jiashizuo
commented
5 months ago Excuse me, have you solved it? I also encountered this error report.
Closed sinaziaee closed 4 months ago
jiashizuo
commented
5 months ago Excuse me, have you solved it? I also encountered this error report.
sinaziaee
commented
5 months ago Excuse me, have you solved it? I also encountered this error report.
No, unfortunately.
htcwf89
commented
5 months ago I'm running into the same error when trying to run region-based training. I traced the issue back to the "ConvertSegmentationToRegionsTransform" class in the batchgeneratorsv2 package (batchgeneratorsv2/batchgeneratorsv2/transforms/utils/seg_to_regions.py). It appears the latest update by @FabianIsensee that was pushed as the result of this comment https://github.com/MIC-DKFZ/nnUNet/issues/2136#issuecomment-2085004267 has broken the region-based training in nnunetv2. However, I tried manually reverting the "ConvertSegmentationToRegionsTransform" class to previous versions visible in the history here but those introduced different errors instead. @FabianIsensee & @GregorKoehler could you guys please take a look?
p.s. I'm using nnunetv2 V2.5, torch 2.1.2+cu118, and batchgeneratorsv2 0.1.1 My OS is Linux and my GPUs are RTX A6000s.
FabianIsensee
commented
5 months ago This should be fixed if you install both the batchgeneratorsv2 and nnunetv2 current master
htcwf89
commented
5 months ago Thanks for the super quick response. I tried it but unfortunately won't work. I installed the current masters through cloning from git but it doesn't seem the loss function likes the bool. The full error traceback is below. I haven't gotten around to experimenting with potential solutions but will report back if I find a fix.
In the meantime, I installed nnunet2 V2.4 and was able to run the region-based training, if others want to get things moving on their work @sinaziaee @jiashizuo
Here's the traceback:
"(/home/my_user/nnunet2_envNew) my_user@01:~$ CUDA_VISIBLE_DEVICES=3 ############################ INFO: You are using the old nnU-Net default plans. We have updated our recommendations. Please consider using those instead! Read more here: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/resenc_presets.md ####################################################################### Please cite the following paper when using nnU-Net: 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. ####################################################################### 2024-05-28 14:24:29.521258: do_dummy_2d_data_aug: True 2024-05-28 14:24:29.534906: Using splits from existing split file: /mnt/Stuff/MultiModal/nnUNet_preprocessed/Dataset727 /splits_final.json 2024-05-28 14:24:29.540675: The split file contains 5 splits. 2024-05-28 14:24:29.544157: Desired fold for training: 0 2024-05-28 14:24:29.547901: This split has 40 training and 10 validation cases. using pin_memory on device 0 using pin_memory on device 0 2024-05-28 14:24:35.571942: Using torch.compile...
This is the configuration used by this training:
Configuration name: 3d_fullres
{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [32, 256, 224], 'median_image_size_in_voxels': [33.0, 256.0, 252.0], 'spacing': [3.0, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False}
These are the global plan.json settings:
{'dataset_name': 'Dataset727, 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.5, 0.5], 'original_median_shape_after_transp': [33, 256, 252], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 300.6848449707031, 'mean': 57.38395309448242, 'median': 55.44499206542969, 'min': -34.248321533203125, 'percentile_00_5': -0.029295789077878, 'percentile_99_5': 161.9104461669922, 'std': 34.14013671875}}}
2024-05-28 14:24:38.170208: unpacking dataset... 2024-05-28 14:24:43.407785: unpacking done... 2024-05-28 14:24:43.435786: Unable to plot network architecture: nnUNet_compile is enabled! 2024-05-28 14:24:43.458476: 2024-05-28 14:24:43.462629: Epoch 0
2024-05-28 14:24:43.466364: Current learning rate: 0.01
Traceback (most recent call last): File "/home/my_user/nnunet2_envNew/bin/nnUNetv2_train", line 8, in
from user code: File "/home/my_user/nnUNetNew/nnunetv2/training/loss/dice.py", line 83, in forward if x.shape == y.shape: Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
You can suppress this exception and fall back to eager by setting: import torch._dynamo torch._dynamo.config.suppress_errors = True
Exception in thread Thread-1 (results_loop): Traceback (most recent call last): File "/home/my_user/nnunet2_envNew/lib/python3.11/threading.py", line 1045, in _bootstrap_inner self.run() File "/home/my_user/nnunet2_envNew/lib/python3.11/threading.py", line 982, in run self._target(*self._args, *self._kwargs) File "/home/my_user/nnunet2_envNew/lib/python3.11/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 125, in results_loop raise e File "/home/my_user/nnunet2_envNew/lib/python3.11/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 103, in results_loop raise RuntimeError("One or more background workers are no longer alive. Exiting. Please check the " RuntimeError: One or more background workers are no longer alive. Exiting. Please check the print statements above for the actual error message Exception in thread Thread-2 (results_loop): Traceback (most recent call last): File "/home/my_user/nnunet2_envNew/lib/python3.11/threading.py", line 1045, in _bootstrap_inner self.run() File "/home/my_user/nnunet2_envNew/lib/python3.11/threading.py", line 982, in run self._target(self._args, **self._kwargs) File "/home/my_user/nnunet2_envNew/lib/python3.11/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 125, in results_loop raise e File "/home/my_user/nnunet2_envNew/lib/python3.11/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 103, in results_loop raise RuntimeError("One or more background workers are no longer alive. Exiting. Please check the " RuntimeError: One or more background workers are no longer alive. Exiting. Please check the print statements above for the actual error message "
FabianIsensee
commented
5 months ago This is a different problem. Please upgrade torch to the latest version. It is not a nnU-Net related error. If upgrading doesn't work you can always disable torch.compile by setting nnUNet_compile=f in your environment variables
GregorKoehler
commented
4 months ago Hi @htcwf89, @sinaziaee, @jiashizuo,
as installing nnUNet v2.4 apparently resolved the original issue regarding region-based training and the new error seems unrelated to nnUNet, I'm closing this issue for now. Please feel free to re-open if you encounter the same issue & find that it's related to nnUNet.
Best, Gregor
Hi I am new to nnUNet and I wanted to use it on Kits23 dataset. To start, I am using selecting 5 cases from the datasets randomly and putting them in a folder. Then I used
Then I used nnUNetv2_plan_and_preprocess -d 220 --verify_dataset_integrity -c 2d 3d_fullres 3d_lowres
and created to preprocess the data.
Then I tried to run nnUNetv2_train 220 2d 0 or nnUNetv2_train 220 3d_lowres 0
export nnUNet_raw="mypath/playground/nnUNet_raw/" export nnUNet_preprocessed="mypath/playground/processed" export nnUNet_results="mypath/playground/nnUNet_results"to set the paths Then I used python Dataset220_KiTS2023.pybut I always get this error: cannot access local variable 'region_labels' where it is not associated with a value
So, I'm lost as I don't know what to do in this step.