├── .gitignore ├── Data_Preprocess ├── match_to_target_histogram.py ├── register.sh ├── rename.py └── rename_unlabeleddata.py ├── README.md ├── figs ├── framework.png ├── reg.png └── results.png └── nnunet_semi_sup ├── __init__.py ├── __pycache__ ├── SurfaceDice.cpython-37.pyc ├── __init__.cpython-37.pyc ├── configuration.cpython-37.pyc └── paths.cpython-37.pyc ├── configuration.py ├── dataset_conversion ├── .ipynb_checkpoints │ ├── Task029_LiverTumorSegmentationChallenge-checkpoint.py │ ├── Task082_BraTS_2020-checkpoint.py │ ├── Task231_SemiSplPan-checkpoint.py │ ├── Task232_SemiSplPanLiTSKiTS-checkpoint.py │ └── Task233_SplPanLiTSKiTS-checkpoint.py ├── Task017_BeyondCranialVaultAbdominalOrganSegmentation.py ├── Task024_Promise2012.py ├── Task027_AutomaticCardiacDetectionChallenge.py ├── Task029_LiverTumorSegmentationChallenge.py ├── Task032_BraTS_2018.py ├── Task035_ISBI_MSLesionSegmentationChallenge.py ├── Task037_038_Chaos_Challenge.py ├── Task040_KiTS.py ├── Task043_BraTS_2019.py ├── Task055_SegTHOR.py ├── Task056_VerSe2019.py ├── Task056_Verse_normalize_orientation.py ├── Task058_ISBI_EM_SEG.py ├── Task059_EPFL_EM_MITO_SEG.py ├── Task061_CREMI.py ├── Task062_NIHPancreas.py ├── Task064_KiTS_labelsFixed.py ├── Task065_KiTS_NicksLabels.py ├── Task069_CovidSeg.py ├── Task075_Fluo_C3DH_A549_ManAndSim.py ├── Task076_Fluo_N3DH_SIM.py ├── Task082_BraTS_2020.py ├── Task083_VerSe2020.py ├── Task089_Fluo-N2DH-SIM.py ├── Task114_heart_MNMs.py ├── Task115_COVIDSegChallenge.py ├── Task120_Massachusetts_RoadSegm.py ├── Task231_SemiSplPan.py ├── Task232_SemiSplPanLiTSKiTS.py ├── Task233_SplPanLiTSKiTS.py ├── __init__.py └── utils.py ├── evaluation ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── evaluator.cpython-37.pyc │ └── metrics.cpython-37.pyc ├── add_dummy_task_with_mean_over_all_tasks.py ├── add_mean_dice_to_json.py ├── collect_results_files.py ├── evaluator.py ├── metrics.py ├── model_selection │ ├── __init__.py │ ├── collect_all_fold0_results_and_summarize_in_one_csv.py │ ├── ensemble.py │ ├── figure_out_what_to_submit.py │ ├── rank_candidates.py │ ├── rank_candidates_StructSeg.py │ ├── rank_candidates_cascade.py │ ├── summarize_results_in_one_json.py │ └── summarize_results_with_plans.py ├── region_based_evaluation.py └── surface_dice.py ├── experiment_planning ├── DatasetAnalyzer.py ├── __init__.py ├── __pycache__ │ ├── DatasetAnalyzer.cpython-37.pyc │ ├── __init__.cpython-37.pyc │ ├── change_batch_size.cpython-37.pyc │ ├── common_utils.cpython-37.pyc │ ├── experiment_planner_baseline_2DUNet.cpython-37.pyc │ ├── experiment_planner_baseline_2DUNet_v21.cpython-37.pyc │ ├── experiment_planner_baseline_3DUNet.cpython-37.pyc │ ├── experiment_planner_baseline_3DUNet_v21.cpython-37.pyc │ ├── summarize_plans.cpython-37.pyc │ └── utils.cpython-37.pyc ├── alternative_experiment_planning │ ├── __init__.py │ ├── experiment_planner_baseline_3DUNet_v21_11GB.py │ ├── experiment_planner_baseline_3DUNet_v21_16GB.py │ ├── experiment_planner_baseline_3DUNet_v21_32GB.py │ ├── experiment_planner_baseline_3DUNet_v21_3convperstage.py │ ├── experiment_planner_baseline_3DUNet_v22.py │ ├── experiment_planner_baseline_3DUNet_v23.py │ ├── experiment_planner_residual_3DUNet_v21.py │ ├── normalization │ │ ├── __init__.py │ │ ├── experiment_planner_2DUNet_v21_RGB_scaleto_0_1.py │ │ ├── experiment_planner_3DUNet_CT2.py │ │ └── experiment_planner_3DUNet_nonCT.py │ ├── patch_size │ │ ├── __init__.py │ │ ├── experiment_planner_3DUNet_isotropic_in_mm.py │ │ └── experiment_planner_3DUNet_isotropic_in_voxels.py │ ├── pooling_and_convs │ │ ├── __init__.py │ │ ├── experiment_planner_baseline_3DUNet_allConv3x3.py │ │ └── experiment_planner_baseline_3DUNet_poolBasedOnSpacing.py │ ├── readme.md │ └── target_spacing │ │ ├── __init__.py │ │ ├── experiment_planner_baseline_3DUNet_targetSpacingForAnisoAxis.py │ │ ├── experiment_planner_baseline_3DUNet_v21_customTargetSpacing_2x2x2.py │ │ └── experiment_planner_baseline_3DUNet_v21_noResampling.py ├── change_batch_size.py ├── common_utils.py ├── experiment_planner_baseline_2DUNet.py ├── experiment_planner_baseline_2DUNet_v21.py ├── experiment_planner_baseline_3DUNet.py ├── experiment_planner_baseline_3DUNet_v21.py ├── nnUNet_convert_decathlon_task.py ├── nnUNet_plan_and_preprocess.py ├── old │ ├── __init__.py │ └── old_plan_and_preprocess_task.py ├── summarize_plans.py └── utils.py ├── inference ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── predict.cpython-37.pyc │ └── segmentation_export.cpython-37.pyc ├── change_trainer.py ├── ensemble_predictions.py ├── predict.py ├── predict_simple.py ├── pretrained_models │ ├── __init__.py │ ├── collect_pretrained_models.py │ └── download_pretrained_model.py └── segmentation_export.py ├── network_architecture ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── generic_UNet.cpython-37.pyc │ ├── initialization.cpython-37.pyc │ └── neural_network.cpython-37.pyc ├── custom_modules │ ├── __init__.py │ ├── conv_blocks.py │ ├── feature_response_normalization.py │ ├── helperModules.py │ └── mish.py ├── generic_UNet.py ├── generic_UNet_DP.py ├── generic_modular_UNet.py ├── generic_modular_residual_UNet.py ├── initialization.py └── neural_network.py ├── paths.py ├── postprocessing ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ └── connected_components.cpython-37.pyc ├── connected_components.py ├── consolidate_all_for_paper.py ├── consolidate_postprocessing.py └── consolidate_postprocessing_simple.py ├── preprocessing ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── cropping.cpython-37.pyc │ ├── preprocessing.cpython-37.pyc │ └── sanity_checks.cpython-37.pyc ├── cropping.py ├── custom_preprocessors │ ├── __init__.py │ └── preprocessor_scale_RGB_to_0_1.py ├── preprocessing.py └── sanity_checks.py ├── run ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── default_configuration.cpython-37.pyc │ └── load_pretrained_weights.cpython-37.pyc ├── default_configuration.py ├── load_pretrained_weights.py ├── run_training.py ├── run_training_DDP.py └── run_training_DP.py ├── test └── plans.pkl ├── training ├── .ipynb_checkpoints │ └── model_restore-checkpoint.py ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ └── model_restore.cpython-37.pyc ├── cascade_stuff │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── predict_next_stage.cpython-37.pyc │ └── predict_next_stage.py ├── data_augmentation │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── custom_transforms.cpython-37.pyc │ │ ├── data_augmentation_moreDA.cpython-37.pyc │ │ ├── default_data_augmentation.cpython-37.pyc │ │ ├── downsampling.cpython-37.pyc │ │ └── pyramid_augmentations.cpython-37.pyc │ ├── custom_transforms.py │ ├── data_augmentation_insaneDA.py │ ├── data_augmentation_insaneDA2.py │ ├── data_augmentation_moreDA.py │ ├── data_augmentation_noDA.py │ ├── default_data_augmentation.py │ ├── downsampling.py │ └── pyramid_augmentations.py ├── dataloading │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── dataset_loading.cpython-37.pyc │ └── dataset_loading.py ├── learning_rate │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── poly_lr.cpython-37.pyc │ └── poly_lr.py ├── loss_functions │ ├── TopK_loss.py │ ├── __init__.py │ ├── __pycache__ │ │ ├── TopK_loss.cpython-37.pyc │ │ ├── __init__.cpython-37.pyc │ │ ├── crossentropy.cpython-37.pyc │ │ ├── deep_supervision.cpython-37.pyc │ │ └── dice_loss.cpython-37.pyc │ ├── crossentropy.py │ ├── deep_supervision.py │ └── dice_loss.py ├── model_restore.py ├── network_training │ ├── .ipynb_checkpoints │ │ └── nnUNetTrainerV2Finetune-checkpoint.py │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── network_trainer.cpython-37.pyc │ │ ├── nnUNetTrainer.cpython-37.pyc │ │ ├── nnUNetTrainerCascadeFullRes.cpython-37.pyc │ │ ├── nnUNetTrainerV2.cpython-37.pyc │ │ ├── nnUNetTrainerV2Finetune.cpython-37.pyc │ │ ├── nnUNetTrainerV2_CascadeFullRes.cpython-37.pyc │ │ └── nnUNetTrainerV2_DDP.cpython-37.pyc │ ├── competitions_with_custom_Trainers │ │ ├── BraTS2020 │ │ │ ├── __init__.py │ │ │ ├── nnUNetTrainerV2BraTSRegions.py │ │ │ └── nnUNetTrainerV2BraTSRegions_moreDA.py │ │ ├── MMS │ │ │ ├── __init__.py │ │ │ └── nnUNetTrainerV2_MMS.py │ │ └── __init__.py │ ├── network_trainer.py │ ├── nnUNetTrainer.py │ ├── nnUNetTrainerCascadeFullRes.py │ ├── nnUNetTrainerV2.py │ ├── nnUNetTrainerV2Finetune.py │ ├── nnUNetTrainerV2_CascadeFullRes.py │ ├── nnUNetTrainerV2_DDP.py │ ├── nnUNetTrainerV2_DP.py │ ├── nnUNetTrainerV2_fp32.py │ └── nnUNet_variants │ │ ├── __init__.py │ │ ├── architectural_variants │ │ ├── __init__.py │ │ ├── nnUNetTrainerV2_3ConvPerStage.py │ │ ├── nnUNetTrainerV2_3ConvPerStage_samefilters.py │ │ ├── nnUNetTrainerV2_BN.py │ │ ├── nnUNetTrainerV2_FRN.py │ │ ├── nnUNetTrainerV2_GN.py │ │ ├── nnUNetTrainerV2_GeLU.py │ │ ├── nnUNetTrainerV2_LReLU_slope_2en1.py │ │ ├── nnUNetTrainerV2_Mish.py │ │ ├── nnUNetTrainerV2_NoNormalization.py │ │ ├── nnUNetTrainerV2_NoNormalization_lr1en3.py │ │ ├── nnUNetTrainerV2_ReLU.py │ │ ├── nnUNetTrainerV2_ReLU_biasInSegOutput.py │ │ ├── nnUNetTrainerV2_ReLU_convReLUIN.py │ │ ├── nnUNetTrainerV2_ResencUNet.py │ │ ├── nnUNetTrainerV2_ResencUNet_DA3.py │ │ ├── nnUNetTrainerV2_ResencUNet_DA3_BN.py │ │ ├── nnUNetTrainerV2_allConv3x3.py │ │ ├── nnUNetTrainerV2_lReLU_biasInSegOutput.py │ │ ├── nnUNetTrainerV2_lReLU_convlReLUIN.py │ │ ├── nnUNetTrainerV2_noDeepSupervision.py │ │ └── nnUNetTrainerV2_softDeepSupervision.py │ │ ├── benchmarking │ │ ├── __init__.py │ │ ├── nnUNetTrainerV2_2epochs.py │ │ └── nnUNetTrainerV2_dummyLoad.py │ │ ├── cascade │ │ ├── __init__.py │ │ ├── nnUNetTrainerV2CascadeFullRes_DAVariants.py │ │ ├── nnUNetTrainerV2CascadeFullRes_lowerLR.py │ │ ├── nnUNetTrainerV2CascadeFullRes_shorter.py │ │ └── nnUNetTrainerV2CascadeFullRes_shorter_lowerLR.py │ │ ├── copies │ │ ├── __init__.py │ │ └── nnUNetTrainerV2_copies.py │ │ ├── data_augmentation │ │ ├── __init__.py │ │ ├── nnUNetTrainerV2_DA2.py │ │ ├── nnUNetTrainerV2_DA3.py │ │ ├── nnUNetTrainerV2_independentScalePerAxis.py │ │ ├── nnUNetTrainerV2_insaneDA.py │ │ ├── nnUNetTrainerV2_noDA.py │ │ └── nnUNetTrainerV2_noMirroring.py │ │ ├── loss_function │ │ ├── __init__.py │ │ ├── nnUNetTrainerV2_ForceBD.py │ │ ├── nnUNetTrainerV2_ForceSD.py │ │ ├── nnUNetTrainerV2_Loss_CE.py │ │ ├── nnUNetTrainerV2_Loss_CEGDL.py │ │ ├── nnUNetTrainerV2_Loss_Dice.py │ │ ├── nnUNetTrainerV2_Loss_DiceTopK10.py │ │ ├── nnUNetTrainerV2_Loss_Dice_lr1en3.py │ │ ├── nnUNetTrainerV2_Loss_Dice_squared.py │ │ ├── nnUNetTrainerV2_Loss_MCC.py │ │ ├── nnUNetTrainerV2_Loss_TopK10.py │ │ ├── nnUNetTrainerV2_focalLoss.py │ │ └── nnUNetTrainerV2_graduallyTransitionFromCEToDice.py │ │ ├── miscellaneous │ │ ├── __init__.py │ │ └── nnUNetTrainerV2_fullEvals.py │ │ ├── nnUNetTrainerCE.py │ │ ├── nnUNetTrainerNoDA.py │ │ ├── optimizer_and_lr │ │ ├── __init__.py │ │ ├── nnUNetTrainerV2_Adam.py │ │ ├── nnUNetTrainerV2_Adam_ReduceOnPlateau.py │ │ ├── nnUNetTrainerV2_Adam_lr_3en4.py │ │ ├── nnUNetTrainerV2_Ranger_lr1en2.py │ │ ├── nnUNetTrainerV2_Ranger_lr3en3.py │ │ ├── nnUNetTrainerV2_Ranger_lr3en4.py │ │ ├── nnUNetTrainerV2_SGD_ReduceOnPlateau.py │ │ ├── nnUNetTrainerV2_SGD_fixedSchedule.py │ │ ├── nnUNetTrainerV2_SGD_fixedSchedule2.py │ │ ├── nnUNetTrainerV2_SGD_lrs.py │ │ ├── nnUNetTrainerV2_cycleAtEnd.py │ │ ├── nnUNetTrainerV2_fp16.py │ │ ├── nnUNetTrainerV2_momentum09.py │ │ ├── nnUNetTrainerV2_momentum095.py │ │ ├── nnUNetTrainerV2_momentum098.py │ │ ├── nnUNetTrainerV2_momentum09in2D.py │ │ ├── nnUNetTrainerV2_reduceMomentumDuringTraining.py │ │ └── nnUNetTrainerV2_warmup.py │ │ └── resampling │ │ ├── __init__.py │ │ └── nnUNetTrainerV2_resample33.py └── optimizer │ ├── __init__.py │ └── ranger.py └── utilities ├── .ipynb_checkpoints └── task_name_id_conversion-checkpoint.py ├── __init__.py ├── __pycache__ ├── __init__.cpython-37.pyc ├── distributed.cpython-37.pyc ├── nd_softmax.cpython-37.pyc ├── one_hot_encoding.cpython-37.pyc ├── random_stuff.cpython-37.pyc ├── sitk_stuff.cpython-37.pyc ├── task_name_id_conversion.cpython-37.pyc ├── tensor_utilities.cpython-37.pyc └── to_torch.cpython-37.pyc ├── distributed.py ├── file_conversions.py ├── file_endings.py ├── folder_names.py ├── nd_softmax.py ├── one_hot_encoding.py ├── overlay_plots.py ├── random_stuff.py ├── recursive_delete_npz.py ├── recursive_rename_taskXX_to_taskXXX.py ├── sitk_stuff.py ├── task_name_id_conversion.py ├── tensor_utilities.py └── to_torch.py /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | .DS_Store 3 | -------------------------------------------------------------------------------- /Data_Preprocess/rename_unlabeleddata.