def cal_acc(loader, netF, netB, netC):
start_test = True
with torch.no_grad():
iter_test = iter(loader)
for i in range(len(loader)):
data = iter_test.next()
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)
_, predict = torch.max(all_output, 1)
accuracy = torch.sum(torch.squeeze(predict).float() == all_label).item() / float(all_label.size()[0])
mean_ent = torch.mean(loss.Entropy(nn.Softmax(dim=1)(all_output))).cpu().data.item()
return accuracy*100, mean_ent
def train_target(args):
dset_loaders = digit_load(args)
## set base network
if args.dset == 'u2m':
netF = network.LeNetBase().cuda()
elif args.dset == 'm2u':
netF = network.LeNetBase().cuda()
elif args.dset == 's2m':
netF = network.DTNBase().cuda()
elif args.dset == 'm2mm':
netF = network.DTNBase_c().cuda()
elif args.dset == 's2u':
netF = network.DTNBase_c().cuda()
netB = network.feat_bootleneck_c().cuda()
netC = network.feat_classifier_c().cuda()
args.modelpath = args.output_dir + '/source_F.pt'
netF.load_state_dict(torch.load(args.modelpath))
args.modelpath = args.output_dir + '/source_B.pt'
netB.load_state_dict(torch.load(args.modelpath))
args.modelpath = args.output_dir + '/source_C.pt'
netC.load_state_dict(torch.load(args.modelpath))
netC.eval()
for k, v in netC.named_parameters():
v.requires_grad = False
param_group = []
for k, v in netF.named_parameters():
param_group += [{'params': v, 'lr': args.lr}]
for k, v in netB.named_parameters():
param_group += [{'params': v, 'lr': args.lr}]
# optimizer = optim.SGD(param_group)
optimizer = optim.Adam(param_group)
optimizer = op_copy(optimizer)
max_iter = args.max_epoch * len(dset_loaders["target"])
interval_iter = len(dset_loaders["target"])
# interval_iter = max_iter // args.interval
iter_num = 0
while iter_num < max_iter:
optimizer.zero_grad()
try:
inputs_test, _, tar_idx = iter_test.next()
except:
iter_test = iter(dset_loaders["target"])
inputs_test, _, tar_idx = iter_test.next()
if inputs_test.size(0) == 1:
continue
if iter_num % interval_iter == 0 and args.cls_par > 0:
netF.eval()
netF.eval()
mem_label = obtain_label(dset_loaders['target_te'], netF, netB,
netC, args)
mem_label = torch.from_numpy(mem_label).cuda()
netF.train()
netB.train()
iter_num += 1
# lr_scheduler(optimizer, iter_num=iter_num, max_iter=max_iter)
inputs_test = inputs_test.cuda()
features_test = netB(netF(inputs_test))
outputs_test = netC(features_test)
if args.cls_par > 0:
pred = mem_label[tar_idx]
classifier_loss = args.cls_par * nn.CrossEntropyLoss()(
outputs_test, pred)
else:
classifier_loss = torch.tensor(0.0).cuda()
if args.ent:
softmax_out = nn.Softmax(dim=1)(outputs_test)
entropy_loss = torch.mean(loss.Entropy(softmax_out))
if args.gent:
msoftmax = softmax_out.mean(dim=0)
entropy_loss -= torch.sum(-msoftmax *
torch.log(msoftmax + 1e-5))
im_loss = entropy_loss * args.ent_par
classifier_loss += im_loss
optimizer.zero_grad()
classifier_loss.backward()
optimizer.step()
if iter_num % interval_iter == 0 or iter_num == max_iter:
netF.eval()
netB.eval()
acc, _ = cal_acc(dset_loaders['test'], netF, netB, netC)
log_str = 'Task: {}, Iter:{}/{}; Accuracy = {:.2f}%'.format(
args.dset, iter_num, max_iter, acc)
args.out_file.write(log_str + '\n')
args.out_file.flush()
print(log_str + '\n')
if args.issave:
torch.save(
netF.state_dict(),
osp.join(args.output_dir, "target_F_" + args.savename + ".pt"))
torch.save(
netB.state_dict(),
osp.join(args.output_dir, "target_B_" + args.savename + ".pt"))
torch.save(
netC.state_dict(),
osp.join(args.output_dir, "target_C_" + args.savename + ".pt"))
return netF, netB, netC