├── .gitignore ├── 1-FullySupervisedLearning ├── Dockerfile ├── FullySupervised-GrandChallenge-Logo.PNG ├── Multi-organ.gif ├── README.md ├── TumorDemo.gif └── nnUNet │ ├── HIP_Logo.png │ ├── LICENSE │ ├── documentation │ ├── common_problems_and_solutions.md │ ├── common_questions.md │ ├── data_format_inference.md │ ├── dataset_conversion.md │ ├── expected_epoch_times.md │ ├── extending_nnunet.md │ ├── inference_example_Prostate.md │ ├── setting_up_paths.md │ ├── training_example_Hippocampus.md │ ├── tutorials │ │ ├── custom_preprocessing.md │ │ ├── custom_spacing.md │ │ └── edit_plans_files.md │ └── using_nnUNet_as_baseline.md │ ├── nnunet │ ├── __init__.py │ ├── configuration.py │ ├── dataset_conversion │ │ ├── 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 │ │ ├── __init__.py │ │ └── utils.py │ ├── evaluation │ │ ├── __init__.py │ │ ├── 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 │ │ ├── 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 │ │ ├── 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 │ │ ├── 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 │ │ ├── connected_components.py │ │ ├── consolidate_all_for_paper.py │ │ ├── consolidate_postprocessing.py │ │ └── consolidate_postprocessing_simple.py │ ├── preprocessing │ │ ├── __init__.py │ │ ├── cropping.py │ │ ├── custom_preprocessors │ │ │ ├── __init__.py │ │ │ └── preprocessor_scale_RGB_to_0_1.py │ │ ├── preprocessing.py │ │ └── sanity_checks.py │ ├── run │ │ ├── __init__.py │ │ ├── default_configuration.py │ │ ├── load_pretrained_weights.py │ │ ├── run_training.py │ │ ├── run_training_DDP.py │ │ └── run_training_DP.py │ ├── training │ │ ├── __init__.py │ │ ├── cascade_stuff │ │ │ ├── __init__.py │ │ │ └── predict_next_stage.py │ │ ├── data_augmentation │ │ │ ├── __init__.py │ │ │ ├── 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 │ │ │ └── dataset_loading.py │ │ ├── learning_rate │ │ │ ├── __init__.py │ │ │ └── poly_lr.py │ │ ├── loss_functions │ │ │ ├── TopK_loss.py │ │ │ ├── __init__.py │ │ │ ├── crossentropy.py │ │ │ ├── deep_supervision.py │ │ │ └── dice_loss.py │ │ ├── model_restore.py │ │ ├── network_training │ │ │ ├── __init__.py │ │ │ ├── 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 │ │ │ ├── nnUNetTrainerV2_CascadeFullRes.py │ │ │ ├── nnUNetTrainerV2_DDP.py │ │ │ ├── nnUNetTrainerV2_DP.py │ │ │ ├── nnUNetTrainerV2_Loss_DiceTopK10_moreDA.py │ │ │ ├── nnUNetTrainerV2_MMS.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 │ │ ├── __init__.py │ │ ├── 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 │ ├── readme.md │ ├── setup.cfg │ ├── setup.py │ └── tests │ └── test_steps_for_sliding_window_prediction.py ├── 2-Semi-supervisedLearning ├── Dockerfile ├── README.md ├── SemiSupervised-GrandChallenge-Logo.png └── nnUNet │ ├── LICENSE │ ├── nnunet │ ├── __init__.py │ ├── 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 │ ├── setup.cfg │ └── setup.py ├── 3-WeaklySupervisedLearning ├── Dockerfile ├── README.md ├── SimpleCRF-master │ ├── LICENSE │ ├── MANIFEST.in │ ├── README.md │ ├── densecrf_python │ │ ├── densecrf.cpp │ │ ├── densecrf3d.cpp │ │ ├── densecrf3d_util.cpp │ │ ├── densecrf3d_util.h │ │ ├── densecrf_core.cpp │ │ ├── densecrf_core.h │ │ ├── wrap2D_py2.cpp │ │ ├── wrap2D_py3.cpp │ │ ├── wrap3D_py2.cpp │ │ └── wrap3D_py3.cpp │ ├── dependency │ │ ├── densecrf │ │ │ ├── CMakeLists.txt │ │ │ ├── README.txt │ │ │ ├── external │ │ │ │ ├── CMakeLists.txt │ │ │ │ └── liblbfgs │ │ │ │ │ ├── AUTHORS │ │ │ │ │ ├── COPYING │ │ │ │ │ ├── ChangeLog │ │ │ │ │ ├── INSTALL │ │ │ │ │ ├── Makefile.am │ │ │ │ │ ├── Makefile.in │ │ │ │ │ ├── NEWS │ │ │ │ │ ├── README │ │ │ │ │ ├── aclocal.m4 │ │ │ │ │ ├── autogen.sh │ │ │ │ │ ├── config.guess │ │ │ │ │ ├── config.h.in │ │ │ │ │ ├── config.sub │ │ │ │ │ ├── configure │ │ │ │ │ ├── configure.in │ │ │ │ │ ├── depcomp │ │ │ │ │ ├── include │ │ │ │ │ └── lbfgs.h │ │ │ │ │ ├── install-sh │ │ │ │ │ ├── lbfgs.sln │ │ │ │ │ ├── lib │ │ │ │ │ ├── Makefile.am │ │ │ │ │ ├── Makefile.in │ │ │ │ │ ├── arithmetic_ansi.h │ │ │ │ │ ├── arithmetic_sse_double.h │ │ │ │ │ ├── arithmetic_sse_float.h │ │ │ │ │ ├── lbfgs.c │ │ │ │ │ └── lib.vcxproj │ │ │ │ │ ├── ltmain.sh │ │ │ │ │ ├── missing │ │ │ │ │ └── sample │ │ │ │ │ ├── Makefile.am │ │ │ │ │ ├── Makefile.in │ │ │ │ │ ├── sample.c │ │ │ │ │ ├── sample.cpp │ │ │ │ │ └── sample.vcxproj │ │ │ ├── include │ │ │ │ ├── Eigen │ │ │ │ │ ├── Array │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ ├── Cholesky │ │ │ │ │ ├── CholmodSupport │ │ │ │ │ ├── Core │ │ │ │ │ ├── Dense │ │ │ │ │ ├── Eigen │ │ │ │ │ ├── Eigen2Support │ │ │ │ │ ├── Eigenvalues │ │ │ │ │ ├── Geometry │ │ │ │ │ ├── Householder │ │ │ │ │ ├── IterativeLinearSolvers │ │ │ │ │ ├── Jacobi │ │ │ │ │ ├── LU │ │ │ │ │ ├── LeastSquares │ │ │ │ │ ├── OrderingMethods │ │ │ │ │ ├── PaStiXSupport │ │ │ │ │ ├── PardisoSupport │ │ │ │ │ ├── QR │ │ │ │ │ ├── QtAlignedMalloc │ │ │ │ │ ├── SVD │ │ │ │ │ ├── Sparse │ │ │ │ │ ├── SparseCholesky │ │ │ │ │ ├── SparseCore │ │ │ │ │ ├── StdDeque │ │ │ │ │ ├── StdList │ │ │ │ │ ├── StdVector │ │ │ │ │ ├── SuperLUSupport │ │ │ │ │ ├── UmfPackSupport │ │ │ │ │ └── src │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── Cholesky │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── LDLT.h │ │ │ │ │ │ ├── LLT.h │ │ │ │ │ │ └── LLT_MKL.h │ │ │ │ │ │ ├── CholmodSupport │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── CholmodSupport.h │ │ │ │ │ │ ├── Core │ │ │ │ │ │ ├── Array.h │ │ │ │ │ │ ├── ArrayBase.h │ │ │ │ │ │ ├── ArrayWrapper.h │ │ │ │ │ │ ├── Assign.h │ │ │ │ │ │ ├── Assign_MKL.h │ │ │ │ │ │ ├── BandMatrix.h │ │ │ │ │ │ ├── Block.h │ │ │ │ │ │ ├── BooleanRedux.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── CommaInitializer.h │ │ │ │ │ │ ├── CwiseBinaryOp.h │ │ │ │ │ │ ├── CwiseNullaryOp.h │ │ │ │ │ │ ├── CwiseUnaryOp.h │ │ │ │ │ │ ├── CwiseUnaryView.h │ │ │ │ │ │ ├── DenseBase.h │ │ │ │ │ │ ├── DenseCoeffsBase.h │ │ │ │ │ │ ├── DenseStorage.h │ │ │ │ │ │ ├── Diagonal.h │ │ │ │ │ │ ├── DiagonalMatrix.h │ │ │ │ │ │ ├── DiagonalProduct.h │ │ │ │ │ │ ├── Dot.h │ │ │ │ │ │ ├── EigenBase.h │ │ │ │ │ │ ├── Flagged.h │ │ │ │ │ │ ├── ForceAlignedAccess.h │ │ │ │ │ │ ├── Functors.h │ │ │ │ │ │ ├── Fuzzy.h │ │ │ │ │ │ ├── GeneralProduct.h │ │ │ │ │ │ ├── GenericPacketMath.h │ │ │ │ │ │ ├── GlobalFunctions.h │ │ │ │ │ │ ├── IO.h │ │ │ │ │ │ ├── Map.h │ │ │ │ │ │ ├── MapBase.h │ │ │ │ │ │ ├── MathFunctions.h │ │ │ │ │ │ ├── Matrix.h │ │ │ │ │ │ ├── MatrixBase.h │ │ │ │ │ │ ├── NestByValue.h │ │ │ │ │ │ ├── NoAlias.h │ │ │ │ │ │ ├── NumTraits.h │ │ │ │ │ │ ├── PermutationMatrix.h │ │ │ │ │ │ ├── PlainObjectBase.h │ │ │ │ │ │ ├── Product.h │ │ │ │ │ │ ├── ProductBase.h │ │ │ │ │ │ ├── Random.h │ │ │ │ │ │ ├── Redux.h │ │ │ │ │ │ ├── Replicate.h │ │ │ │ │ │ ├── ReturnByValue.h │ │ │ │ │ │ ├── Reverse.h │ │ │ │ │ │ ├── Select.h │ │ │ │ │ │ ├── SelfAdjointView.h │ │ │ │ │ │ ├── SelfCwiseBinaryOp.h │ │ │ │ │ │ ├── SolveTriangular.h │ │ │ │ │ │ ├── StableNorm.h │ │ │ │ │ │ ├── Stride.h │ │ │ │ │ │ ├── Swap.h │ │ │ │ │ │ ├── Transpose.h │ │ │ │ │ │ ├── Transpositions.h │ │ │ │ │ │ ├── TriangularMatrix.h │ │ │ │ │ │ ├── VectorBlock.h │ │ │ │ │ │ ├── VectorwiseOp.h │ │ │ │ │ │ ├── Visitor.h │ │ │ │ │ │ ├── arch │ │ │ │ │ │ │ ├── AltiVec │ │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ │ ├── Complex.h │ │ │ │ │ │ │ │ └── PacketMath.h │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ ├── Default │ │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ │ └── Settings.h │ │ │ │ │ │ │ ├── NEON │ │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ │ ├── Complex.h │ │ │ │ │ │ │ │ └── PacketMath.h │ │ │ │ │ │ │ └── SSE │ │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ │ ├── Complex.h │ │ │ │ │ │ │ │ ├── MathFunctions.h │ │ │ │ │ │ │ │ └── PacketMath.h │ │ │ │ │ │ ├── products │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ ├── CoeffBasedProduct.h │ │ │ │ │ │ │ ├── GeneralBlockPanelKernel.h │ │ │ │ │ │ │ ├── GeneralMatrixMatrix.h │ │ │ │ │ │ │ ├── GeneralMatrixMatrixTriangular.h │ │ │ │ │ │ │ ├── GeneralMatrixMatrixTriangular_MKL.h │ │ │ │ │ │ │ ├── GeneralMatrixMatrix_MKL.h │ │ │ │ │ │ │ ├── GeneralMatrixVector.h │ │ │ │ │ │ │ ├── GeneralMatrixVector_MKL.h │ │ │ │ │ │ │ ├── Parallelizer.h │ │ │ │ │ │ │ ├── SelfadjointMatrixMatrix.h │ │ │ │ │ │ │ ├── SelfadjointMatrixMatrix_MKL.h │ │ │ │ │ │ │ ├── SelfadjointMatrixVector.h │ │ │ │ │ │ │ ├── SelfadjointMatrixVector_MKL.h │ │ │ │ │ │ │ ├── SelfadjointProduct.h │ │ │ │ │ │ │ ├── SelfadjointRank2Update.h │ │ │ │ │ │ │ ├── TriangularMatrixMatrix.h │ │ │ │ │ │ │ ├── TriangularMatrixMatrix_MKL.h │ │ │ │ │ │ │ ├── TriangularMatrixVector.h │ │ │ │ │ │ │ ├── TriangularMatrixVector_MKL.h │ │ │ │ │ │ │ ├── TriangularSolverMatrix.h │ │ │ │ │ │ │ ├── TriangularSolverMatrix_MKL.h │ │ │ │ │ │ │ └── TriangularSolverVector.h │ │ │ │ │ │ └── util │ │ │ │ │ │ │ ├── BlasUtil.h │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ ├── Constants.h │ │ │ │ │ │ │ ├── DisableStupidWarnings.h │ │ │ │ │ │ │ ├── ForwardDeclarations.h │ │ │ │ │ │ │ ├── MKL_support.h │ │ │ │ │ │ │ ├── Macros.h │ │ │ │ │ │ │ ├── Memory.h │ │ │ │ │ │ │ ├── Meta.h │ │ │ │ │ │ │ ├── NonMPL2.h │ │ │ │ │ │ │ ├── ReenableStupidWarnings.h │ │ │ │ │ │ │ ├── StaticAssert.h │ │ │ │ │ │ │ └── XprHelper.h │ │ │ │ │ │ ├── Eigen2Support │ │ │ │ │ │ ├── Block.