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # Author: Yao Zhang 4 | # Mail : zhangyao215@mails.ucas.ac.cn 5 | 6 | import os 7 | import csv 8 | import glob 9 | import shutil 10 | import numpy as np 11 | import nibabel as nib 12 | from multiprocessing import Pool 13 | join = os.path.join 14 | 15 | 16 | def check_path(file_path, overwrite=True): 17 | if os.path.exists(file_path): 18 | if overwrite: 19 | shutil.rmtree(file_path) 20 | os.makedirs(file_path) 21 | else: 22 | os.makedirs(file_path) 23 | 24 | 25 | def get_data_list(data_path): 26 | # TODO override regx 27 | regx = data_path+'*/*_sa.nii.gz' 28 | print('we are finding all data by regular expression {}'.format(regx)) 29 | 30 | data_list = glob.glob(regx) 31 | data_list = np.sort(data_list) 32 | print('we found {} volumes'.format(len(data_list))) 33 | return data_list 34 | 35 | 36 | def get_info(filename): 37 | info_dict = {} 38 | with open(filename) as f: 39 | f_csv = csv.reader(f) 40 | for row in f_csv: 41 | info_dict[row[1]] = [row[3], row[4]] 42 | 43 | return info_dict 44 | 45 | 46 | def rename(data_path): 47 | # TODO override seg_filename 48 | data_filename = data_path.split('/')[-1] 49 | print('data filename: {}'.format(data_filename)) 50 | 51 | # TODO override case_id 52 | case_id = data_filename.split('_')[0] 53 | print('case_id: {}'.format(case_id)) 54 | 55 | # TODO make up the output data path 56 | # data new_filename = DATABASE-CASEID-ORIENTATION-SHAPE-INDEX-MODALITY 57 | # seg new_filename = DATABASE-CASEID-ORIENTATION-SHAPE-INDEX 58 | data_volume_nii = nib.load(data_path) 59 | data_volume = data_volume_nii.get_fdata().astype(np.float32) 60 | affine = data_volume_nii.affine 61 | 62 | # get info 63 | info_dict = get_info(info_filename) 64 | 65 | for i in range(data_volume.shape[-1]): 66 | case_sub_id = case_id + '-' + str(i).zfill(2) 67 | orientation = ','.join([str(x) for x in nib.aff2axcodes(affine)]) 68 | vendor = info_dict[case_id][0] 69 | center = info_dict[case_id][1] 70 | modality = '0'.zfill(4) 71 | 72 | new_data_filename = 'MM_{}_{}_{}_{}_{}.nii.gz'.format(case_sub_id, orientation, vendor, center, modality) 73 | new_data_path = join(output_data_path, new_data_filename) 74 | 75 | print('saving {}'.format(new_data_filename)) 76 | nib.save(nib.Nifti1Image(data_volume[..., i], affine), new_data_path) 77 | print('############ finish ############') 78 | 79 | if __name__ == '__main__': 80 | input_path = '../ori_data/Unlabeled/' 81 | info_filename = '../ori_data/M&Ms.csv' 82 | 83 | output_data_path = '../ori_data/unlabeled_data_volume/' 84 | 85 | check_path(output_data_path) 86 | 87 | data_list = get_data_list(input_path) 88 | multiprocess_kernel = 16 89 | pool = Pool(multiprocess_kernel) 90 | pool.map(rename, data_list) 91 | pool.close() 92 | pool.join() 93 | -------------------------------------------------------------------------------- /figs/framework.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/figs/framework.png -------------------------------------------------------------------------------- /figs/reg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/figs/reg.png -------------------------------------------------------------------------------- /figs/results.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/figs/results.png -------------------------------------------------------------------------------- /nnunet_semi_sup/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | print("\n\nPlease cite the following paper when using nnUNet:\n\nIsensee, F., Jaeger, P.F., Kohl, S.A.A. et al. " 3 | "\"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" " 4 | "Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n\n") 5 | print("If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n") 6 | 7 | from . import * -------------------------------------------------------------------------------- /nnunet_semi_sup/__pycache__/SurfaceDice.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/__pycache__/SurfaceDice.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/__pycache__/configuration.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/__pycache__/configuration.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/__pycache__/paths.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/__pycache__/paths.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/configuration.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | default_num_threads = 8 if 'nnUNet_def_n_proc' not in os.environ else int(os.environ['nnUNet_def_n_proc']) 4 | RESAMPLING_SEPARATE_Z_ANISO_THRESHOLD = 3 # determines what threshold to use for resampling the low resolution axis 5 | # separately (with NN) -------------------------------------------------------------------------------- /nnunet_semi_sup/dataset_conversion/Task069_CovidSeg.py: -------------------------------------------------------------------------------- 1 | import shutil 2 | 3 | from batchgenerators.utilities.file_and_folder_operations import * 4 | import SimpleITK as sitk 5 | from nnunet.paths import nnUNet_raw_data 6 | 7 | if __name__ == '__main__': 8 | #data is available at http://medicalsegmentation.com/covid19/ 9 | download_dir = '/home/fabian/Downloads' 10 | 11 | task_id = 69 12 | task_name = "CovidSeg" 13 | 14 | foldername = "Task%03.0d_%s" % (task_id, task_name) 15 | 16 | out_base = join(nnUNet_raw_data, foldername) 17 | imagestr = join(out_base, "imagesTr") 18 | imagests = join(out_base, "imagesTs") 19 | labelstr = join(out_base, "labelsTr") 20 | maybe_mkdir_p(imagestr) 21 | maybe_mkdir_p(imagests) 22 | maybe_mkdir_p(labelstr) 23 | 24 | train_patient_names = [] 25 | test_patient_names = [] 26 | 27 | # the niftis are 3d, but they are just stacks of 2d slices from different patients. So no 3d U-Net, please 28 | 29 | # the training stack has 100 slices, so we split it into 5 equally sized parts (20 slices each) for cross-validation 30 | training_data = sitk.GetArrayFromImage(sitk.ReadImage(join(download_dir, 'tr_im.nii.gz'))) 31 | training_labels = sitk.GetArrayFromImage(sitk.ReadImage(join(download_dir, 'tr_mask.nii.gz'))) 32 | 33 | for f in range(5): 34 | this_name = 'part_%d' % f 35 | data = training_data[f::5] 36 | labels = training_labels[f::5] 37 | sitk.WriteImage(sitk.GetImageFromArray(data), join(imagestr, this_name + '_0000.nii.gz')) 38 | sitk.WriteImage(sitk.GetImageFromArray(labels), join(labelstr, this_name + '.nii.gz')) 39 | train_patient_names.append(this_name) 40 | 41 | shutil.copy(join(download_dir, 'val_im.nii.gz'), join(imagests, 'val_im.nii.gz')) 42 | 43 | test_patient_names.append('val_im') 44 | 45 | json_dict = {} 46 | json_dict['name'] = task_name 47 | json_dict['description'] = "" 48 | json_dict['tensorImageSize'] = "4D" 49 | json_dict['reference'] = "" 50 | json_dict['licence'] = "" 51 | json_dict['release'] = "0.0" 52 | json_dict['modality'] = { 53 | "0": "nonct", 54 | } 55 | json_dict['labels'] = { 56 | "0": "background", 57 | "1": "stuff1", 58 | "2": "stuff2", 59 | "3": "stuff3", 60 | } 61 | 62 | json_dict['numTraining'] = len(train_patient_names) 63 | json_dict['numTest'] = len(test_patient_names) 64 | json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i.split("/")[-1], "label": "./labelsTr/%s.nii.gz" % i.split("/")[-1]} for i in 65 | train_patient_names] 66 | json_dict['test'] = ["./imagesTs/%s.nii.gz" % i.split("/")[-1] for i in test_patient_names] 67 | 68 | save_json(json_dict, os.path.join(out_base, "dataset.json")) 69 | -------------------------------------------------------------------------------- /nnunet_semi_sup/dataset_conversion/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | from . import * -------------------------------------------------------------------------------- /nnunet_semi_sup/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /nnunet_semi_sup/evaluation/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/evaluation/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/evaluation/__pycache__/evaluator.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/evaluation/__pycache__/evaluator.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/evaluation/__pycache__/metrics.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/evaluation/__pycache__/metrics.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/evaluation/add_mean_dice_to_json.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 json 16 | import numpy as np 17 | from batchgenerators.utilities.file_and_folder_operations import subfiles 18 | from collections import OrderedDict 19 | 20 | 21 | def foreground_mean(filename): 22 | with open(filename, 'r') as f: 23 | res = json.load(f) 24 | class_ids = np.array([int(i) for i in res['results']['mean'].keys() if (i != 'mean')]) 25 | class_ids = class_ids[class_ids != 0] 26 | class_ids = class_ids[class_ids != -1] 27 | class_ids = class_ids[class_ids != 99] 28 | 29 | tmp = res['results']['mean'].get('99') 30 | if tmp is not None: 31 | _ = res['results']['mean'].pop('99') 32 | 33 | metrics = res['results']['mean']['1'].keys() 34 | res['results']['mean']["mean"] = OrderedDict() 35 | for m in metrics: 36 | foreground_values = [res['results']['mean'][str(i)][m] for i in class_ids] 37 | res['results']['mean']["mean"][m] = np.nanmean(foreground_values) 38 | with open(filename, 'w') as f: 39 | json.dump(res, f, indent=4, sort_keys=True) 40 | 41 | 42 | def run_in_folder(folder): 43 | json_files = subfiles(folder, True, None, ".json", True) 44 | json_files = [i for i in json_files if not i.split("/")[-1].startswith(".") and not i.endswith("_globalMean.json")] # stupid mac 45 | for j in json_files: 46 | foreground_mean(j) 47 | 48 | 49 | if __name__ == "__main__": 50 | folder = "/media/fabian/Results/nnUNetOutput_final/summary_jsons" 51 | run_in_folder(folder) 52 | -------------------------------------------------------------------------------- /nnunet_semi_sup/evaluation/collect_results_files.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 | import shutil 17 | from batchgenerators.utilities.file_and_folder_operations import subdirs, subfiles 18 | 19 | 20 | def crawl_and_copy(current_folder, out_folder, prefix="fabian_", suffix="ummary.json"): 21 | """ 22 | This script will run recursively through all subfolders of current_folder and copy all files that end with 23 | suffix with some automatically generated prefix into out_folder 24 | :param current_folder: 25 | :param out_folder: 26 | :param prefix: 27 | :return: 28 | """ 29 | s = subdirs(current_folder, join=False) 30 | f = subfiles(current_folder, join=False) 31 | f = [i for i in f if i.endswith(suffix)] 32 | if current_folder.find("fold0") != -1: 33 | for fl in f: 34 | shutil.copy(os.path.join(current_folder, fl), os.path.join(out_folder, prefix+fl)) 35 | for su in s: 36 | if prefix == "": 37 | add = su 38 | else: 39 | add = "__" + su 40 | crawl_and_copy(os.path.join(current_folder, su), out_folder, prefix=prefix+add) 41 | 42 | 43 | if __name__ == "__main__": 44 | from nnunet.paths import network_training_output_dir 45 | output_folder = "/home/fabian/PhD/results/nnUNetV2/leaderboard" 46 | crawl_and_copy(network_training_output_dir, output_folder) 47 | from nnunet.evaluation.add_mean_dice_to_json import run_in_folder 48 | run_in_folder(output_folder) 49 | -------------------------------------------------------------------------------- /nnunet_semi_sup/evaluation/model_selection/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /nnunet_semi_sup/evaluation/surface_dice.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 | 16 | import numpy as np 17 | from medpy.metric.binary import __surface_distances 18 | 19 | 20 | def normalized_surface_dice(a: np.ndarray, b: np.ndarray, threshold: float, spacing: tuple = None, connectivity=1): 21 | """ 22 | This implementation differs from the official surface dice implementation! These two are not comparable!!!!! 23 | 24 | The normalized surface dice is symmetric, so it should not matter whether a or b is the reference image 25 | 26 | This implementation natively supports 2D and 3D images. Whether other dimensions are supported depends on the 27 | __surface_distances implementation in medpy 28 | 29 | :param a: image 1, must have the same shape as b 30 | :param b: image 2, must have the same shape as a 31 | :param threshold: distances below this threshold will be counted as true positives. Threshold is in mm, not voxels! 32 | (if spacing = (1, 1(, 1)) then one voxel=1mm so the threshold is effectively in voxels) 33 | must be a tuple of len dimension(a) 34 | :param spacing: how many mm is one voxel in reality? Can be left at None, we then assume an isotropic spacing of 1mm 35 | :param connectivity: see scipy.ndimage.generate_binary_structure for more information. I suggest you leave that 36 | one alone 37 | :return: 38 | """ 39 | assert all([i == j for i, j in zip(a.shape, b.shape)]), "a and b must have the same shape. a.shape= %s, " \ 40 | "b.shape= %s" % (str(a.shape), str(b.shape)) 41 | if spacing is None: 42 | spacing = tuple([1 for _ in range(len(a.shape))]) 43 | a_to_b = __surface_distances(a, b, spacing, connectivity) 44 | b_to_a = __surface_distances(b, a, spacing, connectivity) 45 | 46 | numel_a = len(a_to_b) 47 | numel_b = len(b_to_a) 48 | 49 | tp_a = np.sum(a_to_b <= threshold) / numel_a 50 | tp_b = np.sum(b_to_a <= threshold) / numel_b 51 | 52 | fp = np.sum(a_to_b > threshold) / numel_a 53 | fn = np.sum(b_to_a > threshold) / numel_b 54 | 55 | dc = (tp_a + tp_b) / (tp_a + tp_b + fp + fn + 1e-8) # 1e-8 just so that we don't get div by 0 56 | return dc 57 | 58 | -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/__pycache__/DatasetAnalyzer.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/experiment_planning/__pycache__/DatasetAnalyzer.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/alternative_experiment_planning/experiment_planner_baseline_3DUNet_v21_3convperstage.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 | from copy import deepcopy 16 | 17 | import numpy as np 18 | from nnunet.experiment_planning.common_utils import get_pool_and_conv_props 19 | from nnunet.experiment_planning.experiment_planner_baseline_3DUNet import ExperimentPlanner 20 | from nnunet.experiment_planning.experiment_planner_baseline_3DUNet_v21 import ExperimentPlanner3D_v21 21 | from nnunet.network_architecture.generic_UNet import Generic_UNet 22 | from nnunet.paths import * 23 | 24 | 25 | class ExperimentPlanner3D_v21_3cps(ExperimentPlanner3D_v21): 26 | """ 27 | have 3x conv-in-lrelu per resolution instead of 2 while remaining in the same memory budget 28 | 29 | This only works with 3d fullres because we use the same data as ExperimentPlanner3D_v21. Lowres would require to 30 | rerun preprocesing (different patch size = different 3d lowres target spacing) 31 | """ 32 | def __init__(self, folder_with_cropped_data, preprocessed_output_folder): 33 | super(ExperimentPlanner3D_v21_3cps, self).__init__(folder_with_cropped_data, preprocessed_output_folder) 34 | self.plans_fname = join(self.preprocessed_output_folder, 35 | "nnUNetPlansv2.1_3cps_plans_3D.pkl") 36 | self.unet_base_num_features = 32 37 | self.conv_per_stage = 3 38 | 39 | def run_preprocessing(self, num_threads): 40 | pass 41 | -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/alternative_experiment_planning/experiment_planner_baseline_3DUNet_v23.