params += [{'params': [value], 'lr': args.multi,
'weight_decay': 0.0005}]
else:
params += [{'params': [value], 'lr': args.multi * 10,
'weight_decay': 0.0005}]
if "resnet" in args.net:
F1 = Predictor_deep(num_class=len(class_list),
inc=inc)
else:
F1 = Predictor(num_class=len(class_list), inc=inc,
temp=args.T)
weights_init(F1)
lr = args.lr
G.cuda()
F1.cuda()
im_data_s = torch.FloatTensor(1)
im_data_t = torch.FloatTensor(1)
im_data_tu = torch.FloatTensor(1)
gt_labels_s = torch.LongTensor(1)
gt_labels_t = torch.LongTensor(1)
sample_labels_t = torch.LongTensor(1)
sample_labels_s = torch.LongTensor(1)
im_data_s = im_data_s.cuda()
im_data_t = im_data_t.cuda()
im_data_tu = im_data_tu.cuda()
gt_labels_s = gt_labels_s.cuda()
gt_labels_t = gt_labels_t.cuda()
sample_labels_t = sample_labels_t.cuda()
'weight_decay': 0.0005
}]
## F1: Source-based Classifier
## F1: Target-based Classifier
if "resnet" in args.net:
F1 = Predictor_deep(num_class=len(class_list), inc=inc)
F2 = Predictor_deep(num_class=4, inc=inc)
else:
#### F1: Semantic Classifier; F2: Rotation Classifier
F1 = Predictor(num_class=len(class_list), inc=inc, temp=args.T)
F2 = Predictor(num_class=4, inc=inc, temp=args.T)
weights_init(F1)
lr = args.lr
G.cuda()
F1.cuda()
F2.cuda()
if os.path.exists(args.checkpath) == False:
os.mkdir(args.checkpath)
def train():
G.train()
F1.train()
F2.train()
optimizer_g = optim.SGD(params,
momentum=0.9,
weight_decay=0.0005,
nesterov=True)
optimizer_f1 = optim.SGD(list(F1.parameters()),
