search

gaudot / SlicerDentalSegmentator

3D Slicer extension for fully-automatic segmentation of CT and CBCT dental volumes.
Apache License 2.0
56 stars 6 forks source link

Failed to load the segmentation. #19

Open Fan-dentist opened 1 week ago

Fan-dentist commented 1 week ago

I am having trouble shoot problems in windows 11, could you help me [Python] Failed to load the segmentation. [Python] Something went wrong during the nnUNet processing. [Python] Please check the logs for potential errors and contact the library maintainers.

2024/10/09 12:55:51.980 :: nnUNet is already installed (2.5.1) and compatible with requested version (nnunetv2). 2024/10/09 12:55:54.078 :: Transferring volume to nnUNet in C:/Users/jb/AppData/Local/Temp/Slicer-TcKeDo 2024/10/09 12:57:01.759 :: Starting nnUNet with the following parameters: 2024/10/09 12:57:01.759 :: 2024/10/09 12:57:01.759 :: F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Scripts\nnUNetv2_predict.exe -i C:/Users/jb/AppData/Local/Temp/Slicer-TcKeDo/input -o C:/Users/jb/AppData/Local/Temp/Slicer-TcKeDo/output -d Dataset111_453CT -tr nnUNetTrainer -p nnUNetPlans -c 3d_fullres -f 0 -npp 1 -nps 1 -step_size 0.5 -device cuda -chk checkpoint_final.pth --disable_tta 2024/10/09 12:57:01.759 :: 2024/10/09 12:57:01.759 :: JSON parameters : 2024/10/09 12:57:01.759 :: { 2024/10/09 12:57:01.759 :: "folds": "0", 2024/10/09 12:57:01.759 :: "device": "cuda", 2024/10/09 12:57:01.759 :: "stepSize": 0.5, 2024/10/09 12:57:01.759 :: "disableTta": true, 2024/10/09 12:57:01.759 :: "nProcessPreprocessing": 1, 2024/10/09 12:57:01.759 :: "nProcessSegmentationExport": 1, 2024/10/09 12:57:01.759 :: "checkPointName": "", 2024/10/09 12:57:01.759 :: "modelPath": { 2024/10/09 12:57:01.759 :: "_path": "F:\3Dslicer\Slicer 5.7.0-2024-10-05\slicer.org\Extensions-33047\DentalSegmentator\lib\Slicer-5.7\qt-scripted-modules\Resources\ML" 2024/10/09 12:57:01.759 :: } 2024/10/09 12:57:01.759 :: } 2024/10/09 12:57:01.788 :: nnUNet preprocessing... 2024/10/09 12:57:09.267 :: F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\site-packages\nnunetv2\inference\predict_from_raw_data.py:84: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. 2024/10/09 12:57:09.267 :: checkpoint = torch.load(join(model_training_outputdir, f'fold{f}', checkpoint_name), 2024/10/09 12:57:09.634 :: F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\site-packages\nnunetv2\utilities\plans_handling\plans_handler.py:37: UserWarning: Detected old nnU-Net plans format. Attempting to reconstruct network architecture parameters. If this fails, rerun nnUNetv2_plan_experiment for your dataset. If you use a custom architecture, please downgrade nnU-Net to the version you implemented this or update your implementation + plans. 2024/10/09 12:57:09.634 :: warnings.warn("Detected old nnU-Net plans format. Attempting to reconstruct network architecture " 2024/10/09 12:57:37.695 :: Process SpawnProcess-16: 2024/10/09 12:57:37.781 :: Traceback (most recent call last): 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\runpy.py", line 197, in _run_module_as_main 2024/10/09 12:57:37.781 :: Traceback (most recent call last): 2024/10/09 12:57:37.781 :: return _run_code(code, main_globals, None, 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\runpy.py", line 87, in _run_code 2024/10/09 12:57:37.781 :: exec(code, run_globals) 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Scripts\nnUNetv2_predict.exe__main__.py", line 7, in 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\multiprocessing\process.py", line 315, in _bootstrap 2024/10/09 12:57:37.781 :: self.run() 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\multiprocessing\process.py", line 108, in run 2024/10/09 12:57:37.781 :: self._target(*self._args, **self._kwargs) 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\site-packages\nnunetv2\inference\data_iterators.py", line 58, in preprocess_fromfiles_save_to_queue 2024/10/09 12:57:37.781 :: raise e 