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 | from nnunet.experiment_planning.experiment_planner_baseline_3DUNet_v21 import \ 16 | ExperimentPlanner3D_v21 17 | from nnunet.paths import * 18 | 19 | 20 | class ExperimentPlanner3D_v23(ExperimentPlanner3D_v21): 21 | """ 22 | """ 23 | def __init__(self, folder_with_cropped_data, preprocessed_output_folder): 24 | super(ExperimentPlanner3D_v23, self).__init__(folder_with_cropped_data, preprocessed_output_folder) 25 | self.data_identifier = "nnUNetData_plans_v2.3" 26 | self.plans_fname = join(self.preprocessed_output_folder, 27 | "nnUNetPlansv2.3_plans_3D.pkl") 28 | self.preprocessor_name = "Preprocessor3DDifferentResampling" 29 | -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/alternative_experiment_planning/normalization/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/experiment_planning/alternative_experiment_planning/normalization/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/alternative_experiment_planning/normalization/experiment_planner_2DUNet_v21_RGB_scaleto_0_1.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 | 16 | from nnunet.experiment_planning.experiment_planner_baseline_2DUNet_v21 import ExperimentPlanner2D_v21 17 | from nnunet.paths import * 18 | 19 | 20 | class ExperimentPlanner2D_v21_RGB_scaleTo_0_1(ExperimentPlanner2D_v21): 21 | """ 22 | used by tutorial nnunet.tutorials.custom_preprocessing 23 | """ 24 | def __init__(self, folder_with_cropped_data, preprocessed_output_folder): 25 | super().__init__(folder_with_cropped_data, preprocessed_output_folder) 26 | self.data_identifier = "nnUNet_RGB_scaleTo_0_1" 27 | self.plans_fname = join(self.preprocessed_output_folder, "nnUNet_RGB_scaleTo_0_1" + "_plans_2D.pkl") 28 | 29 | # The custom preprocessor class we intend to use is GenericPreprocessor_scale_uint8_to_0_1. It must be located 30 | # in nnunet.preprocessing (any file and submodule) and will be found by its name. Make sure to always define 31 | # unique names! 32 | self.preprocessor_name = 'GenericPreprocessor_scale_uint8_to_0_1' 33 | -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/alternative_experiment_planning/normalization/experiment_planner_3DUNet_CT2.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 | 16 | from collections import OrderedDict 17 | 18 | from nnunet.experiment_planning.experiment_planner_baseline_3DUNet import ExperimentPlanner 19 | from nnunet.paths import * 20 | 21 | 22 | class ExperimentPlannerCT2(ExperimentPlanner): 23 | """ 24 | preprocesses CT data with the "CT2" normalization. 25 | 26 | (clip range comes from training set and is the 0.5 and 99.5 percentile of intensities in foreground) 27 | CT = clip to range, then normalize with global mn and sd (computed on foreground in training set) 28 | CT2 = clip to range, normalize each case separately with its own mn and std (computed within the area that was in clip_range) 29 | """ 30 | def __init__(self, folder_with_cropped_data, preprocessed_output_folder): 31 | super(ExperimentPlannerCT2, self).__init__(folder_with_cropped_data, preprocessed_output_folder) 32 | self.data_identifier = "nnUNet_CT2" 33 | self.plans_fname = join(self.preprocessed_output_folder, "nnUNetPlans" + "CT2_plans_3D.pkl") 34 | 35 | def determine_normalization_scheme(self): 36 | schemes = OrderedDict() 37 | modalities = self.dataset_properties['modalities'] 38 | num_modalities = len(list(modalities.keys())) 39 | 40 | for i in range(num_modalities): 41 | if modalities[i] == "CT": 42 | schemes[i] = "CT2" 43 | else: 44 | schemes[i] = "nonCT" 45 | return schemes 46 | -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/alternative_experiment_planning/normalization/experiment_planner_3DUNet_nonCT.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 | 16 | from collections import OrderedDict 17 | 18 | from nnunet.experiment_planning.experiment_planner_baseline_3DUNet import ExperimentPlanner 19 | from nnunet.paths import * 20 | 21 | 22 | class ExperimentPlannernonCT(ExperimentPlanner): 23 | """ 24 | Preprocesses all data in nonCT mode (this is what we use for MRI per default, but here it is applied to CT images 25 | as well) 26 | """ 27 | def __init__(self, folder_with_cropped_data, preprocessed_output_folder): 28 | super(ExperimentPlannernonCT, self).__init__(folder_with_cropped_data, preprocessed_output_folder) 29 | self.data_identifier = "nnUNet_nonCT" 30 | self.plans_fname = join(self.preprocessed_output_folder, "nnUNetPlans" + "nonCT_plans_3D.pkl") 31 | 32 | def determine_normalization_scheme(self): 33 | schemes = OrderedDict() 34 | modalities = self.dataset_properties['modalities'] 35 | num_modalities = len(list(modalities.keys())) 36 | 37 | for i in range(num_modalities): 38 | if modalities[i] == "CT": 39 | schemes[i] = "nonCT" 40 | else: 41 | schemes[i] = "nonCT" 42 | return schemes 43 | 44 | -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/alternative_experiment_planning/patch_size/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/experiment_planning/alternative_experiment_planning/patch_size/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/alternative_experiment_planning/pooling_and_convs/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/experiment_planning/alternative_experiment_planning/pooling_and_convs/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/alternative_experiment_planning/readme.md: -------------------------------------------------------------------------------- 1 | These alternatives are not used in nnU-Net, but you can use them if you believe they might be better suited for you. 2 | I (Fabian) have not found them to be consistently superior. -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/alternative_experiment_planning/target_spacing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/experiment_planning/alternative_experiment_planning/target_spacing/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/alternative_experiment_planning/target_spacing/experiment_planner_baseline_3DUNet_v21_customTargetSpacing_2x2x2.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 numpy as np 16 | from nnunet.experiment_planning.experiment_planner_baseline_3DUNet_v21 import ExperimentPlanner3D_v21 17 | from nnunet.paths import * 18 | 19 | 20 | class ExperimentPlanner3D_v21_customTargetSpacing_2x2x2(ExperimentPlanner3D_v21): 21 | def __init__(self, folder_with_cropped_data, preprocessed_output_folder): 22 | super(ExperimentPlanner3D_v21, self).__init__(folder_with_cropped_data, preprocessed_output_folder) 23 | # we change the data identifier and plans_fname. This will make this experiment planner save the preprocessed 24 | # data in a different folder so that they can co-exist with the default (ExperimentPlanner3D_v21). We also 25 | # create a custom plans file that will be linked to this data 26 | self.data_identifier = "nnUNetData_plans_v2.1_trgSp_2x2x2" 27 | self.plans_fname = join(self.preprocessed_output_folder, 28 | "nnUNetPlansv2.1_trgSp_2x2x2_plans_3D.pkl") 29 | 30 | def get_target_spacing(self): 31 | # simply return the desired spacing as np.array 32 | return np.array([2., 2., 2.]) # make sure this is float!!!! Not int! 33 | 34 | -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/change_batch_size.py: -------------------------------------------------------------------------------- 1 | from batchgenerators.utilities.file_and_folder_operations import * 2 | import numpy as np 3 | 4 | if __name__ == '__main__': 5 | input_file = '/home/fabian/data/nnUNet_preprocessed/Task004_Hippocampus/nnUNetPlansv2.1_plans_3D.pkl' 6 | output_file = '/home/fabian/data/nnUNet_preprocessed/Task004_Hippocampus/nnUNetPlansv2.1_LISA_plans_3D.pkl' 7 | a = load_pickle(input_file) 8 | a['plans_per_stage'][0]['batch_size'] = int(np.floor(6 / 9 * a['plans_per_stage'][0]['batch_size'])) 9 | save_pickle(a, output_file) -------------------------------------------------------------------------------- /nnunet_semi_sup/experiment_planning/old/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/experiment_planning/old/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/inference/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /nnunet_semi_sup/inference/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/inference/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/inference/__pycache__/predict.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/inference/__pycache__/predict.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/inference/__pycache__/segmentation_export.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/inference/__pycache__/segmentation_export.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/inference/change_trainer.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 | 16 | from batchgenerators.utilities.file_and_folder_operations import * 17 | 18 | 19 | def pretend_to_be_nnUNetTrainer(folder, checkpoints=("model_best.model.pkl", "model_final_checkpoint.model.pkl")): 20 | pretend_to_be_other_trainer(folder, "nnUNetTrainer", checkpoints) 21 | 22 | 23 | def pretend_to_be_other_trainer(folder, new_trainer_name, checkpoints=("model_best.model.pkl", "model_final_checkpoint.model.pkl")): 24 | folds = subdirs(folder, prefix="fold_", join=False) 25 | 26 | if isdir(join(folder, 'all')): 27 | folds.append('all') 28 | 29 | for c in checkpoints: 30 | for f in folds: 31 | checkpoint_file = join(folder, f, c) 32 | if isfile(checkpoint_file): 33 | a = load_pickle(checkpoint_file) 34 | a['name'] = new_trainer_name 35 | save_pickle(a, checkpoint_file) 36 | 37 | 38 | def main(): 39 | import argparse 40 | parser = argparse.ArgumentParser(description='Use this script to change the nnunet trainer class of a saved ' 41 | 'model. Useful for models that were trained with trainers that do ' 42 | 'not support inference (multi GPU trainers) or for trainer classes ' 43 | 'whose source code is not available. For this to work the network ' 44 | 'architecture must be identical between the original trainer ' 45 | 'class and the trainer class we are changing to. This script is ' 46 | 'experimental and only to be used by advanced users.') 47 | parser.add_argument('-i', help='Folder containing the trained model. This folder is the one containing the ' 48 | 'fold_X subfolders.') 49 | parser.add_argument('-tr', help='Name of the new trainer class') 50 | args = parser.parse_args() 51 | pretend_to_be_other_trainer(args.i, args.tr) 52 | -------------------------------------------------------------------------------- /nnunet_semi_sup/inference/pretrained_models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/inference/pretrained_models/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/network_architecture/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /nnunet_semi_sup/network_architecture/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/network_architecture/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/network_architecture/__pycache__/generic_UNet.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/network_architecture/__pycache__/generic_UNet.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/network_architecture/__pycache__/initialization.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/network_architecture/__pycache__/initialization.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/network_architecture/__pycache__/neural_network.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/network_architecture/__pycache__/neural_network.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/network_architecture/custom_modules/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/network_architecture/custom_modules/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/network_architecture/custom_modules/feature_response_normalization.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 | 16 | from nnunet.utilities.tensor_utilities import mean_tensor 17 | from torch import nn 18 | import torch 19 | from torch.nn.parameter import Parameter 20 | import torch.jit 21 | 22 | 23 | class FRN3D(nn.Module): 24 | def __init__(self, num_features: int, eps=1e-6, **kwargs): 25 | super().__init__() 26 | self.eps = eps 27 | self.num_features = num_features 28 | self.weight = Parameter(torch.ones(1, num_features, 1, 1, 1), True) 29 | self.bias = Parameter(torch.zeros(1, num_features, 1, 1, 1), True) 30 | self.tau = Parameter(torch.zeros(1, num_features, 1, 1, 1), True) 31 | 32 | def forward(self, x: torch.Tensor): 33 | x = x * torch.rsqrt(mean_tensor(x * x, [2, 3, 4], keepdim=True) + self.eps) 34 | 35 | return torch.max(self.weight * x + self.bias, self.tau) 36 | 37 | 38 | if __name__ == "__main__": 39 | tmp = torch.rand((3, 32, 16, 16, 16)) 40 | 41 | frn = FRN3D(32) 42 | 43 | out = frn(tmp) 44 | -------------------------------------------------------------------------------- /nnunet_semi_sup/network_architecture/custom_modules/helperModules.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 | 16 | from torch import nn 17 | 18 | 19 | class Identity(nn.Module): 20 | def __init__(self, *args, **kwargs): 21 | super().__init__() 22 | 23 | def forward(self, input): 24 | return input 25 | 26 | 27 | class MyGroupNorm(nn.GroupNorm): 28 | def __init__(self, num_channels, eps=1e-5, affine=True, num_groups=8): 29 | super(MyGroupNorm, self).__init__(num_groups, num_channels, eps, affine) 30 | -------------------------------------------------------------------------------- /nnunet_semi_sup/network_architecture/custom_modules/mish.py: -------------------------------------------------------------------------------- 1 | ############ 2 | # https://github.com/lessw2020/mish/blob/master/mish.py 3 | # This code was taken from the repo above and was not created by me (Fabian)! Full credit goes to the original authors 4 | ############ 5 | 6 | import torch 7 | 8 | import torch.nn as nn 9 | import torch.nn.functional as F 10 | 11 | 12 | # Mish - "Mish: A Self Regularized Non-Monotonic Neural Activation Function" 13 | # https://arxiv.org/abs/1908.08681v1 14 | # implemented for PyTorch / FastAI by lessw2020 15 | # github: https://github.com/lessw2020/mish 16 | 17 | class Mish(nn.Module): 18 | def __init__(self): 19 | super().