[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -11,7 +11,7 @@ repos:
         args: ["--py313-plus"]
         files: ^itkit/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.15.21
+    rev: v0.16.5
     hooks:
       - id: ruff-check
         types_or: [ python, pyi ]
@@ -36,7 +36,7 @@ repos:
       - id: requirements-txt-fixer
       - id: trailing-whitespace
   - repo: https://github.com/PyCQA/isort
-    rev: 9.0.0b1
+    rev: 9.0.1
     hooks:
       - id: isort
         files: ^itkit/

SlicerITKIT/ITKITInference/ITKITInference.py

@@ -20,7 +20,6 @@
 import logging
 import os
 import tempfile
-from typing import Optional
 
 import ctk
 import qt
@@ -279,7 +278,6 @@ def setup(self) -> None:
 
     def cleanup(self) -> None:
         """Called when the application closes and the module widget is destroyed."""
-        pass
 
     def onServerUrlChanged(self):
         """Called when server URL changes."""
@@ -330,8 +328,8 @@ def onConnectButton(self):
             LOGGER.exception("Connect to server failed")
             self.serverStatusLabel.setText("Connection failed")
             self.serverStatusLabel.setStyleSheet("color: red;")
-            self.statusLabel.setText(f"Error: {str(e)}")
-            slicer.util.errorDisplay(f"Failed to connect to server: {str(e)}")
+            self.statusLabel.setText(f"Error: {e!s}")
+            slicer.util.errorDisplay(f"Failed to connect to server: {e!s}")
 
     def updateModelStatus(self, model_info):
         """Update the current model status display."""
@@ -453,8 +451,8 @@ def onLoadModelButton(self):
         except Exception as e:
             LOGGER.exception("Load model failed")
             self.progressBar.hide()
-            self.statusLabel.setText(f"Error: {str(e)}")
-            slicer.util.errorDisplay(f"Failed to load model: {str(e)}")
+            self.statusLabel.setText(f"Error: {e!s}")
+            slicer.util.errorDisplay(f"Failed to load model: {e!s}")
 
     def onUnloadModelButton(self):
         """Unload the current model from the server."""
@@ -474,7 +472,7 @@ def onUnloadModelButton(self):
 
         except Exception as e:
             LOGGER.exception("Unload model failed")
-            slicer.util.errorDisplay(f"Failed to unload model: {str(e)}")
+            slicer.util.errorDisplay(f"Failed to unload model: {e!s}")
 
     def onApplyButton(self):
         """Run processing when user clicks "Apply" button."""
@@ -531,8 +529,8 @@ def onComplete():
                 LOGGER.exception("Inference failed")
 
                 def onError():
-                    slicer.util.errorDisplay(f"Inference failed: {str(e)}")
-                    self.statusLabel.setText(f"Error: {str(e)}")
+                    slicer.util.errorDisplay(f"Inference failed: {e!s}")
+                    self.statusLabel.setText(f"Error: {e!s}")
                     self.applyButton.enabled = True
                     self.progressBar.hide()
 
@@ -572,7 +570,7 @@ def load_model(
         self,
         server_url: str,
         backend_type: str,
-        config_path: Optional[str],
+        config_path: str | None,
         model_path: str,
         inference_config: dict,
     ) -> bool:

SlicerITKIT/server/itkit_server.py

@@ -26,6 +26,7 @@
 import torch
 from flask import Flask, jsonify, request, send_file
 from flask_cors import CORS
+
 from itkit.mm.inference import (
     InferenceConfig,
     Inferencer_Seg3D,
@@ -54,9 +55,9 @@ def __init__(
         self,
         name: str,
         backend_type: str,
-        config_path: Optional[str],
+        config_path: str | None,
         model_path: str,
-        inference_config: Optional[dict] = None,
+        inference_config: dict | None = None,
     ):
         self.name = name
         self.backend_type = backend_type  # 'mmengine' or 'onnx'
@@ -119,7 +120,7 @@ def to_dict(self):
 
