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Training of an nnUNet model #27

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plbenveniste opened this issue Jul 25, 2024 · 8 comments
Open

Training of an nnUNet model #27

plbenveniste opened this issue Jul 25, 2024 · 8 comments
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@plbenveniste
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plbenveniste commented Jul 25, 2024 •
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In this issue, I detail the exploration of training an nnUNet model.
The code is in branch plb/new_nnunet.

The script nnunet/convert_msd_to_nnunet.py takes the json file of an MSD dataset and converts it to the nnUNet format.

I created the associated virtual environment: conda create venv_nnunet python=3.9 on koios.
@plbenveniste plbenveniste self-assigned this Jul 25, 2024
@plbenveniste
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plbenveniste commented Jul 25, 2024 •
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To install:

conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia
pip install -r nnunet/requirements.txt
pip install --upgrade git+https://github.com/FabianIsensee/hiddenlayer.git

The MSD dataset was converted to the nnUNet format using the following command:

python nnunet/convert_msd_to_nnunet.py --input ~/net/ms-lesion-agnostic/msd_data/dataset_2024-07-24_seed42_lesionOnly.json -o ~/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/ --tasknumber 101

The environment variables were set

export nnUNet_raw="/home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw"
export nnUNet_results="/home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_results"
export nnUNet_preprocessed="/home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_preprocessed"

The nnUNet raw data was preprocessed:

nnUNetv2_plan_and_preprocess -d 101 --verify_dataset_integrity -c 3d_fullres

I got a message saying:

...
Warning: Direction mismatch between segmentation and corresponding images. 
Direction images: (0.9998246020407097, -0.002916572623419852, 0.01850023567035481, 0.0008972689266514793, -0.9792064375432301, -0.2028643810866037, -0.018707219531766597, -0.2028453752936667, 0.9790320649327531). 
Direction seg: (0.999824551454073, -0.002916848812073564, 0.01850292634995937, 0.0008975451891515451, -0.9792064339919263, -0.20286437942668814, -0.01870990973509375, -0.20284538846575453, 0.9790320144286973). 
Image files: ['/home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset101_msLesionAgnostic/imagesTr/msLesionAgnostic_640_0000.nii.gz']. 
Seg file: /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset101_msLesionAgnostic/labelsTr/msLesionAgnostic_640.nii.gz

Traceback (most recent call last):
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/bin/nnUNetv2_plan_and_preprocess", line 8, in <module>
    sys.exit(plan_and_preprocess_entry())
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/site-packages/nnunetv2/experiment_planning/plan_and_preprocess_entrypoints.py", line 184, in plan_and_preprocess_entry
    extract_fingerprints(args.d, args.fpe, args.npfp, args.verify_dataset_integrity, args.clean, args.verbose)
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/site-packages/nnunetv2/experiment_planning/plan_and_preprocess_api.py", line 47, in extract_fingerprints
    extract_fingerprint_dataset(d, fingerprint_extractor_class, num_processes, check_dataset_integrity, clean,
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/site-packages/nnunetv2/experiment_planning/plan_and_preprocess_api.py", line 30, in extract_fingerprint_dataset
    verify_dataset_integrity(join(nnUNet_raw, dataset_name), num_processes)
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/site-packages/nnunetv2/experiment_planning/verify_dataset_integrity.py", line 220, in verify_dataset_integrity
    raise RuntimeError(
RuntimeError: Some images have errors. Please check text output above to see which one(s) and what's going on.

I need to deal with this when doing the dataset conversion (using sct_register_multimodal -identity 1

@valosekj
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I experienced something similar in the past. If you are sure that your images and labels are in the same space, which is something I would expect (you can check this, for example, by opening them in FSLeyes and check if you get a red warning in the left-down corner), then you can fix ITK direction using this script: https://gist.github.com/valosekj/a03195d9060b0e164faff95102129feb

Alternatively, you can maybe try to change "overwrite_image_reader_writer" to NibabelIO (to use nibabel instead of itk) in dataset.json.

@plbenveniste
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plbenveniste commented Jul 31, 2024 •
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Thanks @valosekj for your message.
I ended up using sct_register_multimodal -identity 1 to have same dimensions in images and labels.
I also than binarized every label to a threshold of 0.5 (I could have avoided doing this by using nearest neigbour in the above process.

Surprisingly, I still had a dimension mismatch for some images as we can see below, but it didn't cause any problem.

