Remove lazy argument feeding from field dataset

map2map/data/fields.py

@@ -34,8 +34,7 @@ class FieldDataset(Dataset):
     """
     def __init__(self, in_patterns, tgt_patterns, in_norms=None, tgt_norms=None,
             augment=False, crop=None, pad=0, scale_factor=1, noise_chan=0,
-            cache=False, div_data=False, rank=None, world_size=None,
-            **kwargs):
+            cache=False, div_data=False, rank=None, world_size=None):
         in_file_lists = [sorted(glob(p)) for p in in_patterns]
         self.in_files = list(zip(* in_file_lists))
 

map2map/test.py

@@ -13,8 +13,14 @@ def test(args):
     test_dataset = FieldDataset(
         in_patterns=args.test_in_patterns,
         tgt_patterns=args.test_tgt_patterns,
+        in_norms=args.in_norms,
+        tgt_norms=args.tgt_norms,
         augment=False,
-        **vars(args),
+        crop=args.crop,
+        pad=args.pad,
+        scale_factor=args.scale_factor,
+        noise_chan=args.noise_chan,
+        cache=args.cache,
     )
     test_loader = DataLoader(
         test_dataset,

map2map/train.py

@@ -48,7 +48,17 @@ def gpu_worker(local_rank, args):
     train_dataset = FieldDataset(
         in_patterns=args.train_in_patterns,
         tgt_patterns=args.train_tgt_patterns,
-        **vars(args),
+        in_norms=args.in_norms,
+        tgt_norms=args.tgt_norms,
+        augment=args.augment,
+        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,
+        world_size=args.world_size,
     )
     if not args.div_data:
         #train_sampler = DistributedSampler(train_dataset, shuffle=True)
@@ -68,8 +78,17 @@ def gpu_worker(local_rank, args):
         val_dataset = FieldDataset(
             in_patterns=args.val_in_patterns,
             tgt_patterns=args.val_tgt_patterns,
+            in_norms=args.in_norms,
+            tgt_norms=args.tgt_norms,
             augment=False,
-            **{k: v for k, v in vars(args).items() if k != 'augment'},
+            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,
+            world_size=args.world_size,
         )
         if not args.div_data:
             #val_sampler = DistributedSampler(val_dataset, shuffle=False)