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

map2map/args.py

@@ -78,6 +78,8 @@ def add_train_args(parser):
             help='enable conditional GAN')
     parser.add_argument('--adv-start', default=0, type=int,
             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,
             help='final fraction of loss (vs adv-loss)')
     parser.add_argument('--loss-halflife', default=20, type=float,

map2map/train.py

@@ -274,8 +274,8 @@ def train(epoch, loader, model, criterion, optimizer, scheduler,
     # loss_adv: generator (model) adversarial loss
     # adv_loss: discriminator (adv_model) loss
     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):
         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()
 
     epoch_loss = torch.zeros(5, dtype=torch.float64, device=device)
-    fake = torch.zeros(1, dtype=torch.float32, device=device)
-    real = torch.ones(1, dtype=torch.float32, device=device)
+    fake = torch.zeros([1], dtype=torch.float32, device=device)
+    real = torch.ones([1], dtype=torch.float32, device=device)
 
     with torch.no_grad():
         for input, target in loader: