def test12(test_loader, nets, epoch):
clf_losses = [AverageMeter() for _ in range(5)]
top1 = [AverageMeter() for _ in range(5)]
consistency_meters = [AverageMeter() for _ in range(2)]
snet = nets['snet']
tnet = nets['tnet']
snet.eval()
tnet.eval()
for idx, (img, target) in enumerate(test_loader, start=1):
img = cpu_gpu(args.cuda, img, volatile=True)
target = cpu_gpu(args.cuda, target, volatile=True)
out1_t, out2_t = tnet(img)
out1_s, out2_s = snet(img)
cls_t1 = F.cross_entropy(out1_t, target)
cls_t2 = F.cross_entropy(out2_t, target)
cls_s1 = F.cross_entropy(out1_s, target)
cls_s2 = F.cross_entropy(out2_s, target)
preds1_t = [out1_t, out1_s]
preds2_t = [out2_t, out2_s]
if 'New' in args.pseudo_label_type:
pseudo, pseudo_loss = get_pseudo_loss_new(preds1_t, preds2_t, consistency_meters, weight_clip=True)
elif 'Old' in args.pseudo_label_type:
pseudo, pseudo_loss = get_pseudo_loss(preds1_t, preds2_t, consistency_meters, softmax_pseudo_label=True)
else:
mean_t = (out1_t + out2_t) * 0.5
mean_s = (out1_s + out2_s) * 0.5
if using_soft_discrepancy:
intra_t = soft_discrepancy(out1_t, out2_t) * intra_t_ratio
intra_s = soft_discrepancy(out1_s, out2_s) * intra_s_ratio
else:
intra_t = discrepancy(out1_t, out2_t) * intra_t_ratio
intra_s = discrepancy(out1_s, out2_s) * intra_s_ratio
length = intra_t + intra_s
wt = intra_t.data / length
ws = intra_s.data / length
pseudo = wt * mean_t.detach() + ws * mean_s.detach()
pseudo_cls = F.cross_entropy(pseudo, target)
out = [out1_t, out2_t, out1_s, out2_s, pseudo]
accu = [accuracy(pred, target, topk=(1,))[0] for pred in out]
for acc, top in zip(accu, top1):
top.update(acc, img.size(0))
cls_loss = [cls_t1, cls_t2, cls_s1, cls_s2, pseudo_cls]
for loss, losses in zip(cls_loss, clf_losses):
losses.update(loss.item(), img.size(0))
result = 'Epoch:{}, cls-loss:({:.3f},{:.3f},{:.3f},{:.3f},{:.3f}), ' \
'top1:({:.4f},{:.4f},{:.4f},{:.4f},{:.4f})'. \
format(epoch, clf_losses[0].avg, clf_losses[1].avg, clf_losses[2].avg, clf_losses[3].avg, clf_losses[4].avg,
top1[0].avg, top1[1].avg, top1[2].avg, top1[3].avg, top1[4].avg)
print(result)
return [top1[0].avg, top1[1].avg, top1[2].avg, top1[3].avg, top1[4].avg]
def train(train_loader, nets, optimizers, epoch, mode=0):
clf_losses = [AverageMeter() for _ in range(5)]
intra_losses = [AverageMeter() for _ in range(2)]
inter_losses = [AverageMeter() for _ in range(4)]
top1 = [AverageMeter() for _ in range(5)]
consistency_meters = [AverageMeter() for _ in range(2)]
pseudo_losses = [AverageMeter() for _ in range(2)]
weight = [AverageMeter() for _ in range(2)]
tnet = nets['tnet']
snet = nets['snet']
tnet.train()
snet.train()
t_fea_opt = optimizers['t_fea_opt']
t_clf_opt = optimizers['t_clf_opt']
s_fea_opt = optimizers['s_fea_opt']
s_clf_opt = optimizers['s_clf_opt']
for idx, (img1, img2, target) in enumerate(train_loader, start=1):
img1 = cpu_gpu(args.cuda, img1, volatile=False)
img2 = cpu_gpu(args.cuda, img2, volatile=False)
