├── Data └── nnUNet_raw │ ├── Dataset801_MedAnaSegPipeline_AbdomenCT │ └── dataset.json │ ├── Dataset802_MedAnaSegPipeline__LiverTumor │ └── dataset.json │ └── Dataset803_MedAnaSegPipeline_part1_lungBAV │ └── dataset.json ├── README.md ├── assets ├── Fig1.png ├── Fig2.png └── video.jpg └── mambaclinix ├── .gitignore ├── LICENSE ├── nnunetv2 ├── __init__.py ├── batch_running │ ├── __init__.py │ ├── benchmarking │ │ ├── __init__.py │ │ ├── generate_benchmarking_commands.py │ │ └── summarize_benchmark_results.py │ ├── collect_results_custom_Decathlon.py │ ├── collect_results_custom_Decathlon_2d.py │ ├── generate_lsf_runs_customDecathlon.py │ └── release_trainings │ │ ├── __init__.py │ │ └── nnunetv2_v1 │ │ ├── __init__.py │ │ ├── collect_results.py │ │ └── generate_lsf_commands.py ├── configuration.py ├── dataset_conversion │ ├── Dataset027_ACDC.py │ ├── Dataset073_Fluo_C3DH_A549_SIM.py │ ├── Dataset114_MNMs.py │ ├── Dataset115_EMIDEC.py │ ├── Dataset120_RoadSegmentation.py │ ├── Dataset137_BraTS21.py │ ├── Dataset218_Amos2022_task1.py │ ├── Dataset219_Amos2022_task2.py │ ├── Dataset220_KiTS2023.py │ ├── Dataset221_AutoPETII_2023.py │ ├── Dataset988_dummyDataset4.py │ ├── __init__.py │ ├── convert_MSD_dataset.py │ ├── convert_raw_dataset_from_old_nnunet_format.py │ ├── datasets_for_integration_tests │ │ ├── Dataset996_IntegrationTest_Hippocampus_regions_ignore.py │ │ ├── Dataset997_IntegrationTest_Hippocampus_regions.py │ │ ├── Dataset998_IntegrationTest_Hippocampus_ignore.py │ │ ├── Dataset999_IntegrationTest_Hippocampus.py │ │ └── __init__.py │ └── generate_dataset_json.py ├── ensembling │ ├── __init__.py │ └── ensemble.py ├── evaluation │ ├── __init__.py │ ├── accumulate_cv_results.py │ ├── evaluate_predictions.py │ └── find_best_configuration.py ├── experiment_planning │ ├── __init__.py │ ├── dataset_fingerprint │ │ ├── __init__.py │ │ └── fingerprint_extractor.py │ ├── experiment_planners │ │ ├── __init__.py │ │ ├── default_experiment_planner.py │ │ ├── network_topology.py │ │ ├── readme.md │ │ └── resencUNet_planner.py │ ├── plan_and_preprocess_api.py │ ├── plan_and_preprocess_entrypoints.py │ ├── plans_for_pretraining │ │ ├── __init__.py │ │ └── move_plans_between_datasets.py │ └── verify_dataset_integrity.py ├── imageio │ ├── __init__.py │ ├── base_reader_writer.py │ ├── natural_image_reader_writer.py │ ├── nibabel_reader_writer.py │ ├── reader_writer_registry.py │ ├── readme.md │ ├── simpleitk_reader_writer.py │ └── tif_reader_writer.py ├── inference │ ├── __init__.py │ ├── data_iterators.py │ ├── examples.py │ ├── export_prediction.py │ ├── predict_from_raw_data.py │ ├── readme.md │ └── sliding_window_prediction.py ├── model_sharing │ ├── __init__.py │ ├── entry_points.py │ ├── model_download.py │ ├── model_export.py │ └── model_import.py ├── nets │ └── MambaClinix_3d.py ├── paths.py ├── postprocessing │ ├── __init__.py │ └── remove_connected_components.py ├── preprocessing │ ├── __init__.py │ ├── cropping │ │ ├── __init__.py │ │ └── cropping.py │ ├── normalization │ │ ├── __init__.py │ │ ├── default_normalization_schemes.py │ │ ├── map_channel_name_to_normalization.py │ │ └── readme.md │ ├── preprocessors │ │ ├── __init__.py │ │ └── default_preprocessor.py │ └── resampling │ │ ├── __init__.py │ │ ├── default_resampling.py │ │ └── utils.py ├── run │ ├── __init__.py │ ├── load_pretrained_weights.py │ └── run_training.py ├── tests │ ├── __init__.py │ └── integration_tests │ │ ├── __init__.py │ │ ├── add_lowres_and_cascade.py │ │ ├── cleanup_integration_test.py │ │ ├── lsf_commands.sh │ │ ├── prepare_integration_tests.sh │ │ ├── readme.md │ │ ├── run_integration_test.sh │ │ ├── run_integration_test_bestconfig_inference.py │ │ └── run_integration_test_trainingOnly_DDP.sh ├── training │ ├── __init__.py │ ├── data_augmentation │ │ ├── __init__.py │ │ ├── compute_initial_patch_size.py │ │ └── custom_transforms │ │ │ ├── __init__.py │ │ │ ├── cascade_transforms.py │ │ │ ├── deep_supervision_donwsampling.py │ │ │ ├── limited_length_multithreaded_augmenter.py │ │ │ ├── manipulating_data_dict.py │ │ │ ├── masking.py │ │ │ ├── region_based_training.py │ │ │ └── transforms_for_dummy_2d.py │ ├── dataloading │ │ ├── __init__.py │ │ ├── base_data_loader.py │ │ ├── data_loader_2d.py │ │ ├── data_loader_3d.py │ │ ├── nnunet_dataset.py │ │ └── utils.py │ ├── logging │ │ ├── __init__.py │ │ └── nnunet_logger.py │ ├── loss │ │ ├── __init__.py │ │ ├── compound_losses.py │ │ ├── deep_supervision.py │ │ ├── dice.py │ │ ├── mambaclinix_loss.py │ │ ├── region_specific_tversky_losses.py │ │ └── robust_ce_loss.py │ ├── lr_scheduler │ │ ├── __init__.py │ │ └── polylr.py │ └── nnUNetTrainer │ │ ├── __init__.py │ │ ├── nnUNetTrainer.py │ │ ├── nnUNetTrainerMambaClinix.py │ │ └── variants │ │ ├── __init__.py │ │ ├── benchmarking │ │ ├── __init__.py │ │ ├── nnUNetTrainerBenchmark_5epochs.py │ │ └── nnUNetTrainerBenchmark_5epochs_noDataLoading.py │ │ ├── data_augmentation │ │ ├── __init__.py │ │ ├── nnUNetTrainerDA5.py │ │ ├── nnUNetTrainerDAOrd0.py │ │ ├── nnUNetTrainerNoDA.py │ │ └── nnUNetTrainerNoMirroring.py │ │ ├── loss │ │ ├── __init__.py │ │ ├── nnUNetTrainerCELoss.py │ │ ├── nnUNetTrainerDiceLoss.py │ │ ├── nnUNetTrainerMambaClinixTRS.py │ │ └── nnUNetTrainerTopkLoss.py │ │ ├── lr_schedule │ │ ├── __init__.py │ │ └── nnUNetTrainerCosAnneal.py │ │ ├── network_architecture │ │ ├── __init__.py │ │ ├── nnUNetTrainerBN.py │ │ └── nnUNetTrainerNoDeepSupervision.py │ │ ├── optimizer │ │ ├── __init__.py │ │ ├── nnUNetTrainerAdam.py │ │ └── nnUNetTrainerAdan.py │ │ ├── sampling │ │ ├── __init__.py │ │ └── nnUNetTrainer_probabilisticOversampling.py │ │ └── training_length │ │ ├── __init__.py │ │ ├── nnUNetTrainer_Xepochs.py │ │ └── nnUNetTrainer_Xepochs_NoMirroring.py └── utilities │ ├── __init__.py │ ├── collate_outputs.py │ ├── dataset_name_id_conversion.py │ ├── ddp_allgather.py │ ├── default_n_proc_DA.py │ ├── file_path_utilities.py │ ├── find_class_by_name.py │ ├── get_network_from_plans.py │ ├── helpers.py │ ├── json_export.py │ ├── label_handling │ ├── __init__.py │ └── label_handling.py │ ├── network_initialization.py │ ├── overlay_plots.py │ ├── plans_handling │ ├── __init__.py │ └── plans_handler.py │ └── utils.py └── setup.py /Data/nnUNet_raw/Dataset801_MedAnaSegPipeline_AbdomenCT/dataset.json: -------------------------------------------------------------------------------- 1 | { 2 | "channel_names": { 3 | "0": "CT" 4 | }, 5 | "labels": { 6 | "background": 0, 7 | "liver": 1, 8 | "right kidney": 2, 9 | "spleen": 3, 10 | "pancreas": 4, 11 | "aorta": 5, 12 | "inferior vena cava": 6, 13 | "right adrenal gland": 7, 14 | "left adrenal gland": 8, 15 | "gallbladder": 9, 16 | "esophagus": 10, 17 | "stomach": 11, 18 | "duodenum": 12, 19 | "left kidney": 13 20 | }, 21 | "numTraining": 50, 22 | "file_ending": ".nii.gz", 23 | "name": "Dataset701_AbdomenCT", 24 | "description": "This dataset was from MICCAI FLARE 2022 Challenge. The training set contained 50 CT scans that were from the MSD Pancreas dataset and the annotations were from AbdomenCT-1K. Another 50 validation cases were from TCIA and the annotations were provided by the challenge organizers." 25 | } -------------------------------------------------------------------------------- /Data/nnUNet_raw/Dataset802_MedAnaSegPipeline__LiverTumor/dataset.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "Liver", 3 | "description": "Liver tumor segmentation", 4 | "licence": "CC-BY-SA 4.0", 5 | "release": "https://github.com/CYB08", 6 | "tensorImageSize": "3D", 7 | "labels": { 8 | "background": 0, 9 | "tumor": 1 10 | }, 11 | "numTraining": 25, 12 | "file_ending": ".nii.gz", 13 | "channel_names": { 14 | "0": "CT" 15 | } 16 | } -------------------------------------------------------------------------------- /Data/nnUNet_raw/Dataset803_MedAnaSegPipeline_part1_lungBAV/dataset.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "MedAna_SegPipeline", 3 | "description": "Segmentation of Clinical Images", 4 | "reference": "https://github.com/CYB08", 5 | "licence": "Apache 2.0", 6 | "release": "2.0", 7 | "channel_names": { 8 | "0": "CT" 9 | }, 10 | "labels": { 11 | "background": 0, 12 | "Bronchus": 1, 13 | "PulmonaryArtery": 2, 14 | "PulmonaryVenous": 3 15 | }, 16 | "numTraining": 55, 17 | "file_ending": ".nii.gz", 18 | "overwrite_image_reader_writer": "SimpleITKIO" 19 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # [MambaClinix](https://arxiv.org/abs/2409.12533) 2 | 3 | - Official repository for "MambaClinix: Hierarchical Gated Convolution and Mamba-Based U-Net for Enhanced 3D Medical Image Segmentation." [arXiv, 2024](https://arxiv.org/abs/2409.12533) 4 | 5 | ![Framework](https://github.com/CYB08/MambaClinix-PyTorch/blob/main/assets/Fig1.png) 6 | 7 | ![Block](https://github.com/CYB08/MambaClinix-PyTorch/blob/main/assets/Fig2.png) 8 | 9 | [![](https://github.com/CYB08/MambaClinix-PyTorch/blob/main/assets/video.jpg)](https://www.bilibili.com/video/BV1VYbFepEDk/?spm_id_from=333.999.list.card_archive.click) 10 | 11 | ## Installation 12 | 13 | Requirements: `ubuntu 22.04` + `Python 3.10` + `torch 2.0.1` + `torchvision 0.15.2 (cuda 11.8)` 14 | 15 | 1. Create a virtual environment: `conda create -n MambaClinix python=3.10 -y` and `conda activate MambaClinix ` 16 | 2. Install [Pytorch](https://pytorch.org/get-started/previous-versions/#linux-and-windows-4) 2.0.1: `pip install torch==2.0.1 torchvision==0.15.2 --index-url https://download.pytorch.org/whl/cu118` 17 | 3. Install [Mamba](https://github.com/state-spaces/mamba): `pip install causal-conv1d>=1.2.0` and `pip install https://github.com/state-spaces/mamba/releases/download/v2.1.0/mamba_ssm-2.1.0+cu118torch2.0cxx11abiFALSE-cp310-cp310-linux_x86_64.whl` 18 | 4. Download code: `git clone https://github.com/CYB08/MambaClinix-PyTorch.git` and `cd MambaClinix-PyTorch/mambaclinix` and run `pip install -e .` 19 | 20 | ## Preprocessing 21 | 22 | We have released a small portion of sample data from our private dataset. Download dataset [here](https://drive.google.com/drive/folders/111n2yo68O3s7kZFjwo7840B-pdNWkAvG?usp=drive_link) and put them into the `Data` folder. `MambaClinix` is built on the popular [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) framework. To preprocess your datasets, please run: 23 | 24 | ``` 25 | nnUNetv2_plan_and_preprocess -d DATASET_ID --verify_dataset_integrity -np 2 26 | ``` 27 | 28 | If you want to train on your own dataset, please adjust the base path in `mambaclinix/nnunetv2/paths.py` as follow: 29 | 30 | ```python 31 | # An example to set base path, 32 | base = '/root/autodl-tmp/Data' # Using your nnUNet data directory locations 33 | nnUNet_raw = join(base, 'nnUNet_raw') 34 | nnUNet_preprocessed = join(base, 'nnUNet_preprocessed') 35 | nnUNet_results = join(base, 'nnUNet_results') 36 | ``` 37 | 38 | 39 | ## Model Training 40 | - Train a `MambaClinix` model 41 | 42 | ``` 43 | nnUNetv2_train DATASET_ID 3d_fullres all -tr nnUNetTrainerMambaClinix 44 | ``` 45 | 46 | - Train a `MambaClinix` model with `region-specific Tversky loss` 47 | 48 | ``` 49 | nnUNetv2_train DATASET_ID 3d_fullres all -tr nnUNetTrainerMambaClinixTRS 50 | ``` 51 | 52 | ## Inference 53 | 54 | - Predict samples with `MambaClinix` model 55 | 56 | ```bash 57 | nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_ID -c 3d_fullres --disable_tta -f all -tr nnUNetTrainerMambaClinix 58 | ``` 59 | 60 | ## Evaluate 61 | 62 | - Evaluate `MambaClinix` model performance 63 | 64 | ```bash 65 | nnUNetv2_evaluate_folder GT_FOLDER PRED_FOLDER -djfile Path_Dataset_json -pfile Path_Plans_json 66 | ``` 67 | 68 | `PRED_FOLDER` refers to the path of the prediction file or folder; `GT_FOLDER` is the path of the corresponding ground truth file or folder; `Path_Dataset_json` represents the path of the `dataset.json`, and `Path_Plans_json` represents the path of the `plans.json`. 69 | 70 | ## Paper 71 | 72 | ``` 73 | @misc{bian2024mambaclinixhierarchicalgatedconvolution, 74 | title={MambaClinix: Hierarchical Gated Convolution and Mamba-Based U-Net forEnhanced 3D Medical Image Segmentation}, 75 | author={Chenyuan Bian and Nan Xia and Xia Yang and Feifei Wang and Fengjiao Wang and Bin Wei and Qian Dong}, 76 | year={2024}, 77 | eprint={2409.12533}, 78 | archivePrefix={arXiv}, 79 | primaryClass={eess.IV}, 80 | url={https://arxiv.org/abs/2409.12533}, 81 | } 82 | ``` 83 | 84 | ## Acknowledgements 85 | 86 | We greatly appreciate the authors of [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) , [Mamba](https://github.com/state-spaces/mamba) , and [U-Mamba](https://github.com/bowang-lab/U-Mamba) projects for open-sourcing their valuable code. Our code is developed based on these outstanding projects. 87 | 88 | 89 | 90 | -------------------------------------------------------------------------------- /assets/Fig1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/assets/Fig1.png -------------------------------------------------------------------------------- /assets/Fig2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/assets/Fig2.png -------------------------------------------------------------------------------- /assets/video.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/assets/video.jpg -------------------------------------------------------------------------------- /mambaclinix/.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # IPython Notebook 70 | .ipynb_checkpoints 71 | 72 | # pyenv 73 | .python-version 74 | 75 | # celery beat schedule file 76 | celerybeat-schedule 77 | 78 | # dotenv 79 | .env 80 | 81 | # virtualenv 82 | venv/ 83 | ENV/ 84 | 85 | # Spyder project settings 86 | .spyderproject 87 | 88 | # Rope project settings 89 | .ropeproject 90 | 91 | *.memmap 92 | *.png 93 | *.zip 94 | *.npz 95 | *.npy 96 | *.jpg 97 | *.jpeg 98 | .idea 99 | *.txt 100 | .idea/* 101 | *.png 102 | *.nii.gz 103 | *.nii 104 | *.tif 105 | *.bmp 106 | *.pkl 107 | *.xml 108 | *.pkl 109 | *.pdf 110 | *.png 111 | *.jpg 112 | *.jpeg 113 | 114 | *.model 115 | 116 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/batch_running/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/batch_running/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/batch_running/benchmarking/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/batch_running/benchmarking/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/batch_running/benchmarking/generate_benchmarking_commands.py: -------------------------------------------------------------------------------- 1 | if __name__ == '__main__': 2 | """ 3 | This code probably only works within the DKFZ infrastructure (using LSF). You will need to adapt it to your scheduler! 4 | """ 5 | gpu_models = [#'NVIDIAA100_PCIE_40GB', 'NVIDIAGeForceRTX2080Ti', 'NVIDIATITANRTX', 'TeslaV100_SXM2_32GB', 6 | 'NVIDIAA100_SXM4_40GB']#, 'TeslaV100_PCIE_32GB'] 7 | datasets = [2, 3, 4, 5] 8 | trainers = ['nnUNetTrainerBenchmark_5epochs', 'nnUNetTrainerBenchmark_5epochs_noDataLoading'] 9 | plans = ['nnUNetPlans'] 10 | configs = ['2d', '2d_bs3x', '2d_bs6x', '3d_fullres', '3d_fullres_bs3x', '3d_fullres_bs6x'] 11 | num_gpus = 1 12 | 13 | benchmark_configurations = {d: configs for d in datasets} 14 | 15 | exclude_hosts = "-R \"select[hname!='e230-dgxa100-1']'\"" 16 | resources = "-R \"tensorcore\"" 17 | queue = "-q gpu" 18 | preamble = "-L /bin/bash \"source ~/load_env_torch210.sh && " 19 | train_command = 'nnUNet_compile=False nnUNet_results=/dkfz/cluster/gpu/checkpoints/OE0441/isensee/nnUNet_results_remake_benchmark nnUNetv2_train' 20 | 21 | folds = (0, ) 22 | 23 | use_these_modules = { 24 | tr: plans for tr in trainers 25 | } 26 | 27 | additional_arguments = f' -num_gpus {num_gpus}' # '' 28 | 29 | output_file = "/home/isensee/deleteme.txt" 30 | with open(output_file, 'w') as f: 31 | for g in gpu_models: 32 | gpu_requirements = f"-gpu num={num_gpus}:j_exclusive=yes:gmodel={g}" 33 | for tr in use_these_modules.keys(): 34 | for p in use_these_modules[tr]: 35 | for dataset in benchmark_configurations.keys(): 36 | for config in benchmark_configurations[dataset]: 37 | for fl in folds: 38 | command = f'bsub {exclude_hosts} {resources} {queue} {gpu_requirements} {preamble} {train_command} {dataset} {config} {fl} -tr {tr} -p {p}' 39 | if additional_arguments is not None and len(additional_arguments) > 0: 40 | command += f' {additional_arguments}' 41 | f.write(f'{command}\"\n') -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/batch_running/benchmarking/summarize_benchmark_results.py: -------------------------------------------------------------------------------- 1 | from batchgenerators.utilities.file_and_folder_operations import join, load_json, isfile 2 | from nnunetv2.utilities.dataset_name_id_conversion import maybe_convert_to_dataset_name 3 | from nnunetv2.paths import nnUNet_results 4 | from nnunetv2.utilities.file_path_utilities import get_output_folder 5 | 6 | if __name__ == '__main__': 7 | trainers = ['nnUNetTrainerBenchmark_5epochs', 'nnUNetTrainerBenchmark_5epochs_noDataLoading'] 8 | datasets = [2, 3, 4, 5] 9 | plans = ['nnUNetPlans'] 10 | configs = ['2d', '2d_bs3x', '2d_bs6x', '3d_fullres', '3d_fullres_bs3x', '3d_fullres_bs6x'] 11 | output_file = join(nnUNet_results, 'benchmark_results.csv') 12 | 13 | torch_version = '2.1.0.dev20230330'#"2.0.0"#"2.1.0.dev20230328" #"1.11.0a0+gitbc2c6ed" # 14 | cudnn_version = 8700 # 8302 # 15 | num_gpus = 1 16 | 17 | unique_gpus = set() 18 | 19 | # collect results in the most janky way possible. Amazing coding skills! 20 | all_results = {} 21 | for tr in trainers: 22 | all_results[tr] = {} 23 | for p in plans: 24 | all_results[tr][p] = {} 25 | for c in configs: 26 | all_results[tr][p][c] = {} 27 | for d in datasets: 28 | dataset_name = maybe_convert_to_dataset_name(d) 29 | output_folder = get_output_folder(dataset_name, tr, p, c, fold=0) 30 | expected_benchmark_file = join(output_folder, 'benchmark_result.json') 31 | all_results[tr][p][c][d] = {} 32 | if isfile(expected_benchmark_file): 33 | # filter results for what we want 34 | results = [i for i in load_json(expected_benchmark_file).values() 35 | if i['num_gpus'] == num_gpus and i['cudnn_version'] == cudnn_version and 36 | i['torch_version'] == torch_version] 37 | for r in results: 38 | all_results[tr][p][c][d][r['gpu_name']] = r 39 | unique_gpus.add(r['gpu_name']) 40 | 41 | # haha. Fuck this. Collect GPUs in the code above. 42 | # unique_gpus = np.unique([i["gpu_name"] for tr in trainers for p in plans for c in configs for d in datasets for i in all_results[tr][p][c][d]]) 43 | 44 | unique_gpus = list(unique_gpus) 45 | unique_gpus.sort() 46 | 47 | with open(output_file, 'w') as f: 48 | f.write('Dataset,Trainer,Plans,Config') 49 | for g in unique_gpus: 50 | f.write(f",{g}") 51 | f.write("\n") 52 | for d in datasets: 53 | for tr in trainers: 54 | for p in plans: 55 | for c in configs: 56 | gpu_results = [] 57 | for g in unique_gpus: 58 | if g in all_results[tr][p][c][d].keys(): 59 | gpu_results.append(round(all_results[tr][p][c][d][g]["fastest_epoch"], ndigits=2)) 60 | else: 61 | gpu_results.append("MISSING") 62 | # skip if all are missing 63 | if all([i == 'MISSING' for i in gpu_results]): 64 | continue 65 | f.write(f"{d},{tr},{p},{c}") 66 | for g in gpu_results: 67 | f.write(f",{g}") 68 | f.write("\n") 69 | f.write("\n") 70 | 71 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/batch_running/collect_results_custom_Decathlon_2d.py: -------------------------------------------------------------------------------- 1 | from batchgenerators.utilities.file_and_folder_operations import * 2 | 3 | from nnunetv2.batch_running.collect_results_custom_Decathlon import collect_results, summarize 4 | from nnunetv2.paths import nnUNet_results 5 | 6 | if __name__ == '__main__': 7 | use_these_trainers = { 8 | 'nnUNetTrainer': ('nnUNetPlans', ), 9 | } 10 | all_results_file = join(nnUNet_results, 'hrnet_results.csv') 11 | datasets = [2, 3, 4, 17, 20, 24, 27, 38, 55, 64, 82] 12 | collect_results(use_these_trainers, datasets, all_results_file) 13 | 14 | folds = (0, ) 15 | configs = ('2d', ) 16 | output_file = join(nnUNet_results, 'hrnet_results_summary_fold0.csv') 17 | summarize(all_results_file, output_file, folds, configs, datasets, use_these_trainers) 18 | 19 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/batch_running/generate_lsf_runs_customDecathlon.py: -------------------------------------------------------------------------------- 1 | from copy import deepcopy 2 | import numpy as np 3 | 4 | 5 | def merge(dict1, dict2): 6 | keys = np.unique(list(dict1.keys()) + list(dict2.keys())) 7 | keys = np.unique(keys) 8 | res = {} 9 | for k in keys: 10 | all_configs = [] 11 | if dict1.get(k) is not None: 12 | all_configs += list(dict1[k]) 13 | if dict2.get(k) is not None: 14 | all_configs += list(dict2[k]) 15 | if len(all_configs) > 0: 16 | res[k] = tuple(np.unique(all_configs)) 17 | return res 18 | 19 | 20 | if __name__ == "__main__": 21 | # after the Nature Methods paper we switch our evaluation to a different (more stable/high quality) set of 22 | # datasets for evaluation and future development 23 | configurations_all = { 24 | 2: ("3d_fullres", "2d"), 25 | 3: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), 26 | 4: ("2d", "3d_fullres"), 27 | 17: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), 28 | 20: ("2d", "3d_fullres"), 29 | 24: ("2d", "3d_fullres"), 30 | 27: ("2d", "3d_fullres"), 31 | 38: ("2d", "3d_fullres"), 32 | 55: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), 33 | 64: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), 34 | 82: ("2d", "3d_fullres"), 35 | # 83: ("2d", "3d_fullres"), 36 | } 37 | 38 | configurations_3d_fr_only = { 39 | i: ("3d_fullres", ) for i in configurations_all if "3d_fullres" in configurations_all[i] 40 | } 41 | 42 | configurations_3d_c_only = { 43 | i: ("3d_cascade_fullres", ) for i in configurations_all if "3d_cascade_fullres" in configurations_all[i] 44 | } 45 | 46 | configurations_3d_lr_only = { 47 | i: ("3d_lowres", ) for i in configurations_all if "3d_lowres" in configurations_all[i] 48 | } 49 | 50 | configurations_2d_only = { 51 | i: ("2d", ) for i in configurations_all if "2d" in configurations_all[i] 52 | } 53 | 54 | num_gpus = 1 55 | exclude_hosts = "-R \"select[hname!='e230-dgx2-2']\" -R \"select[hname!='e230-dgx2-1']\" -R \"select[hname!='e230-dgx1-1']\" -R \"select[hname!='e230-dgxa100-1']\" -R \"select[hname!='e230-dgxa100-2']\" -R \"select[hname!='e230-dgxa100-3']\" -R \"select[hname!