__init__() 20 | 21 | def forward(self, x): 22 | # inlining this saves 1 second per epoch (V100 GPU) vs having a temp x and then returning x(!) 23 | return x * (torch.tanh(F.softplus(x))) 24 | -------------------------------------------------------------------------------- /nnunet_semi_sup/network_architecture/initialization.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 | 16 | from torch import nn 17 | 18 | 19 | class InitWeights_He(object): 20 | def __init__(self, neg_slope=1e-2): 21 | self.neg_slope = neg_slope 22 | 23 | def __call__(self, module): 24 | if isinstance(module, nn.Conv3d) or isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d) or isinstance(module, nn.ConvTranspose3d): 25 | module.weight = nn.init.kaiming_normal_(module.weight, a=self.neg_slope) 26 | if module.bias is not None: 27 | module.bias = nn.init.constant_(module.bias, 0) 28 | 29 | 30 | class InitWeights_XavierUniform(object): 31 | def __init__(self, gain=1): 32 | self.gain = gain 33 | 34 | def __call__(self, module): 35 | if isinstance(module, nn.Conv3d) or isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d) or isinstance(module, nn.ConvTranspose3d): 36 | module.weight = nn.init.xavier_uniform_(module.weight, self.gain) 37 | if module.bias is not None: 38 | module.bias = nn.init.constant_(module.bias, 0) 39 | -------------------------------------------------------------------------------- /nnunet_semi_sup/postprocessing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/postprocessing/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/postprocessing/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/postprocessing/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/postprocessing/__pycache__/connected_components.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/postprocessing/__pycache__/connected_components.cpython-37.pyc -------------------------------------------------------------------------------- /nnunet_semi_sup/postprocessing/consolidate_postprocessing_simple.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 | 16 | import argparse 17 | from nnunet.postprocessing.consolidate_postprocessing import consolidate_folds 18 | from nnunet.utilities.folder_names import get_output_folder_name 19 | from nnunet.utilities.task_name_id_conversion import convert_id_to_task_name 20 | from nnunet.paths import default_cascade_trainer, default_trainer, default_plans_identifier 21 | 22 | 23 | def main(): 24 | argparser = argparse.ArgumentParser(usage="Used to determine the postprocessing for a trained model. Useful for " 25 | "when the best configuration (2d, 3d_fullres etc) as selected manually.") 26 | argparser.add_argument("-m", type=str, required=True, help="U-Net model (2d, 3d_lowres, 3d_fullres or " 27 | "3d_cascade_fullres)") 28 | argparser.add_argument("-t", type=str, required=True, help="Task name or id") 29 | argparser.add_argument("-tr", type=str, required=False, default=None, 30 | help="nnUNetTrainer class. Default: %s, unless 3d_cascade_fullres " 31 | "(then it's %s)" % (default_trainer, default_cascade_trainer)) 32 | argparser.add_argument("-pl", type=str, required=False, default=default_plans_identifier, 33 | help="Plans name, Default=%s" % default_plans_identifier) 34 | argparser.add_argument("-val", type=str, required=False, default="validation_raw", 35 | help="Validation folder name. Default: validation_raw") 36 | 37 | args = argparser.parse_args() 38 | model = args.m 39 | task = args.t 40 | trainer = args.tr 41 | plans = args.pl 42 | val = args.val 43 | 44 | if not task.startswith("Task"): 45 | task_id = int(task) 46 | task = convert_id_to_task_name(task_id) 47 | 48 | if trainer is None: 49 | if model == "3d_cascade_fullres": 50 | trainer = "nnUNetTrainerV2CascadeFullRes" 51 | else: 52 | trainer = "nnUNetTrainerV2" 53 | 54 | folder = get_output_folder_name(model, task, trainer, plans, None) 55 | 56 | consolidate_folds(folder, val) 57 | 58 | 59 | if __name__ == "__main__": 60 | main() 61 | -------------------------------------------------------------------------------- /nnunet_semi_sup/preprocessing/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- 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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 | import torch 15 | 16 | 17 | def load_pretrained_weights(network, fname, verbose=False): 18 | """ 19 | THIS DOES NOT TRANSFER SEGMENTATION HEADS! 20 | """ 21 | saved_model = torch.load(fname) 22 | pretrained_dict = saved_model['state_dict'] 23 | 24 | new_state_dict = {} 25 | 26 | # if state dict comes form nn.DataParallel but we use non-parallel model here then the state dict keys do not 27 | # match. Use heuristic to make it match 28 | for k, value in pretrained_dict.items(): 29 | key = k 30 | # remove module. prefix from DDP models 31 | if key.startswith('module.'): 32 | key = key[7:] 33 | new_state_dict[key] = value 34 | 35 | pretrained_dict = new_state_dict 36 | 37 | model_dict = network.state_dict() 38 | ok = True 39 | for key, _ in model_dict.items(): 40 | if ('conv_blocks' in key): 41 | if (key in pretrained_dict) and (model_dict[key].shape == pretrained_dict[key].shape): 42 | continue 43 | else: 44 | ok = False 45 | break 46 | 47 | # filter unnecessary keys 48 | if ok: 49 | pretrained_dict = {k: v for k, v in pretrained_dict.items() if 50 | (k in model_dict) and (model_dict[k].shape == pretrained_dict[k].shape)} 51 | # 2. overwrite entries in the existing state dict 52 | model_dict.update(pretrained_dict) 53 | print("################### Loading pretrained weights from file ", fname, '###################') 54 | if verbose: 55 | print("Below is the list of overlapping blocks in pretrained model and nnUNet architecture:") 56 | for key, _ in pretrained_dict.items(): 57 | print(key) 58 | print("################### Done ###################") 59 | network.load_state_dict(model_dict) 60 | else: 61 | raise RuntimeError("Pretrained weights are not compatible with the current network architecture") 62 | 63 | -------------------------------------------------------------------------------- /nnunet_semi_sup/test/plans.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/test/plans.pkl -------------------------------------------------------------------------------- /nnunet_semi_sup/training/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * 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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 | 16 | def poly_lr(epoch, max_epochs, initial_lr, exponent=0.9): 17 | return initial_lr * (1 - epoch / max_epochs)**exponent 18 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/loss_functions/TopK_loss.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 numpy as np 16 | import torch 17 | from nnunet.training.loss_functions.crossentropy import RobustCrossEntropyLoss 18 | 19 | 20 | class TopKLoss(RobustCrossEntropyLoss): 21 | """ 22 | Network has to have NO LINEARITY! 23 | """ 24 | def __init__(self, weight=None, ignore_index=-100, k=10): 25 | self.k = k 26 | super(TopKLoss, self).__init__(weight, False, ignore_index, reduce=False) 27 | 28 | def forward(self, inp, target): 29 | target = target[:, 0].long() 30 | res = super(TopKLoss, self).forward(inp, target) 31 | num_voxels = np.prod(res.shape, dtype=np.int64) 32 | res, _ = torch.topk(res.view((-1, )), int(num_voxels * self.k / 100), sorted=False) 33 | return res.mean() 34 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/loss_functions/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /nnunet_semi_sup/training/loss_functions/__pycache__/TopK_loss.cpython-37.pyc: 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-------------------------------------------------------------------------------- /nnunet_semi_sup/training/loss_functions/deep_supervision.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 | 16 | from torch import nn 17 | 18 | 19 | class MultipleOutputLoss2(nn.Module): 20 | def __init__(self, loss, weight_factors=None): 21 | """ 22 | use this if you have several outputs and ground truth (both list of same len) and the loss should be computed 23 | between them (x[0] and y[0], x[1] and y[1] etc) 24 | :param loss: 25 | :param weight_factors: 26 | """ 27 | super(MultipleOutputLoss2, self).__init__() 28 | self.weight_factors = weight_factors 29 | self.loss = loss 30 | 31 | def forward(self, x, y): 32 | assert isinstance(x, (tuple, list)), "x must be either tuple or list" 33 | assert isinstance(y, (tuple, list)), "y must be either tuple or list" 34 | if self.weight_factors is None: 35 | weights = [1] * len(x) 36 | else: 37 | weights = self.weight_factors 38 | 39 | l = weights[0] * self.loss(x[0], y[0]) 40 | for i in range(1, len(x)): 41 | if weights[i] != 0: 42 | l += weights[i] * self.loss(x[i], y[i]) 43 | return l 44 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | import torch 2 | from nnunet.network_architecture.generic_UNet import Generic_UNet 3 | from nnunet.network_architecture.initialization import InitWeights_He 4 | from nnunet.training.network_training.nnUNet_variants.data_augmentation.nnUNetTrainerV2_insaneDA import \ 5 | nnUNetTrainerV2_insaneDA 6 | from nnunet.utilities.nd_softmax import softmax_helper 7 | from torch import nn 8 | 9 | 10 | class nnUNetTrainerV2_MMS(nnUNetTrainerV2_insaneDA): 11 | def setup_DA_params(self): 12 | super().setup_DA_params() 13 | self.data_aug_params["p_rot"] = 0.7 14 | self.data_aug_params["p_eldef"] = 0.1 15 | self.data_aug_params["p_scale"] = 0.3 16 | 17 | self.data_aug_params["independent_scale_factor_for_each_axis"] = True 18 | self.data_aug_params["p_independent_scale_per_axis"] = 0.3 19 | 20 | self.data_aug_params["do_additive_brightness"] = True 21 | self.data_aug_params["additive_brightness_mu"] = 0 22 | self.data_aug_params["additive_brightness_sigma"] = 0.2 23 | self.data_aug_params["additive_brightness_p_per_sample"] = 0.3 24 | self.data_aug_params["additive_brightness_p_per_channel"] = 1 25 | 26 | self.data_aug_params["elastic_deform_alpha"] = (0., 300.) 27 | self.data_aug_params["elastic_deform_sigma"] = (9., 15.) 28 | 29 | self.data_aug_params['gamma_range'] = (0.5, 1.6) 30 | 31 | def initialize_network(self): 32 | if self.threeD: 33 | conv_op = nn.Conv3d 34 | dropout_op = nn.Dropout3d 35 | norm_op = nn.BatchNorm3d 36 | 37 | else: 38 | conv_op = nn.Conv2d 39 | dropout_op = nn.Dropout2d 40 | norm_op = nn.BatchNorm2d 41 | 42 | norm_op_kwargs = {'eps': 1e-5, 'affine': True} 43 | dropout_op_kwargs = {'p': 0, 'inplace': True} 44 | net_nonlin = nn.LeakyReLU 45 | net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} 46 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 47 | len(self.net_num_pool_op_kernel_sizes), 48 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, 49 | dropout_op_kwargs, 50 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2), 51 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) 52 | if torch.cuda.is_available(): 53 | self.network.cuda() 54 | self.network.inference_apply_nonlin = softmax_helper 55 | 56 | """def run_training(self): 57 | from batchviewer import view_batch 58 | a = next(self.tr_gen) 59 | view_batch(a['data']) 60 | import IPython;IPython.embed()""" 61 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/competitions_with_custom_Trainers/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/training/network_training/competitions_with_custom_Trainers/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNetTrainerV2_fp32.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_fp32(nnUNetTrainerV2): 20 | """ 21 | Info for Fabian: same as internal nnUNetTrainerV2_2 22 | """ 23 | 24 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 25 | unpack_data=True, deterministic=True, fp16=False): 26 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 27 | deterministic, False) 28 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_3ConvPerStage.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | from torch import nn 20 | 21 | 22 | class nnUNetTrainerV2_3ConvPerStage(nnUNetTrainerV2): 23 | def initialize_network(self): 24 | self.base_num_features = 24 # otherwise we run out of VRAM 25 | if self.threeD: 26 | conv_op = nn.Conv3d 27 | dropout_op = nn.Dropout3d 28 | norm_op = nn.InstanceNorm3d 29 | 30 | else: 31 | conv_op = nn.Conv2d 32 | dropout_op = nn.Dropout2d 33 | norm_op = nn.InstanceNorm2d 34 | 35 | norm_op_kwargs = {'eps': 1e-5, 'affine': True} 36 | dropout_op_kwargs = {'p': 0, 'inplace': True} 37 | net_nonlin = nn.LeakyReLU 38 | net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} 39 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 40 | len(self.net_num_pool_op_kernel_sizes), 41 | 3, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 42 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2), 43 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) 44 | if torch.cuda.is_available(): 45 | self.network.cuda() 46 | self.network.inference_apply_nonlin = softmax_helper 47 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_3ConvPerStage_samefilters.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | from torch import nn 20 | 21 | 22 | class nnUNetTrainerV2_3ConvPerStageSameFilters(nnUNetTrainerV2): 23 | def initialize_network(self): 24 | if self.threeD: 25 | conv_op = nn.Conv3d 26 | dropout_op = nn.Dropout3d 27 | norm_op = nn.InstanceNorm3d 28 | 29 | else: 30 | conv_op = nn.Conv2d 31 | dropout_op = nn.Dropout2d 32 | norm_op = nn.InstanceNorm2d 33 | 34 | norm_op_kwargs = {'eps': 1e-5, 'affine': True} 35 | dropout_op_kwargs = {'p': 0, 'inplace': True} 36 | net_nonlin = nn.LeakyReLU 37 | net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} 38 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 39 | len(self.net_num_pool_op_kernel_sizes), 40 | 3, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 41 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2), 42 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) 43 | if torch.cuda.is_available(): 44 | self.network.cuda() 45 | self.network.inference_apply_nonlin = softmax_helper 46 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_BN.