feat(cls/shufflenet) use native infinite sampler (#9)

official/vision/classification/shufflenet/train.py

@@ -112,16 +112,6 @@ def get_parameters(model):
     return groups
 
 
-def infinite_iter(loader):
-    iterator = iter(loader)
-    while True:
-        try:
-            yield next(iterator)
-        except StopIteration:
-            iterator = iter(loader)
-            yield next(iterator)
-
-
 def worker(rank, world_size, args):
     if world_size > 1:
         # Initialize distributed process group
@@ -174,9 +164,9 @@ def worker(rank, world_size, args):
     # Build train and valid datasets
     logger.info("preparing dataset..")
     train_dataset = data.dataset.ImageNet(args.data, train=True)
-    train_sampler = data.RandomSampler(
+    train_sampler = data.Infinite(data.RandomSampler(
         train_dataset, batch_size=args.batch_size, drop_last=True
-    )
+    ))
     train_queue = data.DataLoader(
         train_dataset,
         sampler=train_sampler,
@@ -193,7 +183,6 @@ def worker(rank, world_size, args):
         ),
         num_workers=args.workers,
     )
-    train_queue = infinite_iter(train_queue)
 
     valid_dataset = data.dataset.ImageNet(args.data, train=False)
     valid_sampler = data.SequentialSampler(