainer.select_top_data(pred_score)
# add new data
trainer.generate_new_train_data(selected_idx, pred_y, pred_acc)