='e230-dgxa100-4']\"" 56 | resources = "-R \"tensorcore\"" 57 | gpu_requirements = f"-gpu num={num_gpus}:j_exclusive=yes:gmem=33G" 58 | queue = "-q gpu-lowprio" 59 | preamble = "-L /bin/bash \"source ~/load_env_cluster4.sh && " 60 | train_command = 'nnUNet_results=/dkfz/cluster/gpu/checkpoints/OE0441/isensee/nnUNet_results_remake_release nnUNetv2_train' 61 | 62 | folds = (0, ) 63 | # use_this = configurations_2d_only 64 | use_this = merge(configurations_3d_fr_only, configurations_3d_lr_only) 65 | # use_this = merge(use_this, configurations_3d_c_only) 66 | 67 | use_these_modules = { 68 | 'nnUNetTrainer': ('nnUNetPlans',), 69 | 'nnUNetTrainerDiceCELoss_noSmooth': ('nnUNetPlans',), 70 | # 'nnUNetTrainer_DASegOrd0': ('nnUNetPlans',), 71 | } 72 | 73 | additional_arguments = f'--disable_checkpointing -num_gpus {num_gpus}' # '' 74 | 75 | output_file = "/home/isensee/deleteme.txt" 76 | with open(output_file, 'w') as f: 77 | for tr in use_these_modules.keys(): 78 | for p in use_these_modules[tr]: 79 | for dataset in use_this.keys(): 80 | for config in use_this[dataset]: 81 | for fl in folds: 82 | command = f'bsub {exclude_hosts} {resources} {queue} {gpu_requirements} {preamble} {train_command} {dataset} {config} {fl} -tr {tr} -p {p}' 83 | if additional_arguments is not None and len(additional_arguments) > 0: 84 | command += f' {additional_arguments}' 85 | f.write(f'{command}\"\n') 86 | 87 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/batch_running/release_trainings/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/batch_running/release_trainings/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/batch_running/release_trainings/nnunetv2_v1/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/batch_running/release_trainings/nnunetv2_v1/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/batch_running/release_trainings/nnunetv2_v1/generate_lsf_commands.py: -------------------------------------------------------------------------------- 1 | from copy import deepcopy 2 | import numpy as np 3 | 4 | 5 | def merge(dict1, dict2): 6 | keys = np.unique(list(dict1.keys()) + list(dict2.keys())) 7 | keys = np.unique(keys) 8 | res = {} 9 | for k in keys: 10 | all_configs = [] 11 | if dict1.get(k) is not None: 12 | all_configs += list(dict1[k]) 13 | if dict2.get(k) is not None: 14 | all_configs += list(dict2[k]) 15 | if len(all_configs) > 0: 16 | res[k] = tuple(np.unique(all_configs)) 17 | return res 18 | 19 | 20 | if __name__ == "__main__": 21 | # after the Nature Methods paper we switch our evaluation to a different (more stable/high quality) set of 22 | # datasets for evaluation and future development 23 | configurations_all = { 24 | # 1: ("3d_fullres", "2d"), 25 | 2: ("3d_fullres", "2d"), 26 | # 3: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), 27 | # 4: ("2d", "3d_fullres"), 28 | 5: ("2d", "3d_fullres"), 29 | # 6: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), 30 | # 7: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), 31 | # 8: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), 32 | # 9: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), 33 | # 10: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), 34 | # 17: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), 35 | 20: ("2d", "3d_fullres"), 36 | 24: ("2d", "3d_fullres"), 37 | 27: ("2d", "3d_fullres"), 38 | 35: ("2d", "3d_fullres"), 39 | 38: ("2d", "3d_fullres"), 40 | # 55: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), 41 | # 64: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), 42 | # 82: ("2d", "3d_fullres"), 43 | # 83: ("2d", "3d_fullres"), 44 | } 45 | 46 | configurations_3d_fr_only = { 47 | i: ("3d_fullres", ) for i in configurations_all if "3d_fullres" in configurations_all[i] 48 | } 49 | 50 | configurations_3d_c_only = { 51 | i: ("3d_cascade_fullres", ) for i in configurations_all if "3d_cascade_fullres" in configurations_all[i] 52 | } 53 | 54 | configurations_3d_lr_only = { 55 | i: ("3d_lowres", ) for i in configurations_all if "3d_lowres" in configurations_all[i] 56 | } 57 | 58 | configurations_2d_only = { 59 | i: ("2d", ) for i in configurations_all if "2d" in configurations_all[i] 60 | } 61 | 62 | num_gpus = 1 63 | exclude_hosts = "-R \"select[hname!='e230-dgx2-2']\" -R \"select[hname!='e230-dgx2-1']\"" 64 | resources = "-R \"tensorcore\"" 65 | gpu_requirements = f"-gpu num={num_gpus}:j_exclusive=yes:gmem=1G" 66 | queue = "-q gpu-lowprio" 67 | preamble = "-L /bin/bash \"source ~/load_env_cluster4.sh && " 68 | train_command = 'nnUNet_keep_files_open=True nnUNet_results=/dkfz/cluster/gpu/data/OE0441/isensee/nnUNet_results_remake_release_normfix nnUNetv2_train' 69 | 70 | folds = (0, 1, 2, 3, 4) 71 | # use_this = configurations_2d_only 72 | # use_this = merge(configurations_3d_fr_only, configurations_3d_lr_only) 73 | # use_this = merge(use_this, configurations_3d_c_only) 74 | use_this = configurations_all 75 | 76 | use_these_modules = { 77 | 'nnUNetTrainer': ('nnUNetPlans',), 78 | } 79 | 80 | additional_arguments = f'--disable_checkpointing -num_gpus {num_gpus}' # '' 81 | 82 | output_file = "/home/isensee/deleteme.txt" 83 | with open(output_file, 'w') as f: 84 | for tr in use_these_modules.keys(): 85 | for p in use_these_modules[tr]: 86 | for dataset in use_this.keys(): 87 | for config in use_this[dataset]: 88 | for fl in folds: 89 | command = f'bsub {exclude_hosts} {resources} {queue} {gpu_requirements} {preamble} {train_command} {dataset} {config} {fl} -tr {tr} -p {p}' 90 | if additional_arguments is not None and len(additional_arguments) > 0: 91 | command += f' {additional_arguments}' 92 | f.write(f'{command}\"\n') 93 | 94 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/configuration.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | from nnunetv2.utilities.default_n_proc_DA import get_allowed_n_proc_DA 4 | 5 | default_num_processes = 8 if 'nnUNet_def_n_proc' not in os.environ else int(os.environ['nnUNet_def_n_proc']) 6 | 7 | ANISO_THRESHOLD = 3 # determines when a sample is considered anisotropic (3 means that the spacing in the low 8 | # resolution axis must be 3x as large as the next largest spacing) 9 | 10 | default_n_proc_DA = get_allowed_n_proc_DA() 11 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/Dataset027_ACDC.py: -------------------------------------------------------------------------------- 1 | import os 2 | import shutil 3 | from pathlib import Path 4 | 5 | from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json 6 | from nnunetv2.paths import nnUNet_raw 7 | 8 | 9 | def make_out_dirs(dataset_id: int, task_name="ACDC"): 10 | dataset_name = f"Dataset{dataset_id:03d}_{task_name}" 11 | 12 | out_dir = Path(nnUNet_raw.replace('"', "")) / dataset_name 13 | out_train_dir = out_dir / "imagesTr" 14 | out_labels_dir = out_dir / "labelsTr" 15 | out_test_dir = out_dir / "imagesTs" 16 | 17 | os.makedirs(out_dir, exist_ok=True) 18 | os.makedirs(out_train_dir, exist_ok=True) 19 | os.makedirs(out_labels_dir, exist_ok=True) 20 | os.makedirs(out_test_dir, exist_ok=True) 21 | 22 | return out_dir, out_train_dir, out_labels_dir, out_test_dir 23 | 24 | 25 | def copy_files(src_data_folder: Path, train_dir: Path, labels_dir: Path, test_dir: Path): 26 | """Copy files from the ACDC dataset to the nnUNet dataset folder. Returns the number of training cases.""" 27 | patients_train = sorted([f for f in (src_data_folder / "training").iterdir() if f.is_dir()]) 28 | patients_test = sorted([f for f in (src_data_folder / "testing").iterdir() if f.is_dir()]) 29 | 30 | num_training_cases = 0 31 | # Copy training files and corresponding labels. 32 | for patient_dir in patients_train: 33 | for file in patient_dir.iterdir(): 34 | if file.suffix == ".gz" and "_gt" not in file.name and "_4d" not in file.name: 35 | # The stem is 'patient.nii', and the suffix is '.gz'. 36 | # We split the stem and append _0000 to the patient part. 37 | shutil.copy(file, train_dir / f"{file.stem.split('.')[0]}_0000.nii.gz") 38 | num_training_cases += 1 39 | elif file.suffix == ".gz" and "_gt" in file.name: 40 | shutil.copy(file, labels_dir / file.name.replace("_gt", "")) 41 | 42 | # Copy test files. 43 | for patient_dir in patients_test: 44 | for file in patient_dir.iterdir(): 45 | if file.suffix == ".gz" and "_gt" not in file.name and "_4d" not in file.name: 46 | shutil.copy(file, test_dir / f"{file.stem.split('.')[0]}_0000.nii.gz") 47 | 48 | return num_training_cases 49 | 50 | 51 | def convert_acdc(src_data_folder: str, dataset_id=27): 52 | out_dir, train_dir, labels_dir, test_dir = make_out_dirs(dataset_id=dataset_id) 53 | num_training_cases = copy_files(Path(src_data_folder), train_dir, labels_dir, test_dir) 54 | 55 | generate_dataset_json( 56 | str(out_dir), 57 | channel_names={ 58 | 0: "cineMRI", 59 | }, 60 | labels={ 61 | "background": 0, 62 | "RV": 1, 63 | "MLV": 2, 64 | "LVC": 3, 65 | }, 66 | file_ending=".nii.gz", 67 | num_training_cases=num_training_cases, 68 | ) 69 | 70 | 71 | if __name__ == "__main__": 72 | import argparse 73 | 74 | parser = argparse.ArgumentParser() 75 | parser.add_argument( 76 | "-i", 77 | "--input_folder", 78 | type=str, 79 | help="The downloaded ACDC dataset dir. Should contain extracted 'training' and 'testing' folders.", 80 | ) 81 | parser.add_argument( 82 | "-d", "--dataset_id", required=False, type=int, default=27, help="nnU-Net Dataset ID, default: 27" 83 | ) 84 | args = parser.parse_args() 85 | print("Converting...") 86 | convert_acdc(args.input_folder, args.dataset_id) 87 | print("Done!") 88 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/Dataset115_EMIDEC.py: -------------------------------------------------------------------------------- 1 | import shutil 2 | from pathlib import Path 3 | 4 | from nnunetv2.dataset_conversion.Dataset027_ACDC import make_out_dirs 5 | from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json 6 | 7 | 8 | def copy_files(src_data_dir: Path, src_test_dir: Path, train_dir: Path, labels_dir: Path, test_dir: Path): 9 | """Copy files from the EMIDEC dataset to the nnUNet dataset folder. Returns the number of training cases.""" 10 | patients_train = sorted([f for f in src_data_dir.iterdir() if f.is_dir()]) 11 | patients_test = sorted([f for f in src_test_dir.iterdir() if f.is_dir()]) 12 | 13 | # Copy training files and corresponding labels. 14 | for patient in patients_train: 15 | train_file = patient / "Images" / f"{patient.name}.nii.gz" 16 | label_file = patient / "Contours" / f"{patient.name}.nii.gz" 17 | shutil.copy(train_file, train_dir / f"{train_file.stem.split('.')[0]}_0000.nii.gz") 18 | shutil.copy(label_file, labels_dir) 19 | 20 | # Copy test files. 21 | for patient in patients_test: 22 | test_file = patient / "Images" / f"{patient.name}.nii.gz" 23 | shutil.copy(test_file, test_dir / f"{test_file.stem.split('.')[0]}_0000.nii.gz") 24 | 25 | return len(patients_train) 26 | 27 | 28 | def convert_emidec(src_data_dir: str, src_test_dir: str, dataset_id=27): 29 | out_dir, train_dir, labels_dir, test_dir = make_out_dirs(dataset_id=dataset_id, task_name="EMIDEC") 30 | num_training_cases = copy_files(Path(src_data_dir), Path(src_test_dir), train_dir, labels_dir, test_dir) 31 | 32 | generate_dataset_json( 33 | str(out_dir), 34 | channel_names={ 35 | 0: "cineMRI", 36 | }, 37 | labels={ 38 | "background": 0, 39 | "cavity": 1, 40 | "normal_myocardium": 2, 41 | "myocardial_infarction": 3, 42 | "no_reflow": 4, 43 | }, 44 | file_ending=".nii.gz", 45 | num_training_cases=num_training_cases, 46 | ) 47 | 48 | 49 | if __name__ == "__main__": 50 | import argparse 51 | 52 | parser = argparse.ArgumentParser() 53 | parser.add_argument("-i", "--input_dir", type=str, help="The EMIDEC dataset directory.") 54 | parser.add_argument("-t", "--test_dir", type=str, help="The EMIDEC test set directory.") 55 | parser.add_argument( 56 | "-d", "--dataset_id", required=False, type=int, default=115, help="nnU-Net Dataset ID, default: 115" 57 | ) 58 | args = parser.parse_args() 59 | print("Converting...") 60 | convert_emidec(args.input_dir, args.test_dir, args.dataset_id) 61 | print("Done!") 62 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/Dataset120_RoadSegmentation.py: -------------------------------------------------------------------------------- 1 | import multiprocessing 2 | import shutil 3 | from multiprocessing import Pool 4 | 5 | from batchgenerators.utilities.file_and_folder_operations import * 6 | 7 | from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json 8 | from nnunetv2.paths import nnUNet_raw 9 | from skimage import io 10 | from acvl_utils.morphology.morphology_helper import generic_filter_components 11 | from scipy.ndimage import binary_fill_holes 12 | 13 | 14 | def load_and_covnert_case(input_image: str, input_seg: str, output_image: str, output_seg: str, 15 | min_component_size: int = 50): 16 | seg = io.imread(input_seg) 17 | seg[seg == 255] = 1 18 | image = io.imread(input_image) 19 | image = image.sum(2) 20 | mask = image == (3 * 255) 21 | # the dataset has large white areas in which road segmentations can exist but no image information is available. 22 | # Remove the road label in these areas 23 | mask = generic_filter_components(mask, filter_fn=lambda ids, sizes: [i for j, i in enumerate(ids) if 24 | sizes[j] > min_component_size]) 25 | mask = binary_fill_holes(mask) 26 | seg[mask] = 0 27 | io.imsave(output_seg, seg, check_contrast=False) 28 | shutil.copy(input_image, output_image) 29 | 30 | 31 | if __name__ == "__main__": 32 | # extracted archive from https://www.kaggle.com/datasets/insaff/massachusetts-roads-dataset?resource=download 33 | source = '/media/fabian/data/raw_datasets/Massachussetts_road_seg/road_segmentation_ideal' 34 | 35 | dataset_name = 'Dataset120_RoadSegmentation' 36 | 37 | imagestr = join(nnUNet_raw, dataset_name, 'imagesTr') 38 | imagests = join(nnUNet_raw, dataset_name, 'imagesTs') 39 | labelstr = join(nnUNet_raw, dataset_name, 'labelsTr') 40 | labelsts = join(nnUNet_raw, dataset_name, 'labelsTs') 41 | maybe_mkdir_p(imagestr) 42 | maybe_mkdir_p(imagests) 43 | maybe_mkdir_p(labelstr) 44 | maybe_mkdir_p(labelsts) 45 | 46 | train_source = join(source, 'training') 47 | test_source = join(source, 'testing') 48 | 49 | with multiprocessing.get_context("spawn").Pool(8) as p: 50 | 51 | # not all training images have a segmentation 52 | valid_ids = subfiles(join(train_source, 'output'), join=False, suffix='png') 53 | num_train = len(valid_ids) 54 | r = [] 55 | for v in valid_ids: 56 | r.append( 57 | p.starmap_async( 58 | load_and_covnert_case, 59 | (( 60 | join(train_source, 'input', v), 61 | join(train_source, 'output', v), 62 | join(imagestr, v[:-4] + '_0000.png'), 63 | join(labelstr, v), 64 | 50 65 | ),) 66 | ) 67 | ) 68 | 69 | # test set 70 | valid_ids = subfiles(join(test_source, 'output'), join=False, suffix='png') 71 | for v in valid_ids: 72 | r.append( 73 | p.starmap_async( 74 | load_and_covnert_case, 75 | (( 76 | join(test_source, 'input', v), 77 | join(test_source, 'output', v), 78 | join(imagests, v[:-4] + '_0000.png'), 79 | join(labelsts, v), 80 | 50 81 | ),) 82 | ) 83 | ) 84 | _ = [i.get() for i in r] 85 | 86 | generate_dataset_json(join(nnUNet_raw, dataset_name), {0: 'R', 1: 'G', 2: 'B'}, {'background': 0, 'road': 1}, 87 | num_train, '.png', dataset_name=dataset_name) 88 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/Dataset137_BraTS21.py: -------------------------------------------------------------------------------- 1 | import multiprocessing 2 | import shutil 3 | from multiprocessing import Pool 4 | 5 | import SimpleITK as sitk 6 | import numpy as np 7 | from batchgenerators.utilities.file_and_folder_operations import * 8 | from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json 9 | from nnunetv2.paths import nnUNet_raw 10 | 11 | 12 | def copy_BraTS_segmentation_and_convert_labels_to_nnUNet(in_file: str, out_file: str) -> None: 13 | # use this for segmentation only!!! 14 | # nnUNet wants the labels to be continuous. BraTS is 0, 1, 2, 4 -> we make that into 0, 1, 2, 3 15 | img = sitk.ReadImage(in_file) 16 | img_npy = sitk.GetArrayFromImage(img) 17 | 18 | uniques = np.unique(img_npy) 19 | for u in uniques: 20 | if u not in [0, 1, 2, 4]: 21 | raise RuntimeError('unexpected label') 22 | 23 | seg_new = np.zeros_like(img_npy) 24 | seg_new[img_npy == 4] = 3 25 | seg_new[img_npy == 2] = 1 26 | seg_new[img_npy == 1] = 2 27 | img_corr = sitk.GetImageFromArray(seg_new) 28 | img_corr.CopyInformation(img) 29 | sitk.WriteImage(img_corr, out_file) 30 | 31 | 32 | def convert_labels_back_to_BraTS(seg: np.ndarray): 33 | new_seg = np.zeros_like(seg) 34 | new_seg[seg == 1] = 2 35 | new_seg[seg == 3] = 4 36 | new_seg[seg == 2] = 1 37 | return new_seg 38 | 39 | 40 | def load_convert_labels_back_to_BraTS(filename, input_folder, output_folder): 41 | a = sitk.ReadImage(join(input_folder, filename)) 42 | b = sitk.GetArrayFromImage(a) 43 | c = convert_labels_back_to_BraTS(b) 44 | d = sitk.GetImageFromArray(c) 45 | d.CopyInformation(a) 46 | sitk.WriteImage(d, join(output_folder, filename)) 47 | 48 | 49 | def convert_folder_with_preds_back_to_BraTS_labeling_convention(input_folder: str, output_folder: str, num_processes: int = 12): 50 | """ 51 | reads all prediction files (nifti) in the input folder, converts the labels back to BraTS convention and saves the 52 | """ 53 | maybe_mkdir_p(output_folder) 54 | nii = subfiles(input_folder, suffix='.nii.gz', join=False) 55 | with multiprocessing.get_context("spawn").Pool(num_processes) as p: 56 | p.starmap(load_convert_labels_back_to_BraTS, zip(nii, [input_folder] * len(nii), [output_folder] * len(nii))) 57 | 58 | 59 | if __name__ == '__main__': 60 | brats_data_dir = '/home/isensee/drives/E132-Rohdaten/BraTS_2021/training' 61 | 62 | task_id = 137 63 | task_name = "BraTS2021" 64 | 65 | foldername = "Dataset%03.0d_%s" % (task_id, task_name) 66 | 67 | # setting up nnU-Net folders 68 | out_base = join(nnUNet_raw, foldername) 69 | imagestr = join(out_base, "imagesTr") 70 | labelstr = join(out_base, "labelsTr") 71 | maybe_mkdir_p(imagestr) 72 | maybe_mkdir_p(labelstr) 73 | 74 | case_ids = subdirs(brats_data_dir, prefix='BraTS', join=False) 75 | 76 | for c in case_ids: 77 | shutil.copy(join(brats_data_dir, c, c + "_t1.nii.gz"), join(imagestr, c + '_0000.nii.gz')) 78 | shutil.copy(join(brats_data_dir, c, c + "_t1ce.nii.gz"), join(imagestr, c + '_0001.nii.gz')) 79 | shutil.copy(join(brats_data_dir, c, c + "_t2.nii.gz"), join(imagestr, c + '_0002.nii.gz')) 80 | shutil.copy(join(brats_data_dir, c, c + "_flair.nii.gz"), join(imagestr, c + '_0003.nii.gz')) 81 | 82 | copy_BraTS_segmentation_and_convert_labels_to_nnUNet(join(brats_data_dir, c, c + "_seg.nii.gz"), 83 | join(labelstr, c + '.nii.gz')) 84 | 85 | generate_dataset_json(out_base, 86 | channel_names={0: 'T1', 1: 'T1ce', 2: 'T2', 3: 'Flair'}, 87 | labels={ 88 | 'background': 0, 89 | 'whole tumor': (1, 2, 3), 90 | 'tumor core': (2, 3), 91 | 'enhancing tumor': (3, ) 92 | }, 93 | num_training_cases=len(case_ids), 94 | file_ending='.nii.gz', 95 | regions_class_order=(1, 2, 3), 96 | license='see https://www.synapse.org/#!Synapse:syn25829067/wiki/610863', 97 | reference='see https://www.synapse.org/#!Synapse:syn25829067/wiki/610863', 98 | dataset_release='1.0') 99 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/Dataset218_Amos2022_task1.py: -------------------------------------------------------------------------------- 1 | from batchgenerators.utilities.file_and_folder_operations import * 2 | import shutil 3 | from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json 4 | from nnunetv2.paths import nnUNet_raw 5 | 6 | 7 | def convert_amos_task1(amos_base_dir: str, nnunet_dataset_id: int = 218): 8 | """ 9 | AMOS doesn't say anything about how the validation set is supposed to be used. So we just incorporate that into 10 | the train set. Having a 5-fold cross-validation is superior to a single train:val split 11 | """ 12 | task_name = "AMOS2022_postChallenge_task1" 13 | 14 | foldername = "Dataset%03.0d_%s" % (nnunet_dataset_id, task_name) 15 | 16 | # setting up nnU-Net folders 17 | out_base = join(nnUNet_raw, foldername) 18 | imagestr = join(out_base, "imagesTr") 19 | imagests = join(out_base, "imagesTs") 20 | labelstr = join(out_base, "labelsTr") 21 | maybe_mkdir_p(imagestr) 22 | maybe_mkdir_p(imagests) 23 | maybe_mkdir_p(labelstr) 24 | 25 | dataset_json_source = load_json(join(amos_base_dir, 'dataset.json')) 26 | 27 | training_identifiers = [i['image'].split('/')[-1][:-7] for i in dataset_json_source['training']] 28 | tr_ctr = 0 29 | for tr in training_identifiers: 30 | if int(tr.split("_")[-1]) <= 410: # these are the CT images 31 | tr_ctr += 1 32 | shutil.copy(join(amos_base_dir, 'imagesTr', tr + '.nii.gz'), join(imagestr, f'{tr}_0000.nii.gz')) 33 | shutil.copy(join(amos_base_dir, 'labelsTr', tr + '.nii.gz'), join(labelstr, f'{tr}.nii.gz')) 34 | 35 | test_identifiers = [i['image'].split('/')[-1][:-7] for i in dataset_json_source['test']] 36 | for ts in test_identifiers: 37 | if int(ts.split("_")[-1]) <= 500: # these are the CT images 38 | shutil.copy(join(amos_base_dir, 'imagesTs', ts + '.nii.gz'), join(imagests, f'{ts}_0000.nii.gz')) 39 | 40 | val_identifiers = [i['image'].split('/')[-1][:-7] for i in dataset_json_source['validation']] 41 | for vl in val_identifiers: 42 | if int(vl.split("_")[-1]) <= 409: # these are the CT images 43 | tr_ctr += 1 44 | shutil.copy(join(amos_base_dir, 'imagesVa', vl + '.nii.gz'), join(imagestr, f'{vl}_0000.nii.gz')) 45 | shutil.copy(join(amos_base_dir, 'labelsVa', vl + '.nii.gz'), join(labelstr, f'{vl}.nii.gz')) 46 | 47 | generate_dataset_json(out_base, {0: "CT"}, labels={v: int(k) for k,v in dataset_json_source['labels'].items()}, 48 | num_training_cases=tr_ctr, file_ending='.nii.gz', 49 | dataset_name=task_name, reference='https://amos22.grand-challenge.org/', 50 | release='https://zenodo.org/record/7262581', 51 | overwrite_image_reader_writer='NibabelIOWithReorient', 52 | description="This is the dataset as released AFTER the challenge event. It has the " 53 | "validation set gt in it! We just use the validation images as additional " 54 | "training cases because AMOS doesn't specify how they should be used. nnU-Net's" 55 | " 5-fold CV is better than some random train:val split.") 56 | 57 | 58 | if __name__ == '__main__': 59 | import argparse 60 | parser = argparse.ArgumentParser() 61 | parser.add_argument('input_folder', type=str, 62 | help="The downloaded and extracted AMOS2022 (https://amos22.grand-challenge.org/) data. " 63 | "Use this link: https://zenodo.org/record/7262581." 64 | "You need to specify the folder with the imagesTr, imagesVal, labelsTr etc subfolders here!") 65 | parser.add_argument('-d', required=False, type=int, default=218, help='nnU-Net Dataset ID, default: 218') 66 | args = parser.parse_args() 67 | amos_base = args.input_folder 68 | convert_amos_task1(amos_base, args.d) 69 | 70 | 71 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/Dataset219_Amos2022_task2.py: -------------------------------------------------------------------------------- 1 | from batchgenerators.utilities.file_and_folder_operations import * 2 | import shutil 3 | from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json 4 | from nnunetv2.paths import nnUNet_raw 5 | 6 | 7 | def convert_amos_task2(amos_base_dir: str, nnunet_dataset_id: int = 219): 8 | """ 9 | AMOS doesn't say anything about how the validation set is supposed to be used. So we just incorporate that into 10 | the train set. Having a 5-fold cross-validation is superior to a single train:val split 11 | """ 12 | task_name = "AMOS2022_postChallenge_task2" 13 | 14 | foldername = "Dataset%03.0d_%s" % (nnunet_dataset_id, task_name) 15 | 16 | # setting up nnU-Net folders 17 | out_base = join(nnUNet_raw, foldername) 18 | imagestr = join(out_base, "imagesTr") 19 | imagests = join(out_base, "imagesTs") 20 | labelstr = join(out_base, "labelsTr") 21 | maybe_mkdir_p(imagestr) 22 | maybe_mkdir_p(imagests) 23 | maybe_mkdir_p(labelstr) 24 | 25 | dataset_json_source = load_json(join(amos_base_dir, 'dataset.json')) 26 | 27 | training_identifiers = [i['image'].split('/')[-1][:-7] for i in dataset_json_source['training']] 28 | for tr in training_identifiers: 29 | shutil.copy(join(amos_base_dir, 'imagesTr', tr + '.nii.gz'), join(imagestr, f'{tr}_0000.nii.gz')) 30 | shutil.copy(join(amos_base_dir, 'labelsTr', tr + '.nii.gz'), join(labelstr, f'{tr}.nii.gz')) 31 | 32 | test_identifiers = [i['image'].split('/')[-1][:-7] for i in dataset_json_source['test']] 33 | for ts in test_identifiers: 34 | shutil.copy(join(amos_base_dir, 'imagesTs', ts + '.nii.gz'), join(imagests, f'{ts}_0000.nii.gz')) 35 | 36 | val_identifiers = [i['image'].split('/')[-1][:-7] for i in dataset_json_source['validation']] 37 | for vl in val_identifiers: 38 | shutil.copy(join(amos_base_dir, 'imagesVa', vl + '.nii.gz'), join(imagestr, f'{vl}_0000.nii.gz')) 39 | shutil.copy(join(amos_base_dir, 'labelsVa', vl + '.nii.gz'), join(labelstr, f'{vl}.nii.gz')) 40 | 41 | generate_dataset_json(out_base, {0: "either_CT_or_MR"}, labels={v: int(k) for k,v in dataset_json_source['labels'].items()}, 42 | num_training_cases=len(training_identifiers) + len(val_identifiers), file_ending='.nii.gz', 43 | dataset_name=task_name, reference='https://amos22.grand-challenge.org/', 44 | release='https://zenodo.org/record/7262581', 45 | overwrite_image_reader_writer='NibabelIOWithReorient', 46 | description="This is the dataset as released AFTER the challenge event. It has the " 47 | "validation set gt in it! We just use the validation images as additional " 48 | "training cases because AMOS doesn't specify how they should be used. nnU-Net's" 49 | " 5-fold CV is better than some random train:val split.") 50 | 51 | 52 | if __name__ == '__main__': 53 | import argparse 54 | parser = argparse.ArgumentParser() 55 | parser.add_argument('input_folder', type=str, 56 | help="The downloaded and extracted AMOS2022 (https://amos22.grand-challenge.org/) data. " 57 | "Use this link: https://zenodo.org/record/7262581." 