Warning messages 
Direction images: (0.9995568285353207, -4.2305939833177105e-05, 0.029768181674008378, 0.0023210087268018707, 0.9970655078825915, -0.07651788804874493, -0.02967759166276047, 0.07655306134333943, 0.9966237344998277). 
Direction seg: (0.999557527771378, 0.0011393518202445663, 0.029722897004697196, 0.001139351755611898, 0.9970662055766669, -0.07653550708132914, -0.029722896109700924, 0.0765355053186275, 0.9966237331859261). 
Image files: ['/home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/imagesTr/msLesionAgnostic_1371_0000.nii.gz']. 
Seg file: /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/labelsTr/msLesionAgnostic_1371.nii.gz

Warning: Direction mismatch between segmentation and corresponding images. 
Direction images: (0.9998727930631436, -0.01594979726329166, 4.048400076738005e-05, -0.015482158666859506, -0.9699406718540176, 0.24284850674579794, 0.003834117254632673, 0.24281823871084504, 0.9700642252615967). 
Direction seg: (0.9998746193770184, -0.015715991708088715, 0.001937302249608592, -0.01571599139066678, -0.9699406460725575, 0.24283357955510604, 0.001937302317948398, 0.24283358643519834, 0.9700660284230386). 
Image files: ['/home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/imagesTr/msLesionAgnostic_1184_0000.nii.gz']. 
Seg file: /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/labelsTr/msLesionAgnostic_1184.nii.gz

Warning: Direction mismatch between segmentation and corresponding images. 
Direction images: (0.9999650507186807, -3.2498976463678855e-05, -0.008360399655654377, -0.0019455056336788246, 0.9716359354515495, -0.23647371604735118, 0.008130950068450082, 0.23648172843373924, 0.9716019171123297). 
Direction seg: (0.9999655147443567, -0.0009890025352992376, -0.008245677081396018, -0.0009890025711708239, 0.9716363921293472, -0.2364777896768553, 0.008245677411101177, 0.2364777861953439, 0.9716019060289138). 
Image files: ['/home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/imagesTr/msLesionAgnostic_1341_0000.nii.gz']. 
Seg file: /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/labelsTr/msLesionAgnostic_1341.nii.gz

Warning: Direction mismatch between segmentation and corresponding images. 
Direction images: (0.9999461782630069, 1.4256992099983761e-05, 0.010374988239862817, 0.0021222265209242816, 0.9785737025935498, -0.20588591831855255, -0.010155625623308788, 0.2058968391915934, 0.9785210004170319). 
Direction seg: (0.9999467397067836, 0.0010682421584383053, 0.01026531114786726, 0.0010682421705256642, 0.9785742550834928, -0.20589144819860944, -0.01026531092755063, 0.20589144262566617, 0.9785209936152197). 
Image files: ['/home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/imagesTr/msLesionAgnostic_1349_0000.nii.gz']. 
Seg file: /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/labelsTr/msLesionAgnostic_1349.nii.gz

Warning: Direction mismatch between segmentation and corresponding images. 
Direction images: (0.9998737875777984, -0.015846944828360025, 0.0011327979091779968, -0.01587049846636809, -0.9995553321726754, 0.025244112225836113, -0.0007322521810542541, 0.025258904663096143, 0.9996806747991213). 
Direction seg: (0.9998742224498159, -0.01585872568560471, 0.00020027293067782567, -0.015858725394327734, -0.9995553321235096, 0.025251513487364926, 0.00020027292187368913, 0.025251511699089922, 0.9996811096331424). 
Image files: ['/home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/imagesTr/msLesionAgnostic_1199_0000.nii.gz']. 
Seg file: /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/labelsTr/msLesionAgnostic_1199.nii.gz

Warning: Direction mismatch between segmentation and corresponding images. 
Direction images: (0.999964081679984, 4.2173231885602473e-05, -0.00847546859402508, -0.001320598932424257, 0.9885497176643242, -0.15088973555037227, 0.008372058788976455, 0.15089550665014348, 0.9885142660263687). 
Direction seg: (0.9999643151625024, -0.0006392129841412547, -0.008423765271167316, -0.000639212927685442, 0.9885499483048401, -0.15089264395156346, 0.008423764504104498, 0.1508926476448042, 0.9885142640245324). 
Image files: ['/home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/imagesTr/msLesionAgnostic_1353_0000.nii.gz']. 
Seg file: /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/labelsTr/msLesionAgnostic_1353.nii.gz