 def _get_windowing_from_model(
     model: ModelConfig,
-) -> tuple[Optional[float], Optional[float]]:
+) -> tuple[float | None, float | None]:
     """Try to read window level/width from backend metadata or config."""
     if model.backend_type.lower() == "mmengine":
         cfg = getattr(model.backend, "cfg", None)

examples/configs/0.0.AbdomenCT1K_TorchIO/MedNeXt.py

@@ -1,4 +1,5 @@
 from mmengine.config import read_base
+
 with read_base():
     from .mgam import *
 

examples/configs/0.0.AbdomenCT1K_TorchIO/SegFormer3D.py

@@ -1,4 +1,5 @@
 from mmengine.config import read_base
+
 with read_base():
     from .mgam import *
 

examples/configs/0.0.AbdomenCT1K_TorchIO/mgam.py

@@ -1,35 +1,36 @@
-from torch.optim.adamw import AdamW
-from torch.distributed.fsdp.api import ShardingStrategy
-
-from mmengine.runner import ValLoop
-from mmengine.runner import TestLoop
-from mmengine.hooks.iter_timer_hook import IterTimerHook
-from mmengine.hooks.param_scheduler_hook import ParamSchedulerHook
-from mmengine.hooks.checkpoint_hook import CheckpointHook
-from mmengine.hooks import DistSamplerSeedHook
-from mmengine.runner import IterBasedTrainLoop
-from mmengine.optim.scheduler import LinearLR, PolyLR
-from mmengine.optim import OptimWrapper, AmpOptimWrapper
-from mmengine.model.wrappers import MMFullyShardedDataParallel
 from mmengine._strategy.deepspeed import DeepSpeedOptimWrapper, DeepSpeedStrategy
 from mmengine.dataset.sampler import DefaultSampler, InfiniteSampler
 from mmengine.dataset.utils import default_collate
+from mmengine.hooks import DistSamplerSeedHook
+from mmengine.hooks.checkpoint_hook import CheckpointHook
+from mmengine.hooks.iter_timer_hook import IterTimerHook
+from mmengine.hooks.param_scheduler_hook import ParamSchedulerHook
+from mmengine.model.wrappers import MMFullyShardedDataParallel
+from mmengine.optim import AmpOptimWrapper, OptimWrapper
+from mmengine.optim.scheduler import LinearLR, PolyLR
+from mmengine.runner import IterBasedTrainLoop, TestLoop, ValLoop
 from mmengine.visualization import TensorboardVisBackend
+from torch.distributed.fsdp.api import ShardingStrategy
+from torch.optim.adamw import AdamW
+
+from itkit.dataset import ITKITConcatDataset
+from itkit.dataset.AbdomenCT_1K.mm_dataset import AbdomenCT_1K_Mha
+from itkit.dataset.torchio import TorchIO_PatchedDataset
 
 # customize
 from itkit.mm.mmeng_PlugIn import (
-    RemasteredDDP, LoggerJSON, RuntimeInfoHook, multi_sample_collate,
-    RatioSampler, RemasteredFSDP_Strategy)
-from itkit.process.GeneralPreProcess import WindowSet, TypeConvert
-from itkit.process.LoadBiomedicalData import LoadImageFromMHA, LoadMaskFromMHA
+    LoggerJSON,
+    RatioSampler,
+    RemasteredDDP,
+    RemasteredFSDP_Strategy,
+    RuntimeInfoHook,
+    multi_sample_collate,
+)
 from itkit.mm.mmseg_Dev3D import PackSeg3DInputs, Seg3DDataPreProcessor
 from itkit.mm.mmseg_PlugIn import IoUMetric_PerClass
-from itkit.dataset import ITKITConcatDataset
-from itkit.dataset.torchio import TorchIO_PatchedDataset
-from itkit.dataset.AbdomenCT_1K.mm_dataset import AbdomenCT_1K_Mha
-from itkit.mm.visualization import SegViser, BaseVisHook, LocalVisBackend
-
-
+from itkit.mm.visualization import BaseVisHook, LocalVisBackend, SegViser
+from itkit.process.GeneralPreProcess import TypeConvert, WindowSet
+from itkit.process.LoadBiomedicalData import LoadImageFromMHA, LoadMaskFromMHA
 