58 | "You need to specify the folder with the imagesTr, imagesVal, labelsTr etc subfolders here!") 59 | parser.add_argument('-d', required=False, type=int, default=219, help='nnU-Net Dataset ID, default: 219') 60 | args = parser.parse_args() 61 | amos_base = args.input_folder 62 | convert_amos_task2(amos_base, args.d) 63 | 64 | # /home/isensee/Downloads/amos22/amos22/ 65 | 66 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/Dataset220_KiTS2023.py: -------------------------------------------------------------------------------- 1 | from batchgenerators.utilities.file_and_folder_operations import * 2 | import shutil 3 | from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json 4 | from nnunetv2.paths import nnUNet_raw 5 | 6 | 7 | def convert_kits2023(kits_base_dir: str, nnunet_dataset_id: int = 220): 8 | task_name = "KiTS2023" 9 | 10 | foldername = "Dataset%03.0d_%s" % (nnunet_dataset_id, task_name) 11 | 12 | # setting up nnU-Net folders 13 | out_base = join(nnUNet_raw, foldername) 14 | imagestr = join(out_base, "imagesTr") 15 | labelstr = join(out_base, "labelsTr") 16 | maybe_mkdir_p(imagestr) 17 | maybe_mkdir_p(labelstr) 18 | 19 | cases = subdirs(kits_base_dir, prefix='case_', join=False) 20 | for tr in cases: 21 | shutil.copy(join(kits_base_dir, tr, 'imaging.nii.gz'), join(imagestr, f'{tr}_0000.nii.gz')) 22 | shutil.copy(join(kits_base_dir, tr, 'segmentation.nii.gz'), join(labelstr, f'{tr}.nii.gz')) 23 | 24 | generate_dataset_json(out_base, {0: "CT"}, 25 | labels={ 26 | "background": 0, 27 | "kidney": (1, 2, 3), 28 | "masses": (2, 3), 29 | "tumor": 2 30 | }, 31 | regions_class_order=(1, 3, 2), 32 | num_training_cases=len(cases), file_ending='.nii.gz', 33 | dataset_name=task_name, reference='none', 34 | release='prerelease', 35 | overwrite_image_reader_writer='NibabelIOWithReorient', 36 | description="KiTS2023") 37 | 38 | 39 | if __name__ == '__main__': 40 | import argparse 41 | parser = argparse.ArgumentParser() 42 | parser.add_argument('input_folder', type=str, 43 | help="The downloaded and extracted KiTS2023 dataset (must have case_XXXXX subfolders)") 44 | parser.add_argument('-d', required=False, type=int, default=220, help='nnU-Net Dataset ID, default: 220') 45 | args = parser.parse_args() 46 | amos_base = args.input_folder 47 | convert_kits2023(amos_base, args.d) 48 | 49 | # /media/isensee/raw_data/raw_datasets/kits23/dataset 50 | 51 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/Dataset221_AutoPETII_2023.py: -------------------------------------------------------------------------------- 1 | from batchgenerators.utilities.file_and_folder_operations import * 2 | import shutil 3 | from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json 4 | from nnunetv2.paths import nnUNet_raw, nnUNet_preprocessed 5 | 6 | 7 | def convert_autopet(autopet_base_dir:str = '/media/isensee/My Book1/AutoPET/nifti/FDG-PET-CT-Lesions', 8 | nnunet_dataset_id: int = 221): 9 | task_name = "AutoPETII_2023" 10 | 11 | foldername = "Dataset%03.0d_%s" % (nnunet_dataset_id, task_name) 12 | 13 | # setting up nnU-Net folders 14 | out_base = join(nnUNet_raw, foldername) 15 | imagestr = join(out_base, "imagesTr") 16 | labelstr = join(out_base, "labelsTr") 17 | maybe_mkdir_p(imagestr) 18 | maybe_mkdir_p(labelstr) 19 | 20 | patients = subdirs(autopet_base_dir, prefix='PETCT', join=False) 21 | n = 0 22 | identifiers = [] 23 | for pat in patients: 24 | patient_acquisitions = subdirs(join(autopet_base_dir, pat), join=False) 25 | for pa in patient_acquisitions: 26 | n += 1 27 | identifier = f"{pat}_{pa}" 28 | identifiers.append(identifier) 29 | if not isfile(join(imagestr, f'{identifier}_0000.nii.gz')): 30 | shutil.copy(join(autopet_base_dir, pat, pa, 'CTres.nii.gz'), join(imagestr, f'{identifier}_0000.nii.gz')) 31 | if not isfile(join(imagestr, f'{identifier}_0001.nii.gz')): 32 | shutil.copy(join(autopet_base_dir, pat, pa, 'SUV.nii.gz'), join(imagestr, f'{identifier}_0001.nii.gz')) 33 | if not isfile(join(imagestr, f'{identifier}.nii.gz')): 34 | shutil.copy(join(autopet_base_dir, pat, pa, 'SEG.nii.gz'), join(labelstr, f'{identifier}.nii.gz')) 35 | 36 | generate_dataset_json(out_base, {0: "CT", 1:"CT"}, 37 | labels={ 38 | "background": 0, 39 | "tumor": 1 40 | }, 41 | num_training_cases=n, file_ending='.nii.gz', 42 | dataset_name=task_name, reference='https://autopet-ii.grand-challenge.org/', 43 | release='release', 44 | # overwrite_image_reader_writer='NibabelIOWithReorient', 45 | description=task_name) 46 | 47 | # manual split 48 | splits = [] 49 | for fold in range(5): 50 | val_patients = patients[fold :: 5] 51 | splits.append( 52 | { 53 | 'train': [i for i in identifiers if not any([i.startswith(v) for v in val_patients])], 54 | 'val': [i for i in identifiers if any([i.startswith(v) for v in val_patients])], 55 | } 56 | ) 57 | pp_out_dir = join(nnUNet_preprocessed, foldername) 58 | maybe_mkdir_p(pp_out_dir) 59 | save_json(splits, join(pp_out_dir, 'splits_final.json'), sort_keys=False) 60 | 61 | 62 | if __name__ == '__main__': 63 | import argparse 64 | parser = argparse.ArgumentParser() 65 | parser.add_argument('input_folder', type=str, 66 | help="The downloaded and extracted autopet dataset (must have PETCT_XXX subfolders)") 67 | parser.add_argument('-d', required=False, type=int, default=221, help='nnU-Net Dataset ID, default: 221') 68 | args = parser.parse_args() 69 | amos_base = args.input_folder 70 | convert_autopet(amos_base, args.d) 71 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/Dataset988_dummyDataset4.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | from batchgenerators.utilities.file_and_folder_operations import * 4 | 5 | from nnunetv2.paths import nnUNet_raw 6 | from nnunetv2.utilities.utils import get_filenames_of_train_images_and_targets 7 | 8 | if __name__ == '__main__': 9 | # creates a dummy dataset where there are no files in imagestr and labelstr 10 | source_dataset = 'Dataset004_Hippocampus' 11 | 12 | target_dataset = 'Dataset987_dummyDataset4' 13 | target_dataset_dir = join(nnUNet_raw, target_dataset) 14 | maybe_mkdir_p(target_dataset_dir) 15 | 16 | dataset = get_filenames_of_train_images_and_targets(join(nnUNet_raw, source_dataset)) 17 | 18 | # the returned dataset will have absolute paths. We should use relative paths so that you can freely copy 19 | # datasets around between systems. As long as the source dataset is there it will continue working even if 20 | # nnUNet_raw is in different locations 21 | 22 | # paths must be relative to target_dataset_dir!!! 23 | for k in dataset.keys(): 24 | dataset[k]['label'] = os.path.relpath(dataset[k]['label'], target_dataset_dir) 25 | dataset[k]['images'] = [os.path.relpath(i, target_dataset_dir) for i in dataset[k]['images']] 26 | 27 | # load old dataset.json 28 | dataset_json = load_json(join(nnUNet_raw, source_dataset, 'dataset.json')) 29 | dataset_json['dataset'] = dataset 30 | 31 | # save 32 | save_json(dataset_json, join(target_dataset_dir, 'dataset.json'), sort_keys=False) 33 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/dataset_conversion/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/convert_raw_dataset_from_old_nnunet_format.py: -------------------------------------------------------------------------------- 1 | import shutil 2 | from copy import deepcopy 3 | 4 | from batchgenerators.utilities.file_and_folder_operations import join, maybe_mkdir_p, isdir, load_json, save_json 5 | from nnunetv2.paths import nnUNet_raw 6 | 7 | 8 | def convert(source_folder, target_dataset_name): 9 | """ 10 | remember that old tasks were called TaskXXX_YYY and new ones are called DatasetXXX_YYY 11 | source_folder 12 | """ 13 | if isdir(join(nnUNet_raw, target_dataset_name)): 14 | raise RuntimeError(f'Target dataset name {target_dataset_name} already exists. Aborting... ' 15 | f'(we might break something). If you are sure you want to proceed, please manually ' 16 | f'delete {join(nnUNet_raw, target_dataset_name)}') 17 | maybe_mkdir_p(join(nnUNet_raw, target_dataset_name)) 18 | shutil.copytree(join(source_folder, 'imagesTr'), join(nnUNet_raw, target_dataset_name, 'imagesTr')) 19 | shutil.copytree(join(source_folder, 'labelsTr'), join(nnUNet_raw, target_dataset_name, 'labelsTr')) 20 | if isdir(join(source_folder, 'imagesTs')): 21 | shutil.copytree(join(source_folder, 'imagesTs'), join(nnUNet_raw, target_dataset_name, 'imagesTs')) 22 | if isdir(join(source_folder, 'labelsTs')): 23 | shutil.copytree(join(source_folder, 'labelsTs'), join(nnUNet_raw, target_dataset_name, 'labelsTs')) 24 | if isdir(join(source_folder, 'imagesVal')): 25 | shutil.copytree(join(source_folder, 'imagesVal'), join(nnUNet_raw, target_dataset_name, 'imagesVal')) 26 | if isdir(join(source_folder, 'labelsVal')): 27 | shutil.copytree(join(source_folder, 'labelsVal'), join(nnUNet_raw, target_dataset_name, 'labelsVal')) 28 | shutil.copy(join(source_folder, 'dataset.json'), join(nnUNet_raw, target_dataset_name)) 29 | 30 | dataset_json = load_json(join(nnUNet_raw, target_dataset_name, 'dataset.json')) 31 | del dataset_json['tensorImageSize'] 32 | del dataset_json['numTest'] 33 | del dataset_json['training'] 34 | del dataset_json['test'] 35 | dataset_json['channel_names'] = deepcopy(dataset_json['modality']) 36 | del dataset_json['modality'] 37 | 38 | dataset_json['labels'] = {j: int(i) for i, j in dataset_json['labels'].items()} 39 | dataset_json['file_ending'] = ".nii.gz" 40 | save_json(dataset_json, join(nnUNet_raw, target_dataset_name, 'dataset.json'), sort_keys=False) 41 | 42 | 43 | def convert_entry_point(): 44 | import argparse 45 | parser = argparse.ArgumentParser() 46 | parser.add_argument("input_folder", type=str, 47 | help='Raw old nnUNet dataset. This must be the folder with imagesTr,labelsTr etc subfolders! ' 48 | 'Please provide the PATH to the old Task, not just the task name. nnU-Net V2 does not ' 49 | 'know where v1 tasks are.') 50 | parser.add_argument("output_dataset_name", type=str, 51 | help='New dataset NAME (not path!). Must follow the DatasetXXX_NAME convention!') 52 | args = parser.parse_args() 53 | convert(args.input_folder, args.output_dataset_name) 54 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset996_IntegrationTest_Hippocampus_regions_ignore.py: -------------------------------------------------------------------------------- 1 | import SimpleITK as sitk 2 | import shutil 3 | 4 | import numpy as np 5 | from batchgenerators.utilities.file_and_folder_operations import isdir, join, load_json, save_json, nifti_files 6 | 7 | from nnunetv2.utilities.dataset_name_id_conversion import maybe_convert_to_dataset_name 8 | from nnunetv2.paths import nnUNet_raw 9 | from nnunetv2.utilities.label_handling.label_handling import LabelManager 10 | from nnunetv2.utilities.plans_handling.plans_handler import PlansManager, ConfigurationManager 11 | 12 | 13 | def sparsify_segmentation(seg: np.ndarray, label_manager: LabelManager, percent_of_slices: float) -> np.ndarray: 14 | assert label_manager.has_ignore_label, "This preprocessor only works with datasets that have an ignore label!" 15 | seg_new = np.ones_like(seg) * label_manager.ignore_label 16 | x, y, z = seg.shape 17 | # x 18 | num_slices = max(1, round(x * percent_of_slices)) 19 | selected_slices = np.random.choice(x, num_slices, replace=False) 20 | seg_new[selected_slices] = seg[selected_slices] 21 | # y 22 | num_slices = max(1, round(y * percent_of_slices)) 23 | selected_slices = np.random.choice(y, num_slices, replace=False) 24 | seg_new[:, selected_slices] = seg[:, selected_slices] 25 | # z 26 | num_slices = max(1, round(z * percent_of_slices)) 27 | selected_slices = np.random.choice(z, num_slices, replace=False) 28 | seg_new[:, :, selected_slices] = seg[:, :, selected_slices] 29 | return seg_new 30 | 31 | 32 | if __name__ == '__main__': 33 | dataset_name = 'IntegrationTest_Hippocampus_regions_ignore' 34 | dataset_id = 996 35 | dataset_name = f"Dataset{dataset_id:03d}_{dataset_name}" 36 | 37 | try: 38 | existing_dataset_name = maybe_convert_to_dataset_name(dataset_id) 39 | if existing_dataset_name != dataset_name: 40 | raise FileExistsError(f"A different dataset with id {dataset_id} already exists :-(: {existing_dataset_name}. If " 41 | f"you intent to delete it, remember to also remove it in nnUNet_preprocessed and " 42 | f"nnUNet_results!") 43 | except RuntimeError: 44 | pass 45 | 46 | if isdir(join(nnUNet_raw, dataset_name)): 47 | shutil.rmtree(join(nnUNet_raw, dataset_name)) 48 | 49 | source_dataset = maybe_convert_to_dataset_name(4) 50 | shutil.copytree(join(nnUNet_raw, source_dataset), join(nnUNet_raw, dataset_name)) 51 | 52 | # additionally optimize entire hippocampus region, remove Posterior 53 | dj = load_json(join(nnUNet_raw, dataset_name, 'dataset.json')) 54 | dj['labels'] = { 55 | 'background': 0, 56 | 'hippocampus': (1, 2), 57 | 'anterior': 1, 58 | 'ignore': 3 59 | } 60 | dj['regions_class_order'] = (2, 1) 61 | save_json(dj, join(nnUNet_raw, dataset_name, 'dataset.json'), sort_keys=False) 62 | 63 | # now add ignore label to segmentation images 64 | np.random.seed(1234) 65 | lm = LabelManager(label_dict=dj['labels'], regions_class_order=dj.get('regions_class_order')) 66 | 67 | segs = nifti_files(join(nnUNet_raw, dataset_name, 'labelsTr')) 68 | for s in segs: 69 | seg_itk = sitk.ReadImage(s) 70 | seg_npy = sitk.GetArrayFromImage(seg_itk) 71 | seg_npy = sparsify_segmentation(seg_npy, lm, 0.1 / 3) 72 | seg_itk_new = sitk.GetImageFromArray(seg_npy) 73 | seg_itk_new.CopyInformation(seg_itk) 74 | sitk.WriteImage(seg_itk_new, s) 75 | 76 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset997_IntegrationTest_Hippocampus_regions.py: -------------------------------------------------------------------------------- 1 | import shutil 2 | 3 | from batchgenerators.utilities.file_and_folder_operations import isdir, join, load_json, save_json 4 | 5 | from nnunetv2.utilities.dataset_name_id_conversion import maybe_convert_to_dataset_name 6 | from nnunetv2.paths import nnUNet_raw 7 | 8 | if __name__ == '__main__': 9 | dataset_name = 'IntegrationTest_Hippocampus_regions' 10 | dataset_id = 997 11 | dataset_name = f"Dataset{dataset_id:03d}_{dataset_name}" 12 | 13 | try: 14 | existing_dataset_name = maybe_convert_to_dataset_name(dataset_id) 15 | if existing_dataset_name != dataset_name: 16 | raise FileExistsError( 17 | f"A different dataset with id {dataset_id} already exists :-(: {existing_dataset_name}. If " 18 | f"you intent to delete it, remember to also remove it in nnUNet_preprocessed and " 19 | f"nnUNet_results!") 20 | except RuntimeError: 21 | pass 22 | 23 | if isdir(join(nnUNet_raw, dataset_name)): 24 | shutil.rmtree(join(nnUNet_raw, dataset_name)) 25 | 26 | source_dataset = maybe_convert_to_dataset_name(4) 27 | shutil.copytree(join(nnUNet_raw, source_dataset), join(nnUNet_raw, dataset_name)) 28 | 29 | # additionally optimize entire hippocampus region, remove Posterior 30 | dj = load_json(join(nnUNet_raw, dataset_name, 'dataset.json')) 31 | dj['labels'] = { 32 | 'background': 0, 33 | 'hippocampus': (1, 2), 34 | 'anterior': 1 35 | } 36 | dj['regions_class_order'] = (2, 1) 37 | save_json(dj, join(nnUNet_raw, dataset_name, 'dataset.json'), sort_keys=False) 38 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset998_IntegrationTest_Hippocampus_ignore.py: -------------------------------------------------------------------------------- 1 | import shutil 2 | 3 | from batchgenerators.utilities.file_and_folder_operations import isdir, join, load_json, save_json 4 | 5 | from nnunetv2.utilities.dataset_name_id_conversion import maybe_convert_to_dataset_name 6 | from nnunetv2.paths import nnUNet_raw 7 | 8 | 9 | if __name__ == '__main__': 10 | dataset_name = 'IntegrationTest_Hippocampus_ignore' 11 | dataset_id = 998 12 | dataset_name = f"Dataset{dataset_id:03d}_{dataset_name}" 13 | 14 | try: 15 | existing_dataset_name = maybe_convert_to_dataset_name(dataset_id) 16 | if existing_dataset_name != dataset_name: 17 | raise FileExistsError(f"A different dataset with id {dataset_id} already exists :-(: {existing_dataset_name}. If " 18 | f"you intent to delete it, remember to also remove it in nnUNet_preprocessed and " 19 | f"nnUNet_results!") 20 | except RuntimeError: 21 | pass 22 | 23 | if isdir(join(nnUNet_raw, dataset_name)): 24 | shutil.rmtree(join(nnUNet_raw, dataset_name)) 25 | 26 | source_dataset = maybe_convert_to_dataset_name(4) 27 | shutil.copytree(join(nnUNet_raw, source_dataset), join(nnUNet_raw, dataset_name)) 28 | 29 | # set class 2 to ignore label 30 | dj = load_json(join(nnUNet_raw, dataset_name, 'dataset.json')) 31 | dj['labels']['ignore'] = 2 32 | del dj['labels']['Posterior'] 33 | save_json(dj, join(nnUNet_raw, dataset_name, 'dataset.json'), sort_keys=False) 34 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset999_IntegrationTest_Hippocampus.py: -------------------------------------------------------------------------------- 1 | import shutil 2 | 3 | from batchgenerators.utilities.file_and_folder_operations import isdir, join 4 | 5 | from nnunetv2.utilities.dataset_name_id_conversion import maybe_convert_to_dataset_name 6 | from nnunetv2.paths import nnUNet_raw 7 | 8 | 9 | if __name__ == '__main__': 10 | dataset_name = 'IntegrationTest_Hippocampus' 11 | dataset_id = 999 12 | dataset_name = f"Dataset{dataset_id:03d}_{dataset_name}" 13 | 14 | try: 15 | existing_dataset_name = maybe_convert_to_dataset_name(dataset_id) 16 | if existing_dataset_name != dataset_name: 17 | raise FileExistsError(f"A different dataset with id {dataset_id} already exists :-(: {existing_dataset_name}. If " 18 | f"you intent to delete it, remember to also remove it in nnUNet_preprocessed and " 19 | f"nnUNet_results!") 20 | except RuntimeError: 21 | pass 22 | 23 | if isdir(join(nnUNet_raw, dataset_name)): 24 | shutil.rmtree(join(nnUNet_raw, dataset_name)) 25 | 26 | source_dataset = maybe_convert_to_dataset_name(4) 27 | shutil.copytree(join(nnUNet_raw, source_dataset), join(nnUNet_raw, dataset_name)) 28 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/dataset_conversion/datasets_for_integration_tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/dataset_conversion/datasets_for_integration_tests/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/ensembling/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/ensembling/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/evaluation/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/evaluation/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/evaluation/accumulate_cv_results.py: -------------------------------------------------------------------------------- 1 | import shutil 2 | from typing import Union, List, Tuple 3 | 4 | from batchgenerators.utilities.file_and_folder_operations import load_json, join, isdir, maybe_mkdir_p, subfiles, isfile 5 | 6 | from nnunetv2.configuration import default_num_processes 7 | from nnunetv2.evaluation.evaluate_predictions import compute_metrics_on_folder 8 | from nnunetv2.paths import nnUNet_raw, nnUNet_preprocessed 9 | from nnunetv2.utilities.plans_handling.plans_handler import PlansManager 10 | 11 | 12 | def accumulate_cv_results(trained_model_folder, 13 | merged_output_folder: str, 14 | folds: Union[List[int], Tuple[int, ...]], 15 | num_processes: int = default_num_processes, 16 | overwrite: bool = True): 17 | """ 18 | There are a lot of things that can get fucked up, so the simplest way to deal with potential problems is to 19 | collect the cv results into a separate folder and then evaluate them again. No messing with summary_json files! 20 | """ 21 | 22 | if overwrite and isdir(merged_output_folder): 23 | shutil.rmtree(merged_output_folder) 24 | maybe_mkdir_p(merged_output_folder) 25 | 26 | dataset_json = load_json(join(trained_model_folder, 'dataset.json')) 27 | plans_manager = PlansManager(join(trained_model_folder, 'plans.json')) 28 | rw = plans_manager.image_reader_writer_class() 29 | shutil.copy(join(trained_model_folder, 'dataset.json'), join(merged_output_folder, 'dataset.json')) 30 | shutil.copy(join(trained_model_folder, 'plans.json'), join(merged_output_folder, 'plans.json')) 31 | 32 | did_we_copy_something = False 33 | for f in folds: 34 | expected_validation_folder = join(trained_model_folder, f'fold_{f}', 'validation') 35 | if not isdir(expected_validation_folder): 36 | raise RuntimeError(f"fold {f} of model {trained_model_folder} is missing. Please train it!") 37 | predicted_files = subfiles(expected_validation_folder, suffix=dataset_json['file_ending'], join=False) 38 | for pf in predicted_files: 39 | if overwrite and isfile(join(merged_output_folder, pf)): 40 | raise RuntimeError(f'More than one of your folds has a prediction for case {pf}') 41 | if overwrite or not isfile(join(merged_output_folder, pf)): 42 | shutil.copy(join(expected_validation_folder, pf), join(merged_output_folder, pf)) 43 | did_we_copy_something = True 44 | 45 | if did_we_copy_something or not isfile(join(merged_output_folder, 'summary.json')): 46 | label_manager = plans_manager.get_label_manager(dataset_json) 47 | gt_folder = join(nnUNet_raw, plans_manager.dataset_name, 'labelsTr') 48 | if not isdir(gt_folder): 49 | gt_folder = join(nnUNet_preprocessed, plans_manager.dataset_name, 'gt_segmentations') 50 | compute_metrics_on_folder(gt_folder, 51 | merged_output_folder, 52 | join(merged_output_folder, 'summary.json'), 53 | rw, 54 | dataset_json['file_ending'], 55 | label_manager.foreground_regions if label_manager.has_regions else 56 | label_manager.foreground_labels, 57 | label_manager.ignore_label, 58 | num_processes) 59 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/experiment_planning/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/experiment_planning/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/experiment_planning/dataset_fingerprint/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/experiment_planning/dataset_fingerprint/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/experiment_planning/experiment_planners/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/experiment_planning/experiment_planners/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/experiment_planning/experiment_planners/network_topology.py: -------------------------------------------------------------------------------- 1 | from copy import deepcopy 2 | import numpy as np 3 | 4 | 5 | def get_shape_must_be_divisible_by(net_numpool_per_axis): 6 | return 2 ** np.array(net_numpool_per_axis) 7 | 8 | 9 | def pad_shape(shape, must_be_divisible_by): 10 | """ 11 | pads shape so that it is divisible by must_be_divisible_by 12 | :param shape: 13 | :param must_be_divisible_by: 14 | :return: 15 | """ 16 | if not isinstance(must_be_divisible_by, (tuple, list, np.ndarray)): 17 | must_be_divisible_by = [must_be_divisible_by] * len(shape) 18 | else: 19 | assert len(must_be_divisible_by) == len(shape) 20 | 21 | new_shp = [shape[i] + must_be_divisible_by[i] - shape[i] % must_be_divisible_by[i] for i in range(len(shape))] 22 | 23 | for i in range(len(shape)): 24 | if shape[i] % must_be_divisible_by[i] == 0: 25 | new_shp[i] -= must_be_divisible_by[i] 26 | new_shp = np.array(new_shp).astype(int) 27 | return new_shp 28 | 29 | 30 | def get_pool_and_conv_props(spacing, patch_size, min_feature_map_size, max_numpool): 31 | """ 32 | this is the same as get_pool_and_conv_props_v2 from old nnunet 33 | 34 | :param spacing: 35 | :param patch_size: 36 | :param min_feature_map_size: min edge length of feature maps in bottleneck 37 | :param max_numpool: 38 | :return: 39 | """ 40 | # todo review this code 41 | dim = len(spacing) 42 | 43 | current_spacing = deepcopy(list(spacing)) 44 | current_size = deepcopy(list(patch_size)) 45 | 46 | pool_op_kernel_sizes = [[1] * len(spacing)] 47 | conv_kernel_sizes = [] 48 | 49 | num_pool_per_axis = [0] * dim 50 | kernel_size = [1] * dim 51 | 52 | while True: 53 | # exclude axes that we cannot pool further because of min_feature_map_size constraint 54 | valid_axes_for_pool = [i for i in range(dim) if current_size[i] >= 2*min_feature_map_size] 55 | if len(valid_axes_for_pool) < 1: 56 | break 57 | 58 | spacings_of_axes = [current_spacing[i] for i in valid_axes_for_pool] 59 | 60 | # find axis that are within factor of 2 within smallest spacing 61 | min_spacing_of_valid = min(spacings_of_axes) 62 | valid_axes_for_pool = [i for i in valid_axes_for_pool if current_spacing[i] / min_spacing_of_valid < 2] 63 | 64 | # max_numpool constraint 65 | valid_axes_for_pool = [i for i in valid_axes_for_pool if num_pool_per_axis[i] < max_numpool] 66 | 67 | if len(valid_axes_for_pool) == 1: 68 | if current_size[valid_axes_for_pool[0]] >= 3 * min_feature_map_size: 69 | pass 70 | else: 71 | break 72 | if len(valid_axes_for_pool) < 1: 73 | break 74 | 75 | # now we need to find kernel sizes 76 | # kernel sizes are initialized to 1. They are successively set to 3 when their associated axis becomes within 77 | # factor 2 of min_spacing. Once they are 3 they remain 3 78 | for d in range(dim): 79 | if kernel_size[d] == 3: 80 | continue 81 | else: 82 | if current_spacing[d] / min(current_spacing) < 2: 83 | kernel_size[d] = 3 84 | 85 | other_axes = [i for i in range(dim) if i not in valid_axes_for_pool] 86 | 87 | pool_kernel_sizes = [0] * dim 88 | for v in valid_axes_for_pool: 89 | pool_kernel_sizes[v] = 2 90 | num_pool_per_axis[v] += 1 91 | current_spacing[v] *= 2 92 | current_size[v] = np.ceil(current_size[v] / 2) 93 | for nv in other_axes: 94 | pool_kernel_sizes[nv] = 1 95 | 96 | pool_op_kernel_sizes.append(pool_kernel_sizes) 97 | conv_kernel_sizes.append(deepcopy(kernel_size)) 98 | #print(conv_kernel_sizes) 99 | 100 | must_be_divisible_by = get_shape_must_be_divisible_by(num_pool_per_axis) 101 | patch_size = pad_shape(patch_size, must_be_divisible_by) 102 | 103 | # we need to add one more conv_kernel_size for the bottleneck. We always use 3x3(x3) conv here 104 | conv_kernel_sizes.append([3]*dim) 105 | return num_pool_per_axis, pool_op_kernel_sizes, conv_kernel_sizes, patch_size, must_be_divisible_by 106 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/experiment_planning/experiment_planners/readme.md: -------------------------------------------------------------------------------- 1 | What do experiment planners need to do (these are notes for myself while rewriting nnU-Net, they are provided as is 2 | without further explanations. These notes also include new features): 3 | - (done) preprocessor name should be configurable via cli 4 | - (done) gpu memory target should be configurable via cli 5 | - (done) plans name should be configurable via cli 6 | - (done) data name should be specified in plans (plans specify the data they want to use, this will allow us to manually 7 | edit plans files without having to copy the data folders) 8 | - plans must contain: 9 | - (done) transpose forward/backward 10 | - (done) preprocessor name (can differ for each config) 11 | - (done) spacing 12 | - (done) normalization scheme 13 | - (done) target spacing 14 | - (done) conv and pool op kernel sizes 15 | - (done) base num features for architecture 16 | - (done) data identifier 17 | - num conv per stage? 18 | - (done) use mask for norm 19 | - [NO. Handled by LabelManager & dataset.json] num segmentation outputs 20 | - [NO. Handled by LabelManager & dataset.json] ignore class 21 | - [NO. Handled by LabelManager & dataset.json] list of regions or classes 22 | - [NO. Handled by LabelManager & dataset.json] regions class order, if applicable 23 | - (done) resampling function to be used 24 | - (done) the image reader writer class that should be used 25 | 26 | 27 | dataset.json 28 | mandatory: 29 | - numTraining 30 | - labels (value 'ignore' has special meaning. Cannot have more than one ignore_label) 31 | - modalities 32 | - file_ending 33 | 34 | optional 35 | - overwrite_image_reader_writer (if absent, auto) 36 | - regions 37 | - region_class_order 38 | - -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/experiment_planning/experiment_planners/resencUNet_planner.py: -------------------------------------------------------------------------------- 1 | from typing import Union, List, Tuple 2 | 3 | from torch import nn 4 | 5 | from nnunetv2.experiment_planning.experiment_planners.default_experiment_planner import ExperimentPlanner 6 | from dynamic_network_architectures.architectures.unet import ResidualEncoderUNet 7 | 8 | 9 | class ResEncUNetPlanner(ExperimentPlanner): 10 | def __init__(self, dataset_name_or_id: Union[str, int], 11 | gpu_memory_target_in_gb: float = 8, 12 | preprocessor_name: str = 'DefaultPreprocessor', plans_name: str = 'nnUNetResEncUNetPlans', 13 | overwrite_target_spacing: Union[List[float], Tuple[float, ...]] = None, 14 | suppress_transpose: bool = False): 15 | super().__init__(dataset_name_or_id, gpu_memory_target_in_gb, preprocessor_name, plans_name, 16 | overwrite_target_spacing, suppress_transpose) 17 | 18 | self.UNet_base_num_features = 32 19 | self.UNet_class = ResidualEncoderUNet 20 | # the following two numbers are really arbitrary and were set to reproduce default nnU-Net's configurations as 21 | # much as possible 22 | self.UNet_reference_val_3d = 680000000 23 | self.UNet_reference_val_2d = 135000000 24 | self.UNet_reference_com_nfeatures = 32 25 | self.UNet_reference_val_corresp_GB = 8 26 | self.UNet_reference_val_corresp_bs_2d = 12 27 | self.UNet_reference_val_corresp_bs_3d = 2 28 | self.UNet_featuremap_min_edge_length = 4 29 | self.UNet_blocks_per_stage_encoder = (1, 3, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6) 30 | self.UNet_blocks_per_stage_decoder = (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) 31 | self.UNet_min_batch_size = 2 32 | self.UNet_max_features_2d = 512 33 | self.UNet_max_features_3d = 320 34 | 35 | 36 | if __name__ == '__main__': 37 | # we know both of these networks run with batch size 2 and 12 on ~8-10GB, respectively 38 | net = ResidualEncoderUNet(input_channels=1, n_stages=6, features_per_stage=(32, 64, 128, 256, 320, 320), 39 | conv_op=nn.Conv3d, kernel_sizes=3, strides=(1, 2, 2, 2, 2, 2), 40 | n_blocks_per_stage=(1, 3, 4, 6, 6, 6), num_classes=3, 41 | n_conv_per_stage_decoder=(1, 1, 1, 1, 1), 42 | conv_bias=True, norm_op=nn.InstanceNorm3d, norm_op_kwargs={}, dropout_op=None, 43 | nonlin=nn.LeakyReLU, nonlin_kwargs={'inplace': True}, deep_supervision=True) 44 | print(net.compute_conv_feature_map_size((128, 128, 128))) # -> 558319104. The value you see above was finetuned 45 | # from this one to match the regular nnunetplans more closely 46 | 47 | net = ResidualEncoderUNet(input_channels=1, n_stages=7, features_per_stage=(32, 64, 128, 256, 512, 512, 512), 48 | conv_op=nn.Conv2d, kernel_sizes=3, strides=(1, 2, 2, 2, 2, 2, 2), 49 | n_blocks_per_stage=(1, 3, 4, 6, 6, 6, 6), num_classes=3, 50 | n_conv_per_stage_decoder=(1, 1, 1, 1, 1, 1), 51 | conv_bias=True, norm_op=nn.InstanceNorm2d, norm_op_kwargs={}, dropout_op=None, 52 | nonlin=nn.LeakyReLU, nonlin_kwargs={'inplace': True}, deep_supervision=True) 53 | print(net.compute_conv_feature_map_size((512, 512))) # -> 129793792 54 | 55 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/experiment_planning/plans_for_pretraining/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/experiment_planning/plans_for_pretraining/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/imageio/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/imageio/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/imageio/natural_image_reader_writer.py: -------------------------------------------------------------------------------- 1 | # Copyright 2021 HIP Applied Computer Vision Lab, Division of Medical Image Computing, German Cancer Research Center 2 | # (DKFZ), Heidelberg, Germany 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | 16 | from typing import Tuple, Union, List 17 | import numpy as np 18 | from nnunetv2.imageio.base_reader_writer import BaseReaderWriter 19 | from skimage import io 20 | 21 | 22 | class NaturalImage2DIO(BaseReaderWriter): 23 | """ 24 | ONLY SUPPORTS 2D IMAGES!!! 25 | """ 26 | 27 | # there are surely more we could add here. Everything that can be read by skimage.io should be supported 28 | supported_file_endings = [ 29 | '.png', 30 | # '.jpg', 31 | # '.jpeg', # jpg not supported because we cannot allow lossy compression! segmentation maps! 32 | '.bmp', 33 | '.tif' 34 | ] 35 | 36 | def read_images(self, image_fnames: Union[List[str], Tuple[str, ...]]) -> Tuple[np.ndarray, dict]: 37 | images = [] 38 | for f in image_fnames: 39 | npy_img = io.imread(f) 40 | if npy_img.ndim == 3: 41 | # rgb image, last dimension should be the color channel and the size of that channel should be 3 42 | # (or 4 if we have alpha) 43 | assert npy_img.shape[-1] == 3 or npy_img.shape[-1] == 4, "If image has three dimensions then the last " \ 44 | "dimension must have shape 3 or 4 " \ 45 | f"(RGB or RGBA). Image shape here is {npy_img.shape}" 46 | # move RGB(A) to front, add additional dim so that we have shape (1, c, X, Y), where c is either 3 or 4 47 | images.append(npy_img.transpose((2, 0, 1))[:, None]) 48 | elif npy_img.ndim == 2: 49 | # grayscale image 50 | images.append(npy_img[None, None]) 51 | 52 | if not self._check_all_same([i.shape for i in images]): 53 | print('ERROR! Not all input images have the same shape!') 54 | print('Shapes:') 55 | print([i.shape for i in images]) 56 | print('Image files:') 57 | print(image_fnames) 58 | raise RuntimeError() 59 | return np.vstack(images).astype(np.float32), {'spacing': (999, 1, 1)} 60 | 61 | def read_seg(self, seg_fname: str) -> Tuple[np.ndarray, dict]: 62 | return self.read_images((seg_fname, )) 63 | 64 | def write_seg(self, seg: np.ndarray, output_fname: str, properties: dict) -> None: 65 | io.imsave(output_fname, seg[0].astype(np.uint8), check_contrast=False) 66 | 67 | 68 | if __name__ == '__main__': 69 | images = ('/media/fabian/data/nnUNet_raw/Dataset120_RoadSegmentation/imagesTr/img-11_0000.png',) 70 | segmentation = '/media/fabian/data/nnUNet_raw/Dataset120_RoadSegmentation/labelsTr/img-11.png' 71 | imgio = NaturalImage2DIO() 72 | img, props = imgio.read_images(images) 73 | seg, segprops = imgio.read_seg(segmentation) -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/imageio/reader_writer_registry.py: -------------------------------------------------------------------------------- 1 | import traceback 2 | from typing import Type 3 | 4 | from batchgenerators.utilities.file_and_folder_operations import join 5 | 6 | import nnunetv2 7 | from nnunetv2.imageio.natural_image_reader_writer import NaturalImage2DIO 8 | from nnunetv2.imageio.nibabel_reader_writer import NibabelIO, NibabelIOWithReorient 9 | from nnunetv2.imageio.simpleitk_reader_writer import SimpleITKIO 10 | from nnunetv2.imageio.tif_reader_writer import Tiff3DIO 11 | from nnunetv2.imageio.base_reader_writer import BaseReaderWriter 12 | from nnunetv2.utilities.find_class_by_name import recursive_find_python_class 13 | 14 | LIST_OF_IO_CLASSES = [ 15 | NaturalImage2DIO, 16 | SimpleITKIO, 17 | Tiff3DIO, 18 | NibabelIO, 19 | NibabelIOWithReorient 20 | ] 21 | 22 | 23 | def determine_reader_writer_from_dataset_json(dataset_json_content: dict, example_file: str = None, 24 | allow_nonmatching_filename: bool = False, verbose: bool = True 25 | ) -> Type[BaseReaderWriter]: 26 | if 'overwrite_image_reader_writer' in dataset_json_content.keys() and \ 27 | dataset_json_content['overwrite_image_reader_writer'] != 'None': 28 | ioclass_name = dataset_json_content['overwrite_image_reader_writer'] 29 | # trying to find that class in the nnunetv2.imageio module 30 | try: 31 | ret = recursive_find_reader_writer_by_name(ioclass_name) 32 | if verbose: print(f'Using {ret} reader/writer') 33 | return ret 34 | except RuntimeError: 35 | if verbose: print(f'Warning: Unable to find ioclass specified in dataset.json: {ioclass_name}') 36 | if verbose: print('Trying to automatically determine desired class') 37 | return determine_reader_writer_from_file_ending(dataset_json_content['file_ending'], example_file, 38 | allow_nonmatching_filename, verbose) 39 | 40 | 41 | def determine_reader_writer_from_file_ending(file_ending: str, example_file: str = None, allow_nonmatching_filename: bool = False, 42 | verbose: bool = True): 43 | for rw in LIST_OF_IO_CLASSES: 44 | if file_ending.lower() in rw.supported_file_endings: 45 | if example_file is not None: 46 | # if an example file is provided, try if we can actually read it. If not move on to the next reader 47 | try: 48 | tmp = rw() 49 | _ = tmp.read_images((example_file,)) 50 | if verbose: print(f'Using {rw} as reader/writer') 51 | return rw 52 | except: 53 | if verbose: print(f'Failed to open file {example_file} with reader {rw}:') 54 | traceback.print_exc() 55 | pass 56 | else: 57 | if verbose: print(f'Using {rw} as reader/writer') 58 | return rw 59 | else: 60 | if allow_nonmatching_filename and example_file is not None: 61 | try: 62 | tmp = rw() 63 | _ = tmp.read_images((example_file,)) 64 | if verbose: print(f'Using {rw} as reader/writer') 65 | return rw 66 | except: 67 | if verbose: print(f'Failed to open file {example_file} with reader {rw}:') 68 | if verbose: traceback.print_exc() 69 | pass 70 | raise RuntimeError(f"Unable to determine a reader for file ending {file_ending} and file {example_file} (file None means no file provided).") 71 | 72 | 73 | def recursive_find_reader_writer_by_name(rw_class_name: str) -> Type[BaseReaderWriter]: 74 | ret = recursive_find_python_class(join(nnunetv2.__path__[0], "imageio"), rw_class_name, 'nnunetv2.imageio') 75 | if ret is None: 76 | raise RuntimeError("Unable to find reader writer class '%s'. Please make sure this class is located in the " 77 | "nnunetv2.imageio module." % rw_class_name) 78 | else: 79 | return ret 80 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/imageio/readme.md: -------------------------------------------------------------------------------- 1 | - Derive your adapter from `BaseReaderWriter`. 2 | - Reimplement all abstractmethods. 3 | - make sure to support 2d and 3d input images (or raise some error). 4 | - place it in this folder or nnU-Net won't find it! 5 | - add it to LIST_OF_IO_CLASSES in `reader_writer_registry.py` 6 | 7 | Bam, you're done! -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/inference/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/inference/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/inference/sliding_window_prediction.py: -------------------------------------------------------------------------------- 1 | from functools import lru_cache 2 | 3 | import numpy as np 4 | import torch 5 | from typing import Union, Tuple, List 6 | from acvl_utils.cropping_and_padding.padding import pad_nd_image 7 | from scipy.ndimage import gaussian_filter 8 | 9 | 10 | @lru_cache(maxsize=2) 11 | def compute_gaussian(tile_size: Union[Tuple[int, ...], List[int]], sigma_scale: float = 1. / 8, 12 | value_scaling_factor: float = 1, dtype=torch.float16, device=torch.device('cuda', 0)) \ 13 | -> torch.Tensor: 14 | tmp = np.zeros(tile_size) 15 | center_coords = [i // 2 for i in tile_size] 16 | sigmas = [i * sigma_scale for i in tile_size] 17 | tmp[tuple(center_coords)] = 1 18 | gaussian_importance_map = gaussian_filter(tmp, sigmas, 0, mode='constant', cval=0) 19 | 20 | gaussian_importance_map = torch.from_numpy(gaussian_importance_map) 21 | 22 | gaussian_importance_map = gaussian_importance_map / torch.max(gaussian_importance_map) * value_scaling_factor 23 | gaussian_importance_map = gaussian_importance_map.type(dtype).to(device) 24 | 25 | # gaussian_importance_map cannot be 0, otherwise we may end up with nans! 26 | gaussian_importance_map[gaussian_importance_map == 0] = torch.min( 27 | gaussian_importance_map[gaussian_importance_map != 0]) 28 | 29 | return gaussian_importance_map 30 | 31 | 32 | def compute_steps_for_sliding_window(image_size: Tuple[int, ...], tile_size: Tuple[int, ...], tile_step_size: float) -> \ 33 | List[List[int]]: 34 | assert [i >= j for i, j in zip(image_size, tile_size)], "image size must be as large or larger than patch_size" 35 | assert 0 < tile_step_size <= 1, 'step_size must be larger than 0 and smaller or equal to 1' 36 | 37 | # our step width is patch_size*step_size at most, but can be narrower. For example if we have image size of 38 | # 110, patch size of 64 and step_size of 0.5, then we want to make 3 steps starting at coordinate 0, 23, 46 39 | target_step_sizes_in_voxels = [i * tile_step_size for i in tile_size] 40 | 41 | num_steps = [int(np.ceil((i - k) / j)) + 1 for i, j, k in zip(image_size, target_step_sizes_in_voxels, tile_size)] 42 | 43 | steps = [] 44 | for dim in range(len(tile_size)): 45 | # the highest step value for this dimension is 46 | max_step_value = image_size[dim] - tile_size[dim] 47 | if num_steps[dim] > 1: 48 | actual_step_size = max_step_value / (num_steps[dim] - 1) 49 | else: 50 | actual_step_size = 99999999999 # does not matter because there is only one step at 0 51 | 52 | steps_here = [int(np.round(actual_step_size * i)) for i in range(num_steps[dim])] 53 | 54 | steps.append(steps_here) 55 | 56 | return steps 57 | 58 | 59 | if __name__ == '__main__': 60 | a = torch.rand((4, 2, 32, 23)) 61 | a_npy = a.numpy() 62 | 63 | a_padded = pad_nd_image(a, new_shape=(48, 27)) 64 | a_npy_padded = pad_nd_image(a_npy, new_shape=(48, 27)) 65 | assert all([i == j for i, j in zip(a_padded.shape, (4, 2, 48, 27))]) 66 | assert all([i == j for i, j in zip(a_npy_padded.shape, (4, 2, 48, 27))]) 67 | assert np.all(a_padded.numpy() == a_npy_padded) 68 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/model_sharing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/model_sharing/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/model_sharing/entry_points.py: -------------------------------------------------------------------------------- 1 | from nnunetv2.model_sharing.model_download import download_and_install_from_url 2 | from nnunetv2.model_sharing.model_export import export_pretrained_model 3 | from nnunetv2.model_sharing.model_import import install_model_from_zip_file 4 | 5 | 6 | def print_license_warning(): 7 | print('') 8 | print('######################################################') 9 | print('!!!!!!!!!!!!!!!!!!!!!!!!WARNING!!!!!!!!!!!!!!!!!!!!!!!') 10 | print('######################################################') 11 | print("Using the pretrained model weights is subject to the license of the dataset they were trained on. Some " 12 | "allow commercial use, others don't. It is your responsibility to make sure you use them appropriately! Use " 13 | "nnUNet_print_pretrained_model_info(task_name) to see a summary of the dataset and where to find its license!") 14 | print('######################################################') 15 | print('') 16 | 17 | 18 | def download_by_url(): 19 | import argparse 20 | parser = argparse.ArgumentParser( 21 | description="Use this to download pretrained models. This script is intended to download models via url only. " 22 | "CAREFUL: This script will overwrite " 23 | "existing models (if they share the same trainer class and plans as " 24 | "the pretrained model.") 25 | parser.add_argument("url", type=str, help='URL of the pretrained model') 26 | args = parser.parse_args() 27 | url = args.url 28 | download_and_install_from_url(url) 29 | 30 | 31 | def install_from_zip_entry_point(): 32 | import argparse 33 | parser = argparse.ArgumentParser( 34 | description="Use this to install a zip file containing a pretrained model.") 35 | parser.add_argument("zip", type=str, help='zip file') 36 | args = parser.parse_args() 37 | zip = args.zip 38 | install_model_from_zip_file(zip) 39 | 40 | 41 | def export_pretrained_model_entry(): 42 | import argparse 43 | parser = argparse.ArgumentParser( 44 | description="Use this to export a trained model as a zip file.") 45 | parser.add_argument('-d', type=str, required=True, help='Dataset name or id') 46 | parser.add_argument('-o', type=str, required=True, help='Output file name') 47 | parser.add_argument('-c', nargs='+', type=str, required=False, 48 | default=('3d_lowres', '3d_fullres', '2d', '3d_cascade_fullres'), 49 | help="List of configuration names") 50 | parser.add_argument('-tr', required=False, type=str, default='nnUNetTrainer', help='Trainer class') 51 | parser.add_argument('-p', required=False, type=str, default='nnUNetPlans', help='plans identifier') 52 | parser.add_argument('-f', required=False, nargs='+', type=str, default=(0, 1, 2, 3, 4), help='list of fold ids') 53 | parser.add_argument('-chk', required=False, nargs='+', type=str, default=('checkpoint_final.pth', ), 54 | help='Lis tof checkpoint names to export. Default: checkpoint_final.pth') 55 | parser.add_argument('--not_strict', action='store_false', default=False, required=False, help='Set this to allow missing folds and/or configurations') 56 | parser.add_argument('--exp_cv_preds', action='store_true', required=False, help='Set this to export the cross-validation predictions as well') 57 | args = parser.parse_args() 58 | 59 | export_pretrained_model(dataset_name_or_id=args.d, output_file=args.o, configurations=args.c, trainer=args.tr, 60 | plans_identifier=args.p, folds=args.f, strict=not args.not_strict, save_checkpoints=args.chk, 61 | export_crossval_predictions=args.exp_cv_preds) 62 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/model_sharing/model_download.py: -------------------------------------------------------------------------------- 1 | from typing import Optional 2 | 3 | import requests 4 | from batchgenerators.utilities.file_and_folder_operations import * 5 | from time import time 6 | from nnunetv2.model_sharing.model_import import install_model_from_zip_file 7 | from nnunetv2.paths import nnUNet_results 8 | from tqdm import tqdm 9 | 10 | 11 | def download_and_install_from_url(url): 12 | assert nnUNet_results is not None, "Cannot install model because network_training_output_dir is not " \ 13 | "set (RESULTS_FOLDER missing as environment variable, see " \ 14 | "Installation instructions)" 15 | print('Downloading pretrained model from url:', url) 16 | import http.client 17 | http.client.HTTPConnection._http_vsn = 10 18 | http.client.HTTPConnection._http_vsn_str = 'HTTP/1.0' 19 | 20 | import os 21 | home = os.path.expanduser('~') 22 | random_number = int(time() * 1e7) 23 | tempfile = join(home, f'.nnunetdownload_{str(random_number)}') 24 | 25 | try: 26 | download_file(url=url, local_filename=tempfile, chunk_size=8192 * 16) 27 | print("Download finished. Extracting...") 28 | install_model_from_zip_file(tempfile) 29 | print("Done") 30 | except Exception as e: 31 | raise e 32 | finally: 33 | if isfile(tempfile): 34 | os.remove(tempfile) 35 | 36 | 37 | def download_file(url: str, local_filename: str, chunk_size: Optional[int] = 8192 * 16) -> str: 38 | # borrowed from https://stackoverflow.com/questions/16694907/download-large-file-in-python-with-requests 39 | # NOTE the stream=True parameter below 40 | with requests.get(url, stream=True, timeout=100) as r: 41 | r.raise_for_status() 42 | with tqdm.wrapattr(open(local_filename, 'wb'), "write", total=int(r.headers.get("Content-Length"))) as f: 43 | for chunk in r.iter_content(chunk_size=chunk_size): 44 | f.write(chunk) 45 | return local_filename 46 | 47 | 48 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/model_sharing/model_import.py: -------------------------------------------------------------------------------- 1 | import zipfile 2 | 3 | from nnunetv2.paths import nnUNet_results 4 | 5 | 6 | def install_model_from_zip_file(zip_file: str): 7 | with zipfile.ZipFile(zip_file, 'r') as zip_ref: 8 | zip_ref.extractall(nnUNet_results) -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/paths.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import os 16 | join = os.path.join 17 | """ 18 | Please make sure your data is organized as follows: 19 | 20 | data/ 21 | ├── nnUNet_raw/ 22 | │ ├── Dataset701_AbdomenCT/ 23 | │ │ ├── imagesTr 24 | │ │ │ ├── FLARE22_Tr_0001_0000.nii.gz 25 | │ │ │ ├── FLARE22_Tr_0002_0000.nii.gz 26 | │ │ │ ├── ... 27 | │ │ ├── labelsTr 28 | │ │ │ ├── FLARE22_Tr_0001.nii.gz 29 | │ │ │ ├── FLARE22_Tr_0002.nii.gz 30 | │ │ │ ├── ... 31 | │ │ ├── dataset.json 32 | │ ├── Dataset702_AbdomenMR/ 33 | │ │ ├── imagesTr 34 | │ │ │ ├── amos_0507_0000.nii.gz 35 | │ │ │ ├── amos_0508_0000.nii.gz 36 | │ │ │ ├── ... 37 | │ │ ├── labelsTr 38 | │ │ │ ├── amos_0507.nii.gz 39 | │ │ │ ├── amos_0508.nii.gz 40 | │ │ │ ├── ... 41 | │ │ ├── dataset.json 42 | │ ├── ... 43 | """ 44 | base = join(os.sep.join(__file__.split(os.sep)[:-3]), 'data') 45 | # or you can set your own path by 46 | # base = '/root/autodl-tmp/Data' 47 | 48 | nnUNet_raw = join(base, 'nnUNet_raw') # os.environ.get('nnUNet_raw') 49 | nnUNet_preprocessed = join(base, 'nnUNet_preprocessed') # os.environ.get('nnUNet_preprocessed') 50 | nnUNet_results = join(base, 'nnUNet_results') # os.environ.get('nnUNet_results') 51 | 52 | if nnUNet_raw is None: 53 | print("nnUNet_raw is not defined and nnU-Net can only be used on data for which preprocessed files " 54 | "are already present on your system. nnU-Net cannot be used for experiment planning and preprocessing like " 55 | "this. If this is not intended, please read documentation/setting_up_paths.md for information on how to set " 56 | "this up properly.") 57 | 58 | if nnUNet_preprocessed is None: 59 | print("nnUNet_preprocessed is not defined and nnU-Net can not be used for preprocessing " 60 | "or training. If this is not intended, please read documentation/setting_up_paths.md for information on how " 61 | "to set this up.") 62 | 63 | if nnUNet_results is None: 64 | print("nnUNet_results is not defined and nnU-Net cannot be used for training or " 65 | "inference. If this is not intended behavior, please read documentation/setting_up_paths.md for information " 66 | "on how to set this up.") 67 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/postprocessing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/postprocessing/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/preprocessing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/preprocessing/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/preprocessing/cropping/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/preprocessing/cropping/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/preprocessing/cropping/cropping.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | # Hello! crop_to_nonzero is the function you are looking for. Ignore the rest. 5 | from acvl_utils.cropping_and_padding.bounding_boxes import get_bbox_from_mask, crop_to_bbox, bounding_box_to_slice 6 | 7 | 8 | def create_nonzero_mask(data): 9 | """ 10 | 11 | :param data: 12 | :return: the mask is True where the data is nonzero 13 | """ 14 | from scipy.ndimage import binary_fill_holes 15 | assert data.ndim in (3, 4), "data must have shape (C, X, Y, Z) or shape (C, X, Y)" 16 | nonzero_mask = np.zeros(data.shape[1:], dtype=bool) 17 | for c in range(data.shape[0]): 18 | this_mask = data[c] != 0 19 | nonzero_mask = nonzero_mask | this_mask 20 | nonzero_mask = binary_fill_holes(nonzero_mask) 21 | return nonzero_mask 22 | 23 | 24 | def crop_to_nonzero(data, seg=None, nonzero_label=-1): 25 | """ 26 | 27 | :param data: 28 | :param seg: 29 | :param nonzero_label: this will be written into the segmentation map 30 | :return: 31 | """ 32 | nonzero_mask = create_nonzero_mask(data) 33 | bbox = get_bbox_from_mask(nonzero_mask) 34 | 35 | slicer = bounding_box_to_slice(bbox) 36 | data = data[tuple([slice(None), *slicer])] 37 | 38 | if seg is not None: 39 | seg = seg[tuple([slice(None), *slicer])] 40 | 41 | nonzero_mask = nonzero_mask[slicer][None] 42 | if seg is not None: 43 | seg[(seg == 0) & (~nonzero_mask)] = nonzero_label 44 | else: 45 | nonzero_mask = nonzero_mask.astype(np.int8) 46 | nonzero_mask[nonzero_mask == 0] = nonzero_label 47 | nonzero_mask[nonzero_mask > 0] = 0 48 | seg = nonzero_mask 49 | return data, seg, bbox 50 | 51 | 52 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/preprocessing/normalization/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/preprocessing/normalization/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/preprocessing/normalization/map_channel_name_to_normalization.py: -------------------------------------------------------------------------------- 1 | from typing import Type 2 | 3 | from nnunetv2.preprocessing.normalization.default_normalization_schemes import CTNormalization, NoNormalization, \ 4 | ZScoreNormalization, RescaleTo01Normalization, RGBTo01Normalization, ImageNormalization 5 | 6 | channel_name_to_normalization_mapping = { 7 | 'CT': CTNormalization, 8 | 'noNorm': NoNormalization, 9 | 'zscore': ZScoreNormalization, 10 | 'rescale_to_0_1': RescaleTo01Normalization, 11 | 'rgb_to_0_1': RGBTo01Normalization 12 | } 13 | 14 | 15 | def get_normalization_scheme(channel_name: str) -> Type[ImageNormalization]: 16 | """ 17 | If we find the channel_name in channel_name_to_normalization_mapping return the corresponding normalization. If it is 18 | not found, use the default (ZScoreNormalization) 19 | """ 20 | norm_scheme = channel_name_to_normalization_mapping.get(channel_name) 21 | if norm_scheme is None: 22 | norm_scheme = ZScoreNormalization 23 | # print('Using %s for image normalization' % norm_scheme.__name__) 24 | return norm_scheme 25 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/preprocessing/normalization/readme.md: -------------------------------------------------------------------------------- 1 | The channel_names entry in dataset.json only determines the normlaization scheme. So if you want to use something different 2 | then you can just 3 | - create a new subclass of ImageNormalization 4 | - map your custom channel identifier to that subclass in channel_name_to_normalization_mapping 5 | - run plan and preprocess again with your custom normlaization scheme -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/preprocessing/preprocessors/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/preprocessing/preprocessors/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/preprocessing/resampling/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/preprocessing/resampling/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/preprocessing/resampling/utils.py: -------------------------------------------------------------------------------- 1 | from typing import Callable 2 | 3 | import nnunetv2 4 | from batchgenerators.utilities.file_and_folder_operations import join 5 | from nnunetv2.utilities.find_class_by_name import recursive_find_python_class 6 | 7 | 8 | def recursive_find_resampling_fn_by_name(resampling_fn: str) -> Callable: 9 | ret = recursive_find_python_class(join(nnunetv2.__path__[0], "preprocessing", "resampling"), resampling_fn, 10 | 'nnunetv2.preprocessing.resampling') 11 | if ret is None: 12 | raise RuntimeError("Unable to find resampling function named '%s'. Please make sure this fn is located in the " 13 | "nnunetv2.preprocessing.resampling module." % resampling_fn) 14 | else: 15 | return ret 16 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/run/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/run/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/run/load_pretrained_weights.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch._dynamo import OptimizedModule 3 | from torch.nn.parallel import DistributedDataParallel as DDP 4 | 5 | 6 | def load_pretrained_weights(network, fname, verbose=False): 7 | """ 8 | Transfers all weights between matching keys in state_dicts. matching is done by name and we only transfer if the 9 | shape is also the same. Segmentation layers (the 1x1(x1) layers that produce the segmentation maps) 10 | identified by keys ending with '.seg_layers') are not transferred! 11 | 12 | If the pretrained weights were obtained with a training outside nnU-Net and DDP or torch.optimize was used, 13 | you need to change the keys of the pretrained state_dict. DDP adds a 'module.' prefix and torch.optim adds 14 | '_orig_mod'. You DO NOT need to worry about this if pretraining was done with nnU-Net as 15 | nnUNetTrainer.save_checkpoint takes care of that! 16 | 17 | """ 18 | saved_model = torch.load(fname) 19 | pretrained_dict = saved_model['network_weights'] 20 | 21 | skip_strings_in_pretrained = [ 22 | '.seg_layers.', 23 | ] 24 | 25 | if isinstance(network, DDP): 26 | mod = network.module 27 | else: 28 | mod = network 29 | if isinstance(mod, OptimizedModule): 30 | mod = mod._orig_mod 31 | 32 | model_dict = mod.state_dict() 33 | # verify that all but the segmentation layers have the same shape 34 | for key, _ in model_dict.items(): 35 | if all([i not in key for i in skip_strings_in_pretrained]): 36 | assert key in pretrained_dict, \ 37 | f"Key {key} is missing in the pretrained model weights. The pretrained weights do not seem to be " \ 38 | f"compatible with your network." 39 | assert model_dict[key].shape == pretrained_dict[key].shape, \ 40 | f"The shape of the parameters of key {key} is not the same. Pretrained model: " \ 41 | f"{pretrained_dict[key].shape}; your network: {model_dict[key]}. The pretrained model " \ 42 | f"does not seem to be compatible with your network." 43 | 44 | # fun fact: in principle this allows loading from parameters that do not cover the entire network. For example pretrained 45 | # encoders. Not supported by this function though (see assertions above) 46 | 47 | # commenting out this abomination of a dict comprehension for preservation in the archives of 'what not to do' 48 | # pretrained_dict = {'module.' + k if is_ddp else k: v 49 | # for k, v in pretrained_dict.items() 50 | # if (('module.' + k if is_ddp else k) in model_dict) and 51 | # all([i not in k for i in skip_strings_in_pretrained])} 52 | 53 | pretrained_dict = {k: v for k, v in pretrained_dict.items() 54 | if k in model_dict.keys() and all([i not in k for i in skip_strings_in_pretrained])} 55 | 56 | model_dict.update(pretrained_dict) 57 | 58 | print("################### Loading pretrained weights from file ", fname, '###################') 59 | if verbose: 60 | print("Below is the list of overlapping blocks in pretrained model and nnUNet architecture:") 61 | for key, value in pretrained_dict.items(): 62 | print(key, 'shape', value.shape) 63 | print("################### Done ###################") 64 | mod.load_state_dict(model_dict) 65 | 66 | 67 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/tests/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/tests/integration_tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/tests/integration_tests/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/tests/integration_tests/add_lowres_and_cascade.py: -------------------------------------------------------------------------------- 1 | from batchgenerators.utilities.file_and_folder_operations import * 2 | 3 | from nnunetv2.paths import nnUNet_preprocessed 4 | from nnunetv2.utilities.dataset_name_id_conversion import maybe_convert_to_dataset_name 5 | 6 | if __name__ == '__main__': 7 | import argparse 8 | 9 | parser = argparse.ArgumentParser() 10 | parser.add_argument('-d', nargs='+', type=int, help='List of dataset ids') 11 | args = parser.parse_args() 12 | 13 | for d in args.d: 14 | dataset_name = maybe_convert_to_dataset_name(d) 15 | plans = load_json(join(nnUNet_preprocessed, dataset_name, 'nnUNetPlans.json')) 16 | plans['configurations']['3d_lowres'] = { 17 | "data_identifier": "nnUNetPlans_3d_lowres", # do not be a dumbo and forget this. I was a dumbo. And I paid dearly with ~10 min debugging time 18 | 'inherits_from': '3d_fullres', 19 | "patch_size": [20, 28, 20], 20 | "median_image_size_in_voxels": [18.0, 25.0, 18.0], 21 | "spacing": [2.0, 2.0, 2.0], 22 | "n_conv_per_stage_encoder": [2, 2, 2], 23 | "n_conv_per_stage_decoder": [2, 2], 24 | "num_pool_per_axis": [2, 2, 2], 25 | "pool_op_kernel_sizes": [[1, 1, 1], [2, 2, 2], [2, 2, 2]], 26 | "conv_kernel_sizes": [[3, 3, 3], [3, 3, 3], [3, 3, 3]], 27 | "next_stage": "3d_cascade_fullres" 28 | } 29 | plans['configurations']['3d_cascade_fullres'] = { 30 | 'inherits_from': '3d_fullres', 31 | "previous_stage": "3d_lowres" 32 | } 33 | save_json(plans, join(nnUNet_preprocessed, dataset_name, 'nnUNetPlans.json'), sort_keys=False) -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/tests/integration_tests/cleanup_integration_test.py: -------------------------------------------------------------------------------- 1 | import shutil 2 | 3 | from batchgenerators.utilities.file_and_folder_operations import isdir, join 4 | 5 | from nnunetv2.paths import nnUNet_raw, nnUNet_results, nnUNet_preprocessed 6 | 7 | if __name__ == '__main__': 8 | # deletes everything! 9 | dataset_names = [ 10 | 'Dataset996_IntegrationTest_Hippocampus_regions_ignore', 11 | 'Dataset997_IntegrationTest_Hippocampus_regions', 12 | 'Dataset998_IntegrationTest_Hippocampus_ignore', 13 | 'Dataset999_IntegrationTest_Hippocampus', 14 | ] 15 | for fld in [nnUNet_raw, nnUNet_preprocessed, nnUNet_results]: 16 | for d in dataset_names: 17 | if isdir(join(fld, d)): 18 | shutil.rmtree(join(fld, d)) 19 | 20 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/tests/integration_tests/lsf_commands.sh: -------------------------------------------------------------------------------- 1 | bsub -q gpu.legacy -gpu num=1:j_exclusive=yes:gmem=1G -L /bin/bash ". /home/isensee/load_env_cluster4.sh && cd /home/isensee/git_repos/nnunet_remake && export nnUNet_keep_files_open=True && . nnunetv2/tests/integration_tests/run_integration_test.sh 996" 2 | bsub -q gpu.legacy -gpu num=1:j_exclusive=yes:gmem=1G -L /bin/bash ". /home/isensee/load_env_cluster4.sh && cd /home/isensee/git_repos/nnunet_remake && export nnUNet_keep_files_open=True && . nnunetv2/tests/integration_tests/run_integration_test.sh 997" 3 | bsub -q gpu.legacy -gpu num=1:j_exclusive=yes:gmem=1G -L /bin/bash ". /home/isensee/load_env_cluster4.sh && cd /home/isensee/git_repos/nnunet_remake && export nnUNet_keep_files_open=True && . nnunetv2/tests/integration_tests/run_integration_test.sh 998" 4 | bsub -q gpu.legacy -gpu num=1:j_exclusive=yes:gmem=1G -L /bin/bash ". /home/isensee/load_env_cluster4.sh && cd /home/isensee/git_repos/nnunet_remake && export nnUNet_keep_files_open=True && . nnunetv2/tests/integration_tests/run_integration_test.sh 999" 5 | 6 | 7 | bsub -q gpu.legacy -gpu num=2:j_exclusive=yes:gmem=1G -L /bin/bash ". /home/isensee/load_env_cluster4.sh && cd /home/isensee/git_repos/nnunet_remake && export nnUNet_keep_files_open=True && . nnunetv2/tests/integration_tests/run_integration_test_trainingOnly_DDP.sh 996" 8 | bsub -q gpu.legacy -gpu num=2:j_exclusive=yes:gmem=1G -L /bin/bash ". /home/isensee/load_env_cluster4.sh && cd /home/isensee/git_repos/nnunet_remake && export nnUNet_keep_files_open=True && . nnunetv2/tests/integration_tests/run_integration_test_trainingOnly_DDP.sh 997" 9 | bsub -q gpu.legacy -gpu num=2:j_exclusive=yes:gmem=1G -L /bin/bash ". /home/isensee/load_env_cluster4.sh && cd /home/isensee/git_repos/nnunet_remake && export nnUNet_keep_files_open=True && . nnunetv2/tests/integration_tests/run_integration_test_trainingOnly_DDP.sh 998" 10 | bsub -q gpu.legacy -gpu num=2:j_exclusive=yes:gmem=1G -L /bin/bash ". /home/isensee/load_env_cluster4.sh && cd /home/isensee/git_repos/nnunet_remake && export nnUNet_keep_files_open=True && . nnunetv2/tests/integration_tests/run_integration_test_trainingOnly_DDP.sh 999" 11 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/tests/integration_tests/prepare_integration_tests.sh: -------------------------------------------------------------------------------- 1 | # assumes you are in the nnunet repo! 2 | 3 | # prepare raw datasets 4 | python nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset999_IntegrationTest_Hippocampus.py 5 | python nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset998_IntegrationTest_Hippocampus_ignore.py 6 | python nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset997_IntegrationTest_Hippocampus_regions.py 7 | python nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset996_IntegrationTest_Hippocampus_regions_ignore.py 8 | 9 | # now run experiment planning without preprocessing 10 | nnUNetv2_plan_and_preprocess -d 996 997 998 999 --no_pp 11 | 12 | # now add 3d lowres and cascade 13 | python nnunetv2/tests/integration_tests/add_lowres_and_cascade.py -d 996 997 998 999 14 | 15 | # now preprocess everything 16 | nnUNetv2_preprocess -d 996 997 998 999 -c 2d 3d_lowres 3d_fullres -np 8 8 8 # no need to preprocess cascade as its the same data as 3d_fullres 17 | 18 | # done -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/tests/integration_tests/readme.md: -------------------------------------------------------------------------------- 1 | # Preface 2 | 3 | I am just a mortal with many tasks and limited time. Aint nobody got time for unittests. 4 | 5 | HOWEVER, at least some integration tests should be performed testing nnU-Net from start to finish. 6 | 7 | # Introduction - What the heck is happening? 8 | This test covers all possible labeling scenarios (standard labels, regions, ignore labels and regions with 9 | ignore labels). It runs the entire nnU-Net pipeline from start to finish: 10 | 11 | - fingerprint extraction 12 | - experiment planning 13 | - preprocessing 14 | - train all 4 configurations (2d, 3d_lowres, 3d_fullres, 3d_cascade_fullres) as 5-fold CV 15 | - automatically find the best model or ensemble 16 | - determine the postprocessing used for this 17 | - predict some test set 18 | - apply postprocessing to the test set 19 | 20 | To speed things up, we do the following: 21 | - pick Dataset004_Hippocampus because it is quadratisch praktisch gut. MNIST of medical image segmentation 22 | - by default this dataset does not have 3d_lowres or cascade. We just manually add them (cool new feature, eh?). See `add_lowres_and_cascade.py` to learn more! 23 | - we use nnUNetTrainer_5epochs for a short training 24 | 25 | # How to run it? 26 | 27 | Set your pwd to be the nnunet repo folder (the one where the `nnunetv2` folder and the `setup.py` are located!) 28 | 29 | Now generate the 4 dummy datasets (ids 996, 997, 998, 999) from dataset 4. This will crash if you don't have Dataset004! 30 | ```commandline 31 | bash nnunetv2/tests/integration_tests/prepare_integration_tests.sh 32 | ``` 33 | 34 | Now you can run the integration test for each of the datasets: 35 | ```commandline 36 | bash nnunetv2/tests/integration_tests/run_integration_test.sh DATSET_ID 37 | ``` 38 | use DATSET_ID 996, 997, 998 and 999. You can run these independently on different GPUs/systems to speed things up. 39 | This will take i dunno like 10-30 Minutes!? 40 | 41 | Also run 42 | ```commandline 43 | bash nnunetv2/tests/integration_tests/run_integration_test_trainingOnly_DDP.sh DATSET_ID 44 | ``` 45 | to verify DDP is working (needs 2 GPUs!) 46 | 47 | # How to check if the test was successful? 48 | If I was not as lazy as I am I would have programmed some automatism that checks if Dice scores etc are in an acceptable range. 49 | So you need to do the following: 50 | 1) check that none of your runs crashed (duh) 51 | 2) for each run, navigate to `nnUNet_results/DATASET_NAME` and take a look at the `inference_information.json` file. 52 | Does it make sense? If so: NICE! 53 | 54 | Once the integration test is completed you can delete all the temporary files associated with it by running: 55 | 56 | ```commandline 57 | python nnunetv2/tests/integration_tests/cleanup_integration_test.py 58 | ``` -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/tests/integration_tests/run_integration_test.sh: -------------------------------------------------------------------------------- 1 | 2 | 3 | nnUNetv2_train $1 3d_fullres 0 -tr nnUNetTrainer_5epochs --npz 4 | nnUNetv2_train $1 3d_fullres 1 -tr nnUNetTrainer_5epochs --npz 5 | nnUNetv2_train $1 3d_fullres 2 -tr nnUNetTrainer_5epochs --npz 6 | nnUNetv2_train $1 3d_fullres 3 -tr nnUNetTrainer_5epochs --npz 7 | nnUNetv2_train $1 3d_fullres 4 -tr nnUNetTrainer_5epochs --npz 8 | 9 | nnUNetv2_train $1 2d 0 -tr nnUNetTrainer_5epochs --npz 10 | nnUNetv2_train $1 2d 1 -tr nnUNetTrainer_5epochs --npz 11 | nnUNetv2_train $1 2d 2 -tr nnUNetTrainer_5epochs --npz 12 | nnUNetv2_train $1 2d 3 -tr nnUNetTrainer_5epochs --npz 13 | nnUNetv2_train $1 2d 4 -tr nnUNetTrainer_5epochs --npz 14 | 15 | nnUNetv2_train $1 3d_lowres 0 -tr nnUNetTrainer_5epochs --npz 16 | nnUNetv2_train $1 3d_lowres 1 -tr nnUNetTrainer_5epochs --npz 17 | nnUNetv2_train $1 3d_lowres 2 -tr nnUNetTrainer_5epochs --npz 18 | nnUNetv2_train $1 3d_lowres 3 -tr nnUNetTrainer_5epochs --npz 19 | nnUNetv2_train $1 3d_lowres 4 -tr nnUNetTrainer_5epochs --npz 20 | 21 | nnUNetv2_train $1 3d_cascade_fullres 0 -tr nnUNetTrainer_5epochs --npz 22 | nnUNetv2_train $1 3d_cascade_fullres 1 -tr nnUNetTrainer_5epochs --npz 23 | nnUNetv2_train $1 3d_cascade_fullres 2 -tr nnUNetTrainer_5epochs --npz 24 | nnUNetv2_train $1 3d_cascade_fullres 3 -tr nnUNetTrainer_5epochs --npz 25 | nnUNetv2_train $1 3d_cascade_fullres 4 -tr nnUNetTrainer_5epochs --npz 26 | 27 | python nnunetv2/tests/integration_tests/run_integration_test_bestconfig_inference.py -d $1 -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/tests/integration_tests/run_integration_test_trainingOnly_DDP.sh: -------------------------------------------------------------------------------- 1 | nnUNetv2_train $1 3d_fullres 0 -tr nnUNetTrainer_10epochs -num_gpus 2 2 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/data_augmentation/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/data_augmentation/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/data_augmentation/compute_initial_patch_size.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def get_patch_size(final_patch_size, rot_x, rot_y, rot_z, scale_range): 5 | if isinstance(rot_x, (tuple, list)): 6 | rot_x = max(np.abs(rot_x)) 7 | if isinstance(rot_y, (tuple, list)): 8 | rot_y = max(np.abs(rot_y)) 9 | if isinstance(rot_z, (tuple, list)): 10 | rot_z = max(np.abs(rot_z)) 11 | rot_x = min(90 / 360 * 2. * np.pi, rot_x) 12 | rot_y = min(90 / 360 * 2. * np.pi, rot_y) 13 | rot_z = min(90 / 360 * 2. * np.pi, rot_z) 14 | from batchgenerators.augmentations.utils import rotate_coords_3d, rotate_coords_2d 15 | coords = np.array(final_patch_size) 16 | final_shape = np.copy(coords) 17 | if len(coords) == 3: 18 | final_shape = np.max(np.vstack((np.abs(rotate_coords_3d(coords, rot_x, 0, 0)), final_shape)), 0) 19 | final_shape = np.max(np.vstack((np.abs(rotate_coords_3d(coords, 0, rot_y, 0)), final_shape)), 0) 20 | final_shape = np.max(np.vstack((np.abs(rotate_coords_3d(coords, 0, 0, rot_z)), final_shape)), 0) 21 | elif len(coords) == 2: 22 | final_shape = np.max(np.vstack((np.abs(rotate_coords_2d(coords, rot_x)), final_shape)), 0) 23 | final_shape /= min(scale_range) 24 | return final_shape.astype(int) 25 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/data_augmentation/custom_transforms/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/data_augmentation/custom_transforms/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/data_augmentation/custom_transforms/deep_supervision_donwsampling.py: -------------------------------------------------------------------------------- 1 | from typing import Tuple, Union, List 2 | 3 | from batchgenerators.augmentations.utils import resize_segmentation 4 | from batchgenerators.transforms.abstract_transforms import AbstractTransform 5 | import numpy as np 6 | 7 | 8 | class DownsampleSegForDSTransform2(AbstractTransform): 9 | ''' 10 | data_dict['output_key'] will be a list of segmentations scaled according to ds_scales 11 | ''' 12 | def __init__(self, ds_scales: Union[List, Tuple], 13 | order: int = 0, input_key: str = "seg", 14 | output_key: str = "seg", axes: Tuple[int] = None): 15 | """ 16 | Downscales data_dict[input_key] according to ds_scales. Each entry in ds_scales specified one deep supervision 17 | output and its resolution relative to the original data, for example 0.25 specifies 1/4 of the original shape. 18 | ds_scales can also be a tuple of tuples, for example ((1, 1, 1), (0.5, 0.5, 0.5)) to specify the downsampling 19 | for each axis independently 20 | """ 21 | self.axes = axes 22 | self.output_key = output_key 23 | self.input_key = input_key 24 | self.order = order 25 | self.ds_scales = ds_scales 26 | 27 | def __call__(self, **data_dict): 28 | if self.axes is None: 29 | axes = list(range(2, data_dict[self.input_key].ndim)) 30 | else: 31 | axes = self.axes 32 | 33 | output = [] 34 | for s in self.ds_scales: 35 | if not isinstance(s, (tuple, list)): 36 | s = [s] * len(axes) 37 | else: 38 | assert len(s) == len(axes), f'If ds_scales is a tuple for each resolution (one downsampling factor ' \ 39 | f'for each axis) then the number of entried in that tuple (here ' \ 40 | f'{len(s)}) must be the same as the number of axes (here {len(axes)}).' 41 | 42 | if all([i == 1 for i in s]): 43 | output.append(data_dict[self.input_key]) 44 | else: 45 | new_shape = np.array(data_dict[self.input_key].shape).astype(float) 46 | for i, a in enumerate(axes): 47 | new_shape[a] *= s[i] 48 | new_shape = np.round(new_shape).astype(int) 49 | out_seg = np.zeros(new_shape, dtype=data_dict[self.input_key].dtype) 50 | for b in range(data_dict[self.input_key].shape[0]): 51 | for c in range(data_dict[self.input_key].shape[1]): 52 | out_seg[b, c] = resize_segmentation(data_dict[self.input_key][b, c], new_shape[2:], self.order) 53 | output.append(out_seg) 54 | data_dict[self.output_key] = output 55 | return data_dict 56 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/data_augmentation/custom_transforms/limited_length_multithreaded_augmenter.py: -------------------------------------------------------------------------------- 1 | from batchgenerators.dataloading.nondet_multi_threaded_augmenter import NonDetMultiThreadedAugmenter 2 | 3 | 4 | class LimitedLenWrapper(NonDetMultiThreadedAugmenter): 5 | def __init__(self, my_imaginary_length, *args, **kwargs): 6 | super().__init__(*args, **kwargs) 7 | self.len = my_imaginary_length 8 | 9 | def __len__(self): 10 | return self.len 11 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/data_augmentation/custom_transforms/manipulating_data_dict.py: -------------------------------------------------------------------------------- 1 | from batchgenerators.transforms.abstract_transforms import AbstractTransform 2 | 3 | 4 | class RemoveKeyTransform(AbstractTransform): 5 | def __init__(self, key_to_remove: str): 6 | self.key_to_remove = key_to_remove 7 | 8 | def __call__(self, **data_dict): 9 | _ = data_dict.pop(self.key_to_remove, None) 10 | return data_dict 11 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/data_augmentation/custom_transforms/masking.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | 3 | from batchgenerators.transforms.abstract_transforms import AbstractTransform 4 | 5 | 6 | class MaskTransform(AbstractTransform): 7 | def __init__(self, apply_to_channels: List[int], mask_idx_in_seg: int = 0, set_outside_to: int = 0, 8 | data_key: str = "data", seg_key: str = "seg"): 9 | """ 10 | Sets everything outside the mask to 0. CAREFUL! outside is defined as < 0, not =0 (in the Mask)!!! 11 | """ 12 | self.apply_to_channels = apply_to_channels 13 | self.seg_key = seg_key 14 | self.data_key = data_key 15 | self.set_outside_to = set_outside_to 16 | self.mask_idx_in_seg = mask_idx_in_seg 17 | 18 | def __call__(self, **data_dict): 19 | mask = data_dict[self.seg_key][:, self.mask_idx_in_seg] < 0 20 | for c in self.apply_to_channels: 21 | data_dict[self.data_key][:, c][mask] = self.set_outside_to 22 | return data_dict 23 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/data_augmentation/custom_transforms/region_based_training.py: -------------------------------------------------------------------------------- 1 | from typing import List, Tuple, Union 2 | 3 | from batchgenerators.transforms.abstract_transforms import AbstractTransform 4 | import numpy as np 5 | 6 | 7 | class ConvertSegmentationToRegionsTransform(AbstractTransform): 8 | def __init__(self, regions: Union[List, Tuple], 9 | seg_key: str = "seg", output_key: str = "seg", seg_channel: int = 0): 10 | """ 11 | regions are tuple of tuples where each inner tuple holds the class indices that are merged into one region, 12 | example: 13 | regions= ((1, 2), (2, )) will result in 2 regions: one covering the region of labels 1&2 and the other just 2 14 | :param regions: 15 | :param seg_key: 16 | :param output_key: 17 | """ 18 | self.seg_channel = seg_channel 19 | self.output_key = output_key 20 | self.seg_key = seg_key 21 | self.regions = regions 22 | 23 | def __call__(self, **data_dict): 24 | seg = data_dict.get(self.seg_key) 25 | num_regions = len(self.regions) 26 | if seg is not None: 27 | seg_shp = seg.shape 28 | output_shape = list(seg_shp) 29 | output_shape[1] = num_regions 30 | region_output = np.zeros(output_shape, dtype=seg.dtype) 31 | for b in range(seg_shp[0]): 32 | for region_id, region_source_labels in enumerate(self.regions): 33 | if not isinstance(region_source_labels, (list, tuple)): 34 | region_source_labels = (region_source_labels, ) 35 | for label_value in region_source_labels: 36 | region_output[b, region_id][seg[b, self.seg_channel] == label_value] = 1 37 | data_dict[self.output_key] = region_output 38 | return data_dict 39 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/data_augmentation/custom_transforms/transforms_for_dummy_2d.py: -------------------------------------------------------------------------------- 1 | from typing import Tuple, Union, List 2 | 3 | from batchgenerators.transforms.abstract_transforms import AbstractTransform 4 | 5 | 6 | class Convert3DTo2DTransform(AbstractTransform): 7 | def __init__(self, apply_to_keys: Union[List[str], Tuple[str]] = ('data', 'seg')): 8 | """ 9 | Transforms a 5D array (b, c, x, y, z) to a 4D array (b, c * x, y, z) by overloading the color channel 10 | """ 11 | self.apply_to_keys = apply_to_keys 12 | 13 | def __call__(self, **data_dict): 14 | for k in self.apply_to_keys: 15 | shp = data_dict[k].shape 16 | assert len(shp) == 5, 'This transform only works on 3D data, so expects 5D tensor (b, c, x, y, z) as input.' 17 | data_dict[k] = data_dict[k].reshape((shp[0], shp[1] * shp[2], shp[3], shp[4])) 18 | shape_key = f'orig_shape_{k}' 19 | assert shape_key not in data_dict.keys(), f'Convert3DTo2DTransform needs to store the original shape. ' \ 20 | f'It does that using the {shape_key} key. That key is ' \ 21 | f'already taken. Bummer.' 22 | data_dict[shape_key] = shp 23 | return data_dict 24 | 25 | 26 | class Convert2DTo3DTransform(AbstractTransform): 27 | def __init__(self, apply_to_keys: Union[List[str], Tuple[str]] = ('data', 'seg')): 28 | """ 29 | Reverts Convert3DTo2DTransform by transforming a 4D array (b, c * x, y, z) back to 5D (b, c, x, y, z) 30 | """ 31 | self.apply_to_keys = apply_to_keys 32 | 33 | def __call__(self, **data_dict): 34 | for k in self.apply_to_keys: 35 | shape_key = f'orig_shape_{k}' 36 | assert shape_key in data_dict.keys(), f'Did not find key {shape_key} in data_dict. Shitty. ' \ 37 | f'Convert2DTo3DTransform only works in tandem with ' \ 38 | f'Convert3DTo2DTransform and you probably forgot to add ' \ 39 | f'Convert3DTo2DTransform to your pipeline. (Convert3DTo2DTransform ' \ 40 | f'is where the missing key is generated)' 41 | original_shape = data_dict[shape_key] 42 | current_shape = data_dict[k].shape 43 | data_dict[k] = data_dict[k].reshape((original_shape[0], original_shape[1], original_shape[2], 44 | current_shape[-2], current_shape[-1])) 45 | return data_dict 46 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/dataloading/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/dataloading/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/dataloading/data_loader_3d.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from nnunetv2.training.dataloading.base_data_loader import nnUNetDataLoaderBase 3 | from nnunetv2.training.dataloading.nnunet_dataset import nnUNetDataset 4 | 5 | 6 | class nnUNetDataLoader3D(nnUNetDataLoaderBase): 7 | def generate_train_batch(self): 8 | selected_keys = self.get_indices() 9 | # preallocate memory for data and seg 10 | data_all = np.zeros(self.data_shape, dtype=np.float32) 11 | seg_all = np.zeros(self.seg_shape, dtype=np.int16) 12 | case_properties = [] 13 | 14 | for j, i in enumerate(selected_keys): 15 | # oversampling foreground will improve stability of model training, especially if many patches are empty 16 | # (Lung for example) 17 | force_fg = self.get_do_oversample(j) 18 | 19 | data, seg, properties = self._data.load_case(i) 20 | case_properties.append(properties) 21 | 22 | # If we are doing the cascade then the segmentation from the previous stage will already have been loaded by 23 | # self._data.load_case(i) (see nnUNetDataset.load_case) 24 | shape = data.shape[1:] 25 | dim = len(shape) 26 | bbox_lbs, bbox_ubs = self.get_bbox(shape, force_fg, properties['class_locations']) 27 | 28 | # whoever wrote this knew what he was doing (hint: it was me). We first crop the data to the region of the 29 | # bbox that actually lies within the data. This will result in a smaller array which is then faster to pad. 30 | # valid_bbox is just the coord that lied within the data cube. It will be padded to match the patch size 31 | # later 32 | valid_bbox_lbs = [max(0, bbox_lbs[i]) for i in range(dim)] 33 | valid_bbox_ubs = [min(shape[i], bbox_ubs[i]) for i in range(dim)] 34 | 35 | # At this point you might ask yourself why we would treat seg differently from seg_from_previous_stage. 36 | # Why not just concatenate them here and forget about the if statements? Well that's because segneeds to 37 | # be padded with -1 constant whereas seg_from_previous_stage needs to be padded with 0s (we could also 38 | # remove label -1 in the data augmentation but this way it is less error prone) 39 | this_slice = tuple([slice(0, data.shape[0])] + [slice(i, j) for i, j in zip(valid_bbox_lbs, valid_bbox_ubs)]) 40 | data = data[this_slice] 41 | 42 | this_slice = tuple([slice(0, seg.shape[0])] + [slice(i, j) for i, j in zip(valid_bbox_lbs, valid_bbox_ubs)]) 43 | seg = seg[this_slice] 44 | 45 | padding = [(-min(0, bbox_lbs[i]), max(bbox_ubs[i] - shape[i], 0)) for i in range(dim)] 46 | data_all[j] = np.pad(data, ((0, 0), *padding), 'constant', constant_values=0) 47 | seg_all[j] = np.pad(seg, ((0, 0), *padding), 'constant', constant_values=-1) 48 | 49 | return {'data': data_all, 'seg': seg_all, 'properties': case_properties, 'keys': selected_keys} 50 | 51 | 52 | if __name__ == '__main__': 53 | folder = '/media/fabian/data/nnUNet_preprocessed/Dataset002_Heart/3d_fullres' 54 | ds = nnUNetDataset(folder, 0) # this should not load the properties! 55 | dl = nnUNetDataLoader3D(ds, 5, (16, 16, 16), (16, 16, 16), 0.33, None, None) 56 | a = next(dl) 57 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/logging/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/logging/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/loss/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/loss/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/loss/deep_supervision.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | 4 | 5 | class DeepSupervisionWrapper(nn.Module): 6 | def __init__(self, loss, weight_factors=None): 7 | """ 8 | Wraps a loss function so that it can be applied to multiple outputs. Forward accepts an arbitrary number of 9 | inputs. Each input is expected to be a tuple/list. Each tuple/list must have the same length. The loss is then 10 | applied to each entry like this: 11 | l = w0 * loss(input0[0], input1[0], ...) + w1 * loss(input0[1], input1[1], ...) + ... 12 | If weights are None, all w will be 1. 13 | """ 14 | super(DeepSupervisionWrapper, self).__init__() 15 | assert any([x != 0 for x in weight_factors]), "At least one weight factor should be != 0.0" 16 | self.weight_factors = tuple(weight_factors) 17 | self.loss = loss 18 | 19 | def forward(self, *args): 20 | assert all([isinstance(i, (tuple, list)) for i in args]), \ 21 | f"all args must be either tuple or list, got {[type(i) for i in args]}" 22 | # we could check for equal lengths here as well, but we really shouldn't overdo it with checks because 23 | # this code is executed a lot of times! 24 | 25 | if self.weight_factors is None: 26 | weights = (1, ) * len(args[0]) 27 | else: 28 | weights = self.weight_factors 29 | 30 | return sum([weights[i] * self.loss(*inputs) for i, inputs in enumerate(zip(*args)) if weights[i] != 0.0]) 31 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/loss/mambaclinix_loss.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from nnunetv2.training.loss.dice import SoftDiceLoss, MemoryEfficientSoftDiceLoss 3 | from nnunetv2.training.loss.robust_ce_loss import RobustCrossEntropyLoss 4 | from nnunetv2.utilities.helpers import softmax_helper_dim1 5 | from nnunetv2.training.loss.region_specific_tversky_losses import RegionSpecificLoss 6 | from torch import nn 7 | 8 | class MambaClinix_compound_losses(nn.Module): 9 | def __init__(self, soft_dice_kwargs, ce_kwargs, rs_kwargs, 10 | weight_ce=1, weight_dice=1, weight_rs=0.8, 11 | ignore_label=None, 12 | dice_class=SoftDiceLoss): 13 | 14 | super(MambaClinix_compound_losses, self).__init__() 15 | if ignore_label is not None: 16 | ce_kwargs['ignore_index'] = ignore_label 17 | 18 | self.weight_dice = weight_dice 19 | self.weight_ce = weight_ce 20 | self.weight_rs = weight_rs 21 | self.ignore_label = ignore_label 22 | 23 | self.ce = RobustCrossEntropyLoss(**ce_kwargs) 24 | self.dc = dice_class(apply_nonlin=softmax_helper_dim1, **soft_dice_kwargs) 25 | self.rs = RegionSpecificLoss(apply_nonlin=softmax_helper_dim1, **rs_kwargs) 26 | 27 | def forward(self, net_output: torch.Tensor, target: torch.Tensor): 28 | """ 29 | target must be b, c, x, y(, z) with c=1 30 | :param net_output: 31 | :param target: 32 | :return: 33 | """ 34 | if self.ignore_label is not None: 35 | assert target.shape[1] == 1, 'ignore label is not implemented for one hot encoded target variables ' \ 36 | '(DC_and_CE_loss)' 37 | mask = target != self.ignore_label 38 | # remove ignore label from target, replace with one of the known labels. It doesn't matter because we 39 | # ignore gradients in those areas anyway 40 | target_dice = torch.where(mask, target, 0) 41 | num_fg = mask.sum() 42 | else: 43 | target_dice = target 44 | mask = None 45 | 46 | rs_loss = self.rs(net_output, target_dice) \ 47 | if self.weight_rs != 0 else 0 48 | dc_loss = self.dc(net_output, target_dice, loss_mask=mask) \ 49 | if self.weight_dice != 0 else 0 50 | ce_loss = self.ce(net_output, target[:, 0]) \ 51 | if self.weight_ce != 0 and (self.ignore_label is None or num_fg > 0) else 0 52 | 53 | result = self.weight_ce * ce_loss + self.weight_dice * dc_loss + self.weight_rs * rs_loss 54 | 55 | return result 56 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/loss/region_specific_tversky_losses.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from typing import Callable 3 | from torch import nn 4 | 5 | 6 | class RegionSpecificLoss(nn.Module): 7 | def __init__(self, apply_nonlin: Callable = None, smooth=1e-5, num_region_per_axis=(16, 16, 16), 8 | do_bg=False, batch_dice=True, alpha=0.3, beta=0.4): 9 | """ 10 | Region-specific Tversky loss function 11 | """ 12 | super(RegionSpecificLoss, self).__init__() 13 | self.alpha = alpha 14 | self.beta = beta 15 | self.smooth = smooth 16 | self.do_bg = do_bg 17 | self.apply_nonlin = apply_nonlin 18 | self.batch_dice = batch_dice 19 | self.dim = len(num_region_per_axis) 20 | 21 | assert self.dim in [2, 3], "The num of dim must be 2 or 3." 22 | if self.dim == 3: 23 | self.pool = nn.AdaptiveAvgPool3d(num_region_per_axis) 24 | elif self.dim == 2: 25 | self.pool = nn.AdaptiveAvgPool2d(num_region_per_axis) 26 | 27 | 28 | def forward(self, x, y, loss_mask=None): 29 | shp_x, shp_y = x.shape, y.shape 30 | assert self.dim == (len(shp_x) - 2), "The region size must match the data's size." 31 | 32 | if self.apply_nonlin is not None: 33 | x = self.apply_nonlin(x) 34 | 35 | region_tp, region_fp, region_fn, _ = self.get_region_tp_fp_fn(x, y, self.batch_dice, loss_mask) 36 | 37 | alpha = self.alpha + self.beta * (region_fp + self.smooth) / (region_fp + region_fn + self.smooth) 38 | beta = self.alpha + self.beta * (region_fn + self.smooth) / (region_fp + region_fn + self.smooth) 39 | 40 | region_tversky = (region_tp + self.smooth) / (region_tp + alpha * region_fp + beta * region_fn + self.smooth) 41 | 42 | if self.batch_dice: 43 | region_tversky = region_tversky.mean(list(range(1, len(shp_x) - 1))) 44 | else: 45 | region_tversky = region_tversky.mean(list(range(2, len(shp_x)))) 46 | 47 | if not self.do_bg: 48 | if self.batch_dice: 49 | region_tversky = region_tversky[1:] 50 | else: 51 | region_tversky = region_tversky[:, 1:] 52 | 53 | region_tversky = region_tversky.mean() 54 | 55 | return -region_tversky 56 | 57 | 58 | def get_region_tp_fp_fn(self, net_output, gt, batch_dice=None, mask=None): 59 | 60 | with torch.no_grad(): 61 | if net_output.ndim != gt.ndim: 62 | gt = gt.view((gt.shape[0], 1, *gt.shape[1:])) 63 | 64 | if net_output.shape == gt.shape: 65 | # if this is the case then gt is probably already a one hot encoding 66 | y_onehot = gt 67 | else: 68 | y_onehot = torch.zeros(net_output.shape, device=net_output.device) 69 | y_onehot.scatter_(1, gt.long(), 1) 70 | 71 | tp = net_output * y_onehot 72 | fp = net_output * (1 - y_onehot) 73 | fn = (1 - net_output) * y_onehot 74 | tn = (1 - net_output) * (1 - y_onehot) 75 | 76 | if mask is not None: 77 | with torch.no_grad(): 78 | mask_here = torch.tile(mask, (1, tp.shape[1], *[1 for _ in range(2, tp.ndim)])) 79 | tp *= mask_here 80 | fp *= mask_here 81 | fn *= mask_here 82 | 83 | # region specific pooling 84 | tp = self.pool(tp) 85 | fp = self.pool(fp) 86 | fn = self.pool(fn) 87 | 88 | # batch-scale integration 89 | if batch_dice: 90 | tp = tp.sum(dim=0, keepdim=False) 91 | fp = fp.sum(dim=0, keepdim=False) 92 | fn = fn.sum(dim=0, keepdim=False) 93 | 94 | return tp, fp, fn, tn -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/loss/robust_ce_loss.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn, Tensor 3 | import numpy as np 4 | 5 | 6 | class RobustCrossEntropyLoss(nn.CrossEntropyLoss): 7 | """ 8 | this is just a compatibility layer because my target tensor is float and has an extra dimension 9 | 10 | input must be logits, not probabilities! 11 | """ 12 | def forward(self, input: Tensor, target: Tensor) -> Tensor: 13 | if target.ndim == input.ndim: 14 | assert target.shape[1] == 1 15 | target = target[:, 0] 16 | return super().forward(input, target.long()) 17 | 18 | 19 | class TopKLoss(RobustCrossEntropyLoss): 20 | """ 21 | input must be logits, not probabilities! 22 | """ 23 | def __init__(self, weight=None, ignore_index: int = -100, k: float = 10, label_smoothing: float = 0): 24 | self.k = k 25 | super(TopKLoss, self).__init__(weight, False, ignore_index, reduce=False, label_smoothing=label_smoothing) 26 | 27 | def forward(self, inp, target): 28 | target = target[:, 0].long() 29 | res = super(TopKLoss, self).forward(inp, target) 30 | num_voxels = np.prod(res.shape, dtype=np.int64) 31 | res, _ = torch.topk(res.view((-1, )), int(num_voxels * self.k / 100), sorted=False) 32 | return res.mean() 33 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/lr_scheduler/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/lr_scheduler/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/lr_scheduler/polylr.py: -------------------------------------------------------------------------------- 1 | from torch.optim.lr_scheduler import _LRScheduler 2 | 3 | 4 | class PolyLRScheduler(_LRScheduler): 5 | def __init__(self, optimizer, initial_lr: float, max_steps: int, exponent: float = 0.9, current_step: int = None): 6 | self.optimizer = optimizer 7 | self.initial_lr = initial_lr 8 | self.max_steps = max_steps 9 | self.exponent = exponent 10 | self.ctr = 0 11 | super().__init__(optimizer, current_step if current_step is not None else -1, False) 12 | 13 | def step(self, current_step=None): 14 | if current_step is None or current_step == -1: 15 | current_step = self.ctr 16 | self.ctr += 1 17 | 18 | new_lr = self.initial_lr * (1 - current_step / self.max_steps) ** self.exponent 19 | for param_group in self.optimizer.param_groups: 20 | param_group['lr'] = new_lr 21 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/nnUNetTrainer/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/nnUNetTrainerMambaClinix.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 3 | from nnunetv2.utilities.plans_handling.plans_handler import ConfigurationManager, PlansManager 4 | from nnunetv2.nets.MambaClinix_3d import get_mambaclinix_3d_from_plans 5 | from torch.optim import Adam 6 | from nnunetv2.training.lr_scheduler.polylr import PolyLRScheduler 7 | from torch import nn 8 | 9 | class nnUNetTrainerMambaClinix(nnUNetTrainer): 10 | def __init__( 11 | self, 12 | plans: dict, 13 | configuration: str, 14 | fold: int, 15 | dataset_json: dict, 16 | unpack_dataset: bool = True, 17 | device: torch.device = torch.device('cuda') 18 | ): 19 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 20 | 21 | self.initial_lr = 1e-4 22 | self.weight_decay = 1e-5 23 | 24 | @staticmethod 25 | def build_network_architecture(plans_manager: PlansManager, 26 | dataset_json, 27 | configuration_manager: ConfigurationManager, 28 | num_input_channels, 29 | enable_deep_supervision: bool = True) -> nn.Module: 30 | 31 | if len(configuration_manager.patch_size) == 3: 32 | model = get_mambaclinix_3d_from_plans(plans_manager, dataset_json, configuration_manager, 33 | num_input_channels, deep_supervision=enable_deep_supervision) 34 | 35 | else: 36 | raise NotImplementedError("Only 3D models are supported") 37 | 38 | return model 39 | 40 | 41 | def configure_optimizers(self): 42 | optimizer = Adam(self.network.parameters(), lr=self.initial_lr, weight_decay=self.weight_decay, eps=1e-5) 43 | scheduler = PolyLRScheduler(optimizer, self.initial_lr, self.num_epochs, exponent=0.9) 44 | 45 | return optimizer, scheduler 46 | 47 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/nnUNetTrainer/variants/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/benchmarking/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/nnUNetTrainer/variants/benchmarking/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/benchmarking/nnUNetTrainerBenchmark_5epochs.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from batchgenerators.utilities.file_and_folder_operations import save_json, join, isfile, load_json 3 | 4 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 5 | from torch import distributed as dist 6 | 7 | 8 | class nnUNetTrainerBenchmark_5epochs(nnUNetTrainer): 9 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 10 | device: torch.device = torch.device('cuda')): 11 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 12 | assert self.fold == 0, "It makes absolutely no sense to specify a certain fold. Stick with 0 so that we can parse the results." 13 | self.disable_checkpointing = True 14 | self.num_epochs = 5 15 | assert torch.cuda.is_available(), "This only works on GPU" 16 | self.crashed_with_runtime_error = False 17 | 18 | def perform_actual_validation(self, save_probabilities: bool = False): 19 | pass 20 | 21 | def save_checkpoint(self, filename: str) -> None: 22 | # do not trust people to remember that self.disable_checkpointing must be True for this trainer 23 | pass 24 | 25 | def run_training(self): 26 | try: 27 | super().run_training() 28 | except RuntimeError: 29 | self.crashed_with_runtime_error = True 30 | 31 | def on_train_end(self): 32 | super().on_train_end() 33 | 34 | if not self.is_ddp or self.local_rank == 0: 35 | torch_version = torch.__version__ 36 | cudnn_version = torch.backends.cudnn.version() 37 | gpu_name = torch.cuda.get_device_name() 38 | if self.crashed_with_runtime_error: 39 | fastest_epoch = 'Not enough VRAM!' 40 | else: 41 | epoch_times = [i - j for i, j in zip(self.logger.my_fantastic_logging['epoch_end_timestamps'], 42 | self.logger.my_fantastic_logging['epoch_start_timestamps'])] 43 | fastest_epoch = min(epoch_times) 44 | 45 | if self.is_ddp: 46 | num_gpus = dist.get_world_size() 47 | else: 48 | num_gpus = 1 49 | 50 | benchmark_result_file = join(self.output_folder, 'benchmark_result.json') 51 | if isfile(benchmark_result_file): 52 | old_results = load_json(benchmark_result_file) 53 | else: 54 | old_results = {} 55 | # generate some unique key 56 | my_key = f"{cudnn_version}__{torch_version.replace(' ', '')}__{gpu_name.replace(' ', '')}__gpus_{num_gpus}" 57 | old_results[my_key] = { 58 | 'torch_version': torch_version, 59 | 'cudnn_version': cudnn_version, 60 | 'gpu_name': gpu_name, 61 | 'fastest_epoch': fastest_epoch, 62 | 'num_gpus': num_gpus, 63 | } 64 | save_json(old_results, 65 | join(self.output_folder, 'benchmark_result.json')) 66 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/benchmarking/nnUNetTrainerBenchmark_5epochs_noDataLoading.