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vista3d/README.md

@@ -96,9 +96,9 @@ mv model-zoo/models/vista3d vista3dbundle & rm -rf model-zoo
 cd vista3dbundle
 mkdir models
 # minor model weights naming conversion due to monai version change
-wget -O models/model.pt https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_vista3d.pt 
+wget -O models/model.pt https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_vista3d.pt
 ```
-MONAI bundle accepts multiple json config files and input arguments. The latter configs/arguments will overide the previous configs/arguments if they have overlapping keys. 
+MONAI bundle accepts multiple json config files and input arguments. The latter configs/arguments will overide the previous configs/arguments if they have overlapping keys.
 ```python
 # Automatic Segment everything
 python -m monai.bundle run --config_file configs/inference.json --input_dict "{'image':'spleen_03.nii.gz'}
@@ -108,7 +108,7 @@ python -m monai.bundle run --config_file configs/inference.json --input_dict "{'
 python -m monai.bundle run --config_file configs/inference.json --input_dict "{'image':'spleen_03.nii.gz','label_prompt':[3]}
 ```
 ```python
-# Interactive segmentation 
+# Interactive segmentation
 # Points must be three dimensional (x,y,z) in the shape of [[x,y,z],...,[x,y,z]]. Point labels can only be -1(ignore), 0(negative), 1(positive) and 2(negative for special overlaped class like tumor), 3(positive for special class). Only supporting 1 class per inference. The output 255 represents NaN value which means not processed region.
 python -m monai.bundle run --config_file configs/inference.json --input_dict "{'image':'spleen_03.nii.gz','points':[[128,128,16], [100,100,16]],'point_labels':[1, 0]}"
 ```
@@ -158,7 +158,7 @@ python -m monai.bundle run --config_file="['configs/inference.json', 'configs/ba
 ### 1.1 Overlapped classes and postprocessing with [ShapeKit](https://arxiv.org/pdf/2506.24003)
 VISTA3D is trained with binary segmentation, and may produce false positives due to weak false positive supervision. ShapeKit solves this problem with sophisticated postprocessing. ShapeKit requires segmentation mask for each class. VISTA3D by default performs argmax and collaps overlapping classes. Change the `monai.apps.vista3d.transforms.VistaPostTransformd` in `inference.json` to save each class segmentation as a separate channel. Then follow [ShapeKit](https://github.com/BodyMaps/ShapeKit) codebase for processing.
 ```json
-{ 
+{
   "_target_": "Activationsd",
   "sigmoid": true,
   "keys": "pred"
@@ -180,7 +180,7 @@ To segment everything, run
 ```bash
 export CUDA_VISIBLE_DEVICES=0; python -m scripts.infer --config_file 'configs/infer.yaml' - infer_everything --image_file 'example-1.nii.gz'
 ```
-To segment based on point clicks, provide `point` and `point_label`. 
+To segment based on point clicks, provide `point` and `point_label`.
 ```bash
 export CUDA_VISIBLE_DEVICES=0; python -m scripts.infer --config_file 'configs/infer.yaml' - infer --image_file 'example-1.nii.gz' --point "[[128,128,16],[100,100,6]]" --point_label "[1,0]" --save_mask true
 ```

vista3d/cvpr_workshop/infer_cvpr.py

@@ -16,6 +16,7 @@
 
 from train_cvpr import ROI_SIZE
 
+
 def convert_clicks(alldata):
     # indexes = list(alldata.keys())
     # data = [alldata[i] for i in indexes]

vista3d/cvpr_workshop/train_cvpr.py

@@ -22,7 +22,8 @@
 import matplotlib.pyplot as plt
 
 NUM_PATCHES_PER_IMAGE = 2
-ROI_SIZE= [128, 128, 128]
+ROI_SIZE = [128, 128, 128]
+
 
 def plot_to_tensorboard(writer, epoch, inputs, labels, points, outputs):
     """
@@ -109,7 +110,7 @@ def __getitem__(self, idx):
                     keys=["image", "label"],
                     label_key="label",
                     num_classes=label.max() + 1,
-                    ratios=tuple(float(i > 0) for i in range(label.max()+1)),
+                    ratios=tuple(float(i > 0) for i in range(label.max() + 1)),
                     num_samples=NUM_PATCHES_PER_IMAGE,
                 ),
                 monai.transforms.RandScaleIntensityd(
@@ -137,17 +138,19 @@ def __getitem__(self, idx):
                     mode=["constant", "constant"],
                     keys=["image", "label"],
                     spatial_size=ROI_SIZE,
-                )
+                ),
             ]
         )
         data = transforms(data)
         return data
 
+
 import re
 
+
 def get_latest_epoch(directory):
     # Pattern to match filenames like 'model_epoch<number>.pth'
-    pattern = re.compile(r'model_epoch(\d+)\.pth')
+    pattern = re.compile(r"model_epoch(\d+)\.pth")
     max_epoch = -1
 
     for filename in os.listdir(directory):
@@ -159,6 +162,7 @@ def get_latest_epoch(directory):
 
     return max_epoch if max_epoch != -1 else None
 
+
 # Training function
 def train():
     json_file = "allset.json"  # Update with your JSON file
@@ -169,7 +173,6 @@ def train():
     start_epoch = get_latest_epoch(checkpoint_dir)
     start_checkpoint = "./CPRR25_vista3D_model_final_10percent_data.pth"
 
-
     os.makedirs(checkpoint_dir, exist_ok=True)
     dist.init_process_group(backend="nccl")
     world_size = int(os.environ["WORLD_SIZE"])
@@ -189,11 +192,12 @@ def train():
         model.load_state_dict(pretrained_ckpt, strict=True)
     else:
         print(f"Resuming from epoch {start_epoch}")
-        pretrained_ckpt = torch.load(os.path.join(checkpoint_dir, f"model_epoch{start_epoch}.pth"))
-        model.load_state_dict(pretrained_ckpt['model'], strict=True)
+        pretrained_ckpt = torch.load(
+            os.path.join(checkpoint_dir, f"model_epoch{start_epoch}.pth")
+        )
+        model.load_state_dict(pretrained_ckpt["model"], strict=True)
     model = DDP(model, device_ids=[local_rank], find_unused_parameters=True)
 
-    
     optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=1.0e-05)
     lr_scheduler = monai.optimizers.WarmupCosineSchedule(
         optimizer=optimizer,
@@ -265,10 +269,16 @@ def train():
                 if local_rank == 0:
                     writer.add_scalar("loss", loss.item(), step)
         if local_rank == 0 and (epoch + 1) % save_interval == 0:
-            checkpoint_path = os.path.join(checkpoint_dir, f"model_epoch{epoch + 1}.pth")
+            checkpoint_path = os.path.join(
+                checkpoint_dir, f"model_epoch{epoch + 1}.pth"
+            )
             if world_size > 1:
                 torch.save(
-                    {"model": model.module.state_dict(), "epoch": epoch + 1, "step": step},
+                    {
+                        "model": model.module.state_dict(),
+                        "epoch": epoch + 1,
+                        "step": step,
+                    },
                     checkpoint_path,
                 )
                 print(