target = cpu_gpu(args.cuda, target, volatile=False)
out1_t, out2_t = tnet(img1, img2)
out1_s, out2_s = snet(img1, img2)
cls_t1 = F.cross_entropy(out1_t, target)
cls_t2 = F.cross_entropy(out2_t, target)
cls_s1 = F.cross_entropy(out1_s, target)
cls_s2 = F.cross_entropy(out2_s, target)
loss1 = cls_t1 + cls_t2
reset_grad([t_fea_opt, t_clf_opt])
loss1.backward()
set_step([t_fea_opt, t_clf_opt])
loss2 = cls_s1 + cls_s2
reset_grad([s_fea_opt, s_clf_opt])
loss2.backward()
set_step([s_fea_opt, s_clf_opt])
for kk in range(inter_intra_step):
out1_t, out2_t = tnet(img1, img2)
out1_s, out2_s = snet(img1, img2)
cls_t1 = F.cross_entropy(out1_t, target)
cls_t2 = F.cross_entropy(out2_t, target)
cls_s1 = F.cross_entropy(out1_s, target)
cls_s2 = F.cross_entropy(out2_s, target)
if using_soft_discrepancy:
intra_t = soft_discrepancy(out1_t,
out2_t) * intra_t_ratio # a value
intra_s = soft_discrepancy(out1_s, out2_s) * intra_s_ratio
else:
intra_t = discrepancy(out1_t, out2_t) * intra_t_ratio
intra_s = discrepancy(out1_s, out2_s) * intra_s_ratio
loss_1 = intra_t
reset_grad([t_fea_opt])
loss_1.backward()
set_step([t_fea_opt])
loss_2 = intra_s
reset_grad([s_fea_opt])
loss_2.backward()
set_step([s_fea_opt])
for jj in range(pseudo_step):
out1_t, out2_t = tnet(img1, img2)
out1_s, out2_s = snet(img1, img2)
if inter_KL:
inter_t1 = F.kl_div(F.log_softmax(out1_t / 3.0, dim=1),
F.softmax(out1_s.detach() / 3.0, dim=1),
reduction='mean') * (
3.0 * 3.0) / img1.size(0) * inter_ratio
inter_t2 = F.kl_div(F.log_softmax(out2_t / 3.0, dim=1),
F.softmax(out2_s.detach() / 3.0, dim=1),
reduction='mean') * (
3.0 * 3.0) / img1.size(0) * inter_ratio
inter_s1 = F.kl_div(F.log_softmax(out1_s / 3.0, dim=1),
F.softmax(out1_t.detach() / 3.0, dim=1),
reduction='mean') * (
3.0 * 3.0) / img1.size(0) * inter_ratio
inter_s2 = F.kl_div(F.log_softmax(out2_s / 3.0, dim=1),
F.softmax(out2_t.detach() / 3.0, dim=1),
reduction='mean') * (
3.0 * 3.0) / img1.size(0) * inter_ratio
elif using_soft_discrepancy:
inter_t1 = soft_discrepancy(out1_t,
out1_s.detach()) * inter_ratio
inter_t2 = soft_discrepancy(out2_t,
out2_s.detach()) * inter_ratio
inter_s1 = soft_discrepancy(out1_s,
out1_t.detach()) * inter_ratio
inter_s2 = soft_discrepancy(out2_s,
out2_t.detach()) * inter_ratio
else:
inter_t1 = discrepancy(out1_t, out1_s.detach()) * inter_ratio
inter_t2 = discrepancy(out2_t, out2_s.detach()) * inter_ratio
inter_s1 = discrepancy(out1_s, out1_t.detach()) * inter_ratio
inter_s2 = discrepancy(out2_s, out2_t.detach()) * inter_ratio
if using_soft_discrepancy:
intra_t = soft_discrepancy(out1_t,
out2_t) * intra_t_ratio # a value
intra_s = soft_discrepancy(out1_s, out2_s) * intra_s_ratio
else:
intra_t = discrepancy(out1_t, out2_t) * intra_t_ratio
intra_s = discrepancy(out1_s, out2_s) * intra_s_ratio
intra_t_e = torch.exp(-intra_t)
intra_s_e = torch.exp(-intra_s)
length = intra_t_e + intra_s_e
wt = intra_t_e / length
ws = intra_s_e / length
