format and lint hcs_ram

viscy/data/hcs_ram.py

@@ -6,6 +6,7 @@
 
 import numpy as np
 import torch
+import torch.distributed as dist
 from iohub.ngff import Position, open_ome_zarr
 from lightning.pytorch import LightningDataModule
 from monai.data import set_track_meta
@@ -19,11 +20,9 @@
 from torch import Tensor
 from torch.utils.data import DataLoader, Dataset
 
+from viscy.data.distributed import ShardedDistributedSampler
 from viscy.data.hcs import _read_norm_meta
 from viscy.data.typing import ChannelMap, DictTransform, Sample
-from viscy.data.distributed import ShardedDistributedSampler
-from torch.distributed import get_rank
-import torch.distributed as dist
 
 _logger = logging.getLogger("lightning.pytorch")
 
@@ -72,10 +71,12 @@ def _collate_samples(batch: Sequence[Sample]) -> Sample:
         collated[key] = collate_meta_tensor(data)
     return collated
 
+
 def is_ddp_enabled() -> bool:
     """Check if distributed data parallel (DDP) is initialized."""
     return dist.is_available() and dist.is_initialized()
 
+
 class CachedDataset(Dataset):
     """
     A dataset that caches the data in RAM.
@@ -341,7 +342,7 @@ def val_dataloader(self) -> DataLoader:
         else:
             sampler = None
             _logger.info("Using standard sampler for non-distributed validation")
-    
+
         return DataLoader(
             self.val_dataset,
             batch_size=self.batch_size,
@@ -350,5 +351,5 @@ def val_dataloader(self) -> DataLoader:
             pin_memory=True,
             shuffle=False,
             timeout=self.timeout,
-            sampler=sampler
+            sampler=sampler,
         )