pchlap
commented
2 years ago Hi @VincentBourbon, thanks for reaching out with this issue. I believe this might be due to operating system incompatibilities for the nnUNet portion of this tool. At this stage, the tool has only been tested and run on a Linux (Ubuntu) operating system. Are you able to test it out using Ubuntu?
In the future, hopefully, we can support more platforms, however, we are somewhat relying on nnNUnet supporting these other operating systems first.
VincentBourbon
hakim-sina
MarkGardnerUSyd
Hello,
I'm trying to use the Segmentation Cardiac Tool.
After installing PyTorch and nnUnet and running the code, I get the following error.
My setup MacOS BigSur 11.3.1, Python 3.9, PyTorch 11.1.
I'm fairly new to Python scripts. After several searches on the web, I couldn't find a solution for this topic.
Thanks
Vincent
"~ utilisateur$ platipy segmentation cardiac /Users/utilisateur/Documents/Test_AutoSeg/INPUT/P1.nii.gz -o /Users/utilisateur/Documents/Test_AutoSeg/OUTPUT Running cardiac segmentation
Please cite the following paper when using nnUNet:
Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z
If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet
/Users/utilisateur/.platipy/nnUNet_models/nnUNet/3d_lowres/Task400_OPEN_HEART_1FOLD This model expects 1 input modalities for each image Found 1 unique case ids, here are some examples: ['Task400_OPEN_HEART_1FOLD'] If they don't look right, make sure to double check your filenames. They must end with _0000.nii.gz etc emptying cuda cache loading parameters for folds, all using the following model files: ['/Users/utilisateur/.platipy/nnUNet_models/nnUNet/3d_lowres/Task400_OPEN_HEART_1FOLD/nnUNetTrainerV2nnUNetPlansv2.1/all/model_final_checkpoint.model'] starting preprocessing generator starting prediction... Traceback (most recent call last): File "/Library/Frameworks/Python.framework/Versions/3.9/bin/platipy", line 8, in
sys.exit(platipy_cli())
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/platipy/cli/run.py", line 73, in platipy_cli
tools[tool]()
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/click/core.py", line 1130, in call
return self.main(*args, kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/click/core.py", line 1055, in main
rv = self.invoke(ctx)
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/click/core.py", line 1404, in invoke
return ctx.invoke(self.callback, ctx.params)
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/click/core.py", line 760, in invoke
return callback(*args, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/platipy/cli/segmentation.py", line 106, in click_command
results = segmentation_algorithms[algorithm]["algorithm"](image, algorithm_config)
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/platipy/imaging/projects/cardiac/run.py", line 429, in run_hybrid_segmentation
mask_wh = run_segmentation(img, settings["nnunet_settings"])
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/platipy/imaging/projects/nnunet/run.py", line 127, in run_segmentation
predict_from_folder(str(model_folder_name), str(input_path), str(output_path), folds, False,
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/nnunet/inference/predict.py", line 658, in predict_from_folder
return predict_cases(model, list_of_lists[part_id::num_parts], output_files[part_id::num_parts], folds,
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/nnunet/inference/predict.py", line 206, in predict_cases
for preprocessed in preprocessing:
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/nnunet/inference/predict.py", line 109, in preprocess_multithreaded
pr.start()
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/process.py", line 121, in start
self._popen = self._Popen(self)
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 32, in init
super().init(process_obj)
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_fork.py", line 19, in init
self._launch(process_obj)
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 47, in _launch
reduction.dump(process_obj, fp)
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <function at 0x167e4cee0>: attribute lookup on nnunet.utilities.nd_softmax failed"