mean_t = (out1_t + out2_t) * 0.5
mean_s = (out1_s + out2_s) * 0.5
pseudo = wt * mean_t.detach() + ws * mean_s.detach()
kl_t1 = F.kl_div(
F.log_softmax(out1_t / 3.0, dim=1),
F.softmax(pseudo.detach() / 3.0, dim=1),
reduction='mean') * (3.0 * 3.0) / img1.size(0) * kl_loss_t
kl_t2 = F.kl_div(
F.log_softmax(out2_t / 3.0, dim=1),
F.softmax(pseudo.detach() / 3.0, dim=1),
reduction='mean') * (3.0 * 3.0) / img1.size(0) * kl_loss_t
kl_s1 = F.kl_div(
F.log_softmax(out1_s / 3.0, dim=1),
F.softmax(pseudo.detach() / 3.0, dim=1),
reduction='mean') * (3.0 * 3.0) / img1.size(0) * kl_loss_s
kl_s2 = F.kl_div(
F.log_softmax(out2_s / 3.0, dim=1),
F.softmax(pseudo.detach() / 3.0, dim=1),
reduction='mean') * (3.0 * 3.0) / img1.size(0) * kl_loss_s
pseudo_loss_t = kl_t1 + kl_t2
pseudo_loss_s = kl_s1 + kl_s2
if pseudo_burnin == 'exp':
beta = 2 / (1 + math.exp(-10 * epoch / args.epochs)) - 1
elif pseudo_burnin == 'linear':
beta = min(1, 1.25 * (epoch / args.epochs))
else:
assert pseudo_burnin == 'none'
beta = 1
pseudo_cls = F.cross_entropy(pseudo, target)
pseudo_loss_t = pseudo_loss_t * beta * pseudo_loss_ratio
pseudo_loss_s = pseudo_loss_s * beta * pseudo_loss_ratio
loss_2t = inter_t1 + inter_t2 + pseudo_loss_t
reset_grad([t_clf_opt, t_fea_opt])
loss_2t.backward()
set_step([t_clf_opt, t_fea_opt])
loss_2s = inter_s1 + inter_s2 + pseudo_loss_s
reset_grad([s_clf_opt, s_fea_opt])
loss_2s.backward()
set_step([s_clf_opt, s_fea_opt])
out = [out1_t, out2_t, out1_s, out2_s, pseudo]
accu = [accuracy(pred, target, topk=(1, ))[0] for pred in out]
for acc, top in zip(accu, top1):
top.update(acc, img1.size(0))
cls_loss = [cls_t1, cls_t2, cls_s1, cls_s2, pseudo_cls]
for loss, losses in zip(cls_loss, clf_losses):
losses.update(loss.item(), img1.size(0))
intra_list = [intra_t, intra_s]
for intra, intra_meter in zip(intra_list, intra_losses):
intra_meter.update(intra.item(), img1.size(0))
inter_list = [inter_t1, inter_t2, inter_s1, inter_s2]
for inter, inter_meter in zip(inter_list, inter_losses):
inter_meter.update(inter.item(), img1.size(0))
pseudo_losses[0].update(pseudo_loss_t, img1.size(0))
pseudo_losses[1].update(pseudo_loss_s, img1.size(0))
weight[0].update(wt.item())
weight[1].update(ws.item())
if idx % args.print_freq == 0:
result = 'Epoch:{}, cls-loss:({:.3f},{:.3f},{:.3f},{:.3f},{:.3f}), ' \
'intra-loss:({:.3f},{:.3f}), inter-loss:({:.3f},{:.3f},{:.3f},{:.3f}), ' \
'pseudo-loss:({:.3f},{:.3f}), weight:({:.4f},{:.4f})'.format(
epoch, clf_losses[0].avg, clf_losses[1].avg, clf_losses[2].avg, clf_losses[3].avg, clf_losses[4].avg,
intra_losses[0].avg, intra_losses[1].avg,
inter_losses[0].avg, inter_losses[1].avg, inter_losses[2].avg, inter_losses[3].avg,
pseudo_losses[0].avg, pseudo_losses[1].avg, weight[0].avg, weight[1].avg)
print(result)
result1 = 'Epoch:{}, top1:({:.4f},{:.4f},{:.4f},{:.4f},{:.4f})'.format(
epoch, top1[0].avg, top1[1].avg, top1[2].avg, top1[3].avg,
top1[4].avg)
print(result1)
def train(train_loader, nets, optimizers, epoch):
clf_losses = [AverageMeter() for _ in range(5)]
intra_losses = [AverageMeter() for _ in range(2)]
inter_losses = [AverageMeter() for _ in range(2)]
top1 = [AverageMeter() for _ in range(5)]
consistency_meters = [AverageMeter() for _ in range(2)]
pseudo_losses = AverageMeter()
tnet = nets['tnet']
snet = nets['snet']
tnet.train()
snet.train()
t_fea_opt = optimizers['t_fea_opt']
t_clf_opt = optimizers['t_clf_opt']
s_fea_opt = optimizers['s_fea_opt']
s_clf_opt = optimizers['s_clf_opt']
for idx, (img, target) in enumerate(train_loader, start=1):
img = cpu_gpu(args.cuda, img, volatile=False)
target = cpu_gpu(args.cuda, target, volatile=False)
out1_t, out2_t = tnet(img)
out1_s, out2_s = snet(img)
cls_t1 = F.cross_entropy(out1_t, target)
cls_t2 = F.cross_entropy(out2_t, target)
cls_s1 = F.cross_entropy(out1_s, target)
cls_s2 = F.cross_entropy(out2_s, target)
loss1 = cls_t1 + cls_t2
reset_grad([t_fea_opt, t_clf_opt])
loss1.backward()
set_step([t_fea_opt, t_clf_opt])
loss2 = cls_s1 + cls_s2
reset_grad([s_fea_opt, s_clf_opt])
loss2.backward()
set_step([s_fea_opt, s_clf_opt])
# inter_intra to update F
for kk in range(inter_intra_step):
out1_t, out2_t = tnet(img)
out1_s, out2_s = snet(img)
cls_t1 = F.cross_entropy(out1_t, target)
cls_t2 = F.cross_entropy(out2_t, target)
cls_s1 = F.cross_entropy(out1_s, target)
cls_s2 = F.cross_entropy(out2_s, target)
if inter_KL:
inter_t1 = F.kl_div(F.log_softmax(out1_t / 3.0, dim=1), F.softmax(out1_s / 3.0, dim=1), reduction='mean') * (3.0 * 3.0) / img.size(0) * inter_ratio
inter_t2 = F.kl_div(F.log_softmax(out2_t / 3.0, dim=1), F.softmax(out2_s / 3.0, dim=1), reduction='mean') * (3.0 * 3.0) / img.size(0) * inter_ratio
elif using_soft_discrepancy:
inter_t1 = soft_discrepancy(out1_t, out1_s) * inter_ratio
inter_t2 = soft_discrepancy(out2_t, out2_s) * inter_ratio
else:
inter_t1 = discrepancy(out1_t, out1_s) * inter_ratio
inter_t2 = discrepancy(out2_t, out2_s) * inter_ratio
if inter_burnin == 'exp':
beta = 2 / (1 + math.exp(-10 * epoch / args.epochs)) - 1
elif inter_burnin == 'linear':
beta = min(1, 1.25 * (epoch / args.epochs))
else:
assert inter_burnin == 'none'
beta = 1
inter_t1 = inter_t1 * beta
inter_t2 = inter_t2 * beta
if using_soft_discrepancy:
intra_t = soft_discrepancy(out1_t, out2_t) * intra_t_ratio # a value
intra_s = soft_discrepancy(out1_s, out2_s) * intra_s_ratio
else:
intra_t = discrepancy(out1_t, out2_t) * intra_t_ratio
intra_s = discrepancy(out1_s, out2_s) * intra_s_ratio
if args.using_intra1:
loss_1 = inter_t1 + inter_t2 + intra_t + intra_s + intra_1
else:
loss_1 = inter_t1 + inter_t2 + intra_t + intra_s
reset_grad([t_fea_opt, s_fea_opt])
loss_1.backward()
set_step([t_fea_opt, s_fea_opt])
# pseudo to update C
for jj in range(inter_intra_step):
out1_t, out2_t = tnet(img)
out1_s, out2_s = snet(img)
preds1_t = [out1_t, out1_s]
preds2_t = [out2_t, out2_s]
if 'New' in args.pseudo_label_type:
pseudo, pseudo_loss = get_pseudo_loss_new(preds1_t, preds2_t, consistency_meters, smooth=0.1, weight_clip=True)
elif 'Old' in args.pseudo_label_type:
pseudo, pseudo_loss = get_pseudo_loss(preds1_t, preds2_t, consistency_meters, softmax_pseudo_label=True)
else:
if using_soft_discrepancy:
intra_t = soft_discrepancy(out1_t, out2_t) * intra_t_ratio # a value
intra_s = soft_discrepancy(out1_s, out2_s) * intra_s_ratio
else:
intra_t = discrepancy(out1_t, out2_t) * intra_t_ratio
intra_s = discrepancy(out1_s, out2_s) * intra_s_ratio
length = intra_t + intra_s
wt = intra_t / length
ws = intra_s / length
mean_t = (out1_t + out2_t) * 0.5
mean_s = (out1_s + out2_s) * 0.5
pseudo = wt * mean_t + ws * mean_s
kl_t1 = F.kl_div(F.log_softmax(out1_t / 3.0, dim=1), F.softmax(pseudo.detach() / 3.0, dim=1),
reduction='mean') * (3.0 * 3.0) / img.size(0) * kl_loss_t
kl_t2 = F.kl_div(F.log_softmax(out2_t / 3.0, dim=1), F.softmax(pseudo.detach() / 3.0, dim=1),
reduction='mean') * (3.0 * 3.0) / img.size(0) * kl_loss_t
kl_s1 = F.kl_div(F.log_softmax(out1_s / 3.0, dim=1), F.softmax(pseudo.detach() / 3.0, dim=1),
reduction='mean') * (3.0 * 3.0) / img.size(0) * kl_loss_s
kl_s2 = F.kl_div(F.log_softmax(out2_s / 3.0, dim=1), F.softmax(pseudo.detach() / 3.0, dim=1),
reduction='mean') * (3.0 * 3.0) / img.size(0) * kl_loss_s
pseudo_loss = kl_t1 + kl_t2 + kl_s1 + kl_s2
if pseudo_burnin == 'exp':
beta = 2 / (1 + math.exp(-10 * epoch / args.epochs)) - 1
elif pseudo_burnin == 'linear':
beta = min(1, 1.25 * (epoch / args.epochs))
else:
assert pseudo_burnin == 'none'
beta = 1
pseudo_cls = F.cross_entropy(pseudo, target)
pseudo_loss = pseudo_loss * beta * pseudo_loss_ratio
reset_grad([t_clf_opt, s_clf_opt])
pseudo_loss.backward()
set_step([t_clf_opt, s_clf_opt])
out = [out1_t, out2_t, out1_s, out2_s, pseudo]
accu = [accuracy(pred, target, topk=(1,))[0] for pred in out]
for acc, top in zip(accu, top1):
top.update(acc, img.size(0))
cls_loss = [cls_t1, cls_t2, cls_s1, cls_s2, pseudo_cls]
for loss, losses in zip(cls_loss, clf_losses):
losses.update(loss.item(), img.size(0))
intra_list = [intra_t, intra_s]
for intra, intra_meter in zip(intra_list, intra_losses):
intra_meter.update(intra.item(), img.size(0))
inter_list = [inter_t1, inter_t2]
for inter, inter_meter in zip(inter_list, inter_losses):
inter_meter.update(inter.item(), img.size(0))
pseudo_losses.update(pseudo_loss, img.size(0))
if idx % args.print_freq == 0:
result = 'Epoch:{}, cls-loss:({:.3f},{:.3f},{:.3f},{:.3f},{:.3f}), ' \
'intra-loss:({:.3f},{:.3f}), inter-loss:({:.3f},{:.3f}), ' \
'pseudo-loss:({:.3f})'.format(
epoch, clf_losses[0].avg, clf_losses[1].avg, clf_losses[2].avg, clf_losses[3].avg, clf_losses[4].avg,
intra_losses[0].avg, intra_losses[1].avg,
inter_losses[0].avg, inter_losses[1].avg, pseudo_losses.avg)
print(result)
result1 = 'Epoch:{}, top1:({:.4f},{:.4f},{:.4f},{:.4f},{:.4f})'.format(
epoch, top1[0].avg, top1[1].avg, top1[2].avg, top1[3].avg, top1[4].avg)
print(result1)