 # --------------------PARAMETERS-------------------- #
 

examples/configs/0.1.AbdomenCT1K_MONAI/MedNeXt.py

@@ -1,4 +1,5 @@
 from mmengine.config import read_base
+
 with read_base():
     from .mgam import *
 

examples/configs/0.1.AbdomenCT1K_MONAI/SegFormer3D.py

@@ -1,4 +1,5 @@
 from mmengine.config import read_base
+
 with read_base():
     from .mgam import *
 

examples/configs/0.1.AbdomenCT1K_MONAI/mgam.py

@@ -1,35 +1,36 @@
-from torch.optim.adamw import AdamW
-from torch.distributed.fsdp.api import ShardingStrategy
-
-from mmengine.runner import ValLoop
-from mmengine.runner import TestLoop
-from mmengine.hooks.iter_timer_hook import IterTimerHook
-from mmengine.hooks.param_scheduler_hook import ParamSchedulerHook
-from mmengine.hooks.checkpoint_hook import CheckpointHook
-from mmengine.hooks import DistSamplerSeedHook
-from mmengine.runner import IterBasedTrainLoop
-from mmengine.optim.scheduler import LinearLR, PolyLR
-from mmengine.optim import OptimWrapper, AmpOptimWrapper
-from mmengine.model.wrappers import MMFullyShardedDataParallel
 from mmengine._strategy.deepspeed import DeepSpeedOptimWrapper, DeepSpeedStrategy
 from mmengine.dataset.sampler import DefaultSampler, InfiniteSampler
 from mmengine.dataset.utils import default_collate
+from mmengine.hooks import DistSamplerSeedHook
+from mmengine.hooks.checkpoint_hook import CheckpointHook
+from mmengine.hooks.iter_timer_hook import IterTimerHook
+from mmengine.hooks.param_scheduler_hook import ParamSchedulerHook
+from mmengine.model.wrappers import MMFullyShardedDataParallel
+from mmengine.optim import AmpOptimWrapper, OptimWrapper
+from mmengine.optim.scheduler import LinearLR, PolyLR
+from mmengine.runner import IterBasedTrainLoop, TestLoop, ValLoop
 from mmengine.visualization import TensorboardVisBackend
+from torch.distributed.fsdp.api import ShardingStrategy
+from torch.optim.adamw import AdamW
+
+from itkit.dataset import ITKITConcatDataset
+from itkit.dataset.AbdomenCT_1K.mm_dataset import AbdomenCT_1K_Mha
+from itkit.dataset.monai import MONAI_PatchedDataset
 
 # customize
 from itkit.mm.mmeng_PlugIn import (
-    RemasteredDDP, LoggerJSON, RuntimeInfoHook, multi_sample_collate,
-    RatioSampler, RemasteredFSDP_Strategy)
-from itkit.process.GeneralPreProcess import WindowSet, TypeConvert
-from itkit.process.LoadBiomedicalData import LoadImageFromMHA, LoadMaskFromMHA
+    LoggerJSON,
+    RatioSampler,
+    RemasteredDDP,
+    RemasteredFSDP_Strategy,
+    RuntimeInfoHook,
+    multi_sample_collate,
+)
 from itkit.mm.mmseg_Dev3D import PackSeg3DInputs, Seg3DDataPreProcessor
 from itkit.mm.mmseg_PlugIn import IoUMetric_PerClass
-from itkit.dataset import ITKITConcatDataset
-from itkit.dataset.monai import MONAI_PatchedDataset
-from itkit.dataset.AbdomenCT_1K.mm_dataset import AbdomenCT_1K_Mha
-from itkit.mm.visualization import SegViser, BaseVisHook, LocalVisBackend
-
-
+from itkit.mm.visualization import BaseVisHook, LocalVisBackend, SegViser
+from itkit.process.GeneralPreProcess import TypeConvert, WindowSet
+from itkit.process.LoadBiomedicalData import LoadImageFromMHA, LoadMaskFromMHA
 