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── Cwise.h │ │ │ │ │ │ ├── CwiseOperators.h │ │ │ │ │ │ ├── Geometry │ │ │ │ │ │ │ ├── AlignedBox.h │ │ │ │ │ │ │ ├── All.h │ │ │ │ │ │ │ ├── AngleAxis.h │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ ├── Hyperplane.h │ │ │ │ │ │ │ ├── ParametrizedLine.h │ │ │ │ │ │ │ ├── Quaternion.h │ │ │ │ │ │ │ ├── Rotation2D.h │ │ │ │ │ │ │ ├── RotationBase.h │ │ │ │ │ │ │ ├── Scaling.h │ │ │ │ │ │ │ ├── Transform.h │ │ │ │ │ │ │ └── Translation.h │ │ │ │ │ │ ├── LU.h │ │ │ │ │ │ ├── Lazy.h │ │ │ │ │ │ ├── LeastSquares.h │ │ │ │ │ │ ├── Macros.h │ │ │ │ │ │ ├── MathFunctions.h │ │ │ │ │ │ ├── Memory.h │ │ │ │ │ │ ├── Meta.h │ │ │ │ │ │ ├── Minor.h │ │ │ │ │ │ ├── QR.h │ │ │ │ │ │ ├── SVD.h │ │ │ │ │ │ ├── TriangularSolver.h │ │ │ │ │ │ └── VectorBlock.h │ │ │ │ │ │ ├── Eigenvalues │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── ComplexEigenSolver.h │ │ │ │ │ │ ├── ComplexSchur.h │ │ │ │ │ │ ├── ComplexSchur_MKL.h │ │ │ │ │ │ ├── EigenSolver.h │ │ │ │ │ │ ├── GeneralizedSelfAdjointEigenSolver.h │ │ │ │ │ │ ├── HessenbergDecomposition.h │ │ │ │ │ │ ├── MatrixBaseEigenvalues.h │ │ │ │ │ │ ├── RealSchur.h │ │ │ │ │ │ ├── RealSchur_MKL.h │ │ │ │ │ │ ├── SelfAdjointEigenSolver.h │ │ │ │ │ │ ├── SelfAdjointEigenSolver_MKL.h │ │ │ │ │ │ └── Tridiagonalization.h │ │ │ │ │ │ ├── Geometry │ │ │ │ │ │ ├── AlignedBox.h │ │ │ │ │ │ ├── AngleAxis.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── EulerAngles.h │ │ │ │ │ │ ├── Homogeneous.h │ │ │ │ │ │ ├── Hyperplane.h │ │ │ │ │ │ ├── OrthoMethods.h │ │ │ │ │ │ ├── ParametrizedLine.h │ │ │ │ │ │ ├── Quaternion.h │ │ │ │ │ │ ├── Rotation2D.h │ │ │ │ │ │ ├── RotationBase.h │ │ │ │ │ │ ├── Scaling.h │ │ │ │ │ │ ├── Transform.h │ │ │ │ │ │ ├── Translation.h │ │ │ │ │ │ ├── Umeyama.h │ │ │ │ │ │ └── arch │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ └── Geometry_SSE.h │ │ │ │ │ │ ├── Householder │ │ │ │ │ │ ├── BlockHouseholder.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── Householder.h │ │ │ │ │ │ └── HouseholderSequence.h │ │ │ │ │ │ ├── IterativeLinearSolvers │ │ │ │ │ │ ├── BasicPreconditioners.h │ │ │ │ │ │ ├── BiCGSTAB.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── ConjugateGradient.h │ │ │ │ │ │ ├── IncompleteLUT.h │ │ │ │ │ │ └── IterativeSolverBase.h │ │ │ │ │ │ ├── Jacobi │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── Jacobi.h │ │ │ │ │ │ ├── LU │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── Determinant.h │ │ │ │ │ │ ├── FullPivLU.h │ │ │ │ │ │ ├── Inverse.h │ │ │ │ │ │ ├── PartialPivLU.h │ │ │ │ │ │ ├── PartialPivLU_MKL.h │ │ │ │ │ │ └── arch │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ └── Inverse_SSE.h │ │ │ │ │ │ ├── OrderingMethods │ │ │ │ │ │ ├── Amd.h │ │ │ │ │ │ └── CMakeLists.txt │ │ │ │ │ │ ├── PaStiXSupport │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── PaStiXSupport.h │ │ │ │ │ │ ├── PardisoSupport │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── PardisoSupport.h │ │ │ │ │ │ ├── QR │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── ColPivHouseholderQR.h │ │ │ │ │ │ ├── ColPivHouseholderQR_MKL.h │ │ │ │ │ │ ├── FullPivHouseholderQR.h │ │ │ │ │ │ ├── HouseholderQR.h │ │ │ │ │ │ └── HouseholderQR_MKL.h │ │ │ │ │ │ ├── SVD │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── JacobiSVD.h │ │ │ │ │ │ ├── JacobiSVD_MKL.h │ │ │ │ │ │ └── UpperBidiagonalization.h │ │ │ │ │ │ ├── SparseCholesky │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── SimplicialCholesky.h │ │ │ │ │ │ ├── SparseCore │ │ │ │ │ │ ├── AmbiVector.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── CompressedStorage.h │ │ │ │ │ │ ├── ConservativeSparseSparseProduct.h │ │ │ │ │ │ ├── CoreIterators.h │ │ │ │ │ │ ├── MappedSparseMatrix.h │ │ │ │ │ │ ├── SparseAssign.h │ │ │ │ │ │ ├── SparseBlock.h │ │ │ │ │ │ ├── SparseCwiseBinaryOp.h │ │ │ │ │ │ ├── SparseCwiseUnaryOp.h │ │ │ │ │ │ ├── SparseDenseProduct.h │ │ │ │ │ │ ├── SparseDiagonalProduct.h │ │ │ │ │ │ ├── SparseDot.h │ │ │ │ │ │ ├── SparseFuzzy.h │ │ │ │ │ │ ├── SparseMatrix.h │ │ │ │ │ │ ├── SparseMatrixBase.h │ │ │ │ │ │ ├── SparsePermutation.h │ │ │ │ │ │ ├── SparseProduct.h │ │ │ │ │ │ ├── SparseRedux.h │ │ │ │ │ │ ├── SparseSelfAdjointView.h │ │ │ │ │ │ ├── SparseSparseProductWithPruning.h │ │ │ │ │ │ ├── SparseTranspose.h │ │ │ │ │ │ ├── SparseTriangularView.h │ │ │ │ │ │ ├── SparseUtil.h │ │ │ │ │ │ ├── SparseVector.h │ │ │ │ │ │ ├── SparseView.h │ │ │ │ │ │ └── TriangularSolver.h │ │ │ │ │ │ ├── StlSupport │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── StdDeque.h │ │ │ │ │ │ ├── StdList.h │ │ │ │ │ │ ├── StdVector.h │ │ │ │ │ │ └── details.h │ │ │ │ │ │ ├── SuperLUSupport │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── SuperLUSupport.h │ │ │ │ │ │ ├── UmfPackSupport │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── UmfPackSupport.h │ │ │ │ │ │ ├── misc │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── Image.h │ │ │ │ │ │ ├── Kernel.h │ │ │ │ │ │ ├── Solve.h │ │ │ │ │ │ ├── SparseSolve.h │ │ │ │ │ │ └── blas.h │ │ │ │ │ │ └── plugins │ │ │ │ │ │ ├── ArrayCwiseBinaryOps.h │ │ │ │ │ │ ├── ArrayCwiseUnaryOps.h │ │ │ │ │ │ ├── BlockMethods.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── CommonCwiseBinaryOps.h │ │ │ │ │ │ ├── CommonCwiseUnaryOps.h │ │ │ │ │ │ ├── MatrixCwiseBinaryOps.h │ │ │ │ │ │ └── MatrixCwiseUnaryOps.h │ │ │ │ ├── densecrf.h │ │ │ │ ├── labelcompatibility.h │ │ │ │ ├── objective.h │ │ │ │ ├── optimization.h │ │ │ │ ├── pairwise.h │ │ │ │ ├── permutohedral.h │ │ │ │ └── unary.h │ │ │ └── src │ │ │ │ ├── CMakeLists.txt │ │ │ │ ├── densecrf.cpp │ │ │ │ ├── labelcompatibility.cpp │ │ │ │ ├── objective.cpp │ │ │ │ ├── optimization.cpp │ │ │ │ ├── pairwise.cpp │ │ │ │ ├── permutohedral.cpp │ │ │ │ ├── unary.cpp │ │ │ │ ├── util.cpp │ │ │ │ └── util.h │ │ ├── densecrf3d │ │ │ ├── CMakeLists.txt │ │ │ ├── COPYRIGHT.md │ │ │ ├── README.txt │ │ │ ├── external │ │ │ │ ├── CMakeLists.txt │ │ │ │ └── liblbfgs │ │ │ │ │ ├── AUTHORS │ │ │ │ │ ├── COPYING │ │ │ │ │ ├── ChangeLog │ │ │ │ │ ├── INSTALL │ │ │ │ │ ├── Makefile.am │ │ │ │ │ ├── Makefile.in │ │ │ │ │ ├── NEWS │ │ │ │ │ ├── README │ │ │ │ │ ├── aclocal.m4 │ │ │ │ │ ├── autogen.sh │ │ │ │ │ ├── config.guess │ │ │ │ │ ├── config.h.in │ │ │ │ │ ├── config.sub │ │ │ │ │ ├── configure │ │ │ │ │ ├── configure.in │ │ │ │ │ ├── depcomp │ │ │ │ │ ├── include │ │ │ │ │ └── lbfgs.h │ │ │ │ │ ├── install-sh │ │ │ │ │ ├── lbfgs.sln │ │ │ │ │ ├── lib │ │ │ │ │ ├── Makefile.am │ │ │ │ │ ├── Makefile.in │ │ │ │ │ ├── arithmetic_ansi.h │ │ │ │ │ ├── arithmetic_sse_double.h │ │ │ │ │ ├── arithmetic_sse_float.h │ │ │ │ │ ├── lbfgs.c │ │ │ │ │ └── lib.vcxproj │ │ │ │ │ ├── ltmain.sh │ │ │ │ │ ├── missing │ │ │ │ │ └── sample │ │ │ │ │ ├── Makefile.am │ │ │ │ │ ├── Makefile.in │ │ │ │ │ ├── sample.c │ │ │ │ │ ├── sample.cpp │ │ │ │ │ └── sample.vcxproj │ │ │ ├── include │ │ │ │ ├── Eigen │ │ │ │ │ ├── Array │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ ├── Cholesky │ │ │ │ │ ├── CholmodSupport │ │ │ │ │ ├── Core │ │ │ │ │ ├── Dense │ │ │ │ │ ├── Eigen │ │ │ │ │ ├── Eigen2Support │ │ │ │ │ ├── Eigenvalues │ │ │ │ │ ├── Geometry │ │ │ │ │ ├── Householder │ │ │ │ │ ├── IterativeLinearSolvers │ │ │ │ │ ├── Jacobi │ │ │ │ │ ├── LU │ │ │ │ │ ├── LeastSquares │ │ │ │ │ ├── OrderingMethods │ │ │ │ │ ├── PaStiXSupport │ │ │ │ │ ├── PardisoSupport │ │ │ │ │ ├── QR │ │ │ │ │ ├── QtAlignedMalloc │ │ │ │ │ ├── SVD │ │ │ │ │ ├── Sparse │ │ │ │ │ ├── SparseCholesky │ │ │ │ │ ├── SparseCore │ │ │ │ │ ├── StdDeque │ │ │ │ │ ├── StdList │ │ │ │ │ ├── StdVector │ │ │ │ │ ├── SuperLUSupport │ │ │ │ │ ├── UmfPackSupport │ │ │ │ │ └── src │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── Cholesky │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── LDLT.h │ │ │ │ │ │ ├── LLT.h │ │ │ │ │ │ └── LLT_MKL.h │ │ │ │ │ │ ├── CholmodSupport │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── CholmodSupport.h │ │ │ │ │ │ ├── Core │ │ │ │ │ │ ├── Array.h │ │ │ │ │ │ ├── ArrayBase.h │ │ │ │ │ │ ├── ArrayWrapper.h │ │ │ │ │ │ ├── Assign.h │ │ │ │ │ │ ├── Assign_MKL.h │ │ │ │ │ │ ├── BandMatrix.h │ │ │ │ │ │ ├── Block.h │ │ │ │ │ │ ├── BooleanRedux.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── CommaInitializer.h │ │ │ │ │ │ ├── CwiseBinaryOp.h │ │ │ │ │ │ ├── CwiseNullaryOp.h │ │ │ │ │ │ ├── CwiseUnaryOp.h │ │ │ │ │ │ ├── CwiseUnaryView.h │ │ │ │ │ │ ├── DenseBase.h │ │ │ │ │ │ ├── DenseCoeffsBase.h │ │ │ │ │ │ ├── DenseStorage.h │ │ │ │ │ │ ├── Diagonal.h │ │ │ │ │ │ ├── DiagonalMatrix.h │ │ │ │ │ │ ├── DiagonalProduct.h │ │ │ │ │ │ ├── Dot.h │ │ │ │ │ │ ├── EigenBase.h │ │ │ │ │ │ ├── Flagged.h │ │ │ │ │ │ ├── ForceAlignedAccess.h │ │ │ │ │ │ ├── Functors.h │ │ │ │ │ │ ├── Fuzzy.h │ │ │ │ │ │ ├── GeneralProduct.h │ │ │ │ │ │ ├── GenericPacketMath.h │ │ │ │ │ │ ├── GlobalFunctions.h │ │ │ │ │ │ ├── IO.h │ │ │ │ │ │ ├── Map.h │ │ │ │ │ │ ├── MapBase.h │ │ │ │ │ │ ├── MathFunctions.h │ │ │ │ │ │ ├── Matrix.h │ │ │ │ │ │ ├── MatrixBase.h │ │ │ │ │ │ ├── NestByValue.h │ │ │ │ │ │ ├── NoAlias.h │ │ │ │ │ │ ├── NumTraits.h │ │ │ │ │ │ ├── PermutationMatrix.h │ │ │ │ │ │ ├── PlainObjectBase.h │ │ │ │ │ │ ├── Product.h │ │ │ │ │ │ ├── ProductBase.h │ │ │ │ │ │ ├── Random.h │ │ │ │ │ │ ├── Redux.h │ │ │ │ │ │ ├── Replicate.h │ │ │ │ │ │ ├── ReturnByValue.h │ │ │ │ │ │ ├── Reverse.h │ │ │ │ │ │ ├── Select.h │ │ │ │ │ │ ├── SelfAdjointView.h │ │ │ │ │ │ ├── SelfCwiseBinaryOp.h │ │ │ │ │ │ ├── SolveTriangular.h │ │ │ │ │ │ ├── StableNorm.h │ │ │ │ │ │ ├── Stride.h │ │ │ │ │ │ ├── Swap.h │ │ │ │ │ │ ├── Transpose.h │ │ │ │ │ │ ├── Transpositions.h │ │ │ │ │ │ ├── TriangularMatrix.h │ │ │ │ │ │ ├── VectorBlock.h │ │ │ │ │ │ ├── VectorwiseOp.h │ │ │ │ │ │ ├── Visitor.h │ │ │ │ │ │ ├── arch │ │ │ │ │ │ │ ├── AltiVec │ │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ │ ├── Complex.h │ │ │ │ │ │ │ │ └── PacketMath.h │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ ├── Default │ │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ │ └── Settings.h │ │ │ │ │ │ │ ├── NEON │ │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ │ ├── Complex.h │ │ │ │ │ │ │ │ └── PacketMath.h │ │ │ │ │ │ │ └── SSE │ │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ │ ├── Complex.h │ │ │ │ │ │ │ │ ├── MathFunctions.h │ │ │ │ │ │ │ │ └── PacketMath.h │ │ │ │ │ │ ├── products │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ ├── CoeffBasedProduct.h │ │ │ │ │ │ │ ├── GeneralBlockPanelKernel.h │ │ │ │ │ │ │ ├── GeneralMatrixMatrix.h │ │ │ │ │ │ │ ├── GeneralMatrixMatrixTriangular.h │ │ │ │ │ │ │ ├── GeneralMatrixMatrixTriangular_MKL.h │ │ │ │ │ │ │ ├── GeneralMatrixMatrix_MKL.h │ │ │ │ │ │ │ ├── GeneralMatrixVector.h │ │ │ │ │ │ │ ├── GeneralMatrixVector_MKL.h │ │ │ │ │ │ │ ├── Parallelizer.h │ │ │ │ │ │ │ ├── SelfadjointMatrixMatrix.h │ │ │ │ │ │ │ ├── SelfadjointMatrixMatrix_MKL.h │ │ │ │ │ │ │ ├── SelfadjointMatrixVector.h │ │ │ │ │ │ │ ├── SelfadjointMatrixVector_MKL.h │ │ │ │ │ │ │ ├── SelfadjointProduct.h │ │ │ │ │ │ │ ├── SelfadjointRank2Update.h │ │ │ │ │ │ │ ├── TriangularMatrixMatrix.h │ │ │ │ │ │ │ ├── TriangularMatrixMatrix_MKL.h │ │ │ │ │ │ │ ├── TriangularMatrixVector.h │ │ │ │ │ │ │ ├── TriangularMatrixVector_MKL.h │ │ │ │ │ │ │ ├── TriangularSolverMatrix.h │ │ │ │ │ │ │ ├── TriangularSolverMatrix_MKL.h │ │ │ │ │ │ │ └── TriangularSolverVector.h │ │ │ │ │ │ └── util │ │ │ │ │ │ │ ├── BlasUtil.h │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ ├── Constants.h │ │ │ │ │ │ │ ├── DisableStupidWarnings.h │ │ │ │ │ │ │ ├── ForwardDeclarations.h │ │ │ │ │ │ │ ├── MKL_support.h │ │ │ │ │ │ │ ├── Macros.h │ │ │ │ │ │ │ ├── Memory.h │ │ │ │ │ │ │ ├── Meta.h │ │ │ │ │ │ │ ├── NonMPL2.h │ │ │ │ │ │ │ ├── ReenableStupidWarnings.h │ │ │ │ │ │ │ ├── StaticAssert.h │ │ │ │ │ │ │ └── XprHelper.h │ │ │ │ │ │ ├── Eigen2Support │ │ │ │ │ │ ├── Block.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── Cwise.h │ │ │ │ │ │ ├── CwiseOperators.h │ │ │ │ │ │ ├── Geometry │ │ │ │ │ │ │ ├── AlignedBox.h │ │ │ │ │ │ │ ├── All.h │ │ │ │ │ │ │ ├── AngleAxis.h │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ ├── Hyperplane.h │ │ │ │ │ │ │ ├── ParametrizedLine.h │ │ │ │ │ │ │ ├── Quaternion.h │ │ │ │ │ │ │ ├── Rotation2D.h │ │ │ │ │ │ │ ├── RotationBase.h │ │ │ │ │ │ │ ├── Scaling.h │ │ │ │ │ │ │ ├── Transform.h │ │ │ │ │ │ │ └── Translation.h │ │ │ │ │ │ ├── LU.h │ │ │ │ │ │ ├── Lazy.h │ │ │ │ │ │ ├── LeastSquares.h │ │ │ │ │ │ ├── Macros.h │ │ │ │ │ │ ├── MathFunctions.h │ │ │ │ │ │ ├── Memory.h │ │ │ │ │ │ ├── Meta.h │ │ │ │ │ │ ├── Minor.h │ │ │ │ │ │ ├── QR.h │ │ │ │ │ │ ├── SVD.h │ │ │ │ │ │ ├── TriangularSolver.h │ │ │ │ │ │ └── VectorBlock.h │ │ │ │ │ │ ├── Eigenvalues │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── ComplexEigenSolver.h │ │ │ │ │ │ ├── ComplexSchur.h │ │ │ │ │ │ ├── ComplexSchur_MKL.h │ │ │ │ │ │ ├── EigenSolver.h │ │ │ │ │ │ ├── GeneralizedSelfAdjointEigenSolver.h │ │ │ │ │ │ ├── HessenbergDecomposition.h │ │ │ │ │ │ ├── MatrixBaseEigenvalues.h │ │ │ │ │ │ ├── RealSchur.h │ │ │ │ │ │ ├── RealSchur_MKL.h │ │ │ │ │ │ ├── SelfAdjointEigenSolver.h │ │ │ │ │ │ ├── SelfAdjointEigenSolver_MKL.h │ │ │ │ │ │ └── Tridiagonalization.h │ │ │ │ │ │ ├── Geometry │ │ │ │ │ │ ├── AlignedBox.h │ │ │ │ │ │ ├── AngleAxis.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── EulerAngles.h │ │ │ │ │ │ ├── Homogeneous.h │ │ │ │ │ │ ├── Hyperplane.h │ │ │ │ │ │ ├── OrthoMethods.h │ │ │ │ │ │ ├── ParametrizedLine.h │ │ │ │ │ │ ├── Quaternion.h │ │ │ │ │ │ ├── Rotation2D.h │ │ │ │ │ │ ├── RotationBase.h │ │ │ │ │ │ ├── Scaling.h │ │ │ │ │ │ ├── Transform.h │ │ │ │ │ │ ├── Translation.h │ │ │ │ │ │ ├── Umeyama.h │ │ │ │ │ │ └── arch │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ └── Geometry_SSE.h │ │ │ │ │ │ ├── Householder │ │ │ │ │ │ ├── BlockHouseholder.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── Householder.h │ │ │ │ │ │ └── HouseholderSequence.h │ │ │ │ │ │ ├── IterativeLinearSolvers │ │ │ │ │ │ ├── BasicPreconditioners.h │ │ │ │ │ │ ├── BiCGSTAB.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── ConjugateGradient.h │ │ │ │ │ │ ├── IncompleteLUT.h │ │ │ │ │ │ └── IterativeSolverBase.h │ │ │ │ │ │ ├── Jacobi │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── Jacobi.h │ │ │ │ │ │ ├── LU │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── Determinant.h │ │ │ │ │ │ ├── FullPivLU.h │ │ │ │ │ │ ├── Inverse.h │ │ │ │ │ │ ├── PartialPivLU.h │ │ │ │ │ │ ├── PartialPivLU_MKL.h │ │ │ │ │ │ └── arch │ │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ │ └── Inverse_SSE.h │ │ │ │ │ │ ├── OrderingMethods │ │ │ │ │ │ ├── Amd.h │ │ │ │ │ │ └── CMakeLists.txt │ │ │ │ │ │ ├── PaStiXSupport │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── PaStiXSupport.h │ │ │ │ │ │ ├── PardisoSupport │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── PardisoSupport.h │ │ │ │ │ │ ├── QR │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── ColPivHouseholderQR.h │ │ │ │ │ │ ├── ColPivHouseholderQR_MKL.h │ │ │ │ │ │ ├── FullPivHouseholderQR.h │ │ │ │ │ │ ├── HouseholderQR.h │ │ │ │ │ │ └── HouseholderQR_MKL.h │ │ │ │ │ │ ├── SVD │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── JacobiSVD.h │ │ │ │ │ │ ├── JacobiSVD_MKL.h │ │ │ │ │ │ └── UpperBidiagonalization.h │ │ │ │ │ │ ├── SparseCholesky │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── SimplicialCholesky.h │ │ │ │ │ │ ├── SparseCore │ │ │ │ │ │ ├── AmbiVector.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── CompressedStorage.h │ │ │ │ │ │ ├── ConservativeSparseSparseProduct.h │ │ │ │ │ │ ├── CoreIterators.h │ │ │ │ │ │ ├── MappedSparseMatrix.h │ │ │ │ │ │ ├── SparseAssign.h │ │ │ │ │ │ ├── SparseBlock.h │ │ │ │ │ │ ├── SparseCwiseBinaryOp.h │ │ │ │ │ │ ├── SparseCwiseUnaryOp.h │ │ │ │ │ │ ├── SparseDenseProduct.h │ │ │ │ │ │ ├── SparseDiagonalProduct.h │ │ │ │ │ │ ├── SparseDot.h │ │ │ │ │ │ ├── SparseFuzzy.h │ │ │ │ │ │ ├── SparseMatrix.h │ │ │ │ │ │ ├── SparseMatrixBase.h │ │ │ │ │ │ ├── SparsePermutation.h │ │ │ │ │ │ ├── SparseProduct.h │ │ │ │ │ │ ├── SparseRedux.h │ │ │ │ │ │ ├── SparseSelfAdjointView.h │ │ │ │ │ │ ├── SparseSparseProductWithPruning.h │ │ │ │ │ │ ├── SparseTranspose.h │ │ │ │ │ │ ├── SparseTriangularView.h │ │ │ │ │ │ ├── SparseUtil.h │ │ │ │ │ │ ├── SparseVector.h │ │ │ │ │ │ ├── SparseView.h │ │ │ │ │ │ └── TriangularSolver.h │ │ │ │ │ │ ├── StlSupport │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── StdDeque.h │ │ │ │ │ │ ├── StdList.h │ │ │ │ │ │ ├── StdVector.h │ │ │ │ │ │ └── details.h │ │ │ │ │ │ ├── SuperLUSupport │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── SuperLUSupport.h │ │ │ │ │ │ ├── UmfPackSupport │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ └── UmfPackSupport.h │ │ │ │ │ │ ├── misc │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── Image.h │ │ │ │ │ │ ├── Kernel.h │ │ │ │ │ │ ├── Solve.h │ │ │ │ │ │ ├── SparseSolve.h │ │ │ │ │ │ └── blas.h │ │ │ │ │ │ └── plugins │ │ │ │ │ │ ├── ArrayCwiseBinaryOps.h │ │ │ │ │ │ ├── ArrayCwiseUnaryOps.h │ │ │ │ │ │ ├── BlockMethods.h │ │ │ │ │ │ ├── CMakeLists.txt │ │ │ │ │ │ ├── CommonCwiseBinaryOps.h │ │ │ │ │ │ ├── CommonCwiseUnaryOps.h │ │ │ │ │ │ ├── MatrixCwiseBinaryOps.h │ │ │ │ │ │ └── MatrixCwiseUnaryOps.h │ │ │ │ ├── densecrf.h │ │ │ │ ├── labelcompatibility.h │ │ │ │ ├── objective.h │ │ │ │ ├── optimization.h │ │ │ │ ├── pairwise.h │ │ │ │ ├── permutohedral.h │ │ │ │ └── unary.h │ │ │ └── src │ │ │ │ ├── CMakeLists.txt │ │ │ │ ├── densecrf.cpp │ │ │ │ ├── labelcompatibility.cpp │ │ │ │ ├── objective.cpp │ │ │ │ ├── optimization.cpp │ │ │ │ ├── pairwise.cpp │ │ │ │ ├── permutohedral.cpp │ │ │ │ ├── permutohedral_couts.cpp │ │ │ │ ├── unary.cpp │ │ │ │ ├── util.cpp │ │ │ │ └── util.h │ │ └── maxflow-v3.0 │ │ │ ├── CHANGES.TXT │ │ │ ├── README.TXT │ │ │ ├── block.h │ │ │ ├── graph.cpp │ │ │ ├── graph.h │ │ │ ├── instances.inc │ │ │ └── maxflow.cpp │ ├── dist │ │ ├── SimpleCRF-0.1.0-py3.7-linux-x86_64.egg │ │ └── SimpleCRF-0.1.0-py3.8-linux-x86_64.egg │ ├── examples │ │ ├── AbdominalOrganCRFSeg.py │ │ ├── demo_densecrf.py │ │ ├── demo_densecrf3d.py │ │ └── demo_maxflow.py │ ├── maxflow_python │ │ ├── maxflow.cpp │ │ ├── util.cpp │ │ ├── util.h │ │ ├── wrap_py2.cpp │ │ └── wrap_py3.cpp │ ├── requirements.txt │ └── setup.py ├── WeaklySupervised-GrandChallenge-Logo.jpeg └── nnUNet │ ├── HIP_Logo.png │ ├── LICENSE │ ├── documentation │ ├── common_problems_and_solutions.md │ ├── common_questions.md │ ├── data_format_inference.md │ ├── dataset_conversion.md │ ├── expected_epoch_times.md │ ├── extending_nnunet.md │ ├── inference_example_Prostate.md │ ├── setting_up_paths.md │ ├── training_example_Hippocampus.md │ ├── tutorials │ │ ├── custom_preprocessing.md │ │ ├── custom_spacing.md │ │ └── edit_plans_files.md │ └── using_nnUNet_as_baseline.md │ ├── nnunet │ ├── __init__.py │ ├── configuration.py │ ├── dataset_conversion │ │ ├── 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 │ │ ├── __init__.py │ │ └── utils.py │ ├── evaluation │ │ ├── __init__.py │ │ ├── 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 │ │ ├── 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 │ │ ├── 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 │ │ ├── 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 │ │ ├── connected_components.py │ │ ├── consolidate_all_for_paper.py │ │ ├── consolidate_postprocessing.py │ │ └── consolidate_postprocessing_simple.py │ ├── preprocessing │ │ ├── __init__.py │ │ ├── cropping.py │ │ ├── custom_preprocessors │ │ │ ├── __init__.py │ │ │ └── preprocessor_scale_RGB_to_0_1.py │ │ ├── preprocessing.py │ │ └── sanity_checks.py │ ├── run │ │ ├── __init__.py │ │ ├── default_configuration.py │ │ ├── load_pretrained_weights.py │ │ ├── run_training.py │ │ ├── run_training_DDP.py │ │ └── run_training_DP.py │ ├── training │ │ ├── __init__.py │ │ ├── cascade_stuff │ │ │ ├── __init__.py │ │ │ └── predict_next_stage.py │ │ ├── data_augmentation │ │ │ ├── __init__.py │ │ │ ├── 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 │ │ │ └── dataset_loading.py │ │ ├── learning_rate │ │ │ ├── __init__.py │ │ │ └── poly_lr.py │ │ ├── loss_functions │ │ │ ├── TopK_loss.py │ │ │ ├── __init__.py │ │ │ ├── crossentropy.py │ │ │ ├── deep_supervision.py │ │ │ └── dice_loss.py │ │ ├── model_restore.py │ │ ├── network_training │ │ │ ├── __init__.py │ │ │ ├── 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 │ │ │ ├── nnUNetTrainerV2_CascadeFullRes.py │ │ │ ├── nnUNetTrainerV2_DDP.py │ │ │ ├── nnUNetTrainerV2_DP.py │ │ │ ├── nnUNetTrainerV2_Loss_DiceTopK10_moreDA.py │ │ │ ├── nnUNetTrainerV2_MMS.