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | from torch import nn 20 | 21 | 22 | class nnUNetTrainerV2_BN(nnUNetTrainerV2): 23 | def initialize_network(self): 24 | """ 25 | changed deep supervision to False 26 | :return: 27 | """ 28 | if self.threeD: 29 | conv_op = nn.Conv3d 30 | dropout_op = nn.Dropout3d 31 | norm_op = nn.BatchNorm3d 32 | 33 | else: 34 | conv_op = nn.Conv2d 35 | dropout_op = nn.Dropout2d 36 | norm_op = nn.BatchNorm2d 37 | 38 | norm_op_kwargs = {'eps': 1e-5, 'affine': True} 39 | dropout_op_kwargs = {'p': 0, 'inplace': True} 40 | net_nonlin = nn.LeakyReLU 41 | net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} 42 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 43 | len(self.net_num_pool_op_kernel_sizes), 44 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 45 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2), 46 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) 47 | if torch.cuda.is_available(): 48 | self.network.cuda() 49 | self.network.inference_apply_nonlin = softmax_helper 50 | 51 | 52 | nnUNetTrainerV2_BN_copy1 = nnUNetTrainerV2_BN 53 | nnUNetTrainerV2_BN_copy2 = nnUNetTrainerV2_BN 54 | nnUNetTrainerV2_BN_copy3 = nnUNetTrainerV2_BN 55 | nnUNetTrainerV2_BN_copy4 = nnUNetTrainerV2_BN 56 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_FRN.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 | 16 | from nnunet.network_architecture.custom_modules.feature_response_normalization import FRN3D 17 | from nnunet.network_architecture.generic_UNet import Generic_UNet 18 | from nnunet.network_architecture.initialization import InitWeights_He 19 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 20 | from nnunet.utilities.nd_softmax import softmax_helper 21 | from torch import nn 22 | from nnunet.network_architecture.custom_modules.helperModules import Identity 23 | import torch 24 | 25 | 26 | class nnUNetTrainerV2_FRN(nnUNetTrainerV2): 27 | def initialize_network(self): 28 | """ 29 | changed deep supervision to False 30 | :return: 31 | """ 32 | if self.threeD: 33 | conv_op = nn.Conv3d 34 | dropout_op = nn.Dropout3d 35 | norm_op = FRN3D 36 | 37 | else: 38 | conv_op = nn.Conv2d 39 | dropout_op = nn.Dropout2d 40 | raise NotImplementedError 41 | norm_op = nn.BatchNorm2d 42 | 43 | norm_op_kwargs = {'eps': 1e-6} 44 | dropout_op_kwargs = {'p': 0, 'inplace': True} 45 | net_nonlin = Identity 46 | net_nonlin_kwargs = {} 47 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 48 | len(self.net_num_pool_op_kernel_sizes), 49 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 50 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2), 51 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) 52 | if torch.cuda.is_available(): 53 | self.network.cuda() 54 | self.network.inference_apply_nonlin = softmax_helper 55 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_GN.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | from nnunet.network_architecture.custom_modules.helperModules import MyGroupNorm 19 | from nnunet.utilities.nd_softmax import softmax_helper 20 | from torch import nn 21 | 22 | 23 | class nnUNetTrainerV2_GN(nnUNetTrainerV2): 24 | def initialize_network(self): 25 | """ 26 | changed deep supervision to False 27 | :return: 28 | """ 29 | if self.threeD: 30 | conv_op = nn.Conv3d 31 | dropout_op = nn.Dropout3d 32 | norm_op = MyGroupNorm 33 | 34 | else: 35 | conv_op = nn.Conv2d 36 | dropout_op = nn.Dropout2d 37 | norm_op = MyGroupNorm 38 | 39 | norm_op_kwargs = {'eps': 1e-5, 'affine': True, 'num_groups': 8} 40 | dropout_op_kwargs = {'p': 0, 'inplace': True} 41 | net_nonlin = nn.LeakyReLU 42 | net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} 43 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 44 | len(self.net_num_pool_op_kernel_sizes), 45 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 46 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2), 47 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) 48 | if torch.cuda.is_available(): 49 | self.network.cuda() 50 | self.network.inference_apply_nonlin = softmax_helper 51 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_GeLU.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | 18 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 19 | from nnunet.utilities.nd_softmax import softmax_helper 20 | from torch import nn 21 | 22 | try: 23 | from torch.nn.functional import gelu 24 | except ImportError: 25 | gelu = None 26 | 27 | 28 | class GeLU(nn.Module): 29 | def __init__(self): 30 | super().__init__() 31 | if gelu is None: 32 | raise ImportError('You need to have at least torch==1.7.0 to use GeLUs') 33 | 34 | def forward(self, x): 35 | return gelu(x) 36 | 37 | 38 | class nnUNetTrainerV2_GeLU(nnUNetTrainerV2): 39 | def initialize_network(self): 40 | """ 41 | - momentum 0.99 42 | - SGD instead of Adam 43 | - self.lr_scheduler = None because we do poly_lr 44 | - deep supervision = True 45 | - ReLU 46 | - i am sure I forgot something here 47 | 48 | Known issue: forgot to set neg_slope=0 in InitWeights_He; should not make a difference though 49 | :return: 50 | """ 51 | if self.threeD: 52 | conv_op = nn.Conv3d 53 | dropout_op = nn.Dropout3d 54 | norm_op = nn.InstanceNorm3d 55 | 56 | else: 57 | conv_op = nn.Conv2d 58 | dropout_op = nn.Dropout2d 59 | norm_op = nn.InstanceNorm2d 60 | 61 | norm_op_kwargs = {'eps': 1e-5, 'affine': True} 62 | dropout_op_kwargs = {'p': 0, 'inplace': True} 63 | net_nonlin = GeLU 64 | net_nonlin_kwargs = {} 65 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 66 | len(self.net_num_pool_op_kernel_sizes), 67 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 68 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(), 69 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) 70 | if torch.cuda.is_available(): 71 | self.network.cuda() 72 | self.network.inference_apply_nonlin = softmax_helper 73 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_LReLU_slope_2en1.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | from torch import nn 20 | 21 | 22 | class nnUNetTrainerV2_LReLU_slope_2en1(nnUNetTrainerV2): 23 | def initialize_network(self): 24 | if self.threeD: 25 | conv_op = nn.Conv3d 26 | dropout_op = nn.Dropout3d 27 | norm_op = nn.InstanceNorm3d 28 | 29 | else: 30 | conv_op = nn.Conv2d 31 | dropout_op = nn.Dropout2d 32 | norm_op = nn.InstanceNorm2d 33 | 34 | norm_op_kwargs = {'eps': 1e-5, 'affine': True} 35 | dropout_op_kwargs = {'p': 0, 'inplace': True} 36 | net_nonlin = nn.LeakyReLU 37 | net_nonlin_kwargs = {'inplace': True, 'negative_slope': 2e-1} 38 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 39 | len(self.net_num_pool_op_kernel_sizes), 40 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 41 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(0), 42 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) 43 | if torch.cuda.is_available(): 44 | self.network.cuda() 45 | self.network.inference_apply_nonlin = softmax_helper 46 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_Mish.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | from torch import nn 20 | from nnunet.network_architecture.custom_modules.mish import Mish 21 | 22 | 23 | class nnUNetTrainerV2_Mish(nnUNetTrainerV2): 24 | def initialize_network(self): 25 | if self.threeD: 26 | conv_op = nn.Conv3d 27 | dropout_op = nn.Dropout3d 28 | norm_op = nn.InstanceNorm3d 29 | 30 | else: 31 | conv_op = nn.Conv2d 32 | dropout_op = nn.Dropout2d 33 | norm_op = nn.InstanceNorm2d 34 | 35 | norm_op_kwargs = {'eps': 1e-5, 'affine': True} 36 | dropout_op_kwargs = {'p': 0, 'inplace': True} 37 | net_nonlin = Mish 38 | net_nonlin_kwargs = {} 39 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 40 | len(self.net_num_pool_op_kernel_sizes), 41 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 42 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(0), 43 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) 44 | if torch.cuda.is_available(): 45 | self.network.cuda() 46 | self.network.inference_apply_nonlin = softmax_helper 47 | 48 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_NoNormalization.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | from nnunet.network_architecture.custom_modules.helperModules import Identity 19 | from nnunet.utilities.nd_softmax import softmax_helper 20 | from torch import nn 21 | 22 | 23 | class nnUNetTrainerV2_NoNormalization(nnUNetTrainerV2): 24 | def initialize_network(self): 25 | if self.threeD: 26 | conv_op = nn.Conv3d 27 | dropout_op = nn.Dropout3d 28 | norm_op = Identity 29 | 30 | else: 31 | conv_op = nn.Conv2d 32 | dropout_op = nn.Dropout2d 33 | norm_op = Identity 34 | 35 | norm_op_kwargs = {} 36 | dropout_op_kwargs = {'p': 0, 'inplace': True} 37 | net_nonlin = nn.LeakyReLU 38 | net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} 39 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 40 | len(self.net_num_pool_op_kernel_sizes), 41 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 42 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2), 43 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) 44 | if torch.cuda.is_available(): 45 | self.network.cuda() 46 | self.network.inference_apply_nonlin = softmax_helper 47 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_NoNormalization_lr1en3.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 | 16 | from nnunet.training.network_training.nnUNet_variants.architectural_variants.nnUNetTrainerV2_NoNormalization import \ 17 | nnUNetTrainerV2_NoNormalization 18 | 19 | 20 | class nnUNetTrainerV2_NoNormalization_lr1en3(nnUNetTrainerV2_NoNormalization): 21 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 22 | unpack_data=True, deterministic=True, fp16=False): 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | self.initial_lr = 1e-3 26 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ReLU.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | from torch import nn 20 | 21 | 22 | class nnUNetTrainerV2_ReLU(nnUNetTrainerV2): 23 | def initialize_network(self): 24 | if self.threeD: 25 | conv_op = nn.Conv3d 26 | dropout_op = nn.Dropout3d 27 | norm_op = nn.InstanceNorm3d 28 | 29 | else: 30 | conv_op = nn.Conv2d 31 | dropout_op = nn.Dropout2d 32 | norm_op = nn.InstanceNorm2d 33 | 34 | norm_op_kwargs = {'eps': 1e-5, 'affine': True} 35 | dropout_op_kwargs = {'p': 0, 'inplace': True} 36 | net_nonlin = nn.ReLU 37 | net_nonlin_kwargs = {'inplace': True} 38 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 39 | len(self.net_num_pool_op_kernel_sizes), 40 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 41 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(0), 42 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) 43 | if torch.cuda.is_available(): 44 | self.network.cuda() 45 | self.network.inference_apply_nonlin = softmax_helper 46 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ReLU_biasInSegOutput.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | from torch import nn 20 | 21 | 22 | class nnUNetTrainerV2_ReLU_biasInSegOutput(nnUNetTrainerV2): 23 | def initialize_network(self): 24 | if self.threeD: 25 | conv_op = nn.Conv3d 26 | dropout_op = nn.Dropout3d 27 | norm_op = nn.InstanceNorm3d 28 | 29 | else: 30 | conv_op = nn.Conv2d 31 | dropout_op = nn.Dropout2d 32 | norm_op = nn.InstanceNorm2d 33 | 34 | norm_op_kwargs = {'eps': 1e-5, 'affine': True} 35 | dropout_op_kwargs = {'p': 0, 'inplace': True} 36 | net_nonlin = nn.ReLU 37 | net_nonlin_kwargs = {'inplace': True} 38 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 39 | len(self.net_num_pool_op_kernel_sizes), 40 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 41 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(0), 42 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True, 43 | seg_output_use_bias=True) 44 | if torch.cuda.is_available(): 45 | self.network.cuda() 46 | self.network.inference_apply_nonlin = softmax_helper 47 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ReLU_convReLUIN.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet, ConvDropoutNonlinNorm 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | from torch import nn 20 | 21 | 22 | class nnUNetTrainerV2_ReLU_convReLUIN(nnUNetTrainerV2): 23 | def initialize_network(self): 24 | if self.threeD: 25 | conv_op = nn.Conv3d 26 | dropout_op = nn.Dropout3d 27 | norm_op = nn.InstanceNorm3d 28 | 29 | else: 30 | conv_op = nn.Conv2d 31 | dropout_op = nn.Dropout2d 32 | norm_op = nn.InstanceNorm2d 33 | 34 | norm_op_kwargs = {'eps': 1e-5, 'affine': True} 35 | dropout_op_kwargs = {'p': 0, 'inplace': True} 36 | net_nonlin = nn.ReLU 37 | net_nonlin_kwargs = {'inplace': True} 38 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 39 | len(self.net_num_pool_op_kernel_sizes), 40 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 41 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(0), 42 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True, 43 | basic_block=ConvDropoutNonlinNorm) 44 | if torch.cuda.is_available(): 45 | self.network.cuda() 46 | self.network.inference_apply_nonlin = softmax_helper 47 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ResencUNet_DA3_BN.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 torch 16 | 17 | from nnunet.network_architecture.generic_modular_residual_UNet import FabiansUNet, get_default_network_config 18 | from nnunet.network_architecture.initialization import InitWeights_He 19 | from nnunet.training.network_training.nnUNet_variants.architectural_variants.nnUNetTrainerV2_ResencUNet_DA3 import \ 20 | nnUNetTrainerV2_ResencUNet_DA3 21 | from nnunet.utilities.nd_softmax import softmax_helper 22 | 23 | 24 | class nnUNetTrainerV2_ResencUNet_DA3_BN(nnUNetTrainerV2_ResencUNet_DA3): 25 | def initialize_network(self): 26 | if self.threeD: 27 | cfg = get_default_network_config(3, None, norm_type="bn") 28 | 29 | else: 30 | cfg = get_default_network_config(1, None, norm_type="bn") 31 | 32 | stage_plans = self.plans['plans_per_stage'][self.stage] 33 | conv_kernel_sizes = stage_plans['conv_kernel_sizes'] 34 | blocks_per_stage_encoder = stage_plans['num_blocks_encoder'] 35 | blocks_per_stage_decoder = stage_plans['num_blocks_decoder'] 36 | pool_op_kernel_sizes = stage_plans['pool_op_kernel_sizes'] 37 | 38 | self.network = FabiansUNet(self.num_input_channels, self.base_num_features, blocks_per_stage_encoder, 2, 39 | pool_op_kernel_sizes, conv_kernel_sizes, cfg, self.num_classes, 40 | blocks_per_stage_decoder, True, False, 320, InitWeights_He(1e-2)) 41 | 42 | if torch.cuda.is_available(): 43 | self.network.cuda() 44 | self.network.inference_apply_nonlin = softmax_helper 45 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_allConv3x3.