[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

@@ -291,7 +291,7 @@ def diff_model_infer(
         output_size,
         divisor,
         logger,
-        include_body_region = include_body_region,
+        include_body_region=include_body_region,
     )
 
     timestamp = datetime.now().strftime("%Y%m%d%H%M%S")

generation/maisi/scripts/diff_model_train.py

@@ -288,7 +288,7 @@ def train_one_epoch(
                     "noise scheduler prediction type has to be chosen from ",
                     f"[{DDPMPredictionType.EPSILON},{DDPMPredictionType.SAMPLE},{DDPMPredictionType.V_PREDICTION}]",
                 )
-            
+
             loss = loss_pt(model_output.float(), model_gt.float())
 
         if amp:

generation/maisi/scripts/infer_controlnet.py

@@ -188,7 +188,7 @@ def main():
             noise_factor=1.0,
             num_inference_steps=args.controlnet_infer["num_inference_steps"],
             autoencoder_sliding_window_infer_size=args.controlnet_infer["autoencoder_sliding_window_infer_size"],
-            autoencoder_sliding_window_infer_overlap=args.controlnet_infer["autoencoder_sliding_window_infer_overlap"]
+            autoencoder_sliding_window_infer_overlap=args.controlnet_infer["autoencoder_sliding_window_infer_overlap"],
         )
         # save image/label pairs
         labels = decollate_batch(batch)[0]["label"]

generation/maisi/scripts/sample.py

@@ -182,13 +182,13 @@ def ldm_conditional_sample_one_image(
     noise_scheduler,
     scale_factor,
     device,
-    combine_label_or,    
+    combine_label_or,
     spacing_tensor,
     latent_shape,
     output_size,
     noise_factor,
-    top_region_index_tensor = None,
-    bottom_region_index_tensor = None,
+    top_region_index_tensor=None,
+    bottom_region_index_tensor=None,
     num_inference_steps=1000,
     autoencoder_sliding_window_infer_size=[96, 96, 96],
     autoencoder_sliding_window_infer_overlap=0.6667,
@@ -203,7 +203,7 @@ def ldm_conditional_sample_one_image(
         noise_scheduler: The noise scheduler for the diffusion process.
         scale_factor (float): Scaling factor for the latent space.
         device (torch.device): The device to run the computation on.
-        combine_label_or (torch.Tensor): The combined label tensor.        
+        combine_label_or (torch.Tensor): The combined label tensor.
         spacing_tensor (torch.Tensor): Tensor specifying the spacing.
         latent_shape (tuple): The shape of the latent space.
         output_size (tuple): The desired output size of the image.

generation/maisi/scripts/train_controlnet.py

@@ -51,7 +51,7 @@ def main():
         "--training-config",
         default="./configs/config_maisi_controlnet_train.json",
         help="config json file that stores training hyper-parameters",
-    )    
+    )
     parser.add_argument("-g", "--gpus", default=1, type=int, help="number of gpus per node")
     parser.add_argument(
         "--include_body_region",
@@ -199,7 +199,9 @@ def main():
                 if isinstance(noise_scheduler, RFlowScheduler):
                     timesteps = noise_scheduler.sample_timesteps(images)
                 else:
-                    timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (images.shape[0],), device=images.device).long()
+                    timesteps = torch.randint(
+                        0, noise_scheduler.num_train_timesteps, (images.shape[0],), device=images.device
+                    ).long()
 
                 # create noisy latent
                 noisy_latent = noise_scheduler.add_noise(original_samples=images, noise=noise, timesteps=timesteps)
@@ -241,7 +243,7 @@ def main():
                     "noise scheduler prediction type has to be chosen from ",
                     f"[{DDPMPredictionType.EPSILON},{DDPMPredictionType.SAMPLE},{DDPMPredictionType.V_PREDICTION}]",
                 )
-                    
+
             if weighted_loss > 1.0:
                 weights = torch.ones_like(images).to(images.device)
                 roi = torch.zeros([noise_shape[0]] + [1] + noise_shape[2:]).to(images.device)
@@ -253,7 +255,7 @@ def main():
                 loss = (F.l1_loss(noise_pred.float(), model_gt.float(), reduction="none") * weights).mean()
             else:
                 loss = F.l1_loss(model_output.float(), model_gt.float())
-                
+
             scaler.scale(loss).backward()
             scaler.step(optimizer)
             scaler.update()