[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

UNETR/BTCV/config.py

@@ -1,3 +1,3 @@
 NIFTI_DATA_ROOT = 'data/images' # nifti image directory
 NIFTI_LABEL_ROOT = 'data/labels' # nifti label directory
-PREDICT_DATA_ROOT = 'data/predict' # predict image directory
+PREDICT_DATA_ROOT = 'data/predict' # predict image directory

UNETR/BTCV/dataset/customDataset.py

@@ -23,9 +23,9 @@ def collate_fn(batch):
 
         images = torch.stack(images, dim=0)
         labels = torch.stack(labels, dim=0)
-        
+
         return [torch.Tensor(images), torch.Tensor(labels)]
-    
+
     return collate_fn
 
 def getDatasetLoader(args):
@@ -80,7 +80,7 @@ def getDatasetLoader(args):
                 transforms.ToTensord(keys=["image", "label"]),
             ]
         )
-    
+
     trainDataset = Dataset(data=trainDicts, transform=train_transform)
     valDataset = Dataset(data=valDicts, transform=val_transform)
     trainLoader = DataLoader(trainDataset,batch_size=args.batch_size,shuffle=True,num_workers=args.workers, collate_fn=_get_collate_fn(isTrain=True))

UNETR/BTCV/main.py

@@ -104,7 +104,7 @@ def main():
         print("Found total gpus", args.ngpus_per_node)
         args.world_size = args.ngpus_per_node * args.world_size
         mp.spawn(main_worker, nprocs=args.ngpus_per_node, args=(args,))
-    else:   
+    else:
         main_worker(gpu=0, args=args)
 
 def main_worker(gpu, args):
@@ -121,7 +121,7 @@ def main_worker(gpu, args):
     torch.backends.cudnn.benchmark = True
     args.test_mode = False
     loader = get_loader(args) if args.btcv else getDatasetLoader(args)
-    
+
     print(args.rank, " gpu", args.gpu)
     if args.rank == 0:
         print("Batch size is:", args.batch_size, "epochs", args.max_epochs)

UNETR/BTCV/requirements.txt

@@ -3,4 +3,4 @@ numpy==2.3.2
 opencv_python
 simpleitk==2.5.2
 tensorboardx==2.6.4
-torch
+torch

UNETR/BTCV/test.py

@@ -138,9 +138,9 @@ def main():
                     print_log=True,
                 )
             ])
-            
+
             for d in loader:
-        
+
                 input_data = d['image'].cuda() # (b, c, h, w, d)
                 predict_raw = inference(input_data, model, args) # shape: (B, H, W, D)
                 predict_tensor = torch.from_numpy(predict_raw.astype(np.float32)) # shape: (B, H, W, D)

UNETR/BTCV/utils/data_utils.py

@@ -165,4 +165,4 @@ def get_loader(args):
         )
         loader = [train_loader, val_loader]
 
-    return loader
+    return loader