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 │ │ ├── __init__.py │ │ ├── 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 │ ├── readme.md │ ├── setup.cfg │ ├── setup.py │ └── tests │ └── test_steps_for_sliding_window_prediction.py ├── 4-ContinualLearning ├── Continual-Learning-GrandChallenge-Logo.jpg ├── Dockerfile ├── README.md └── nnUNet │ ├── HIP_Logo.png │ ├── LICENSE │ ├── documentation │ ├── common_problems_and_solutions.md │ ├── common_questions.md │ ├── data_format_inference.md │ ├── dataset_conversion.md │ ├── expected_epoch_times.md │ ├── extending_nnunet.md │ ├── inference_example_Prostate.md │ ├── setting_up_paths.md │ ├── training_example_Hippocampus.md │ ├── tutorials │ │ ├── custom_preprocessing.md │ │ ├── custom_spacing.md │ │ └── edit_plans_files.md │ └── using_nnUNet_as_baseline.md │ ├── nnunet │ ├── __init__.py │ ├── configuration.py │ ├── dataset_conversion │ │ ├── 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 │ │ ├── __init__.py │ │ └── utils.py │ ├── evaluation │ │ ├── __init__.py │ │ ├── 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 │ │ ├── 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 │ │ ├── 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 │ │ ├── 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 │ │ ├── connected_components.py │ │ ├── consolidate_all_for_paper.py │ │ ├── consolidate_postprocessing.py │ │ └── consolidate_postprocessing_simple.py │ ├── preprocessing │ │ ├── __init__.py │ │ ├── cropping.py │ │ ├── custom_preprocessors │ │ │ ├── __init__.py │ │ │ └── preprocessor_scale_RGB_to_0_1.py │ │ ├── preprocessing.py │ │ └── sanity_checks.py │ ├── run │ │ ├── __init__.py │ │ ├── default_configuration.py │ │ ├── load_pretrained_weights.py │ │ ├── run_training.py │ │ ├── run_training_DDP.py │ │ └── run_training_DP.py │ ├── training │ │ ├── __init__.py │ │ ├── cascade_stuff │ │ │ ├── __init__.py │ │ │ └── predict_next_stage.py │ │ ├── data_augmentation │ │ │ ├── __init__.py │ │ │ ├── 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 │ │ │ └── dataset_loading.py │ │ ├── learning_rate │ │ │ ├── __init__.py │ │ │ └── poly_lr.py │ │ ├── loss_functions │ │ │ ├── TopK_loss.py │ │ │ ├── __init__.py │ │ │ ├── crossentropy.py │ │ │ ├── deep_supervision.py │ │ │ └── dice_loss.py │ │ ├── model_restore.py │ │ ├── network_training │ │ │ ├── __init__.py │ │ │ ├── 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 │ │ │ ├── nnUNetTrainerV2_CascadeFullRes.py │ │ │ ├── nnUNetTrainerV2_DDP.py │ │ │ ├── nnUNetTrainerV2_DP.py │ │ │ ├── nnUNetTrainerV2_Loss_DiceTopK10_moreDA.py │ │ │ ├── nnUNetTrainerV2_MMS.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 │ │ ├── __init__.py │ │ ├── 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 │ ├── readme.md │ ├── setup.cfg │ ├── setup.py │ └── tests │ └── test_steps_for_sliding_window_prediction.py ├── Abdomen1K-supp.pdf ├── LICENSE └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | .DS_Store 3 | -------------------------------------------------------------------------------- /1-FullySupervisedLearning/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM ashishsingh21/nnunet-gpu:0.1.0 2 | 3 | RUN mkdir -p /workspace/datasets/nnUNet/3d_fullres/Task121_Subtask1/ 4 | COPY . /workspace/datasets/nnUNet/3d_fullres/Task121_Subtask1/ 5 | 6 | COPY predict.sh /workspace/ -------------------------------------------------------------------------------- /1-FullySupervisedLearning/FullySupervised-GrandChallenge-Logo.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/FullySupervised-GrandChallenge-Logo.PNG -------------------------------------------------------------------------------- /1-FullySupervisedLearning/Multi-organ.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/Multi-organ.gif -------------------------------------------------------------------------------- /1-FullySupervisedLearning/TumorDemo.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/TumorDemo.gif -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/HIP_Logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/nnUNet/HIP_Logo.png -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/documentation/using_nnUNet_as_baseline.md: -------------------------------------------------------------------------------- 1 | (The U-Net is the current punching bag of methods development. nnU-Net is going to be that looking forward. That is 2 | cool (great, in fact!), but it should be done correctly. Here are tips on how to benchmark against nnU-Net) 3 | 4 | This is work in progress -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/__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 * -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/configuration.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | default_num_threads = 4 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) -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/dataset_conversion/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | from . import * -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/evaluation/model_selection/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/experiment_planning/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/__init__.py -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/normalization/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/normalization/__init__.py -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/patch_size/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/patch_size/__init__.py -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/pooling_and_convs/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/pooling_and_convs/__init__.py -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/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. -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/__init__.py -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/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) 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https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/nnUNet/nnunet/inference/pretrained_models/__init__.py -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/network_architecture/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/network_architecture/custom_modules/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/nnUNet/nnunet/network_architecture/custom_modules/__init__.py -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/postprocessing/__init__.py: -------------------------------------------------------------------------------- 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/1-FullySupervisedLearning/nnUNet/nnunet/run/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/training/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/training/cascade_stuff/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/training/data_augmentation/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/training/dataloading/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/training/learning_rate/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/nnUNet/nnunet/training/learning_rate/__init__.py -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/training/learning_rate/poly_lr.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 | def poly_lr(epoch, max_epochs, initial_lr, exponent=0.9): 17 | return initial_lr * (1 - epoch / max_epochs)**exponent 18 | -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/training/loss_functions/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/training/loss_functions/crossentropy.py: -------------------------------------------------------------------------------- 1 | from torch import nn, Tensor 2 | 3 | 4 | class RobustCrossEntropyLoss(nn.CrossEntropyLoss): 5 | """ 6 | this is just a compatibility layer because my target tensor is float and has an extra dimension 7 | """ 8 | def forward(self, input: Tensor, target: Tensor) -> Tensor: 9 | if len(target.shape) == len(input.shape): 10 | assert target.shape[1] == 1 11 | target = target[:, 0] 12 | return super().forward(input, target.long()) 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/1-FullySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/architectural_variants/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/architectural_variants/__init__.py -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/benchmarking/__init__.py: 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-------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/loss_function/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/1-FullySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/loss_function/__init__.py -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/miscellaneous/__init__.py: -------------------------------------------------------------------------------- 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/1-FullySupervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /1-FullySupervisedLearning/nnUNet/setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | description-file = readme.md -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM ashishsingh21/nnunet-gpu:0.1.0 2 | 3 | RUN mkdir -p /workspace/datasets/nnUNet/3d_fullres/Task200_Subtask1/ 4 | COPY . /workspace/datasets/nnUNet/3d_fullres/Task200_Subtask1/ 5 | 6 | COPY predict.sh /workspace/ -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/SemiSupervised-GrandChallenge-Logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/2-Semi-supervisedLearning/SemiSupervised-GrandChallenge-Logo.png -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/__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 * -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/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) -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/dataset_conversion/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | from . import * -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/evaluation/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/2-Semi-supervisedLearning/nnUNet/nnunet/evaluation/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/experiment_planning/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/experiment_planning/__pycache__/DatasetAnalyzer.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/2-Semi-supervisedLearning/nnUNet/nnunet/experiment_planning/__pycache__/DatasetAnalyzer.cpython-37.pyc -------------------------------------------------------------------------------- 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'/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) -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/experiment_planning/old/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/2-Semi-supervisedLearning/nnUNet/nnunet/experiment_planning/old/__init__.py -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/inference/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * 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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 | -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/postprocessing/__init__.py: -------------------------------------------------------------------------------- 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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 | -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/training/loss_functions/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ 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len(target.shape) == len(input.shape): 10 | assert target.shape[1] == 1 11 | target = target[:, 0] 12 | return super().forward(input, target.long()) -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/training/network_training/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/training/network_training/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/2-Semi-supervisedLearning/nnUNet/nnunet/training/network_training/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- 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/2-Semi-supervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/loss_function/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/2-Semi-supervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/loss_function/__init__.py -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/miscellaneous/__init__.py: 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https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/2-Semi-supervisedLearning/nnUNet/nnunet/utilities/__pycache__/to_torch.cpython-37.pyc -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /2-Semi-supervisedLearning/nnUNet/setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | description-file = readme.md -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM ashishsingh21/nnunet-gpu:0.1.0 2 | 3 | RUN mkdir -p /workspace/datasets/nnUNet/3d_fullres/Task311_Subtask1/ 4 | COPY . /workspace/datasets/nnUNet/3d_fullres/Task311_Subtask1/ 5 | 6 | COPY predict.sh /workspace/ 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/MANIFEST.in: -------------------------------------------------------------------------------- 1 | include maxflow_python/*h 2 | include densecrf_python/*h 3 | include dependency/maxflow-v3.0/*h 4 | include dependency/maxflow-v3.0/*inc 5 | recursive-include dependency/densecrf/include/ * 6 | recursive-include dependency/densecrf/external/liblbfgs * 7 | include dependency/densecrf/src/*h 8 | include dependency/densecrf/external/liblbfgs/include/*h 9 | recursive-include dependency/densecrf3d/include/ * 10 | recursive-include dependency/densecrf3d/external/liblbfgs * 11 | include dependency/densecrf3d/src/*h 12 | include dependency/densecrf3d/external/liblbfgs/include/*h 13 | 14 | 15 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/densecrf_python/densecrf3d_util.h: -------------------------------------------------------------------------------- 1 | #include "densecrf.h" 2 | 3 | void addPairwiseBilateral( 4 | DenseCRF3D & crf3d, int numberOfModalities, double BilateralRStd, double BilateralCStd, double BilateralZStd, 5 | std::vector BilateralModsStds, double BilateralW, 6 | unsigned char * arrayWithMyImWithAllModalities); -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/densecrf_python/densecrf_core.h: -------------------------------------------------------------------------------- 1 | #include "densecrf.h" 2 | #include 3 | #include 4 | #include 5 | #include 6 | using namespace std; 7 | const float GT_PROB = 0.5; 8 | 9 | struct CRFParam{ 10 | double w1=0; 11 | double alpha=0; 12 | double beta=0; 13 | double w2=0; 14 | double gamma=0; 15 | int iter=0; 16 | }; 17 | // VectorXs dense_crf_inference(const unsigned char * img, const float * prob, int W, int H, CRFParam param); 18 | VectorXs dense_crf_inference(const unsigned char * img, const float * prob, int H, int W, int C, CRFParam param); -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/densecrf_python/wrap2D_py2.cpp: -------------------------------------------------------------------------------- 1 | #include "densecrf.cpp" 2 | 3 | PyMODINIT_FUNC 4 | initdenseCRF(void) { 5 | (void) Py_InitModule("denseCRF", Methods); 6 | import_array(); 7 | } 8 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/densecrf_python/wrap2D_py3.cpp: -------------------------------------------------------------------------------- 1 | #include "densecrf.cpp" 2 | 3 | static struct PyModuleDef sDenseCRF = 4 | { 5 | PyModuleDef_HEAD_INIT, 6 | "denseCRF", /* name of module */ 7 | "", /* module documentation, may be NULL */ 8 | -1, /* size of per-interpreter state of the module, or -1 if the module keeps state in global variables. */ 9 | Methods 10 | }; 11 | 12 | 13 | PyMODINIT_FUNC PyInit_denseCRF(void) { 14 | import_array(); 15 | return PyModule_Create(&sDenseCRF); 16 | } -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/densecrf_python/wrap3D_py2.cpp: -------------------------------------------------------------------------------- 1 | #include "densecrf3d.cpp" 2 | 3 | PyMODINIT_FUNC 4 | initdenseCRF3D(void) { 5 | (void) Py_InitModule("denseCRF3D", Methods); 6 | import_array(); 7 | } 8 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/densecrf_python/wrap3D_py3.cpp: -------------------------------------------------------------------------------- 1 | #include "densecrf3d.cpp" 2 | 3 | static struct PyModuleDef sDenseCRF = 4 | { 5 | PyModuleDef_HEAD_INIT, 6 | "denseCRF3D", /* name of module */ 7 | "", /* module documentation, may be NULL */ 8 | -1, /* size of per-interpreter state of the module, or -1 if the module keeps state in global variables. */ 9 | Methods 10 | }; 11 | 12 | 13 | PyMODINIT_FUNC PyInit_denseCRF3D(void) { 14 | import_array(); 15 | return PyModule_Create(&sDenseCRF); 16 | } -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required (VERSION 2.6) 2 | project(densecrf) 3 | 4 | set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PROJECT_SOURCE_DIR}/cmake) 5 | 6 | include_directories( include ) 7 | 8 | add_subdirectory (src) 9 | add_subdirectory (examples) 10 | add_subdirectory (external) 11 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/external/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | include_directories( liblbfgs/include ) 2 | add_library( lbfgs liblbfgs/lib/lbfgs.c ) 3 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/external/liblbfgs/AUTHORS: -------------------------------------------------------------------------------- 1 | Naoaki Okazaki 2 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/external/liblbfgs/Makefile.am: -------------------------------------------------------------------------------- 1 | # $Id$ 2 | 3 | SUBDIRS = lib sample 4 | 5 | docdir = $(prefix)/share/doc/@PACKAGE@ 6 | doc_DATA = README INSTALL COPYING AUTHORS ChangeLog NEWS 7 | 8 | EXTRA_DIST = \ 9 | autogen.sh \ 10 | lbfgs.sln 11 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/external/liblbfgs/NEWS: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/external/liblbfgs/NEWS -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/external/liblbfgs/autogen.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # $Id$ 3 | 4 | if [ "$1" = "--force" ]; 5 | then 6 | FORCE=--force 7 | NOFORCE= 8 | FORCE_MISSING=--force-missing 9 | else 10 | FORCE= 11 | NOFORCE=--no-force 12 | FORCE_MISSING= 13 | fi 14 | 15 | libtoolize --copy $FORCE 2>&1 | sed '/^You should/d' || { 16 | echo "libtoolize failed!" 17 | exit 1 18 | } 19 | 20 | aclocal $FORCE || { 21 | echo "aclocal failed!" 22 | exit 1 23 | } 24 | 25 | autoheader $FORCE || { 26 | echo "autoheader failed!" 27 | exit 1 28 | } 29 | 30 | automake -a -c $NOFORCE || { 31 | echo "automake failed!" 32 | exit 1 33 | } 34 | 35 | autoconf $FORCE || { 36 | echo "autoconf failed!" 37 | exit 1 38 | } 39 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/external/liblbfgs/lib/Makefile.am: -------------------------------------------------------------------------------- 1 | # $Id$ 2 | 3 | EXTRA_DIST = \ 4 | lib.vcxproj 5 | 6 | liblbfgsincludedir = $(includedir) 7 | liblbfgsinclude_HEADERS = \ 8 | ../include/lbfgs.h 9 | 10 | lib_LTLIBRARIES = liblbfgs.la 11 | 12 | liblbfgs_la_SOURCES = \ 13 | arithmetic_ansi.h \ 14 | arithmetic_sse_double.h \ 15 | arithmetic_sse_float.h \ 16 | ../include/lbfgs.h \ 17 | lbfgs.c 18 | 19 | liblbfgs_la_LDFLAGS = \ 20 | -no-undefined \ 21 | -release @VERSION@ 22 | 23 | AM_CFLAGS = @CFLAGS@ 24 | INCLUDES = @INCLUDES@ 25 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/external/liblbfgs/sample/Makefile.am: -------------------------------------------------------------------------------- 1 | # $Id$ 2 | 3 | EXTRA_DIST = \ 4 | sample.cpp \ 5 | sample.vcxproj 6 | 7 | noinst_PROGRAMS = sample 8 | 9 | sample_SOURCES = \ 10 | sample.c 11 | 12 | sample_LDADD = ../lib/liblbfgs.la 13 | 14 | AM_CFLAGS = @CFLAGS@ 15 | INCLUDES = @INCLUDES@ 16 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/Array: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_ARRAY_MODULE_H 2 | #define EIGEN_ARRAY_MODULE_H 3 | 4 | // include Core first to handle Eigen2 support macros 5 | #include "Core" 6 | 7 | #ifndef EIGEN2_SUPPORT 8 | #error The Eigen/Array header does no longer exist in Eigen3. All that functionality has moved to Eigen/Core. 9 | #endif 10 | 11 | #endif // EIGEN_ARRAY_MODULE_H 12 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | include(RegexUtils) 2 | test_escape_string_as_regex() 3 | 4 | file(GLOB Eigen_directory_files "*") 5 | 6 | escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}") 7 | 8 | foreach(f ${Eigen_directory_files}) 9 | if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/src") 10 | list(APPEND Eigen_directory_files_to_install ${f}) 11 | endif() 12 | endforeach(f ${Eigen_directory_files}) 13 | 14 | install(FILES 15 | ${Eigen_directory_files_to_install} 16 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel 17 | ) 18 | 19 | add_subdirectory(src) 20 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/Cholesky: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_CHOLESKY_MODULE_H 2 | #define EIGEN_CHOLESKY_MODULE_H 3 | 4 | #include "Core" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | /** \defgroup Cholesky_Module Cholesky module 9 | * 10 | * 11 | * 12 | * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices. 13 | * Those decompositions are accessible via the following MatrixBase methods: 14 | * - MatrixBase::llt(), 15 | * - MatrixBase::ldlt() 16 | * 17 | * \code 18 | * #include 19 | * \endcode 20 | */ 21 | 22 | #include "src/misc/Solve.h" 23 | #include "src/Cholesky/LLT.h" 24 | #include "src/Cholesky/LDLT.h" 25 | #ifdef EIGEN_USE_LAPACKE 26 | #include "src/Cholesky/LLT_MKL.h" 27 | #endif 28 | 29 | #include "src/Core/util/ReenableStupidWarnings.h" 30 | 31 | #endif // EIGEN_CHOLESKY_MODULE_H 32 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 33 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/Dense: -------------------------------------------------------------------------------- 1 | #include "Core" 2 | #include "LU" 3 | #include "Cholesky" 4 | #include "QR" 5 | #include "SVD" 6 | #include "Geometry" 7 | #include "Eigenvalues" 8 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/Eigen: -------------------------------------------------------------------------------- 1 | #include "Dense" 2 | //#include "Sparse" 3 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/Householder: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_HOUSEHOLDER_MODULE_H 2 | #define EIGEN_HOUSEHOLDER_MODULE_H 3 | 4 | #include "Core" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | /** \defgroup Householder_Module Householder module 9 | * This module provides Householder transformations. 10 | * 11 | * \code 12 | * #include 13 | * \endcode 14 | */ 15 | 16 | #include "src/Householder/Householder.h" 17 | #include "src/Householder/HouseholderSequence.h" 18 | #include "src/Householder/BlockHouseholder.h" 19 | 20 | #include "src/Core/util/ReenableStupidWarnings.h" 21 | 22 | #endif // EIGEN_HOUSEHOLDER_MODULE_H 23 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 24 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/Jacobi: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_JACOBI_MODULE_H 2 | #define EIGEN_JACOBI_MODULE_H 3 | 4 | #include "Core" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | /** \defgroup Jacobi_Module Jacobi module 9 | * This module provides Jacobi and Givens rotations. 10 | * 11 | * \code 12 | * #include 13 | * \endcode 14 | * 15 | * In addition to listed classes, it defines the two following MatrixBase methods to apply a Jacobi or Givens rotation: 16 | * - MatrixBase::applyOnTheLeft() 17 | * - MatrixBase::applyOnTheRight(). 18 | */ 19 | 20 | #include "src/Jacobi/Jacobi.h" 21 | 22 | #include "src/Core/util/ReenableStupidWarnings.h" 23 | 24 | #endif // EIGEN_JACOBI_MODULE_H 25 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 26 | 27 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/LU: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_LU_MODULE_H 2 | #define EIGEN_LU_MODULE_H 3 | 4 | #include "Core" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | /** \defgroup LU_Module LU module 9 | * This module includes %LU decomposition and related notions such as matrix inversion and determinant. 10 | * This module defines the following MatrixBase methods: 11 | * - MatrixBase::inverse() 12 | * - MatrixBase::determinant() 13 | * 14 | * \code 15 | * #include 16 | * \endcode 17 | */ 18 | 19 | #include "src/misc/Solve.h" 20 | #include "src/misc/Kernel.h" 21 | #include "src/misc/Image.h" 22 | #include "src/LU/FullPivLU.h" 23 | #include "src/LU/PartialPivLU.h" 24 | #ifdef EIGEN_USE_LAPACKE 25 | #include "src/LU/PartialPivLU_MKL.h" 26 | #endif 27 | #include "src/LU/Determinant.h" 28 | #include "src/LU/Inverse.h" 29 | 30 | #if defined EIGEN_VECTORIZE_SSE 31 | #include "src/LU/arch/Inverse_SSE.h" 32 | #endif 33 | 34 | #ifdef EIGEN2_SUPPORT 35 | #include "src/Eigen2Support/LU.h" 36 | #endif 37 | 38 | #include "src/Core/util/ReenableStupidWarnings.h" 39 | 40 | #endif // EIGEN_LU_MODULE_H 41 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 42 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/LeastSquares: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_REGRESSION_MODULE_H 2 | #define EIGEN_REGRESSION_MODULE_H 3 | 4 | #ifndef EIGEN2_SUPPORT 5 | #error LeastSquares is only available in Eigen2 support mode (define EIGEN2_SUPPORT) 6 | #endif 7 | 8 | // exclude from normal eigen3-only documentation 9 | #ifdef EIGEN2_SUPPORT 10 | 11 | #include "Core" 12 | 13 | #include "src/Core/util/DisableStupidWarnings.h" 14 | 15 | #include "Eigenvalues" 16 | #include "Geometry" 17 | 18 | /** \defgroup LeastSquares_Module LeastSquares module 19 | * This module provides linear regression and related features. 20 | * 21 | * \code 22 | * #include 23 | * \endcode 24 | */ 25 | 26 | #include "src/Eigen2Support/LeastSquares.h" 27 | 28 | #include "src/Core/util/ReenableStupidWarnings.h" 29 | 30 | #endif // EIGEN2_SUPPORT 31 | 32 | #endif // EIGEN_REGRESSION_MODULE_H 33 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/OrderingMethods: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_ORDERINGMETHODS_MODULE_H 2 | #define EIGEN_ORDERINGMETHODS_MODULE_H 3 | 4 | #include "SparseCore" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | /** \ingroup Sparse_modules 9 | * \defgroup OrderingMethods_Module OrderingMethods module 10 | * 11 | * This module is currently for internal use only. 12 | * 13 | * 14 | * \code 15 | * #include 16 | * \endcode 17 | */ 18 | 19 | #include "src/OrderingMethods/Amd.h" 20 | 21 | #include "src/Core/util/ReenableStupidWarnings.h" 22 | 23 | #endif // EIGEN_ORDERINGMETHODS_MODULE_H 24 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/PardisoSupport: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_PARDISOSUPPORT_MODULE_H 2 | #define EIGEN_PARDISOSUPPORT_MODULE_H 3 | 4 | #include "SparseCore" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | #include 9 | 10 | #include 11 | 12 | /** \ingroup Support_modules 13 | * \defgroup PardisoSupport_Module PardisoSupport module 14 | * 15 | * This module brings support for the Intel(R) MKL PARDISO direct sparse solvers. 16 | * 17 | * \code 18 | * #include 19 | * \endcode 20 | * 21 | * In order to use this module, the MKL headers must be accessible from the include paths, and your binary must be linked to the MKL library and its dependencies. 22 | * See this \ref TopicUsingIntelMKL "page" for more information on MKL-Eigen integration. 