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Add simple label smoothing by --adv-real-label

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Yin Li 2020-02-13 16:32:47 -05:00
parent d2840f01b0
commit 1818e11265
2 changed files with 6 additions and 4 deletions
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2
map2map/args.py
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@ -78,6 +78,8 @@ def add_train_args(parser):
help='enable conditional GAN') help='enable conditional GAN')
parser.add_argument('--adv-start', default=0, type=int, parser.add_argument('--adv-start', default=0, type=int,
help='epoch to start adversarial training') help='epoch to start adversarial training')
parser.add_argument('--adv-real-label', default=1, type=float,
help='label for real samples, e.g. 0.9 for label smoothing')
parser.add_argument('--loss-fraction', default=0.5, type=float, parser.add_argument('--loss-fraction', default=0.5, type=float,
help='final fraction of loss (vs adv-loss)') help='final fraction of loss (vs adv-loss)')
parser.add_argument('--loss-halflife', default=20, type=float, parser.add_argument('--loss-halflife', default=20, type=float,

8
map2map/train.py
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@ -274,8 +274,8 @@ def train(epoch, loader, model, criterion, optimizer, scheduler,
# loss_adv: generator (model) adversarial loss # loss_adv: generator (model) adversarial loss
# adv_loss: discriminator (adv_model) loss # adv_loss: discriminator (adv_model) loss
epoch_loss = torch.zeros(5, dtype=torch.float64, device=device) epoch_loss = torch.zeros(5, dtype=torch.float64, device=device)
real = torch.ones(1, dtype=torch.float32, device=device) fake = torch.zeros([1], dtype=torch.float32, device=device)
fake = torch.zeros(1, dtype=torch.float32, device=device) real = torch.full([1], args.adv_real_label, dtype=torch.float32, device=device)
for i, (input, target) in enumerate(loader): for i, (input, target) in enumerate(loader):
input = input.to(device, non_blocking=True) input = input.to(device, non_blocking=True)
@ -383,8 +383,8 @@ def validate(epoch, loader, model, criterion, adv_model, adv_criterion,
world_size = dist.get_world_size() world_size = dist.get_world_size()
epoch_loss = torch.zeros(5, dtype=torch.float64, device=device) epoch_loss = torch.zeros(5, dtype=torch.float64, device=device)
fake = torch.zeros(1, dtype=torch.float32, device=device) fake = torch.zeros([1], dtype=torch.float32, device=device)
real = torch.ones(1, dtype=torch.float32, device=device) real = torch.ones([1], dtype=torch.float32, device=device)
with torch.no_grad(): with torch.no_grad():
for input, target in loader: for input, target in loader: