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

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

Author: pre-commit-ci[bot]

generation/maisi/scripts/diff_model_infer.py

@@ -95,7 +95,9 @@ def prepare_tensors(args: argparse.Namespace, device: torch.device) -> tuple:
     top_region_index_tensor = torch.from_numpy(top_region_index_tensor[np.newaxis, :]).half().to(device)
     bottom_region_index_tensor = torch.from_numpy(bottom_region_index_tensor[np.newaxis, :]).half().to(device)
     spacing_tensor = torch.from_numpy(spacing_tensor[np.newaxis, :]).half().to(device)
-    modality_tensor = args.diffusion_unet_inference["modality"]*torch.ones((len(spacing_tensor)),dtype=torch.long).to(device)
+    modality_tensor = args.diffusion_unet_inference["modality"] * torch.ones(
+        (len(spacing_tensor)), dtype=torch.long
+    ).to(device)
 
     return top_region_index_tensor, bottom_region_index_tensor, spacing_tensor, modality_tensor
 

generation/maisi/scripts/infer_controlnet.py

@@ -161,7 +161,7 @@ def main():
             top_region_index_tensor = None
             bottom_region_index_tensor = None
         spacing_tensor = batch["spacing"].to(device)
-        modality_tensor = args.controlnet_infer["modality"]*torch.ones((len(labels),),dtype=torch.long).to(device)
+        modality_tensor = args.controlnet_infer["modality"] * torch.ones((len(labels),), dtype=torch.long).to(device)
         out_spacing = tuple((batch["spacing"].squeeze().numpy() / 100).tolist())
         # get target dimension
         dim = batch["dim"]

generation/maisi/scripts/utils.py

@@ -306,10 +306,12 @@ def prepare_maisi_controlnet_json_dataloader(
         LoadImaged(keys=["image", "label"], image_only=True, ensure_channel_first=True),
         Orientationd(keys=["label"], axcodes="RAS"),
         EnsureTyped(keys=["label"], dtype=torch.uint8, track_meta=True),
-        Lambdad(keys="top_region_index", func=lambda x: torch.FloatTensor(x),allow_missing_keys=True),
-        Lambdad(keys="bottom_region_index", func=lambda x: torch.FloatTensor(x),allow_missing_keys=True),
+        Lambdad(keys="top_region_index", func=lambda x: torch.FloatTensor(x), allow_missing_keys=True),
+        Lambdad(keys="bottom_region_index", func=lambda x: torch.FloatTensor(x), allow_missing_keys=True),
         Lambdad(keys="spacing", func=lambda x: torch.FloatTensor(x)),
-        Lambdad(keys=["top_region_index", "bottom_region_index", "spacing"], func=lambda x: x * 1e2,allow_missing_keys=True),
+        Lambdad(
+            keys=["top_region_index", "bottom_region_index", "spacing"], func=lambda x: x * 1e2, allow_missing_keys=True
+        ),
     ]
     train_transforms, val_transforms = Compose(common_transform), Compose(common_transform)