Warning: Direction mismatch between segmentation and corresponding images. 
Direction images: (0.999967560007628, 2.4250750962094284e-05, -0.008054708809685418, -0.003024104554356132, 0.9279712714617492, -0.3726394708944929, 0.007465502262755987, 0.37265173923324924, 0.9279412408107873). 
Direction seg: (0.9999687649609699, -0.001499927893738001, -0.007760110282334256, -0.0014999279201193896, 0.9279724411610171, -0.37264582077690295, 0.007760110738075229, 0.3726458085930611, 0.9279412012330923). 
Image files: ['/home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/imagesTr/msLesionAgnostic_2180_0000.nii.gz']. 
Seg file: /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/labelsTr/msLesionAgnostic_2180.nii.gz

Warning: Direction mismatch between segmentation and corresponding images. 
Direction images: (0.9999675164049507, 2.735942794877295e-05, -0.008060110927302988, -0.0030159611672683187, 0.9286202535678794, -0.3710192836777322, 0.007474631305437654, 0.3710315672756014, 0.928590181808478). 
Direction seg: (0.9999687169716581, -0.0014943018706426896, -0.007767376468637484, -0.0014943019824960953, 0.9286214214917144, -0.3710256655337067, 0.007767376625409159, 0.37102563605312655, 0.9285901266856461). 
Image files: ['/home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/imagesTr/msLesionAgnostic_2183_0000.nii.gz']. 
Seg file: /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/labelsTr/msLesionAgnostic_2183.nii.gz

The dataset is being preprocessed using:

nnUNetv2_plan_and_preprocess -d 201 --verify_dataset_integrity -c 3d_fullres 2d

@plbenveniste
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plbenveniste commented Jul 31, 2024 •
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The model is now training on kronos and koios using:

  • On kronos:
CUDA_VISIBLE_DEVICES=2 nnUNetv2_train 201 3d_fullres 0 && CUDA_VISIBLE_DEVICES=2 nnUNetv2_train 201 3d_fullres 1
CUDA_VISIBLE_DEVICES=3 nnUNetv2_train 201 3d_fullres 2 && CUDA_VISIBLE_DEVICES=3 nnUNetv2_train 201 3d_fullres 3
  • on koios
CUDA_VISIBLE_DEVICES=1 nnUNetv2_train 201 2d 0 && CUDA_VISIBLE_DEVICES=1 nnUNetv2_train 201 3d_fullres 4

@plbenveniste
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plbenveniste commented Jul 31, 2024 •
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I was faced with this issue:

Error message 
2024-07-31 14:20:25.881668: unpacking dataset...
Error when checking /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_preprocessed/Dataset201_msLesionAgnostic/nnUNetPlans_3d_fullres/msLesionAgnostic_793.npy and /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_preprocessed/Dataset201_msLesionAgnostic/nnUNetPlans_3d_fullres/msLesionAgnostic_793_seg.npy, fixing...
multiprocessing.pool.RemoteTraceback:
"""
Traceback (most recent call last):
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/multiprocessing/pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/multiprocessing/pool.py", line 51, in starmapstar
    return list(itertools.starmap(args[0], args[1]))
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/site-packages/nnunetv2/training/dataloading/utils.py", line 40, in _convert_to_npy
    np.load(seg_npy, mmap_mode='r')
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/site-packages/numpy/lib/_npyio_impl.py", line 464, in load
    raise EOFError("No data left in file")
EOFError: No data left in file
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/bin/nnUNetv2_train", line 8, in <module>
    sys.exit(run_training_entry())
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/site-packages/nnunetv2/run/run_training.py", line 274, in run_training_entry
    run_training(args.dataset_name_or_id, args.configuration, args.fold, args.tr, args.p, args.pretrained_weights,
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/site-packages/nnunetv2/run/run_training.py", line 210, in run_training
    nnunet_trainer.run_training()
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/site-packages/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py", line 1287, in run_training
    self.on_train_start()
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/site-packages/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py", line 847, in on_train_start
    unpack_dataset(self.preprocessed_dataset_folder, unpack_segmentation=True, overwrite_existing=False,
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/site-packages/nnunetv2/training/dataloading/utils.py", line 66, in unpack_dataset
    p.starmap(_convert_to_npy, zip(npz_files,
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/multiprocessing/pool.py", line 372, in starmap
    return self._map_async(func, iterable, starmapstar, chunksize).get()
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/multiprocessing/pool.py", line 771, in get
    raise self._value
EOFError: No data left in file
Exception in thread Thread-1:
Traceback (most recent call last):
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/threading.py", line 980, in _bootstrap_inner
    self.run()
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/threading.py", line 917, in run
    self._target(*self._args, **self._kwargs)
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 125, in results_loop
    raise e
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/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:
Traceback (most recent call last):
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/threading.py", line 980, in _bootstrap_inner
    self.run()
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/threading.py", line 917, in run
    self._target(*self._args, **self._kwargs)
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 125, in results_loop
    raise e
  File "/home/plbenveniste/miniconda3/envs/venv_nnunet/lib/python3.9/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