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\site-packages\nnunetv2\inference\data_iterators.py", line 31, in preprocess_fromfiles_save_to_queue 2024/10/09 12:57:37.781 :: data, seg, data_properties = preprocessor.run_case(list_of_lists[idx], 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\site-packages\nnunetv2\preprocessing\preprocessors\default_preprocessor.py", line 139, in run_case 2024/10/09 12:57:37.781 :: data, seg = self.run_case_npy(data, seg, data_properties, plans_manager, configuration_manager, 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\site-packages\nnunetv2\preprocessing\preprocessors\default_preprocessor.py", line 84, in run_case_npy 2024/10/09 12:57:37.781 :: data = configuration_manager.resampling_fn_data(data, new_shape, original_spacing, target_spacing) 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\site-packages\nnunetv2\preprocessing\resampling\default_resampling.py", line 111, in resample_data_or_seg_to_shape 2024/10/09 12:57:37.781 :: data_reshaped = resample_data_or_seg(data, new_shape, is_seg, axis, order, do_separate_z, order_z=order_z) 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\site-packages\nnunetv2\preprocessing\resampling\default_resampling.py", line 144, in resample_data_or_seg 2024/10/09 12:57:37.781 :: data = data.astype(float, copy=False) 2024/10/09 12:57:37.781 :: numpy.core._exceptions._ArrayMemoryError: Unable to allocate 2.20 GiB for an array with shape (1, 672, 678, 648) and data type float64 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\site-packages\nnunetv2\inference\predict_from_raw_data.py", line 866, in predict_entry_point 2024/10/09 12:57:37.781 :: predictor.predict_from_files(args.i, args.o, save_probabilities=args.save_probabilities, 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\site-packages\nnunetv2\inference\predict_from_raw_data.py", line 258, in predict_from_files 2024/10/09 12:57:37.781 :: return self.predict_from_data_iterator(data_iterator, save_probabilities, num_processes_segmentation_export) 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\site-packages\nnunetv2\inference\predict_from_raw_data.py", line 351, in predict_from_data_iterator 2024/10/09 12:57:37.781 :: for preprocessed in data_iterator: 2024/10/09 12:57:37.781 :: File "F:\3Dslicer\Slicer 5.7.0-2024-10-05\lib\Python\Lib\site-packages\nnunetv2\inference\data_iterators.py", line 111, in preprocessing_iterator_fromfiles 2024/10/09 12:57:37.781 :: raise RuntimeError('Background workers died. Look for the error message further up! If there is ' 2024/10/09 12:57:37.781 :: RuntimeError: Background workers died. Look for the error message further up! If there is none then your RAM was full and the worker was killed by the OS. Use fewer workers or get more RAM in that case! 2024/10/09 12:57:38.255 :: ####################################################################### 2024/10/09 12:57:38.255 :: Please cite the following paper when using nnU-Net: 2024/10/09 12:57:38.255 :: Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211. 2024/10/09 12:57:38.255 :: ####################################################################### 2024/10/09 12:57:38.255 :: 2024/10/09 12:57:38.255 :: There are 1 cases in the source folder 2024/10/09 12:57:38.255 :: I am process 0 out of 1 (max process ID is 0, we start counting with 0!) 2024/10/09 12:57:38.255 :: There are 1 cases that I would like to predict 2024/10/09 12:57:41.729 :: Loading inference results... 2024/10/09 12:59:12.916 :: Error loading results : 2024/10/09 12:59:12.916 :: Failed to load the segmentation. 2024/10/09 12:59:12.916 :: Something went wrong during the nnUNet processing. 2024/10/09 12:59:12.916 :: Please check the logs for potential errors and contact the library maintainers

soheilkooklan commented 4 days ago

Hi there! You seem to be having memory issues during the nnUNet processing on Windows 11. Based on the logs, the error Unable to allocate 2.20 GiB for an array suggests that your system might be running out of RAM.

Let me know if you need further assistance.