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | from torch import nn 20 | 21 | 22 | class nnUNetTrainerV2_allConv3x3(nnUNetTrainerV2): 23 | def initialize_network(self): 24 | """ 25 | - momentum 0.99 26 | - SGD instead of Adam 27 | - self.lr_scheduler = None because we do poly_lr 28 | - deep supervision = True 29 | - i am sure I forgot something here 30 | 31 | Known issue: forgot to set neg_slope=0 in InitWeights_He; should not make a difference though 32 | :return: 33 | """ 34 | if self.threeD: 35 | conv_op = nn.Conv3d 36 | dropout_op = nn.Dropout3d 37 | norm_op = nn.InstanceNorm3d 38 | 39 | else: 40 | conv_op = nn.Conv2d 41 | dropout_op = nn.Dropout2d 42 | norm_op = nn.InstanceNorm2d 43 | 44 | for s in range(len(self.net_conv_kernel_sizes)): 45 | for i in range(len(self.net_conv_kernel_sizes[s])): 46 | self.net_conv_kernel_sizes[s][i] = 3 47 | 48 | norm_op_kwargs = {'eps': 1e-5, 'affine': True} 49 | dropout_op_kwargs = {'p': 0, 'inplace': True} 50 | net_nonlin = nn.LeakyReLU 51 | net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} 52 | 53 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 54 | len(self.net_num_pool_op_kernel_sizes), 55 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 56 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2), 57 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) 58 | if torch.cuda.is_available(): 59 | self.network.cuda() 60 | self.network.inference_apply_nonlin = softmax_helper 61 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_lReLU_biasInSegOutput.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | from torch import nn 20 | 21 | 22 | class nnUNetTrainerV2_lReLU_biasInSegOutput(nnUNetTrainerV2): 23 | def initialize_network(self): 24 | if self.threeD: 25 | conv_op = nn.Conv3d 26 | dropout_op = nn.Dropout3d 27 | norm_op = nn.InstanceNorm3d 28 | 29 | else: 30 | conv_op = nn.Conv2d 31 | dropout_op = nn.Dropout2d 32 | norm_op = nn.InstanceNorm2d 33 | 34 | norm_op_kwargs = {'eps': 1e-5, 'affine': True} 35 | dropout_op_kwargs = {'p': 0, 'inplace': True} 36 | net_nonlin = nn.LeakyReLU 37 | net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} 38 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 39 | len(self.net_num_pool_op_kernel_sizes), 40 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 41 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(0), 42 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True, 43 | seg_output_use_bias=True) 44 | if torch.cuda.is_available(): 45 | self.network.cuda() 46 | self.network.inference_apply_nonlin = softmax_helper 47 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_lReLU_convlReLUIN.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 | import torch 15 | from nnunet.network_architecture.generic_UNet import Generic_UNet, ConvDropoutNonlinNorm 16 | from nnunet.network_architecture.initialization import InitWeights_He 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | from torch import nn 20 | 21 | 22 | class nnUNetTrainerV2_lReLU_convReLUIN(nnUNetTrainerV2): 23 | def initialize_network(self): 24 | if self.threeD: 25 | conv_op = nn.Conv3d 26 | dropout_op = nn.Dropout3d 27 | norm_op = nn.InstanceNorm3d 28 | 29 | else: 30 | conv_op = nn.Conv2d 31 | dropout_op = nn.Dropout2d 32 | norm_op = nn.InstanceNorm2d 33 | 34 | norm_op_kwargs = {'eps': 1e-5, 'affine': True} 35 | dropout_op_kwargs = {'p': 0, 'inplace': True} 36 | net_nonlin = nn.LeakyReLU 37 | net_nonlin_kwargs = {'inplace': True, 'negative_slope': 1e-2} 38 | self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, 39 | len(self.net_num_pool_op_kernel_sizes), 40 | self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, 41 | net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2), 42 | self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True, 43 | basic_block=ConvDropoutNonlinNorm) 44 | if torch.cuda.is_available(): 45 | self.network.cuda() 46 | self.network.inference_apply_nonlin = softmax_helper 47 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/benchmarking/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/training/network_training/nnUNet_variants/benchmarking/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/cascade/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/training/network_training/nnUNet_variants/cascade/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/cascade/nnUNetTrainerV2CascadeFullRes_lowerLR.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2_CascadeFullRes import nnUNetTrainerV2CascadeFullRes 17 | 18 | 19 | class nnUNetTrainerV2CascadeFullRes_lowerLR(nnUNetTrainerV2CascadeFullRes): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, previous_trainer="nnUNetTrainerV2", fp16=False): 22 | super().__init__(plans_file, fold, output_folder, dataset_directory, 23 | batch_dice, stage, unpack_data, deterministic, 24 | previous_trainer, fp16) 25 | self.initial_lr = 1e-3 26 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/cascade/nnUNetTrainerV2CascadeFullRes_shorter.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2_CascadeFullRes import nnUNetTrainerV2CascadeFullRes 17 | 18 | 19 | class nnUNetTrainerV2CascadeFullRes_shorter(nnUNetTrainerV2CascadeFullRes): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, previous_trainer="nnUNetTrainerV2", fp16=False): 22 | super().__init__(plans_file, fold, output_folder, dataset_directory, 23 | batch_dice, stage, unpack_data, deterministic, 24 | previous_trainer, fp16) 25 | self.max_num_epochs = 500 26 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/cascade/nnUNetTrainerV2CascadeFullRes_shorter_lowerLR.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2_CascadeFullRes import nnUNetTrainerV2CascadeFullRes 17 | 18 | 19 | class nnUNetTrainerV2CascadeFullRes_shorter_lowerLR(nnUNetTrainerV2CascadeFullRes): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, previous_trainer="nnUNetTrainerV2", fp16=False): 22 | super().__init__(plans_file, fold, output_folder, dataset_directory, 23 | batch_dice, stage, unpack_data, deterministic, 24 | previous_trainer, fp16) 25 | self.max_num_epochs = 500 26 | self.initial_lr = 1e-3 27 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/copies/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/training/network_training/nnUNet_variants/copies/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/copies/nnUNetTrainerV2_copies.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | # This stuff is just so that we can check stability of results. Training is nondeterministic and by renaming the trainer 20 | # class we can have several trained models coexist although the trainer is effectively the same 21 | 22 | 23 | class nnUNetTrainerV2_copy1(nnUNetTrainerV2): 24 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 25 | unpack_data=True, deterministic=True, fp16=False): 26 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 27 | deterministic, fp16) 28 | 29 | 30 | class nnUNetTrainerV2_copy2(nnUNetTrainerV2): 31 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 32 | unpack_data=True, deterministic=True, fp16=False): 33 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 34 | deterministic, fp16) 35 | 36 | 37 | class nnUNetTrainerV2_copy3(nnUNetTrainerV2): 38 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 39 | unpack_data=True, deterministic=True, fp16=False): 40 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 41 | deterministic, fp16) 42 | 43 | 44 | class nnUNetTrainerV2_copy4(nnUNetTrainerV2): 45 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 46 | unpack_data=True, deterministic=True, fp16=False): 47 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 48 | deterministic, fp16) 49 | 50 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/data_augmentation/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/training/network_training/nnUNet_variants/data_augmentation/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/data_augmentation/nnUNetTrainerV2_DA2.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_DA2(nnUNetTrainerV2): 20 | def setup_DA_params(self): 21 | super().setup_DA_params() 22 | 23 | self.data_aug_params["independent_scale_factor_for_each_axis"] = True 24 | 25 | if self.threeD: 26 | self.data_aug_params["rotation_p_per_axis"] = 0.5 27 | else: 28 | self.data_aug_params["rotation_p_per_axis"] = 1 29 | 30 | self.data_aug_params["do_additive_brightness"] = True 31 | 32 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/data_augmentation/nnUNetTrainerV2_independentScalePerAxis.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_independentScalePerAxis(nnUNetTrainerV2): 20 | def setup_DA_params(self): 21 | super().setup_DA_params() 22 | self.data_aug_params["independent_scale_factor_for_each_axis"] = True 23 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/data_augmentation/nnUNetTrainerV2_noMirroring.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_noMirroring(nnUNetTrainerV2): 20 | def validate(self, do_mirroring: bool = True, use_sliding_window: bool = True, 21 | step_size: float = 0.5, save_softmax: bool = True, use_gaussian: bool = True, overwrite: bool = True, 22 | validation_folder_name: str = 'validation_raw', debug: bool = False, all_in_gpu: bool = False, 23 | segmentation_export_kwargs: dict = None, run_postprocessing_on_folds: bool = True): 24 | """ 25 | We need to wrap this because we need to enforce self.network.do_ds = False for prediction 26 | """ 27 | ds = self.network.do_ds 28 | if do_mirroring: 29 | print("WARNING! do_mirroring was True but we cannot do that because we trained without mirroring. " 30 | "do_mirroring was set to False") 31 | do_mirroring = False 32 | self.network.do_ds = False 33 | ret = super().validate(do_mirroring=do_mirroring, use_sliding_window=use_sliding_window, step_size=step_size, 34 | save_softmax=save_softmax, use_gaussian=use_gaussian, 35 | overwrite=overwrite, validation_folder_name=validation_folder_name, debug=debug, 36 | all_in_gpu=all_in_gpu, segmentation_export_kwargs=segmentation_export_kwargs, 37 | run_postprocessing_on_folds=run_postprocessing_on_folds) 38 | self.network.do_ds = ds 39 | return ret 40 | 41 | def setup_DA_params(self): 42 | super().setup_DA_params() 43 | self.data_aug_params["do_mirror"] = False 44 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/loss_function/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/training/network_training/nnUNet_variants/loss_function/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_ForceBD.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_ForceBD(nnUNetTrainerV2): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, fp16=False): 22 | batch_dice = True 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_ForceSD.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_ForceSD(nnUNetTrainerV2): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, fp16=False): 22 | batch_dice = False 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_CE.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 nnunet.training.loss_functions.crossentropy import RobustCrossEntropyLoss 15 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 16 | 17 | 18 | class nnUNetTrainerV2_Loss_CE(nnUNetTrainerV2): 19 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 20 | unpack_data=True, deterministic=True, fp16=False): 21 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 22 | deterministic, fp16) 23 | self.loss = RobustCrossEntropyLoss() 24 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_CEGDL.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | from nnunet.training.loss_functions.dice_loss import GDL_and_CE_loss 18 | 19 | 20 | class nnUNetTrainerV2_Loss_CEGDL(nnUNetTrainerV2): 21 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 22 | unpack_data=True, deterministic=True, fp16=False): 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | self.loss = GDL_and_CE_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, {}) 26 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_Dice.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | from nnunet.training.loss_functions.dice_loss import SoftDiceLoss 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | 20 | 21 | class nnUNetTrainerV2_Loss_Dice(nnUNetTrainerV2): 22 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 23 | unpack_data=True, deterministic=True, fp16=False): 24 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 25 | deterministic, fp16) 26 | self.loss = SoftDiceLoss(**{'apply_nonlin': softmax_helper, 'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}) 27 | 28 | 29 | class nnUNetTrainerV2_Loss_DicewithBG(nnUNetTrainerV2): 30 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 31 | unpack_data=True, deterministic=True, fp16=False): 32 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 33 | deterministic, fp16) 34 | self.loss = SoftDiceLoss(**{'apply_nonlin': softmax_helper, 'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': True}) 35 | 36 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_DiceTopK10.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | from nnunet.training.loss_functions.dice_loss import DC_and_topk_loss 18 | 19 | 20 | class nnUNetTrainerV2_Loss_DiceTopK10(nnUNetTrainerV2): 21 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 22 | unpack_data=True, deterministic=True, fp16=False): 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | self.loss = DC_and_topk_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, 26 | {'k': 10}) 27 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_Dice_lr1en3.