lint

mmcls/core/utils/__init__.py

@@ -2,4 +2,6 @@
 from .dist_utils import DistOptimizerHook, allreduce_grads, sync_random_seed
 from .misc import multi_apply
 
-__all__ = ['allreduce_grads', 'DistOptimizerHook', 'multi_apply', 'sync_random_seed']
+__all__ = [
+    'allreduce_grads', 'DistOptimizerHook', 'multi_apply', 'sync_random_seed'
+]

mmcls/core/utils/dist_utils.py

@@ -95,4 +95,4 @@ def sync_random_seed(seed=None, device='cuda'):
     else:
         random_num = torch.tensor(0, dtype=torch.int32, device=device)
     dist.broadcast(random_num, src=0)
-    return random_num.item()
+    return random_num.item()

mmcls/datasets/builder.py

@@ -104,7 +104,8 @@ def build_dataloader(dataset,
         sampler = build_sampler(
             sampler_cfg,
             default_args=dict(
-                dataset=dataset, num_replicas=world_size, rank=rank, seed=seed))
+                dataset=dataset, num_replicas=world_size, rank=rank,
+                seed=seed))
     # Default sampler logic
     elif dist:
         sampler = build_sampler(

mmcls/datasets/samplers/repeat_aug.py

@@ -21,17 +21,15 @@ class RepeatAugSampler(Sampler):
     Copyright (c) 2015-present, Facebook, Inc.
     """
 
-    def __init__(
-        self,
-        dataset,
-        num_replicas=None,
-        rank=None,
-        shuffle=True,
-        num_repeats=3,
-        selected_round=256,
-        selected_ratio=0,
-        seed=0
-    ):
+    def __init__(self,
+                 dataset,
+                 num_replicas=None,
+                 rank=None,
+                 shuffle=True,
+                 num_repeats=3,
+                 selected_round=256,
+                 selected_ratio=0,
+                 seed=0):
         default_rank, default_world_size = get_dist_info()
         rank = default_rank if rank is None else rank
         num_replicas = (