Looking into this now.

I found the solution in this issue: MIC-DKFZ/nnUNet#441 . I should just deleted all the .npy files and when I run training, I should wait for one of the trainings to have reached GPU stage before launching the others.

@plbenveniste
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plbenveniste commented Aug 2, 2024 •
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The inference were performed with the 2d model on koios using :

CUDA_VISIBLE_DEVICES=1 nnUNetv2_predict -i /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/imagesTs/ -o /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_results/Dataset201_msLesionAgnostic/nnUNetTrainer__nnUNetPlans__2d/fold_0/test_set -d 201 -c 2d -f 0 -chk checkpoint_best.pth

The results were computed using:

python nnunet/evaluate_predictions.py -pred-folder ~/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_results/Dataset201_msLesionAgnostic/nnUNetTrainer__nnUNetPlans__2d/fold_0/test_set/ -label-folder ~/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/labelsTs  -image-folder ~/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/imagesTs/ -conversion-dict ~/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/conversion_dict.json -output-folder ~/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_results/Dataset201_msLesionAgnostic/nnUNetTrainer__nnUNetPlans__2d/fold_0/test_set/

And the plots were done using:

python nnunet/plot_performance.py --pred-dir-path /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_results/Dataset201_msLesionAgnostic/nnUNetTrainer__nnUNetPlans__2d/fold_0/test_set/ --data-json-path /home/plbenveniste/net/ms-lesion-agnostic/msd_data/dataset_2024-07-24_seed42_lesionOnly.json --split test

The results are the following:
dice_scores_contrast
dice_scores_orientation
dice_scores_site

@plbenveniste
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plbenveniste commented Aug 2, 2024 •
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When comparing the results of the nnUNet compared to the results of the monai Unet (#21 (comment)), it seems that the 2d nnUnet underperforms, but its performances are more regular (lower variance).
Why? To be investigated...

Also need to look into the results of the 3D model

@plbenveniste
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plbenveniste commented Aug 9, 2024 •
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Here we focus on the results of the 3d nnUNet. We used the fold 3 for inference as it presented the best dice score:

  • fold 0 = 0.6470999717712402
  • fold 1 =0.6467999815940857
  • fold 2 =0.6449000239372253
  • fold 3 =0.6585999727249146
  • fold 4 =0.6327999830245972

Inference was done using:

CUDA_VISIBLE_DEVICES=1 nnUNetv2_predict -i /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/imagesTs/ -o /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_results/Dataset201_msLesionAgnostic/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_3/test_set -d 201 -c 3d_fullres -f 3 -chk checkpoint_best.pth

python nnunet/evaluate_predictions.py -pred-folder ~/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_results/Dataset201_msLesionAgnostic/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_3/test_set/ -label-folder ~/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/labelsTs  -image-folder ~/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/imagesTs/ -conversion-dict ~/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_raw/Dataset201_msLesionAgnostic/conversion_dict.json -output-folder ~/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_results/Dataset201_msLesionAgnostic/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_3/test_set/

python nnunet/plot_performance.py --pred-dir-path /home/plbenveniste/net/ms-lesion-agnostic/nnunet_experiments/nnUNet_results/Dataset201_msLesionAgnostic/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_3/test_set/ --data-json-path /home/plbenveniste/net/ms-lesion-agnostic/msd_data/dataset_2024-07-24_seed42_lesionOnly.json --split test

Here are the results for fold 3 of the 3d_fullres model:
dice_scores_contrast
dice_scores_orientation
dice_scores_site

The same conclusion as the 2d model stands when comparing the 3d_fullres model to the monai attentionUnet model.

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