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 | 16 | from nnunet.training.network_training.nnUNet_variants.loss_function.nnUNetTrainerV2_Loss_Dice import \ 17 | nnUNetTrainerV2_Loss_Dice, nnUNetTrainerV2_Loss_DicewithBG 18 | 19 | 20 | class nnUNetTrainerV2_Loss_Dice_LR1en3(nnUNetTrainerV2_Loss_Dice): 21 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 22 | unpack_data=True, deterministic=True, fp16=False): 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | self.initial_lr = 1e-3 26 | 27 | 28 | class nnUNetTrainerV2_Loss_DicewithBG_LR1en3(nnUNetTrainerV2_Loss_DicewithBG): 29 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 30 | unpack_data=True, deterministic=True, fp16=False): 31 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 32 | deterministic, fp16) 33 | self.initial_lr = 1e-3 34 | 35 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_Dice_squared.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | from nnunet.training.loss_functions.dice_loss import SoftDiceLossSquared 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | 20 | 21 | class nnUNetTrainerV2_Loss_Dice_squared(nnUNetTrainerV2): 22 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 23 | unpack_data=True, deterministic=True, fp16=False): 24 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 25 | deterministic, fp16) 26 | self.initial_lr = 1e-3 27 | self.loss = SoftDiceLossSquared(**{'apply_nonlin': softmax_helper, 'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}) 28 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_MCC.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | from nnunet.training.loss_functions.dice_loss import MCCLoss 18 | from nnunet.utilities.nd_softmax import softmax_helper 19 | 20 | 21 | class nnUNetTrainerV2_Loss_MCC(nnUNetTrainerV2): 22 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 23 | unpack_data=True, deterministic=True, fp16=False): 24 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 25 | deterministic, fp16) 26 | self.initial_lr = 1e-3 27 | self.loss = MCCLoss(apply_nonlin=softmax_helper, batch_mcc=self.batch_dice, do_bg=True, smooth=0.0) 28 | 29 | 30 | class nnUNetTrainerV2_Loss_MCCnoBG(nnUNetTrainerV2): 31 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 32 | unpack_data=True, deterministic=True, fp16=False): 33 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 34 | deterministic, fp16) 35 | self.initial_lr = 1e-3 36 | self.loss = MCCLoss(apply_nonlin=softmax_helper, batch_mcc=self.batch_dice, do_bg=False, smooth=0.0) 37 | 38 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_TopK10.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | from nnunet.training.loss_functions.TopK_loss import TopKLoss 18 | 19 | 20 | class nnUNetTrainerV2_Loss_TopK10(nnUNetTrainerV2): 21 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 22 | unpack_data=True, deterministic=True, fp16=False): 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | self.loss = TopKLoss(k=10) 26 | 27 | 28 | nnUNetTrainerV2_Loss_TopK10_copy1 = nnUNetTrainerV2_Loss_TopK10 29 | nnUNetTrainerV2_Loss_TopK10_copy2 = nnUNetTrainerV2_Loss_TopK10 30 | nnUNetTrainerV2_Loss_TopK10_copy3 = nnUNetTrainerV2_Loss_TopK10 31 | nnUNetTrainerV2_Loss_TopK10_copy4 = nnUNetTrainerV2_Loss_TopK10 32 | 33 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_graduallyTransitionFromCEToDice.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 | 16 | from nnunet.training.loss_functions.deep_supervision import MultipleOutputLoss2 17 | from nnunet.training.loss_functions.dice_loss import DC_and_CE_loss 18 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 19 | 20 | 21 | class nnUNetTrainerV2_graduallyTransitionFromCEToDice(nnUNetTrainerV2): 22 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 23 | unpack_data=True, deterministic=True, fp16=False): 24 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 25 | deterministic, fp16) 26 | self.loss = DC_and_CE_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, {}, weight_ce=2, weight_dice=0) 27 | 28 | def update_loss(self): 29 | # we train the first 500 epochs with CE, then transition to Dice between 500 and 750. The last 250 epochs will be Dice only 30 | 31 | if self.epoch <= 500: 32 | weight_ce = 2 33 | weight_dice = 0 34 | elif 500 < self.epoch <= 750: 35 | weight_ce = 2 - 2 / 250 * (self.epoch - 500) 36 | weight_dice = 0 + 2 / 250 * (self.epoch - 500) 37 | elif 750 < self.epoch <= self.max_num_epochs: 38 | weight_ce = 0 39 | weight_dice = 2 40 | else: 41 | raise RuntimeError("Invalid epoch: %d" % self.epoch) 42 | 43 | self.print_to_log_file("weight ce", weight_ce, "weight dice", weight_dice) 44 | 45 | self.loss = DC_and_CE_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, {}, weight_ce=weight_ce, 46 | weight_dice=weight_dice) 47 | 48 | self.loss = MultipleOutputLoss2(self.loss, self.ds_loss_weights) 49 | 50 | def on_epoch_end(self): 51 | ret = super().on_epoch_end() 52 | self.update_loss() 53 | return ret 54 | 55 | def load_checkpoint_ram(self, checkpoint, train=True): 56 | ret = super().load_checkpoint_ram(checkpoint, train) 57 | self.update_loss() 58 | return ret 59 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/miscellaneous/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/training/network_training/nnUNet_variants/miscellaneous/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/nnUNetTrainerCE.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 nnunet.training.loss_functions.crossentropy import RobustCrossEntropyLoss 15 | from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer 16 | 17 | 18 | class nnUNetTrainerCE(nnUNetTrainer): 19 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 20 | unpack_data=True, deterministic=True, fp16=False): 21 | super(nnUNetTrainerCE, self).__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, 22 | unpack_data, deterministic, fp16) 23 | self.loss = RobustCrossEntropyLoss() 24 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer/d927ab8cd961c91b4aee28abf007bd6ca7596225/nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/__init__.py -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_Adam.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 | 16 | import torch 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | 19 | 20 | class nnUNetTrainerV2_Adam(nnUNetTrainerV2): 21 | 22 | def initialize_optimizer_and_scheduler(self): 23 | self.optimizer = torch.optim.Adam(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, amsgrad=True) 24 | self.lr_scheduler = None 25 | 26 | 27 | nnUNetTrainerV2_Adam_copy1 = nnUNetTrainerV2_Adam 28 | nnUNetTrainerV2_Adam_copy2 = nnUNetTrainerV2_Adam 29 | nnUNetTrainerV2_Adam_copy3 = nnUNetTrainerV2_Adam 30 | nnUNetTrainerV2_Adam_copy4 = nnUNetTrainerV2_Adam 31 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_Adam_ReduceOnPlateau.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 | 16 | import torch 17 | from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer 18 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 19 | from torch.optim import lr_scheduler 20 | 21 | 22 | class nnUNetTrainerV2_Adam_ReduceOnPlateau(nnUNetTrainerV2): 23 | """ 24 | Same schedule as nnUNetTrainer 25 | """ 26 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 27 | unpack_data=True, deterministic=True, fp16=False): 28 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 29 | deterministic, fp16) 30 | self.initial_lr = 3e-4 31 | 32 | def initialize_optimizer_and_scheduler(self): 33 | assert self.network is not None, "self.initialize_network must be called first" 34 | self.optimizer = torch.optim.Adam(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 35 | amsgrad=True) 36 | self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, mode='min', factor=0.2, 37 | patience=self.lr_scheduler_patience, 38 | verbose=True, threshold=self.lr_scheduler_eps, 39 | threshold_mode="abs") 40 | 41 | def maybe_update_lr(self, epoch=None): 42 | # maybe update learning rate 43 | if self.lr_scheduler is not None: 44 | assert isinstance(self.lr_scheduler, (lr_scheduler.ReduceLROnPlateau, lr_scheduler._LRScheduler)) 45 | 46 | if isinstance(self.lr_scheduler, lr_scheduler.ReduceLROnPlateau): 47 | # lr scheduler is updated with moving average val loss. should be more robust 48 | if self.epoch > 0 and self.train_loss_MA is not None: # otherwise self.train_loss_MA is None 49 | self.lr_scheduler.step(self.train_loss_MA) 50 | else: 51 | self.lr_scheduler.step(self.epoch + 1) 52 | self.print_to_log_file("lr is now (scheduler) %s" % str(self.optimizer.param_groups[0]['lr'])) 53 | 54 | def on_epoch_end(self): 55 | return nnUNetTrainer.on_epoch_end(self) 56 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_Adam_lr_3en4.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 | 16 | from nnunet.training.network_training.nnUNet_variants.optimizer_and_lr.nnUNetTrainerV2_Adam import nnUNetTrainerV2_Adam 17 | 18 | 19 | class nnUNetTrainerV2_Adam_nnUNetTrainerlr(nnUNetTrainerV2_Adam): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, fp16=False): 22 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 23 | deterministic, fp16) 24 | self.initial_lr = 3e-4 25 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_Ranger_lr1en2.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | from nnunet.training.optimizer.ranger import Ranger 18 | 19 | 20 | class nnUNetTrainerV2_Ranger_lr1en2(nnUNetTrainerV2): 21 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 22 | unpack_data=True, deterministic=True, fp16=False): 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | self.initial_lr = 1e-2 26 | 27 | def initialize_optimizer_and_scheduler(self): 28 | self.optimizer = Ranger(self.network.parameters(), self.initial_lr, k=6, N_sma_threshhold=5, 29 | weight_decay=self.weight_decay) 30 | self.lr_scheduler = None 31 | 32 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_Ranger_lr3en3.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | from nnunet.training.optimizer.ranger import Ranger 18 | 19 | 20 | class nnUNetTrainerV2_Ranger_lr3en3(nnUNetTrainerV2): 21 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 22 | unpack_data=True, deterministic=True, fp16=False): 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | self.initial_lr = 3e-3 26 | 27 | def initialize_optimizer_and_scheduler(self): 28 | self.optimizer = Ranger(self.network.parameters(), self.initial_lr, k=6, N_sma_threshhold=5, 29 | weight_decay=self.weight_decay) 30 | self.lr_scheduler = None 31 | 32 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_Ranger_lr3en4.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | from nnunet.training.optimizer.ranger import Ranger 18 | 19 | 20 | class nnUNetTrainerV2_Ranger_lr3en4(nnUNetTrainerV2): 21 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 22 | unpack_data=True, deterministic=True, fp16=False): 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | self.initial_lr = 3e-4 26 | 27 | def initialize_optimizer_and_scheduler(self): 28 | self.optimizer = Ranger(self.network.parameters(), self.initial_lr, k=6, N_sma_threshhold=5, 29 | weight_decay=self.weight_decay) 30 | self.lr_scheduler = None 31 | 32 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_SGD_ReduceOnPlateau.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 | 16 | import torch 17 | from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer 18 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 19 | from torch.optim import lr_scheduler 20 | 21 | 22 | class nnUNetTrainerV2_SGD_ReduceOnPlateau(nnUNetTrainerV2): 23 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 24 | unpack_data=True, deterministic=True, fp16=False): 25 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 26 | deterministic, fp16) 27 | 28 | def initialize_optimizer_and_scheduler(self): 29 | self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 30 | momentum=0.99, nesterov=True) 31 | self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, mode='min', factor=0.2, 32 | patience=self.lr_scheduler_patience, 33 | verbose=True, threshold=self.lr_scheduler_eps, 34 | threshold_mode="abs") 35 | 36 | def maybe_update_lr(self, epoch=None): 37 | # maybe update learning rate 38 | if self.lr_scheduler is not None: 39 | assert isinstance(self.lr_scheduler, (lr_scheduler.ReduceLROnPlateau, lr_scheduler._LRScheduler)) 40 | 41 | if isinstance(self.lr_scheduler, lr_scheduler.ReduceLROnPlateau): 42 | # lr scheduler is updated with moving average val loss. should be more robust 43 | if self.epoch > 0: # otherwise self.train_loss_MA is None 44 | self.lr_scheduler.step(self.train_loss_MA) 45 | else: 46 | self.lr_scheduler.step(self.epoch + 1) 47 | self.print_to_log_file("lr is now (scheduler) %s" % str(self.optimizer.param_groups[0]['lr'])) 48 | 49 | def on_epoch_end(self): 50 | return nnUNetTrainer.on_epoch_end(self) 51 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_SGD_fixedSchedule.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_SGD_fixedSchedule(nnUNetTrainerV2): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, fp16=False): 22 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 23 | deterministic, fp16) 24 | 25 | def maybe_update_lr(self, epoch=None): 26 | if epoch is None: 27 | ep = self.epoch + 1 28 | else: 29 | ep = epoch 30 | 31 | if 0 <= ep < 500: 32 | new_lr = self.initial_lr 33 | elif 500 <= ep < 675: 34 | new_lr = self.initial_lr * 0.1 35 | elif 675 <= ep < 850: 36 | new_lr = self.initial_lr * 0.01 37 | elif ep >= 850: 38 | new_lr = self.initial_lr * 0.001 39 | else: 40 | raise RuntimeError("Really unexpected things happened, ep=%d" % ep) 41 | 42 | self.optimizer.param_groups[0]['lr'] = new_lr 43 | self.print_to_log_file("lr:", self.optimizer.param_groups[0]['lr']) 44 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_SGD_fixedSchedule2.