 # --------------------PARAMETERS-------------------- #
 

examples/configs/0.2.AbdomenCT1K_ITKIT/MedNeXt.py

@@ -1,4 +1,5 @@
 from mmengine.config import read_base
+
 with read_base():
     from .mgam import *
 

examples/configs/0.2.AbdomenCT1K_ITKIT/SegFormer3D.py

@@ -1,4 +1,5 @@
 from mmengine.config import read_base
+
 with read_base():
     from .mgam import *
 

examples/configs/0.2.AbdomenCT1K_ITKIT/mgam.py

@@ -1,34 +1,35 @@
-from torch.optim.adamw import AdamW
-from torch.distributed.fsdp.api import ShardingStrategy
-
-from mmengine.runner import ValLoop
-from mmengine.runner import TestLoop
-from mmengine.hooks.iter_timer_hook import IterTimerHook
-from mmengine.hooks.param_scheduler_hook import ParamSchedulerHook
-from mmengine.hooks.checkpoint_hook import CheckpointHook
-from mmengine.hooks import DistSamplerSeedHook
-from mmengine.runner import IterBasedTrainLoop
-from mmengine.optim.scheduler import LinearLR, PolyLR
-from mmengine.optim import OptimWrapper, AmpOptimWrapper
-from mmengine.model.wrappers import MMFullyShardedDataParallel
 from mmengine._strategy.deepspeed import DeepSpeedOptimWrapper, DeepSpeedStrategy
 from mmengine.dataset.sampler import DefaultSampler, InfiniteSampler
 from mmengine.dataset.utils import default_collate
+from mmengine.hooks import DistSamplerSeedHook
+from mmengine.hooks.checkpoint_hook import CheckpointHook
+from mmengine.hooks.iter_timer_hook import IterTimerHook
+from mmengine.hooks.param_scheduler_hook import ParamSchedulerHook
+from mmengine.model.wrappers import MMFullyShardedDataParallel
+from mmengine.optim import AmpOptimWrapper, OptimWrapper
+from mmengine.optim.scheduler import LinearLR, PolyLR
+from mmengine.runner import IterBasedTrainLoop, TestLoop, ValLoop
 from mmengine.visualization import TensorboardVisBackend
+from torch.distributed.fsdp.api import ShardingStrategy
+from torch.optim.adamw import AdamW
+
+from itkit.dataset import ITKITConcatDataset
+from itkit.dataset.AbdomenCT_1K.mm_dataset import AbdomenCT_1K_Mha, AbdomenCT_1K_Patch
 
 # customize
 from itkit.mm.mmeng_PlugIn import (
-    RemasteredDDP, LoggerJSON, RuntimeInfoHook, multi_sample_collate,
-    RatioSampler, RemasteredFSDP_Strategy)
-from itkit.process.GeneralPreProcess import WindowSet, TypeConvert
-from itkit.process.LoadBiomedicalData import LoadImageFromMHA, LoadMaskFromMHA
+    LoggerJSON,
+    RatioSampler,
+    RemasteredDDP,
+    RemasteredFSDP_Strategy,
+    RuntimeInfoHook,
+    multi_sample_collate,
+)
 from itkit.mm.mmseg_Dev3D import PackSeg3DInputs, Seg3DDataPreProcessor
 from itkit.mm.mmseg_PlugIn import IoUMetric_PerClass
-from itkit.dataset import ITKITConcatDataset
-from itkit.dataset.AbdomenCT_1K.mm_dataset import AbdomenCT_1K_Mha, AbdomenCT_1K_Patch
-from itkit.mm.visualization import SegViser, BaseVisHook, LocalVisBackend
-
-
+from itkit.mm.visualization import BaseVisHook, LocalVisBackend, SegViser
+from itkit.process.GeneralPreProcess import TypeConvert, WindowSet
+from itkit.process.LoadBiomedicalData import LoadImageFromMHA, LoadMaskFromMHA
 
