use randomsampler in train_dataloader

alonet/raft/data_modules/chairs2raft.py

@@ -1,4 +1,4 @@
-from torch.utils.data import SequentialSampler
+from torch.utils.data import SequentialSampler, RandomSampler
 import pytorch_lightning as pl
 
 from alodataset import FlyingChairs2Dataset, Split
@@ -13,7 +13,7 @@ def __init__(self, args):
     def train_dataloader(self):
         split = Split.VAL if self.train_on_val else Split.TRAIN
         dataset = FlyingChairs2Dataset(split=split, transform_fn=self.train_transform, sample=self.sample)
-        sampler = SequentialSampler if self.sequential else None
+        sampler = SequentialSampler if self.sequential else RandomSampler
         return dataset.train_loader(batch_size=self.batch_size, num_workers=self.num_workers, sampler=sampler)
 
     def val_dataloader(self):

alonet/raft/data_modules/chairssdhom2raft.py

@@ -1,4 +1,4 @@
-from torch.utils.data import SequentialSampler
+from torch.utils.data import SequentialSampler, RandomSampler
 
 from alodataset import ChairsSDHomDataset, Split
 from alonet.raft.data_modules import Data2RAFT
@@ -12,7 +12,7 @@ def __init__(self, args):
     def train_dataloader(self):
         split = Split.VAL if self.train_on_val else Split.TRAIN
         dataset = ChairsSDHomDataset(split=split, transform_fn=self.train_transform, sample=self.sample)
-        sampler = SequentialSampler if self.sequential else None
+        sampler = SequentialSampler if self.sequential else RandomSampler
         return dataset.train_loader(batch_size=self.batch_size, num_workers=self.num_workers, sampler=sampler)
 
     def val_dataloader(self):

alonet/raft/data_modules/sintel2raft.py

@@ -1,4 +1,4 @@
-from torch.utils.data import SequentialSampler
+from torch.utils.data import SequentialSampler, RandomSampler
 
 # import pytorch_lightning as pl
 
@@ -24,7 +24,7 @@ def train_dataloader(self):
             transform_fn=lambda f: self.train_transform(f["left"]),
         )
 
-        sampler = SequentialSampler if self.sequential else None
+        sampler = SequentialSampler if self.sequential else RandomSampler
         return dataset.train_loader(batch_size=self.batch_size, num_workers=self.num_workers, sampler=sampler)
 
     def val_dataloader(self):

alonet/raft/data_modules/things2raft.py

@@ -1,4 +1,4 @@
-from torch.utils.data import SequentialSampler
+from torch.utils.data import SequentialSampler, RandomSampler
 
 # import pytorch_lightning as pl
 
@@ -42,7 +42,7 @@ def train_dataloader(self):
 
         dataset = MergeDataset(datasets)
 
-        sampler = SequentialSampler if self.sequential else None
+        sampler = SequentialSampler if self.sequential else RandomSampler
         return dataset.train_loader(batch_size=self.batch_size, num_workers=self.num_workers, sampler=sampler)
 
     def val_dataloader(self):