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from nnunetv2.training.nnUNetTrainer.variants.benchmarking.nnUNetTrainerBenchmark_5epochs import ( 4 | nnUNetTrainerBenchmark_5epochs, 5 | ) 6 | from nnunetv2.utilities.label_handling.label_handling import determine_num_input_channels 7 | 8 | 9 | class nnUNetTrainerBenchmark_5epochs_noDataLoading(nnUNetTrainerBenchmark_5epochs): 10 | def __init__( 11 | self, 12 | plans: dict, 13 | configuration: str, 14 | fold: int, 15 | dataset_json: dict, 16 | unpack_dataset: bool = True, 17 | device: torch.device = torch.device("cuda"), 18 | ): 19 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 20 | self._set_batch_size_and_oversample() 21 | num_input_channels = determine_num_input_channels( 22 | self.plans_manager, self.configuration_manager, self.dataset_json 23 | ) 24 | patch_size = self.configuration_manager.patch_size 25 | dummy_data = torch.rand((self.batch_size, num_input_channels, *patch_size), device=self.device) 26 | if self.enable_deep_supervision: 27 | dummy_target = [ 28 | torch.round( 29 | torch.rand((self.batch_size, 1, *[int(i * j) for i, j in zip(patch_size, k)]), device=self.device) 30 | * max(self.label_manager.all_labels) 31 | ) 32 | for k in self._get_deep_supervision_scales() 33 | ] 34 | else: 35 | raise NotImplementedError("This trainer does not support deep supervision") 36 | self.dummy_batch = {"data": dummy_data, "target": dummy_target} 37 | 38 | def get_dataloaders(self): 39 | return None, None 40 | 41 | def run_training(self): 42 | try: 43 | self.on_train_start() 44 | 45 | for epoch in range(self.current_epoch, self.num_epochs): 46 | self.on_epoch_start() 47 | 48 | self.on_train_epoch_start() 49 | train_outputs = [] 50 | for batch_id in range(self.num_iterations_per_epoch): 51 | train_outputs.append(self.train_step(self.dummy_batch)) 52 | self.on_train_epoch_end(train_outputs) 53 | 54 | with torch.no_grad(): 55 | self.on_validation_epoch_start() 56 | val_outputs = [] 57 | for batch_id in range(self.num_val_iterations_per_epoch): 58 | val_outputs.append(self.validation_step(self.dummy_batch)) 59 | self.on_validation_epoch_end(val_outputs) 60 | 61 | self.on_epoch_end() 62 | 63 | self.on_train_end() 64 | except RuntimeError: 65 | self.crashed_with_runtime_error = True 66 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/data_augmentation/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/nnUNetTrainer/variants/data_augmentation/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerNoDA.py: -------------------------------------------------------------------------------- 1 | from typing import Union, Tuple, List 2 | 3 | from batchgenerators.transforms.abstract_transforms import AbstractTransform 4 | 5 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 6 | import numpy as np 7 | 8 | 9 | class nnUNetTrainerNoDA(nnUNetTrainer): 10 | @staticmethod 11 | def get_training_transforms(patch_size: Union[np.ndarray, Tuple[int]], 12 | rotation_for_DA: dict, 13 | deep_supervision_scales: Union[List, Tuple, None], 14 | mirror_axes: Tuple[int, ...], 15 | do_dummy_2d_data_aug: bool, 16 | order_resampling_data: int = 1, 17 | order_resampling_seg: int = 0, 18 | border_val_seg: int = -1, 19 | use_mask_for_norm: List[bool] = None, 20 | is_cascaded: bool = False, 21 | foreground_labels: Union[Tuple[int, ...], List[int]] = None, 22 | regions: List[Union[List[int], Tuple[int, ...], int]] = None, 23 | ignore_label: int = None) -> AbstractTransform: 24 | return nnUNetTrainer.get_validation_transforms(deep_supervision_scales, is_cascaded, foreground_labels, 25 | regions, ignore_label) 26 | 27 | def get_plain_dataloaders(self, initial_patch_size: Tuple[int, ...], dim: int): 28 | return super().get_plain_dataloaders( 29 | initial_patch_size=self.configuration_manager.patch_size, 30 | dim=dim 31 | ) 32 | 33 | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): 34 | # we need to disable mirroring here so that no mirroring will be applied in inferene! 35 | rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \ 36 | super().configure_rotation_dummyDA_mirroring_and_inital_patch_size() 37 | mirror_axes = None 38 | self.inference_allowed_mirroring_axes = None 39 | return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes 40 | 41 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerNoMirroring.py: -------------------------------------------------------------------------------- 1 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 2 | 3 | 4 | class nnUNetTrainerNoMirroring(nnUNetTrainer): 5 | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): 6 | rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \ 7 | super().configure_rotation_dummyDA_mirroring_and_inital_patch_size() 8 | mirror_axes = None 9 | self.inference_allowed_mirroring_axes = None 10 | return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes 11 | 12 | 13 | class nnUNetTrainer_onlyMirror01(nnUNetTrainer): 14 | """ 15 | Only mirrors along spatial axes 0 and 1 for 3D and 0 for 2D 16 | """ 17 | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): 18 | rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \ 19 | super().configure_rotation_dummyDA_mirroring_and_inital_patch_size() 20 | patch_size = self.configuration_manager.patch_size 21 | dim = len(patch_size) 22 | if dim == 2: 23 | mirror_axes = (0, ) 24 | else: 25 | mirror_axes = (0, 1) 26 | self.inference_allowed_mirroring_axes = mirror_axes 27 | return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes 28 | 29 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/loss/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/nnUNetTrainer/variants/loss/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/loss/nnUNetTrainerCELoss.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from nnunetv2.training.loss.deep_supervision import DeepSupervisionWrapper 3 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 4 | from nnunetv2.training.loss.robust_ce_loss import RobustCrossEntropyLoss 5 | import numpy as np 6 | 7 | 8 | class nnUNetTrainerCELoss(nnUNetTrainer): 9 | def _build_loss(self): 10 | assert not self.label_manager.has_regions, "regions not supported by this trainer" 11 | loss = RobustCrossEntropyLoss( 12 | weight=None, ignore_index=self.label_manager.ignore_label if self.label_manager.has_ignore_label else -100 13 | ) 14 | 15 | # we give each output a weight which decreases exponentially (division by 2) as the resolution decreases 16 | # this gives higher resolution outputs more weight in the loss 17 | if self.enable_deep_supervision: 18 | deep_supervision_scales = self._get_deep_supervision_scales() 19 | weights = np.array([1 / (2**i) for i in range(len(deep_supervision_scales))]) 20 | weights[-1] = 0 21 | 22 | # we don't use the lowest 2 outputs. Normalize weights so that they sum to 1 23 | weights = weights / weights.sum() 24 | # now wrap the loss 25 | loss = DeepSupervisionWrapper(loss, weights) 26 | return loss 27 | 28 | 29 | class nnUNetTrainerCELoss_5epochs(nnUNetTrainerCELoss): 30 | def __init__( 31 | self, 32 | plans: dict, 33 | configuration: str, 34 | fold: int, 35 | dataset_json: dict, 36 | unpack_dataset: bool = True, 37 | device: torch.device = torch.device("cuda"), 38 | ): 39 | """used for debugging plans etc""" 40 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 41 | self.num_epochs = 5 42 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/loss/nnUNetTrainerDiceLoss.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | 4 | from nnunetv2.training.loss.compound_losses import DC_and_BCE_loss, DC_and_CE_loss 5 | from nnunetv2.training.loss.deep_supervision import DeepSupervisionWrapper 6 | from nnunetv2.training.loss.dice import MemoryEfficientSoftDiceLoss 7 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 8 | from nnunetv2.utilities.helpers import softmax_helper_dim1 9 | 10 | 11 | class nnUNetTrainerDiceLoss(nnUNetTrainer): 12 | def _build_loss(self): 13 | loss = MemoryEfficientSoftDiceLoss(**{'batch_dice': self.configuration_manager.batch_dice, 14 | 'do_bg': self.label_manager.has_regions, 'smooth': 1e-5, 'ddp': self.is_ddp}, 15 | apply_nonlin=torch.sigmoid if self.label_manager.has_regions else softmax_helper_dim1) 16 | 17 | if self.enable_deep_supervision: 18 | deep_supervision_scales = self._get_deep_supervision_scales() 19 | 20 | # we give each output a weight which decreases exponentially (division by 2) as the resolution decreases 21 | # this gives higher resolution outputs more weight in the loss 22 | weights = np.array([1 / (2 ** i) for i in range(len(deep_supervision_scales))]) 23 | weights[-1] = 0 24 | 25 | # we don't use the lowest 2 outputs. Normalize weights so that they sum to 1 26 | weights = weights / weights.sum() 27 | # now wrap the loss 28 | loss = DeepSupervisionWrapper(loss, weights) 29 | return loss 30 | 31 | 32 | class nnUNetTrainerDiceCELoss_noSmooth(nnUNetTrainer): 33 | def _build_loss(self): 34 | # set smooth to 0 35 | if self.label_manager.has_regions: 36 | loss = DC_and_BCE_loss({}, 37 | {'batch_dice': self.configuration_manager.batch_dice, 38 | 'do_bg': True, 'smooth': 0, 'ddp': self.is_ddp}, 39 | use_ignore_label=self.label_manager.ignore_label is not None, 40 | dice_class=MemoryEfficientSoftDiceLoss) 41 | else: 42 | loss = DC_and_CE_loss({'batch_dice': self.configuration_manager.batch_dice, 43 | 'smooth': 0, 'do_bg': False, 'ddp': self.is_ddp}, {}, weight_ce=1, weight_dice=1, 44 | ignore_label=self.label_manager.ignore_label, 45 | dice_class=MemoryEfficientSoftDiceLoss) 46 | 47 | if self.enable_deep_supervision: 48 | deep_supervision_scales = self._get_deep_supervision_scales() 49 | 50 | # we give each output a weight which decreases exponentially (division by 2) as the resolution decreases 51 | # this gives higher resolution outputs more weight in the loss 52 | weights = np.array([1 / (2 ** i) for i in range(len(deep_supervision_scales))]) 53 | weights[-1] = 0 54 | 55 | # we don't use the lowest 2 outputs. Normalize weights so that they sum to 1 56 | weights = weights / weights.sum() 57 | # now wrap the loss 58 | loss = DeepSupervisionWrapper(loss, weights) 59 | return loss 60 | 61 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/loss/nnUNetTrainerMambaClinixTRS.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from nnunetv2.training.loss.deep_supervision import DeepSupervisionWrapper 3 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainerMambaClinix import nnUNetTrainerMambaClinix 4 | from nnunetv2.training.loss.mambaclinix_loss import MambaClinix_compound_losses 5 | from nnunetv2.training.loss.dice import MemoryEfficientSoftDiceLoss 6 | import numpy as np 7 | 8 | class nnUNetTrainerMambaClinixTRS(nnUNetTrainerMambaClinix): 9 | def _build_loss(self): 10 | assert not self.label_manager.has_regions, "regions not supported by this trainer" 11 | loss = MambaClinix_compound_losses( 12 | {'batch_dice': self.configuration_manager.batch_dice, 'smooth': 1e-5, 13 | 'do_bg': False, 'ddp': self.is_ddp}, 14 | {}, 15 | {'batch_dice': self.configuration_manager.batch_dice, 'smooth': 1e-5, 16 | 'do_bg': False, 'alpha': 0.3, 'beta': 0.4, 17 | 'num_region_per_axis': (8, 8, 14)}, 18 | weight_ce=1, weight_dice=1, 19 | ignore_label=self.label_manager.ignore_label, 20 | dice_class=MemoryEfficientSoftDiceLoss) 21 | 22 | if self.enable_deep_supervision: 23 | deep_supervision_scales = self._get_deep_supervision_scales() 24 | weights = np.array([1 / (2**i) for i in range(len(deep_supervision_scales))]) 25 | weights[-1] = 0 26 | 27 | # we don't use the lowest 2 outputs. Normalize weights so that they sum to 1 28 | weights = weights / weights.sum() 29 | # now wrap the loss 30 | loss = DeepSupervisionWrapper(loss, weights) 31 | return loss 32 | 33 | 34 | class nnUNetTrainerMambaClinixTRS_500epochs(nnUNetTrainerMambaClinixTRS): 35 | def __init__( 36 | self, 37 | plans: dict, 38 | configuration: str, 39 | fold: int, 40 | dataset_json: dict, 41 | unpack_dataset: bool = True, 42 | device: torch.device = torch.device("cuda"), 43 | ): 44 | """used for debugging plans etc""" 45 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 46 | self.num_epochs = 500 47 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/loss/nnUNetTrainerTopkLoss.py: -------------------------------------------------------------------------------- 1 | from nnunetv2.training.loss.compound_losses import DC_and_topk_loss 2 | from nnunetv2.training.loss.deep_supervision import DeepSupervisionWrapper 3 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 4 | import numpy as np 5 | from nnunetv2.training.loss.robust_ce_loss import TopKLoss 6 | 7 | 8 | class nnUNetTrainerTopk10Loss(nnUNetTrainer): 9 | def _build_loss(self): 10 | assert not self.label_manager.has_regions, "regions not supported by this trainer" 11 | loss = TopKLoss( 12 | ignore_index=self.label_manager.ignore_label if self.label_manager.has_ignore_label else -100, k=10 13 | ) 14 | 15 | if self.enable_deep_supervision: 16 | deep_supervision_scales = self._get_deep_supervision_scales() 17 | 18 | # we give each output a weight which decreases exponentially (division by 2) as the resolution decreases 19 | # this gives higher resolution outputs more weight in the loss 20 | weights = np.array([1 / (2**i) for i in range(len(deep_supervision_scales))]) 21 | weights[-1] = 0 22 | 23 | # we don't use the lowest 2 outputs. Normalize weights so that they sum to 1 24 | weights = weights / weights.sum() 25 | # now wrap the loss 26 | loss = DeepSupervisionWrapper(loss, weights) 27 | return loss 28 | 29 | 30 | class nnUNetTrainerTopk10LossLS01(nnUNetTrainer): 31 | def _build_loss(self): 32 | assert not self.label_manager.has_regions, "regions not supported by this trainer" 33 | loss = TopKLoss( 34 | ignore_index=self.label_manager.ignore_label if self.label_manager.has_ignore_label else -100, 35 | k=10, 36 | label_smoothing=0.1, 37 | ) 38 | 39 | if self.enable_deep_supervision: 40 | deep_supervision_scales = self._get_deep_supervision_scales() 41 | 42 | # we give each output a weight which decreases exponentially (division by 2) as the resolution decreases 43 | # this gives higher resolution outputs more weight in the loss 44 | weights = np.array([1 / (2**i) for i in range(len(deep_supervision_scales))]) 45 | weights[-1] = 0 46 | 47 | # we don't use the lowest 2 outputs. Normalize weights so that they sum to 1 48 | weights = weights / weights.sum() 49 | # now wrap the loss 50 | loss = DeepSupervisionWrapper(loss, weights) 51 | return loss 52 | 53 | 54 | class nnUNetTrainerDiceTopK10Loss(nnUNetTrainer): 55 | def _build_loss(self): 56 | assert not self.label_manager.has_regions, "regions not supported by this trainer" 57 | loss = DC_and_topk_loss( 58 | {"batch_dice": self.configuration_manager.batch_dice, "smooth": 1e-5, "do_bg": False, "ddp": self.is_ddp}, 59 | {"k": 10, "label_smoothing": 0.0}, 60 | weight_ce=1, 61 | weight_dice=1, 62 | ignore_label=self.label_manager.ignore_label, 63 | ) 64 | if self.enable_deep_supervision: 65 | deep_supervision_scales = self._get_deep_supervision_scales() 66 | 67 | # we give each output a weight which decreases exponentially (division by 2) as the resolution decreases 68 | # this gives higher resolution outputs more weight in the loss 69 | weights = np.array([1 / (2**i) for i in range(len(deep_supervision_scales))]) 70 | weights[-1] = 0 71 | 72 | # we don't use the lowest 2 outputs. Normalize weights so that they sum to 1 73 | weights = weights / weights.sum() 74 | # now wrap the loss 75 | loss = DeepSupervisionWrapper(loss, weights) 76 | return loss 77 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/lr_schedule/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/nnUNetTrainer/variants/lr_schedule/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/lr_schedule/nnUNetTrainerCosAnneal.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.optim.lr_scheduler import CosineAnnealingLR 3 | 4 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 5 | 6 | 7 | class nnUNetTrainerCosAnneal(nnUNetTrainer): 8 | def configure_optimizers(self): 9 | optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 10 | momentum=0.99, nesterov=True) 11 | lr_scheduler = CosineAnnealingLR(optimizer, T_max=self.num_epochs) 12 | return optimizer, lr_scheduler 13 | 14 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/network_architecture/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/nnUNetTrainer/variants/network_architecture/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/network_architecture/nnUNetTrainerBN.py: -------------------------------------------------------------------------------- 1 | from dynamic_network_architectures.architectures.unet import ResidualEncoderUNet, PlainConvUNet 2 | from dynamic_network_architectures.building_blocks.helper import convert_dim_to_conv_op, get_matching_batchnorm 3 | from dynamic_network_architectures.initialization.weight_init import init_last_bn_before_add_to_0, InitWeights_He 4 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 5 | from nnunetv2.utilities.plans_handling.plans_handler import ConfigurationManager, PlansManager 6 | from torch import nn 7 | 8 | 9 | class nnUNetTrainerBN(nnUNetTrainer): 10 | @staticmethod 11 | def build_network_architecture(plans_manager: PlansManager, 12 | dataset_json, 13 | configuration_manager: ConfigurationManager, 14 | num_input_channels, 15 | enable_deep_supervision: bool = True) -> nn.Module: 16 | num_stages = len(configuration_manager.conv_kernel_sizes) 17 | 18 | dim = len(configuration_manager.conv_kernel_sizes[0]) 19 | conv_op = convert_dim_to_conv_op(dim) 20 | 21 | label_manager = plans_manager.get_label_manager(dataset_json) 22 | 23 | segmentation_network_class_name = configuration_manager.UNet_class_name 24 | mapping = { 25 | 'PlainConvUNet': PlainConvUNet, 26 | 'ResidualEncoderUNet': ResidualEncoderUNet 27 | } 28 | kwargs = { 29 | 'PlainConvUNet': { 30 | 'conv_bias': True, 31 | 'norm_op': get_matching_batchnorm(conv_op), 32 | 'norm_op_kwargs': {'eps': 1e-5, 'affine': True}, 33 | 'dropout_op': None, 'dropout_op_kwargs': None, 34 | 'nonlin': nn.LeakyReLU, 'nonlin_kwargs': {'inplace': True}, 35 | }, 36 | 'ResidualEncoderUNet': { 37 | 'conv_bias': True, 38 | 'norm_op': get_matching_batchnorm(conv_op), 39 | 'norm_op_kwargs': {'eps': 1e-5, 'affine': True}, 40 | 'dropout_op': None, 'dropout_op_kwargs': None, 41 | 'nonlin': nn.LeakyReLU, 'nonlin_kwargs': {'inplace': True}, 42 | } 43 | } 44 | assert segmentation_network_class_name in mapping.keys(), 'The network architecture specified by the plans file ' \ 45 | 'is non-standard (maybe your own?). Yo\'ll have to dive ' \ 46 | 'into either this ' \ 47 | 'function (get_network_from_plans) or ' \ 48 | 'the init of your nnUNetModule to accommodate that.' 49 | network_class = mapping[segmentation_network_class_name] 50 | 51 | conv_or_blocks_per_stage = { 52 | 'n_conv_per_stage' 53 | if network_class != ResidualEncoderUNet else 'n_blocks_per_stage': configuration_manager.n_conv_per_stage_encoder, 54 | 'n_conv_per_stage_decoder': configuration_manager.n_conv_per_stage_decoder 55 | } 56 | # network class name!! 57 | model = network_class( 58 | input_channels=num_input_channels, 59 | n_stages=num_stages, 60 | features_per_stage=[min(configuration_manager.UNet_base_num_features * 2 ** i, 61 | configuration_manager.unet_max_num_features) for i in range(num_stages)], 62 | conv_op=conv_op, 63 | kernel_sizes=configuration_manager.conv_kernel_sizes, 64 | strides=configuration_manager.pool_op_kernel_sizes, 65 | num_classes=label_manager.num_segmentation_heads, 66 | deep_supervision=enable_deep_supervision, 67 | **conv_or_blocks_per_stage, 68 | **kwargs[segmentation_network_class_name] 69 | ) 70 | model.apply(InitWeights_He(1e-2)) 71 | if network_class == ResidualEncoderUNet: 72 | model.apply(init_last_bn_before_add_to_0) 73 | return model 74 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/network_architecture/nnUNetTrainerNoDeepSupervision.py: -------------------------------------------------------------------------------- 1 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 2 | import torch 3 | 4 | 5 | class nnUNetTrainerNoDeepSupervision(nnUNetTrainer): 6 | def __init__( 7 | self, 8 | plans: dict, 9 | configuration: str, 10 | fold: int, 11 | dataset_json: dict, 12 | unpack_dataset: bool = True, 13 | device: torch.device = torch.device("cuda"), 14 | ): 15 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 16 | self.enable_deep_supervision = False 17 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/optimizer/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/nnUNetTrainer/variants/optimizer/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/optimizer/nnUNetTrainerAdam.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.optim import Adam, AdamW 3 | 4 | from nnunetv2.training.lr_scheduler.polylr import PolyLRScheduler 5 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 6 | 7 | 8 | class nnUNetTrainerAdam(nnUNetTrainer): 9 | def configure_optimizers(self): 10 | optimizer = AdamW(self.network.parameters(), 11 | lr=self.initial_lr, 12 | weight_decay=self.weight_decay, 13 | amsgrad=True) 14 | # optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 15 | # momentum=0.99, nesterov=True) 16 | lr_scheduler = PolyLRScheduler(optimizer, self.initial_lr, self.num_epochs) 17 | return optimizer, lr_scheduler 18 | 19 | 20 | class nnUNetTrainerVanillaAdam(nnUNetTrainer): 21 | def configure_optimizers(self): 22 | optimizer = Adam(self.network.parameters(), 23 | lr=self.initial_lr, 24 | weight_decay=self.weight_decay) 25 | # optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 26 | # momentum=0.99, nesterov=True) 27 | lr_scheduler = PolyLRScheduler(optimizer, self.initial_lr, self.num_epochs) 28 | return optimizer, lr_scheduler 29 | 30 | 31 | class nnUNetTrainerVanillaAdam1en3(nnUNetTrainerVanillaAdam): 32 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 33 | device: torch.device = torch.device('cuda')): 34 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 35 | self.initial_lr = 1e-3 36 | 37 | 38 | class nnUNetTrainerVanillaAdam3en4(nnUNetTrainerVanillaAdam): 39 | # https://twitter.com/karpathy/status/801621764144971776?lang=en 40 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 41 | device: torch.device = torch.device('cuda')): 42 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 43 | self.initial_lr = 3e-4 44 | 45 | 46 | class nnUNetTrainerAdam1en3(nnUNetTrainerAdam): 47 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 48 | device: torch.device = torch.device('cuda')): 49 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 50 | self.initial_lr = 1e-3 51 | 52 | 53 | class nnUNetTrainerAdam3en4(nnUNetTrainerAdam): 54 | # https://twitter.com/karpathy/status/801621764144971776?lang=en 55 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 56 | device: torch.device = torch.device('cuda')): 57 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 58 | self.initial_lr = 3e-4 59 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/optimizer/nnUNetTrainerAdan.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from nnunetv2.training.lr_scheduler.polylr import PolyLRScheduler 4 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 5 | from torch.optim.lr_scheduler import CosineAnnealingLR 6 | try: 7 | from adan_pytorch import Adan 8 | except ImportError: 9 | Adan = None 10 | 11 | 12 | class nnUNetTrainerAdan(nnUNetTrainer): 13 | def configure_optimizers(self): 14 | if Adan is None: 15 | raise RuntimeError('This trainer requires adan_pytorch to be installed, install with "pip install adan-pytorch"') 16 | optimizer = Adan(self.network.parameters(), 17 | lr=self.initial_lr, 18 | # betas=(0.02, 0.08, 0.01), defaults 19 | weight_decay=self.weight_decay) 20 | # optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 21 | # momentum=0.99, nesterov=True) 22 | lr_scheduler = PolyLRScheduler(optimizer, self.initial_lr, self.num_epochs) 23 | return optimizer, lr_scheduler 24 | 25 | 26 | class nnUNetTrainerAdan1en3(nnUNetTrainerAdan): 27 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 28 | device: torch.device = torch.device('cuda')): 29 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 30 | self.initial_lr = 1e-3 31 | 32 | 33 | class nnUNetTrainerAdan3en4(nnUNetTrainerAdan): 34 | # https://twitter.com/karpathy/status/801621764144971776?lang=en 35 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 36 | device: torch.device = torch.device('cuda')): 37 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 38 | self.initial_lr = 3e-4 39 | 40 | 41 | class nnUNetTrainerAdan1en1(nnUNetTrainerAdan): 42 | # this trainer makes no sense -> nan! 43 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 44 | device: torch.device = torch.device('cuda')): 45 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 46 | self.initial_lr = 1e-1 47 | 48 | 49 | class nnUNetTrainerAdanCosAnneal(nnUNetTrainerAdan): 50 | # def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 51 | # device: torch.device = torch.device('cuda')): 52 | # super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 53 | # self.num_epochs = 15 54 | 55 | def configure_optimizers(self): 56 | if Adan is None: 57 | raise RuntimeError('This trainer requires adan_pytorch to be installed, install with "pip install adan-pytorch"') 58 | optimizer = Adan(self.network.parameters(), 59 | lr=self.initial_lr, 60 | # betas=(0.02, 0.08, 0.01), defaults 61 | weight_decay=self.weight_decay) 62 | # optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 63 | # momentum=0.99, nesterov=True) 64 | lr_scheduler = CosineAnnealingLR(optimizer, T_max=self.num_epochs) 65 | return optimizer, lr_scheduler 66 | 67 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/sampling/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/nnUNetTrainer/variants/sampling/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/training_length/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/training/nnUNetTrainer/variants/training_length/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/training_length/nnUNetTrainer_Xepochs.