 # --------------------PARAMETERS-------------------- #
 

itkit/dataset/BraTs2024/convert_nii_mha.py

@@ -131,15 +131,14 @@ def convert_brats_to_mha(input_dir, dest_root, spacing=None, size=None, use_mp=F
     partial_convert_func = partial(convert_case, dest_root=dest_root, spacing=spacing, size=size)
 
     if use_mp:
-        with mp.Pool(mp.cpu_count()) as pool:
-            with tqdm(
-                total=len(case_dirs),
-                desc="Converting BraTs2024",
-                dynamic_ncols=True,
-                leave=False
-            ) as pbar:
-                for _ in pool.imap_unordered(partial_convert_func, case_dirs):
-                    pbar.update()
+        with mp.Pool(mp.cpu_count()) as pool, tqdm(
+            total=len(case_dirs),
+            desc="Converting BraTs2024",
+            dynamic_ncols=True,
+            leave=False
+        ) as pbar:
+            for _ in pool.imap_unordered(partial_convert_func, case_dirs):
+                pbar.update()
     else:
         with tqdm(total=len(case_dirs), desc="Converting Cases") as pbar:
             for case_dir in case_dirs:

itkit/dataset/Totalsegmentator/fix_ITKUnreadable_files.py

@@ -19,7 +19,7 @@ def fix_nifti_file(input_path, output_path):
         nib.save(img, output_path)
         return True
     except Exception as e:
-        print(f"处理文件 {input_path} 时出错: {str(e)}")
+        print(f"处理文件 {input_path} 时出错: {e!s}")
         return False
 
 
@@ -39,7 +39,7 @@ def process_file(file_path, input_root, output_root):
         output_path = output_root / rel_path
         return fix_nifti_file(file_path, output_path)
     except ValueError as e:
-        print(f"计算相对路径时出错: {str(e)}")
+        print(f"计算相对路径时出错: {e!s}")
         return False
 
 

itkit/dataset/base.py

@@ -223,7 +223,7 @@ def sample_iterator(self) -> Generator[tuple[str, str]]:
             if not os.path.exists(image_mha_path):
                 print_log(f"{series} image mha file not found.\nFullPath: {image_mha_path}",
                           MMLogger.get_current_instance(),
-                          logging.WARN)
+                          logging.WARNING)
                 continue
             yield (image_mha_path, label_mha_path)
 
@@ -253,11 +253,11 @@ def sample_iterator(self) -> Generator[tuple[str, str]]:
             # List all image files that match the current series UID
             # Files are in format: <seriesUID>_<patchID>.mha (e.g., 1.3.6.1.4.1.9328.50.4.0095_p0.mha)
             if not os.path.exists(image_folder):
-                print_log(f"Image folder not found: {image_folder}", MMLogger.get_current_instance(), logging.WARN)
+                print_log(f"Image folder not found: {image_folder}", MMLogger.get_current_instance(), logging.WARNING)
                 continue
 
             if series not in self.precrop_meta["patch_meta"]:
-                print_log(f"Series {series} not found in patch metadata", MMLogger.get_current_instance(), logging.WARN)
+                print_log(f"Series {series} not found in patch metadata", MMLogger.get_current_instance(), logging.WARNING)
                 continue
             series_patch_files = self.precrop_meta["patch_meta"][series]["class_within_patch"].keys()