Remove noise channels

map2map/args.py

@@ -37,9 +37,6 @@ def add_common_args(parser):
     parser.add_argument('--scale-factor', default=1, type=int,
             help='input upsampling factor for super-resolution purpose, in '
             'which case crop and pad will be taken at the original resolution')
-    parser.add_argument('--noise-chan', default=0, type=int,
-            help='input noise channels to produce the output stochasticity, '
-            'if the input does not completely determines the output')
 
     parser.add_argument('--model', required=True, type=str,
             help='model from .models')

map2map/data/fields.py

@@ -27,8 +27,6 @@ class FieldDataset(Dataset):
     the input for super-resolution, in which case `crop` and `pad` are sizes of
     the input resolution.
 
-    Noise channels can be concatenated to the input.
-
     `cache` enables data caching.
     `div_data` enables data division, useful when combined with caching.
     """
@@ -142,11 +140,6 @@ class FieldDataset(Dataset):
             for norm, x in zip(self.tgt_norms, tgt_fields):
                 norm(x)
 
-        if self.noise_chan > 0:
-            in_fields.append(
-                torch.randn((self.noise_chan,) + in_fields[0].shape[1:],
-                    dtype=torch.float32))
-
         in_fields = torch.cat(in_fields, dim=0)
         tgt_fields = torch.cat(tgt_fields, dim=0)
 

map2map/test.py

@@ -22,7 +22,6 @@ def test(args):
         crop=args.crop,
         pad=args.pad,
         scale_factor=args.scale_factor,
-        noise_chan=args.noise_chan,
         cache=args.cache,
     )
     test_loader = DataLoader(
@@ -35,7 +34,7 @@ def test(args):
     in_chan, out_chan = test_dataset.in_chan, test_dataset.tgt_chan
 
     model = getattr(models, args.model)
-    model = model(sum(in_chan) + args.noise_chan, sum(out_chan))
+    model = model(sum(in_chan), sum(out_chan))
     criterion = getattr(torch.nn, args.criterion)
     criterion = criterion()
 

map2map/train.py

@@ -66,7 +66,6 @@ def gpu_worker(local_rank, node, args):
         crop=args.crop,
         pad=args.pad,
         scale_factor=args.scale_factor,
-        noise_chan=args.noise_chan,
         cache=args.cache,
         div_data=args.div_data,
         rank=rank,
@@ -96,7 +95,6 @@ def gpu_worker(local_rank, node, args):
             crop=args.crop,
             pad=args.pad,
             scale_factor=args.scale_factor,
-            noise_chan=args.noise_chan,
             cache=args.cache,
             div_data=args.div_data,
             rank=rank,
@@ -119,7 +117,7 @@ def gpu_worker(local_rank, node, args):
     args.in_chan, args.out_chan = train_dataset.in_chan, train_dataset.tgt_chan
 
     model = getattr(models, args.model)
-    model = model(sum(args.in_chan) + args.noise_chan, sum(args.out_chan))
+    model = model(sum(args.in_chan), sum(args.out_chan))
     model.to(device)
     model = DistributedDataParallel(model, device_ids=[device],
             process_group=dist.new_group())
@@ -306,8 +304,6 @@ def train(epoch, loader, model, criterion, optimizer, scheduler,
         output = model(input)
 
         target = narrow_like(target, output)  # FIXME pad
-        if args.noise_chan > 0:
-            input = input[:, :-args.noise_chan]  # remove noise channels
         if hasattr(model, 'scale_factor') and model.scale_factor != 1:
             input = F.interpolate(input,
                     scale_factor=model.scale_factor, mode='nearest')
@@ -450,8 +446,6 @@ def validate(epoch, loader, model, criterion, adv_model, adv_criterion,
             output = model(input)
 
             target = narrow_like(target, output)  # FIXME pad
-            if args.noise_chan > 0:
-                input = input[:, :-args.noise_chan]  # remove noise channels
             if hasattr(model, 'scale_factor') and model.scale_factor != 1:
                 input = F.interpolate(input,
                         scale_factor=model.scale_factor, mode='nearest')