if torch.cuda.is_available():
model_g1.cuda()
model_g2.cuda()
model_f1.cuda()
weight = weight.cuda()
criterion = CrossEntropyLoss2d(weight)
configure(args.tflog_dir, flush_secs=5)
model_g1.train()
model_g2.train()
model_f1.train()
if args.fix_bn:
print(emphasize_str("BN layers are NOT trained!"))
fix_batchnorm_when_training(model_g1)
fix_batchnorm_when_training(model_g2)
fix_batchnorm_when_training(model_f1)
# check_training(model)
for epoch in range(start_epoch, args.epochs):
epoch_loss = 0
for ind, (images, labels) in tqdm(enumerate(train_loader)):
imgs = Variable(images)
lbls = Variable(labels)
if torch.cuda.is_available():
imgs, lbls = imgs.cuda(), lbls.cuda()
# update generator and classifiers by source samples
model_g_3ch.train()
model_g_1ch.train()
model_f1.train()
model_f2.train()
if args.no_dropout:
print("NO DROPOUT")
fix_dropout_when_training(model_g_3ch)
fix_dropout_when_training(model_g_1ch)
fix_dropout_when_training(model_f1)
fix_dropout_when_training(model_f2)
if args.fix_bn:
print(emphasize_str("BN layers are NOT trained!"))
fix_batchnorm_when_training(model_g_3ch)
fix_batchnorm_when_training(model_g_1ch)
fix_batchnorm_when_training(model_f1)
fix_batchnorm_when_training(model_f2)
for epoch in range(args.epochs):
d_loss_per_epoch = 0
c_loss_per_epoch = 0
for ind, (source, target) in tqdm.tqdm(enumerate(train_loader)):
src_imgs, src_lbls = Variable(source[0]), Variable(source[1])
tgt_imgs = Variable(target[0])
if torch.cuda.is_available():
src_imgs, src_lbls, tgt_imgs = src_imgs.cuda(), src_lbls.cuda(
), tgt_imgs.cuda()
'pin_memory': True
} if torch.cuda.is_available() else {}
train_loader = torch.utils.data.DataLoader(src_dataset,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
if torch.cuda.is_available():
model.cuda()
configure(args.tflog_dir, flush_secs=5)
model.train()
if args.fix_bn:
print(emphasize_str("BN layers are NOT trained!"))
fix_batchnorm_when_training(model)
# check_training(model)
for epoch in range(start_epoch, args.epochs):
epoch_loss = 0
epoch_base_loss = 0
epoch_uncertain_loss = 0
for ind, (images, labels) in tqdm(enumerate(train_loader)):
imgs = Variable(images)
lbls = Variable(labels)
if torch.cuda.is_available():
imgs, lbls = imgs.cuda(), lbls.cuda()
# update generator and classifiers by source samples
if torch.cuda.is_available():
model_enc.cuda()
model_dec.cuda()
weight = weight.cuda()
model_enc.train()
model_dec.train()
if args.no_dropout:
print("NO DROPOUT")
fix_dropout_when_training(model_enc)
fix_dropout_when_training(model_dec)
if args.fix_bn:
print(emphasize_str("BN layers are NOT trained!"))
fix_batchnorm_when_training(model_enc)
fix_batchnorm_when_training(model_dec)
for epoch in range(start_epoch, args.epochs):
c_loss_per_epoch = 0
d_loss_per_epoch = 0
src_semseg_loss_per_epoch = 0
src_boundary_loss_per_epoch = 0
tgt_psuedo_boundary_loss_per_epoch = 0
src_extra_boundary_loss_per_epoch = 0
for ind, (source, target) in tqdm.tqdm(enumerate(train_loader)):
src_imgs, src_gt_semseg = Variable(source[0]), Variable(source[1])
tgt_imgs = Variable(target[0])
model_f.cuda()
model_d.cuda()
weight = weight.cuda()
criterion = CrossEntropyLoss2d(weight)
criterion_d = nn.CrossEntropyLoss()
ploter = LinePlotter()
configure(tflog_dir, flush_secs=5)
model_g.train()
model_f.train()
model_d.train()
if args.fix_bn:
print(emphasize_str("BN layers are NOT trained!"))
fix_batchnorm_when_training(model_g)
fix_batchnorm_when_training(model_f)
fix_batchnorm_when_training(model_d)
src_domain_lbl = Variable(torch.ones(args.batch_size).long())
tgt_domain_lbl = Variable(torch.zeros(args.batch_size).long())
for epoch in range(args.start_epoch, args.epochs):
d_loss_per_epoch = 0
c_loss_per_epoch = 0
for ind, (source, target) in tqdm.tqdm(enumerate(train_loader)):
src_imgs, src_lbls = Variable(source[0]), Variable(source[1])
tgt_imgs = Variable(target[0])
if torch.cuda.is_available():