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 | 16 | from nnunet.training.learning_rate.poly_lr import poly_lr 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | 19 | 20 | class nnUNetTrainerV2_SGD_fixedSchedule2(nnUNetTrainerV2): 21 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 22 | unpack_data=True, deterministic=True, fp16=False): 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | 26 | def maybe_update_lr(self, epoch=None): 27 | """ 28 | here we go one step, then use polyLR 29 | :param epoch: 30 | :return: 31 | """ 32 | if epoch is None: 33 | ep = self.epoch + 1 34 | else: 35 | ep = epoch 36 | 37 | if 0 <= ep < 500: 38 | new_lr = self.initial_lr 39 | elif 500 <= ep < 675: 40 | new_lr = self.initial_lr * 0.1 41 | elif ep >= 675: 42 | new_lr = poly_lr(ep - 675, self.max_num_epochs - 675, self.initial_lr * 0.1, 0.9) 43 | else: 44 | raise RuntimeError("Really unexpected things happened, ep=%d" % ep) 45 | 46 | self.optimizer.param_groups[0]['lr'] = new_lr 47 | self.print_to_log_file("lr:", self.optimizer.param_groups[0]['lr']) 48 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_SGD_lrs.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_SGD_lr1en1(nnUNetTrainerV2): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, fp16=False): 22 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 23 | deterministic, fp16) 24 | self.initial_lr = 1e-1 25 | 26 | 27 | class nnUNetTrainerV2_SGD_lr1en3(nnUNetTrainerV2): 28 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 29 | unpack_data=True, deterministic=True, fp16=False): 30 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 31 | deterministic, fp16) 32 | self.initial_lr = 1e-3 33 | 34 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_fp16.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_fp16(nnUNetTrainerV2): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, fp16=False): 22 | assert fp16, "This one only accepts fp16=True" 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_momentum09.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 | 16 | import torch 17 | 18 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 19 | 20 | 21 | class nnUNetTrainerV2_momentum09(nnUNetTrainerV2): 22 | def initialize_optimizer_and_scheduler(self): 23 | assert self.network is not None, "self.initialize_network must be called first" 24 | self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 25 | momentum=0.9, nesterov=True) 26 | self.lr_scheduler = None 27 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_momentum095.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 | 16 | import torch 17 | 18 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 19 | 20 | 21 | class nnUNetTrainerV2_momentum095(nnUNetTrainerV2): 22 | def initialize_optimizer_and_scheduler(self): 23 | assert self.network is not None, "self.initialize_network must be called first" 24 | self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 25 | momentum=0.95, nesterov=True) 26 | self.lr_scheduler = None 27 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_momentum098.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 | 16 | import torch 17 | 18 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 19 | 20 | 21 | class nnUNetTrainerV2_momentum098(nnUNetTrainerV2): 22 | def initialize_optimizer_and_scheduler(self): 23 | assert self.network is not None, "self.initialize_network must be called first" 24 | self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 25 | momentum=0.98, nesterov=True) 26 | self.lr_scheduler = None 27 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_momentum09in2D.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 | 16 | import torch 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | 19 | 20 | class nnUNetTrainerV2_momentum09in2D(nnUNetTrainerV2): 21 | def initialize_optimizer_and_scheduler(self): 22 | if self.threeD: 23 | momentum = 0.99 24 | else: 25 | momentum = 0.9 26 | assert self.network is not None, "self.initialize_network must be called first" 27 | self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 28 | momentum=momentum, nesterov=True) 29 | self.lr_scheduler = None 30 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_reduceMomentumDuringTraining.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 | 16 | import torch 17 | 18 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 19 | 20 | 21 | class nnUNetTrainerV2_reduceMomentumDuringTraining(nnUNetTrainerV2): 22 | """ 23 | This implementation will not work with LR scheduler!!!!!!!!!! 24 | 25 | After epoch 800, linearly decrease momentum from 0.99 to 0.9 26 | """ 27 | def initialize_optimizer_and_scheduler(self): 28 | current_momentum = 0.99 29 | min_momentum = 0.9 30 | 31 | if self.epoch > 800: 32 | current_momentum = current_momentum - (current_momentum - min_momentum) / 200 * (self.epoch - 800) 33 | 34 | self.print_to_log_file("current momentum", current_momentum) 35 | assert self.network is not None, "self.initialize_network must be called first" 36 | if self.optimizer is None: 37 | self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 38 | momentum=0.99, nesterov=True) 39 | else: 40 | # can't reinstantiate because that would break NVIDIA AMP 41 | self.optimizer.param_groups[0]["momentum"] = current_momentum 42 | self.lr_scheduler = None 43 | 44 | def on_epoch_end(self): 45 | self.initialize_optimizer_and_scheduler() 46 | return super().on_epoch_end() 47 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_warmup.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 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_warmup(nnUNetTrainerV2): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, fp16=False): 22 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 23 | deterministic, fp16) 24 | self.max_num_epochs = 1050 25 | 26 | def maybe_update_lr(self, epoch=None): 27 | if self.epoch < 50: 28 | # epoch 49 is max 29 | # we increase lr linearly from 0 to initial_lr 30 | lr = (self.epoch + 1) / 50 * self.initial_lr 31 | self.optimizer.param_groups[0]['lr'] = lr 32 | self.print_to_log_file("epoch:", self.epoch, "lr:", lr) 33 | else: 34 | if epoch is not None: 35 | ep = epoch - 49 36 | else: 37 | ep = self.epoch - 49 38 | assert ep > 0, "epoch must be >0" 39 | return super().maybe_update_lr(ep) 40 | -------------------------------------------------------------------------------- /nnunet_semi_sup/training/network_training/nnUNet_variants/resampling/__init__.py: 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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 | 16 | from batchgenerators.utilities.file_and_folder_operations import * 17 | 18 | 19 | def remove_trailing_slash(filename: str): 20 | while filename.endswith('/'): 21 | filename = filename[:-1] 22 | return filename 23 | 24 | 25 | def maybe_add_0000_to_all_niigz(folder): 26 | nii_gz = subfiles(folder, suffix='.nii.gz') 27 | for n in nii_gz: 28 | n = remove_trailing_slash(n) 29 | if not n.endswith('_0000.nii.gz'): 30 | os.rename(n, n[:-7] + '_0000.nii.gz') 31 | -------------------------------------------------------------------------------- /nnunet_semi_sup/utilities/folder_names.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 | 16 | from batchgenerators.utilities.file_and_folder_operations import * 17 | from nnunet.paths import network_training_output_dir 18 | 19 | 20 | def get_output_folder_name(model: str, task: str = None, trainer: str = None, plans: str = None, fold: int = None, 21 | overwrite_training_output_dir: str = None): 22 | """ 23 | Retrieves the correct output directory for the nnU-Net model described by the input parameters 24 | 25 | :param model: 26 | :param task: 27 | :param trainer: 28 | :param plans: 29 | :param fold: 30 | :param overwrite_training_output_dir: 31 | :return: 32 | """ 33 | assert model in ["2d", "3d_cascade_fullres", '3d_fullres', '3d_lowres'] 34 | 35 | if overwrite_training_output_dir is not None: 36 | tr_dir = overwrite_training_output_dir 37 | else: 38 | tr_dir = network_training_output_dir 39 | 40 | current = join(tr_dir, model) 41 | if task is not None: 42 | current = join(current, task) 43 | if trainer is not None and plans is not None: 44 | current = join(current, trainer + "__" + plans) 45 | if fold is not None: 46 | current = join(current, "fold_%d" % fold) 47 | return current 48 | -------------------------------------------------------------------------------- /nnunet_semi_sup/utilities/nd_softmax.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 torch 16 | from torch import nn 17 | import torch.nn.functional as F 18 | 19 | 20 | softmax_helper = lambda x: F.softmax(x, 1) 21 | 22 | -------------------------------------------------------------------------------- /nnunet_semi_sup/utilities/one_hot_encoding.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 numpy as np 16 | 17 | 18 | def to_one_hot(seg, all_seg_labels=None): 19 | if all_seg_labels is None: 20 | all_seg_labels = np.unique(seg) 21 | result = np.zeros((len(all_seg_labels), *seg.shape), dtype=seg.dtype) 22 | for i, l in enumerate(all_seg_labels): 23 | result[i][seg == l] = 1 24 | return result 25 | -------------------------------------------------------------------------------- /nnunet_semi_sup/utilities/random_stuff.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 | 16 | class no_op(object): 17 | def __enter__(self): 18 | pass 19 | 20 | def __exit__(self, *args): 21 | pass 22 | -------------------------------------------------------------------------------- /nnunet_semi_sup/utilities/recursive_delete_npz.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 | 16 | from batchgenerators.utilities.file_and_folder_operations import * 17 | import argparse 18 | import os 19 | 20 | 21 | def recursive_delete_npz(current_directory: str): 22 | npz_files = subfiles(current_directory, join=True, suffix=".npz") 23 | npz_files = [i for i in npz_files if not i.endswith("segFromPrevStage.npz")] # to be extra safe 24 | _ = [os.remove(i) for i in npz_files] 25 | for d in subdirs(current_directory, join=False): 26 | if d != "pred_next_stage": 27 | recursive_delete_npz(join(current_directory, d)) 28 | 29 | 30 | if __name__ == "__main__": 31 | parser = argparse.ArgumentParser(usage="USE THIS RESPONSIBLY! DANGEROUS! I (Fabian) use this to remove npz files " 32 | "after I ran figure_out_what_to_submit") 33 | parser.add_argument("-f", help="folder", required=True) 34 | 35 | args = parser.parse_args() 36 | 37 | recursive_delete_npz(args.f) 38 | -------------------------------------------------------------------------------- /nnunet_semi_sup/utilities/recursive_rename_taskXX_to_taskXXX.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 | 16 | from batchgenerators.utilities.file_and_folder_operations import * 17 | import os 18 | 19 | 20 | def recursive_rename(folder): 21 | s = subdirs(folder, join=False) 22 | for ss in s: 23 | if ss.startswith("Task") and ss.find("_") == 6: 24 | task_id = int(ss[4:6]) 25 | name = ss[7:] 26 | os.rename(join(folder, ss), join(folder, "Task%03.0d_" % task_id + name)) 27 | s = subdirs(folder, join=True) 28 | for ss in s: 29 | recursive_rename(ss) 30 | 31 | if __name__ == "__main__": 32 | recursive_rename("/media/fabian/Results/nnUNet") 33 | recursive_rename("/media/fabian/nnunet") 34 | recursive_rename("/media/fabian/My Book/MedicalDecathlon") 35 | recursive_rename("/home/fabian/drives/datasets/nnUNet_raw") 36 | recursive_rename("/home/fabian/drives/datasets/nnUNet_preprocessed") 37 | recursive_rename("/home/fabian/drives/datasets/nnUNet_testSets") 38 | recursive_rename("/home/fabian/drives/datasets/results/nnUNet") 39 | recursive_rename("/home/fabian/drives/e230-dgx2-1-data_fabian/Decathlon_raw") 40 | recursive_rename("/home/fabian/drives/e230-dgx2-1-data_fabian/nnUNet_preprocessed") 41 | 42 | -------------------------------------------------------------------------------- /nnunet_semi_sup/utilities/sitk_stuff.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 | 16 | import SimpleITK as sitk 17 | 18 | 19 | def copy_geometry(image: sitk.Image, ref: sitk.Image): 20 | image.SetOrigin(ref.GetOrigin()) 21 | image.SetDirection(ref.GetDirection()) 22 | image.SetSpacing(ref.GetSpacing()) 23 | return image 24 | -------------------------------------------------------------------------------- /nnunet_semi_sup/utilities/tensor_utilities.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 numpy as np 16 | import torch 17 | from torch import nn 18 | 19 | 20 | def sum_tensor(inp, axes, keepdim=False): 21 | axes = np.unique(axes).astype(int) 22 | if keepdim: 23 | for ax in axes: 24 | inp = inp.sum(int(ax), keepdim=True) 25 | else: 26 | for ax in sorted(axes, reverse=True): 27 | inp = inp.sum(int(ax)) 28 | return inp 29 | 30 | 31 | def mean_tensor(inp, axes, keepdim=False): 32 | axes = np.unique(axes).astype(int) 33 | if keepdim: 34 | for ax in axes: 35 | inp = inp.mean(int(ax), keepdim=True) 36 | else: 37 | for ax in sorted(axes, reverse=True): 38 | inp = inp.mean(int(ax)) 39 | return inp 40 | 41 | 42 | def flip(x, dim): 43 | """ 44 | flips the tensor at dimension dim (mirroring!) 45 | :param x: 46 | :param dim: 47 | :return: 48 | """ 49 | indices = [slice(None)] * x.dim() 50 | indices[dim] = torch.arange(x.size(dim) - 1, -1, -1, 51 | dtype=torch.long, device=x.device) 52 | return x[tuple(indices)] 53 | 54 | 55 | -------------------------------------------------------------------------------- /nnunet_semi_sup/utilities/to_torch.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 torch 16 | 17 | 18 | def maybe_to_torch(d): 19 | if isinstance(d, list): 20 | d = [maybe_to_torch(i) if not isinstance(i, torch.Tensor) else i for i in d] 21 | elif not isinstance(d, torch.Tensor): 22 | d = torch.from_numpy(d).float() 23 | return d 24 | 25 | 26 | def to_cuda(data, non_blocking=True, gpu_id=0): 27 | if isinstance(data, list): 28 | data = [i.cuda(gpu_id, non_blocking=non_blocking) for i in data] 29 | else: 30 | data = data.cuda(gpu_id, non_blocking=non_blocking) 31 | return data 32 | --------------------------------------------------------------------------------