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 4 | 5 | 6 | class nnUNetTrainer_5epochs(nnUNetTrainer): 7 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 8 | device: torch.device = torch.device('cuda')): 9 | """used for debugging plans etc""" 10 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 11 | self.num_epochs = 5 12 | 13 | 14 | class nnUNetTrainer_1epoch(nnUNetTrainer): 15 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 16 | device: torch.device = torch.device('cuda')): 17 | """used for debugging plans etc""" 18 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 19 | self.num_epochs = 1 20 | 21 | 22 | class nnUNetTrainer_10epochs(nnUNetTrainer): 23 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 24 | device: torch.device = torch.device('cuda')): 25 | """used for debugging plans etc""" 26 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 27 | self.num_epochs = 10 28 | 29 | 30 | class nnUNetTrainer_20epochs(nnUNetTrainer): 31 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 32 | device: torch.device = torch.device('cuda')): 33 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 34 | self.num_epochs = 20 35 | 36 | 37 | class nnUNetTrainer_50epochs(nnUNetTrainer): 38 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 39 | device: torch.device = torch.device('cuda')): 40 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 41 | self.num_epochs = 50 42 | 43 | 44 | class nnUNetTrainer_100epochs(nnUNetTrainer): 45 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 46 | device: torch.device = torch.device('cuda')): 47 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 48 | self.num_epochs = 100 49 | 50 | 51 | class nnUNetTrainer_250epochs(nnUNetTrainer): 52 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 53 | device: torch.device = torch.device('cuda')): 54 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 55 | self.num_epochs = 250 56 | 57 | 58 | class nnUNetTrainer_2000epochs(nnUNetTrainer): 59 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 60 | device: torch.device = torch.device('cuda')): 61 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 62 | self.num_epochs = 2000 63 | 64 | 65 | class nnUNetTrainer_4000epochs(nnUNetTrainer): 66 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 67 | device: torch.device = torch.device('cuda')): 68 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 69 | self.num_epochs = 4000 70 | 71 | 72 | class nnUNetTrainer_8000epochs(nnUNetTrainer): 73 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 74 | device: torch.device = torch.device('cuda')): 75 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 76 | self.num_epochs = 8000 77 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/training/nnUNetTrainer/variants/training_length/nnUNetTrainer_Xepochs_NoMirroring.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer 4 | 5 | 6 | class nnUNetTrainer_250epochs_NoMirroring(nnUNetTrainer): 7 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 8 | device: torch.device = torch.device('cuda')): 9 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 10 | self.num_epochs = 250 11 | 12 | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): 13 | rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \ 14 | super().configure_rotation_dummyDA_mirroring_and_inital_patch_size() 15 | mirror_axes = None 16 | self.inference_allowed_mirroring_axes = None 17 | return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes 18 | 19 | 20 | class nnUNetTrainer_2000epochs_NoMirroring(nnUNetTrainer): 21 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 22 | device: torch.device = torch.device('cuda')): 23 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 24 | self.num_epochs = 2000 25 | 26 | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): 27 | rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \ 28 | super().configure_rotation_dummyDA_mirroring_and_inital_patch_size() 29 | mirror_axes = None 30 | self.inference_allowed_mirroring_axes = None 31 | return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes 32 | 33 | 34 | class nnUNetTrainer_4000epochs_NoMirroring(nnUNetTrainer): 35 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 36 | device: torch.device = torch.device('cuda')): 37 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 38 | self.num_epochs = 4000 39 | 40 | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): 41 | rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \ 42 | super().configure_rotation_dummyDA_mirroring_and_inital_patch_size() 43 | mirror_axes = None 44 | self.inference_allowed_mirroring_axes = None 45 | return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes 46 | 47 | 48 | class nnUNetTrainer_8000epochs_NoMirroring(nnUNetTrainer): 49 | def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True, 50 | device: torch.device = torch.device('cuda')): 51 | super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device) 52 | self.num_epochs = 8000 53 | 54 | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): 55 | rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \ 56 | super().configure_rotation_dummyDA_mirroring_and_inital_patch_size() 57 | mirror_axes = None 58 | self.inference_allowed_mirroring_axes = None 59 | return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes 60 | 61 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/utilities/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/utilities/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/utilities/collate_outputs.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | 3 | import numpy as np 4 | 5 | 6 | def collate_outputs(outputs: List[dict]): 7 | """ 8 | used to collate default train_step and validation_step outputs. If you want something different then you gotta 9 | extend this 10 | 11 | we expect outputs to be a list of dictionaries where each of the dict has the same set of keys 12 | """ 13 | collated = {} 14 | for k in outputs[0].keys(): 15 | if np.isscalar(outputs[0][k]): 16 | collated[k] = [o[k] for o in outputs] 17 | elif isinstance(outputs[0][k], np.ndarray): 18 | collated[k] = np.vstack([o[k][None] for o in outputs]) 19 | elif isinstance(outputs[0][k], list): 20 | collated[k] = [item for o in outputs for item in o[k]] 21 | else: 22 | raise ValueError(f'Cannot collate input of type {type(outputs[0][k])}. ' 23 | f'Modify collate_outputs to add this functionality') 24 | return collated -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/utilities/dataset_name_id_conversion.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | from typing import Union 15 | 16 | from nnunetv2.paths import nnUNet_preprocessed, nnUNet_raw, nnUNet_results 17 | from batchgenerators.utilities.file_and_folder_operations import * 18 | import numpy as np 19 | 20 | 21 | def find_candidate_datasets(dataset_id: int): 22 | startswith = "Dataset%03.0d" % dataset_id 23 | if nnUNet_preprocessed is not None and isdir(nnUNet_preprocessed): 24 | candidates_preprocessed = subdirs(nnUNet_preprocessed, prefix=startswith, join=False) 25 | else: 26 | candidates_preprocessed = [] 27 | 28 | if nnUNet_raw is not None and isdir(nnUNet_raw): 29 | candidates_raw = subdirs(nnUNet_raw, prefix=startswith, join=False) 30 | else: 31 | candidates_raw = [] 32 | 33 | candidates_trained_models = [] 34 | if nnUNet_results is not None and isdir(nnUNet_results): 35 | candidates_trained_models += subdirs(nnUNet_results, prefix=startswith, join=False) 36 | 37 | all_candidates = candidates_preprocessed + candidates_raw + candidates_trained_models 38 | unique_candidates = np.unique(all_candidates) 39 | return unique_candidates 40 | 41 | 42 | def convert_id_to_dataset_name(dataset_id: int): 43 | unique_candidates = find_candidate_datasets(dataset_id) 44 | if len(unique_candidates) > 1: 45 | raise RuntimeError("More than one dataset name found for dataset id %d. Please correct that. (I looked in the " 46 | "following folders:\n%s\n%s\n%s" % (dataset_id, nnUNet_raw, nnUNet_preprocessed, nnUNet_results)) 47 | if len(unique_candidates) == 0: 48 | raise RuntimeError(f"Could not find a dataset with the ID {dataset_id}. Make sure the requested dataset ID " 49 | f"exists and that nnU-Net knows where raw and preprocessed data are located " 50 | f"(see Documentation - Installation). Here are your currently defined folders:\n" 51 | f"nnUNet_preprocessed={os.environ.get('nnUNet_preprocessed') if os.environ.get('nnUNet_preprocessed') is not None else 'None'}\n" 52 | f"nnUNet_results={os.environ.get('nnUNet_results') if os.environ.get('nnUNet_results') is not None else 'None'}\n" 53 | f"nnUNet_raw={os.environ.get('nnUNet_raw') if os.environ.get('nnUNet_raw') is not None else 'None'}\n" 54 | f"If something is not right, adapt your environment variables.") 55 | return unique_candidates[0] 56 | 57 | 58 | def convert_dataset_name_to_id(dataset_name: str): 59 | assert dataset_name.startswith("Dataset") 60 | dataset_id = int(dataset_name[7:10]) 61 | return dataset_id 62 | 63 | 64 | def maybe_convert_to_dataset_name(dataset_name_or_id: Union[int, str]) -> str: 65 | if isinstance(dataset_name_or_id, str) and dataset_name_or_id.startswith("Dataset"): 66 | return dataset_name_or_id 67 | if isinstance(dataset_name_or_id, str): 68 | try: 69 | dataset_name_or_id = int(dataset_name_or_id) 70 | except ValueError: 71 | raise ValueError("dataset_name_or_id was a string and did not start with 'Dataset' so we tried to " 72 | "convert it to a dataset ID (int). That failed, however. Please give an integer number " 73 | "('1', '2', etc) or a correct dataset name. Your input: %s" % dataset_name_or_id) 74 | return convert_id_to_dataset_name(dataset_name_or_id) 75 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/utilities/ddp_allgather.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | from typing import Any, Optional, Tuple 15 | 16 | import torch 17 | from torch import distributed 18 | 19 | 20 | def print_if_rank0(*args): 21 | if distributed.get_rank() == 0: 22 | print(*args) 23 | 24 | 25 | class AllGatherGrad(torch.autograd.Function): 26 | # stolen from pytorch lightning 27 | @staticmethod 28 | def forward( 29 | ctx: Any, 30 | tensor: torch.Tensor, 31 | group: Optional["torch.distributed.ProcessGroup"] = None, 32 | ) -> torch.Tensor: 33 | ctx.group = group 34 | 35 | gathered_tensor = [torch.zeros_like(tensor) for _ in range(torch.distributed.get_world_size())] 36 | 37 | torch.distributed.all_gather(gathered_tensor, tensor, group=group) 38 | gathered_tensor = torch.stack(gathered_tensor, dim=0) 39 | 40 | return gathered_tensor 41 | 42 | @staticmethod 43 | def backward(ctx: Any, *grad_output: torch.Tensor) -> Tuple[torch.Tensor, None]: 44 | grad_output = torch.cat(grad_output) 45 | 46 | torch.distributed.all_reduce(grad_output, op=torch.distributed.ReduceOp.SUM, async_op=False, group=ctx.group) 47 | 48 | return grad_output[torch.distributed.get_rank()], None 49 | 50 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/utilities/default_n_proc_DA.py: -------------------------------------------------------------------------------- 1 | import subprocess 2 | import os 3 | 4 | 5 | def get_allowed_n_proc_DA(): 6 | """ 7 | This function is used to set the number of processes used on different Systems. It is specific to our cluster 8 | infrastructure at DKFZ. You can modify it to suit your needs. Everything is allowed. 9 | 10 | IMPORTANT: if the environment variable nnUNet_n_proc_DA is set it will overwrite anything in this script 11 | (see first line). 12 | 13 | Interpret the output as the number of processes used for data augmentation PER GPU. 14 | 15 | The way it is implemented here is simply a look up table. We know the hostnames, CPU and GPU configurations of our 16 | systems and set the numbers accordingly. For example, a system with 4 GPUs and 48 threads can use 12 threads per 17 | GPU without overloading the CPU (technically 11 because we have a main process as well), so that's what we use. 18 | """ 19 | 20 | if 'nnUNet_n_proc_DA' in os.environ.keys(): 21 | use_this = int(os.environ['nnUNet_n_proc_DA']) 22 | else: 23 | hostname = subprocess.getoutput(['hostname']) 24 | if hostname in ['Fabian', ]: 25 | use_this = 12 26 | elif hostname in ['hdf19-gpu16', 'hdf19-gpu17', 'hdf19-gpu18', 'hdf19-gpu19', 'e230-AMDworkstation']: 27 | use_this = 16 28 | elif hostname.startswith('e230-dgx1'): 29 | use_this = 10 30 | elif hostname.startswith('hdf18-gpu') or hostname.startswith('e132-comp'): 31 | use_this = 16 32 | elif hostname.startswith('e230-dgx2'): 33 | use_this = 6 34 | elif hostname.startswith('e230-dgxa100-'): 35 | use_this = 28 36 | elif hostname.startswith('lsf22-gpu'): 37 | use_this = 28 38 | elif hostname.startswith('hdf19-gpu') or hostname.startswith('e071-gpu'): 39 | use_this = 12 40 | else: 41 | use_this = 12 # default value 42 | 43 | use_this = min(use_this, os.cpu_count()) 44 | return use_this 45 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/utilities/find_class_by_name.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import pkgutil 3 | 4 | from batchgenerators.utilities.file_and_folder_operations import * 5 | 6 | 7 | def recursive_find_python_class(folder: str, class_name: str, current_module: str): 8 | tr = None 9 | for importer, modname, ispkg in pkgutil.iter_modules([folder]): 10 | # print(modname, ispkg) 11 | if not ispkg: 12 | m = importlib.import_module(current_module + "." + modname) 13 | if hasattr(m, class_name): 14 | tr = getattr(m, class_name) 15 | break 16 | 17 | if tr is None: 18 | for importer, modname, ispkg in pkgutil.iter_modules([folder]): 19 | if ispkg: 20 | next_current_module = current_module + "." + modname 21 | tr = recursive_find_python_class(join(folder, modname), class_name, current_module=next_current_module) 22 | if tr is not None: 23 | break 24 | return tr -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/utilities/get_network_from_plans.py: -------------------------------------------------------------------------------- 1 | from dynamic_network_architectures.architectures.unet import PlainConvUNet, ResidualEncoderUNet 2 | from dynamic_network_architectures.building_blocks.helper import get_matching_instancenorm, convert_dim_to_conv_op 3 | from dynamic_network_architectures.initialization.weight_init import init_last_bn_before_add_to_0 4 | from nnunetv2.utilities.network_initialization import InitWeights_He 5 | from nnunetv2.utilities.plans_handling.plans_handler import ConfigurationManager, PlansManager 6 | from torch import nn 7 | 8 | 9 | def get_network_from_plans(plans_manager: PlansManager, 10 | dataset_json: dict, 11 | configuration_manager: ConfigurationManager, 12 | num_input_channels: int, 13 | deep_supervision: bool = True): 14 | """ 15 | we may have to change this in the future to accommodate other plans -> network mappings 16 | 17 | num_input_channels can differ depending on whether we do cascade. Its best to make this info available in the 18 | trainer rather than inferring it again from the plans here. 19 | """ 20 | num_stages = len(configuration_manager.conv_kernel_sizes) 21 | 22 | dim = len(configuration_manager.conv_kernel_sizes[0]) 23 | conv_op = convert_dim_to_conv_op(dim) 24 | 25 | label_manager = plans_manager.get_label_manager(dataset_json) 26 | 27 | segmentation_network_class_name = configuration_manager.UNet_class_name 28 | mapping = { 29 | 'PlainConvUNet': PlainConvUNet, 30 | 'ResidualEncoderUNet': ResidualEncoderUNet 31 | } 32 | kwargs = { 33 | 'PlainConvUNet': { 34 | 'conv_bias': True, 35 | 'norm_op': get_matching_instancenorm(conv_op), 36 | 'norm_op_kwargs': {'eps': 1e-5, 'affine': True}, 37 | 'dropout_op': None, 'dropout_op_kwargs': None, 38 | 'nonlin': nn.LeakyReLU, 'nonlin_kwargs': {'inplace': True}, 39 | }, 40 | 'ResidualEncoderUNet': { 41 | 'conv_bias': True, 42 | 'norm_op': get_matching_instancenorm(conv_op), 43 | 'norm_op_kwargs': {'eps': 1e-5, 'affine': True}, 44 | 'dropout_op': None, 'dropout_op_kwargs': None, 45 | 'nonlin': nn.LeakyReLU, 'nonlin_kwargs': {'inplace': True}, 46 | } 47 | } 48 | assert segmentation_network_class_name in mapping.keys(), 'The network architecture specified by the plans file ' \ 49 | 'is non-standard (maybe your own?). Yo\'ll have to dive ' \ 50 | 'into either this ' \ 51 | 'function (get_network_from_plans) or ' \ 52 | 'the init of your nnUNetModule to accommodate that.' 53 | network_class = mapping[segmentation_network_class_name] 54 | 55 | conv_or_blocks_per_stage = { 56 | 'n_conv_per_stage' 57 | if network_class != ResidualEncoderUNet else 'n_blocks_per_stage': configuration_manager.n_conv_per_stage_encoder, 58 | 'n_conv_per_stage_decoder': configuration_manager.n_conv_per_stage_decoder 59 | } 60 | # network class name!! 61 | model = network_class( 62 | input_channels=num_input_channels, 63 | n_stages=num_stages, 64 | features_per_stage=[min(configuration_manager.UNet_base_num_features * 2 ** i, 65 | configuration_manager.unet_max_num_features) for i in range(num_stages)], 66 | conv_op=conv_op, 67 | kernel_sizes=configuration_manager.conv_kernel_sizes, 68 | strides=configuration_manager.pool_op_kernel_sizes, 69 | num_classes=label_manager.num_segmentation_heads, 70 | deep_supervision=deep_supervision, 71 | **conv_or_blocks_per_stage, 72 | **kwargs[segmentation_network_class_name] 73 | ) 74 | model.apply(InitWeights_He(1e-2)) 75 | if network_class == ResidualEncoderUNet: 76 | model.apply(init_last_bn_before_add_to_0) 77 | return model 78 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/utilities/helpers.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | def softmax_helper_dim0(x: torch.Tensor) -> torch.Tensor: 5 | return torch.softmax(x, 0) 6 | 7 | 8 | def softmax_helper_dim1(x: torch.Tensor) -> torch.Tensor: 9 | return torch.softmax(x, 1) 10 | 11 | 12 | def empty_cache(device: torch.device): 13 | if device.type == 'cuda': 14 | torch.cuda.empty_cache() 15 | elif device.type == 'mps': 16 | from torch import mps 17 | mps.empty_cache() 18 | else: 19 | pass 20 | 21 | 22 | class dummy_context(object): 23 | def __enter__(self): 24 | pass 25 | 26 | def __exit__(self, exc_type, exc_val, exc_tb): 27 | pass 28 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/utilities/json_export.py: -------------------------------------------------------------------------------- 1 | from collections.abc import Iterable 2 | 3 | import numpy as np 4 | import torch 5 | 6 | 7 | def recursive_fix_for_json_export(my_dict: dict): 8 | # json is stupid. 'cannot serialize object of type bool_/int64/float64'. Come on bro. 9 | keys = list(my_dict.keys()) # cannot iterate over keys() if we change keys.... 10 | for k in keys: 11 | if isinstance(k, (np.int64, np.int32, np.int8, np.uint8)): 12 | tmp = my_dict[k] 13 | del my_dict[k] 14 | my_dict[int(k)] = tmp 15 | del tmp 16 | k = int(k) 17 | 18 | if isinstance(my_dict[k], dict): 19 | recursive_fix_for_json_export(my_dict[k]) 20 | elif isinstance(my_dict[k], np.ndarray): 21 | assert my_dict[k].ndim == 1, 'only 1d arrays are supported' 22 | my_dict[k] = fix_types_iterable(my_dict[k], output_type=list) 23 | elif isinstance(my_dict[k], (np.bool_,)): 24 | my_dict[k] = bool(my_dict[k]) 25 | elif isinstance(my_dict[k], (np.int64, np.int32, np.int8, np.uint8)): 26 | my_dict[k] = int(my_dict[k]) 27 | elif isinstance(my_dict[k], (np.float32, np.float64, np.float16)): 28 | my_dict[k] = float(my_dict[k]) 29 | elif isinstance(my_dict[k], list): 30 | my_dict[k] = fix_types_iterable(my_dict[k], output_type=type(my_dict[k])) 31 | elif isinstance(my_dict[k], tuple): 32 | my_dict[k] = fix_types_iterable(my_dict[k], output_type=tuple) 33 | elif isinstance(my_dict[k], torch.device): 34 | my_dict[k] = str(my_dict[k]) 35 | else: 36 | pass # pray it can be serialized 37 | 38 | 39 | def fix_types_iterable(iterable, output_type): 40 | # this sh!t is hacky as hell and will break if you use it for anything outside nnunet. Keep you hands off of this. 41 | out = [] 42 | for i in iterable: 43 | if type(i) in (np.int64, np.int32, np.int8, np.uint8): 44 | out.append(int(i)) 45 | elif isinstance(i, dict): 46 | recursive_fix_for_json_export(i) 47 | out.append(i) 48 | elif type(i) in (np.float32, np.float64, np.float16): 49 | out.append(float(i)) 50 | elif type(i) in (np.bool_,): 51 | out.append(bool(i)) 52 | elif isinstance(i, str): 53 | out.append(i) 54 | elif isinstance(i, Iterable): 55 | # print('recursive call on', i, type(i)) 56 | out.append(fix_types_iterable(i, type(i))) 57 | else: 58 | out.append(i) 59 | return output_type(out) 60 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/utilities/label_handling/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/utilities/label_handling/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/utilities/network_initialization.py: -------------------------------------------------------------------------------- 1 | from torch import nn 2 | 3 | 4 | class InitWeights_He(object): 5 | def __init__(self, neg_slope=1e-2): 6 | self.neg_slope = neg_slope 7 | 8 | def __call__(self, module): 9 | if isinstance(module, nn.Conv3d) or isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d) or isinstance(module, nn.ConvTranspose3d): 10 | module.weight = nn.init.kaiming_normal_(module.weight, a=self.neg_slope) 11 | if module.bias is not None: 12 | module.bias = nn.init.constant_(module.bias, 0) 13 | -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/utilities/plans_handling/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CYB08/MambaClinix-PyTorch/ba1af654b644a9c7be78cc9cff048c48164056e6/mambaclinix/nnunetv2/utilities/plans_handling/__init__.py -------------------------------------------------------------------------------- /mambaclinix/nnunetv2/utilities/utils.py: -------------------------------------------------------------------------------- 1 | # Copyright 2021 HIP Applied Computer Vision Lab, Division of Medical Image Computing, German Cancer Research Center 2 | # (DKFZ), Heidelberg, Germany 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | import os.path 16 | from functools import lru_cache 17 | from typing import Union 18 | 19 | from batchgenerators.utilities.file_and_folder_operations import * 20 | import numpy as np 21 | import re 22 | 23 | from nnunetv2.paths import nnUNet_raw 24 | 25 | 26 | def get_identifiers_from_splitted_dataset_folder(folder: str, file_ending: str): 27 | files = subfiles(folder, suffix=file_ending, join=False) 28 | # all files have a 4 digit channel index (_XXXX) 29 | crop = len(file_ending) + 5 30 | files = [i[:-crop] for i in files] 31 | # only unique image ids 32 | files = np.unique(files) 33 | return files 34 | 35 | 36 | def create_lists_from_splitted_dataset_folder(folder: str, file_ending: str, identifiers: List[str] = None) -> List[ 37 | List[str]]: 38 | """ 39 | does not rely on dataset.json 40 | """ 41 | if identifiers is None: 42 | identifiers = get_identifiers_from_splitted_dataset_folder(folder, file_ending) 43 | files = subfiles(folder, suffix=file_ending, join=False, sort=True) 44 | list_of_lists = [] 45 | for f in identifiers: 46 | p = re.compile(re.escape(f) + r"_\d\d\d\d" + re.escape(file_ending)) 47 | list_of_lists.append([join(folder, i) for i in files if p.fullmatch(i)]) 48 | return list_of_lists 49 | 50 | 51 | def get_filenames_of_train_images_and_targets(raw_dataset_folder: str, dataset_json: dict = None): 52 | if dataset_json is None: 53 | dataset_json = load_json(join(raw_dataset_folder, 'dataset.json')) 54 | 55 | if 'dataset' in dataset_json.keys(): 56 | dataset = dataset_json['dataset'] 57 | for k in dataset.keys(): 58 | dataset[k]['label'] = os.path.abspath(join(raw_dataset_folder, dataset[k]['label'])) if not os.path.isabs(dataset[k]['label']) else dataset[k]['label'] 59 | dataset[k]['images'] = [os.path.abspath(join(raw_dataset_folder, i)) if not os.path.isabs(i) else i for i in dataset[k]['images']] 60 | else: 61 | identifiers = get_identifiers_from_splitted_dataset_folder(join(raw_dataset_folder, 'imagesTr'), dataset_json['file_ending']) 62 | images = create_lists_from_splitted_dataset_folder(join(raw_dataset_folder, 'imagesTr'), dataset_json['file_ending'], identifiers) 63 | segs = [join(raw_dataset_folder, 'labelsTr', i + dataset_json['file_ending']) for i in identifiers] 64 | dataset = {i: {'images': im, 'label': se} for i, im, se in zip(identifiers, images, segs)} 65 | return dataset 66 | 67 | 68 | if __name__ == '__main__': 69 | print(get_filenames_of_train_images_and_targets(join(nnUNet_raw, 'Dataset002_Heart'))) 70 | --------------------------------------------------------------------------------