23 | * 24 | */ 25 | 26 | #include "src/PardisoSupport/PardisoSupport.h" 27 | 28 | #include "src/Core/util/ReenableStupidWarnings.h" 29 | 30 | #endif // EIGEN_PARDISOSUPPORT_MODULE_H 31 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/QR: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_QR_MODULE_H 2 | #define EIGEN_QR_MODULE_H 3 | 4 | #include "Core" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | #include "Cholesky" 9 | #include "Jacobi" 10 | #include "Householder" 11 | 12 | /** \defgroup QR_Module QR module 13 | * 14 | * 15 | * 16 | * This module provides various QR decompositions 17 | * This module also provides some MatrixBase methods, including: 18 | * - MatrixBase::qr(), 19 | * 20 | * \code 21 | * #include 22 | * \endcode 23 | */ 24 | 25 | #include "src/misc/Solve.h" 26 | #include "src/QR/HouseholderQR.h" 27 | #include "src/QR/FullPivHouseholderQR.h" 28 | #include "src/QR/ColPivHouseholderQR.h" 29 | #ifdef EIGEN_USE_LAPACKE 30 | #include "src/QR/HouseholderQR_MKL.h" 31 | #include "src/QR/ColPivHouseholderQR_MKL.h" 32 | #endif 33 | 34 | #ifdef EIGEN2_SUPPORT 35 | #include "src/Eigen2Support/QR.h" 36 | #endif 37 | 38 | #include "src/Core/util/ReenableStupidWarnings.h" 39 | 40 | #ifdef EIGEN2_SUPPORT 41 | #include "Eigenvalues" 42 | #endif 43 | 44 | #endif // EIGEN_QR_MODULE_H 45 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 46 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/QtAlignedMalloc: -------------------------------------------------------------------------------- 1 | 2 | #ifndef EIGEN_QTMALLOC_MODULE_H 3 | #define EIGEN_QTMALLOC_MODULE_H 4 | 5 | #include "Core" 6 | 7 | #if (!EIGEN_MALLOC_ALREADY_ALIGNED) 8 | 9 | #include "src/Core/util/DisableStupidWarnings.h" 10 | 11 | void *qMalloc(size_t size) 12 | { 13 | return Eigen::internal::aligned_malloc(size); 14 | } 15 | 16 | void qFree(void *ptr) 17 | { 18 | Eigen::internal::aligned_free(ptr); 19 | } 20 | 21 | void *qRealloc(void *ptr, size_t size) 22 | { 23 | void* newPtr = Eigen::internal::aligned_malloc(size); 24 | memcpy(newPtr, ptr, size); 25 | Eigen::internal::aligned_free(ptr); 26 | return newPtr; 27 | } 28 | 29 | #include "src/Core/util/ReenableStupidWarnings.h" 30 | 31 | #endif 32 | 33 | #endif // EIGEN_QTMALLOC_MODULE_H 34 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 35 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/SVD: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_SVD_MODULE_H 2 | #define EIGEN_SVD_MODULE_H 3 | 4 | #include "QR" 5 | #include "Householder" 6 | #include "Jacobi" 7 | 8 | #include "src/Core/util/DisableStupidWarnings.h" 9 | 10 | /** \defgroup SVD_Module SVD module 11 | * 12 | * 13 | * 14 | * This module provides SVD decomposition for matrices (both real and complex). 15 | * This decomposition is accessible via the following MatrixBase method: 16 | * - MatrixBase::jacobiSvd() 17 | * 18 | * \code 19 | * #include 20 | * \endcode 21 | */ 22 | 23 | #include "src/misc/Solve.h" 24 | #include "src/SVD/JacobiSVD.h" 25 | #if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT) 26 | #include "src/SVD/JacobiSVD_MKL.h" 27 | #endif 28 | #include "src/SVD/UpperBidiagonalization.h" 29 | 30 | #ifdef EIGEN2_SUPPORT 31 | #include "src/Eigen2Support/SVD.h" 32 | #endif 33 | 34 | #include "src/Core/util/ReenableStupidWarnings.h" 35 | 36 | #endif // EIGEN_SVD_MODULE_H 37 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 38 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/Sparse: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_SPARSE_MODULE_H 2 | #define EIGEN_SPARSE_MODULE_H 3 | 4 | /** \defgroup Sparse_modules Sparse modules 5 | * 6 | * Meta-module including all related modules: 7 | * - SparseCore 8 | * - OrderingMethods 9 | * - SparseCholesky 10 | * - IterativeLinearSolvers 11 | * 12 | * \code 13 | * #include 14 | * \endcode 15 | */ 16 | 17 | #include "SparseCore" 18 | #include "OrderingMethods" 19 | #include "SparseCholesky" 20 | #include "IterativeLinearSolvers" 21 | 22 | #endif // EIGEN_SPARSE_MODULE_H 23 | 24 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/SparseCholesky: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_SPARSECHOLESKY_MODULE_H 2 | #define EIGEN_SPARSECHOLESKY_MODULE_H 3 | 4 | #include "SparseCore" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | /** \ingroup Sparse_modules 9 | * \defgroup SparseCholesky_Module SparseCholesky module 10 | * 11 | * This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian) matrices. 12 | * Those decompositions are accessible via the following classes: 13 | * - SimplicialLLt, 14 | * - SimplicialLDLt 15 | * 16 | * Such problems can also be solved using the ConjugateGradient solver from the IterativeLinearSolvers module. 17 | * 18 | * \code 19 | * #include 20 | * \endcode 21 | */ 22 | 23 | #include "src/misc/Solve.h" 24 | #include "src/misc/SparseSolve.h" 25 | 26 | #include "src/SparseCholesky/SimplicialCholesky.h" 27 | 28 | #include "src/Core/util/ReenableStupidWarnings.h" 29 | 30 | #endif // EIGEN_SPARSECHOLESKY_MODULE_H 31 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/StdDeque: -------------------------------------------------------------------------------- 1 | // This file is part of Eigen, a lightweight C++ template library 2 | // for linear algebra. 3 | // 4 | // Copyright (C) 2009 Gael Guennebaud 5 | // Copyright (C) 2009 Hauke Heibel 6 | // 7 | // This Source Code Form is subject to the terms of the Mozilla 8 | // Public License v. 2.0. If a copy of the MPL was not distributed 9 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 10 | 11 | #ifndef EIGEN_STDDEQUE_MODULE_H 12 | #define EIGEN_STDDEQUE_MODULE_H 13 | 14 | #include "Core" 15 | #include 16 | 17 | #if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */ 18 | 19 | #define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...) 20 | 21 | #else 22 | 23 | #include "src/StlSupport/StdDeque.h" 24 | 25 | #endif 26 | 27 | #endif // EIGEN_STDDEQUE_MODULE_H 28 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/StdList: -------------------------------------------------------------------------------- 1 | // This file is part of Eigen, a lightweight C++ template library 2 | // for linear algebra. 3 | // 4 | // Copyright (C) 2009 Hauke Heibel 5 | // 6 | // This Source Code Form is subject to the terms of the Mozilla 7 | // Public License v. 2.0. If a copy of the MPL was not distributed 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9 | 10 | #ifndef EIGEN_STDLIST_MODULE_H 11 | #define EIGEN_STDLIST_MODULE_H 12 | 13 | #include "Core" 14 | #include 15 | 16 | #if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */ 17 | 18 | #define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...) 19 | 20 | #else 21 | 22 | #include "src/StlSupport/StdList.h" 23 | 24 | #endif 25 | 26 | #endif // EIGEN_STDLIST_MODULE_H 27 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/StdVector: -------------------------------------------------------------------------------- 1 | // This file is part of Eigen, a lightweight C++ template library 2 | // for linear algebra. 3 | // 4 | // Copyright (C) 2009 Gael Guennebaud 5 | // Copyright (C) 2009 Hauke Heibel 6 | // 7 | // This Source Code Form is subject to the terms of the Mozilla 8 | // Public License v. 2.0. If a copy of the MPL was not distributed 9 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 10 | 11 | #ifndef EIGEN_STDVECTOR_MODULE_H 12 | #define EIGEN_STDVECTOR_MODULE_H 13 | 14 | #include "Core" 15 | #include 16 | 17 | #if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */ 18 | 19 | #define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...) 20 | 21 | #else 22 | 23 | #include "src/StlSupport/StdVector.h" 24 | 25 | #endif 26 | 27 | #endif // EIGEN_STDVECTOR_MODULE_H 28 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | file(GLOB Eigen_src_subdirectories "*") 2 | escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}") 3 | foreach(f ${Eigen_src_subdirectories}) 4 | if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" ) 5 | add_subdirectory(${f}) 6 | endif() 7 | endforeach() 8 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Cholesky/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Cholesky_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Cholesky_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Cholesky COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/CholmodSupport/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_CholmodSupport_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_CholmodSupport_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/CholmodSupport COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Core/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core COMPONENT Devel 6 | ) 7 | 8 | ADD_SUBDIRECTORY(products) 9 | ADD_SUBDIRECTORY(util) 10 | ADD_SUBDIRECTORY(arch) 11 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Core/arch/AltiVec/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_arch_AltiVec_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_arch_AltiVec_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/AltiVec COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Core/arch/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | ADD_SUBDIRECTORY(SSE) 2 | ADD_SUBDIRECTORY(AltiVec) 3 | ADD_SUBDIRECTORY(NEON) 4 | ADD_SUBDIRECTORY(Default) 5 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Core/arch/Default/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_arch_Default_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_arch_Default_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/Default COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Core/arch/NEON/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_arch_NEON_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_arch_NEON_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/NEON COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Core/arch/SSE/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_arch_SSE_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_arch_SSE_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/SSE COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Core/products/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_Product_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_Product_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/products COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Core/util/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_util_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_util_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/util COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Core/util/NonMPL2.h: -------------------------------------------------------------------------------- 1 | #ifdef EIGEN_MPL2_ONLY 2 | #error Including non-MPL2 code in EIGEN_MPL2_ONLY mode 3 | #endif 4 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Core/util/ReenableStupidWarnings.h: -------------------------------------------------------------------------------- 1 | #ifdef EIGEN_WARNINGS_DISABLED 2 | #undef EIGEN_WARNINGS_DISABLED 3 | 4 | #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS 5 | #ifdef _MSC_VER 6 | #pragma warning( pop ) 7 | #elif defined __INTEL_COMPILER 8 | #pragma warning pop 9 | #elif defined __clang__ 10 | #pragma clang diagnostic pop 11 | #endif 12 | #endif 13 | 14 | #endif // EIGEN_WARNINGS_DISABLED 15 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Eigen2Support/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Eigen2Support_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Eigen2Support_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Eigen2Support COMPONENT Devel 6 | ) 7 | 8 | ADD_SUBDIRECTORY(Geometry) -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Eigen2Support/Geometry/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Eigen2Support_Geometry_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Eigen2Support_Geometry_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Eigen2Support/Geometry 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Eigen2Support/Macros.h: -------------------------------------------------------------------------------- 1 | // This file is part of Eigen, a lightweight C++ template library 2 | // for linear algebra. 3 | // 4 | // Copyright (C) 2011 Benoit Jacob 5 | // 6 | // This Source Code Form is subject to the terms of the Mozilla 7 | // Public License v. 2.0. If a copy of the MPL was not distributed 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9 | 10 | #ifndef EIGEN2_MACROS_H 11 | #define EIGEN2_MACROS_H 12 | 13 | #define ei_assert eigen_assert 14 | #define ei_internal_assert eigen_internal_assert 15 | 16 | #define EIGEN_ALIGN_128 EIGEN_ALIGN16 17 | 18 | #define EIGEN_ARCH_WANTS_ALIGNMENT EIGEN_ALIGN_STATICALLY 19 | 20 | #endif // EIGEN2_MACROS_H 21 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Eigenvalues/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_EIGENVALUES_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_EIGENVALUES_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Eigenvalues COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Geometry/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Geometry_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Geometry_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Geometry COMPONENT Devel 6 | ) 7 | 8 | ADD_SUBDIRECTORY(arch) 9 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Geometry/arch/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Geometry_arch_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Geometry_arch_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Geometry/arch COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Householder/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Householder_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Householder_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Householder COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/IterativeLinearSolvers/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_IterativeLinearSolvers_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_IterativeLinearSolvers_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/IterativeLinearSolvers COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/Jacobi/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Jacobi_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Jacobi_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Jacobi COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/LU/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_LU_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_LU_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/LU COMPONENT Devel 6 | ) 7 | 8 | ADD_SUBDIRECTORY(arch) 9 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/LU/arch/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_LU_arch_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_LU_arch_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/LU/arch COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/OrderingMethods/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_OrderingMethods_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_OrderingMethods_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/OrderingMethods COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/PaStiXSupport/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_PastixSupport_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_PastixSupport_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/PaStiXSupport COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/PardisoSupport/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_PardisoSupport_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_PardisoSupport_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/PardisoSupport COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/QR/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_QR_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_QR_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/QR COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/SVD/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_SVD_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_SVD_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SVD COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/SparseCholesky/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_SparseCholesky_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_SparseCholesky_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SparseCholesky COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/SparseCore/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_SparseCore_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_SparseCore_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SparseCore COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/SparseCore/SparseAssign.h: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/SparseCore/SparseAssign.h -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/SparseCore/SparseFuzzy.h: -------------------------------------------------------------------------------- 1 | // This file is part of Eigen, a lightweight C++ template library 2 | // for linear algebra. 3 | // 4 | // Copyright (C) 2008 Gael Guennebaud 5 | // 6 | // This Source Code Form is subject to the terms of the Mozilla 7 | // Public License v. 2.0. If a copy of the MPL was not distributed 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9 | 10 | #ifndef EIGEN_SPARSE_FUZZY_H 11 | #define EIGEN_SPARSE_FUZZY_H 12 | 13 | // template 14 | // template 15 | // bool SparseMatrixBase::isApprox( 16 | // const OtherDerived& other, 17 | // typename NumTraits::Real prec 18 | // ) const 19 | // { 20 | // const typename internal::nested::type nested(derived()); 21 | // const typename internal::nested::type otherNested(other.derived()); 22 | // return (nested - otherNested).cwise().abs2().sum() 23 | // <= prec * prec * (std::min)(nested.cwise().abs2().sum(), otherNested.cwise().abs2().sum()); 24 | // } 25 | 26 | #endif // EIGEN_SPARSE_FUZZY_H 27 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/StlSupport/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_StlSupport_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_StlSupport_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/StlSupport COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/SuperLUSupport/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_SuperLUSupport_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_SuperLUSupport_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SuperLUSupport COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/UmfPackSupport/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_UmfPackSupport_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_UmfPackSupport_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/UmfPackSupport COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/misc/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_misc_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_misc_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/misc COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/include/Eigen/src/plugins/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_plugins_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_plugins_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/plugins COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf/src/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | include_directories( ../external/liblbfgs/include ../external/ ) 2 | 3 | add_library( densecrf util.cpp permutohedral.cpp unary.cpp pairwise.cpp objective.cpp densecrf.cpp labelcompatibility.cpp ) 4 | 5 | add_library( optimization optimization.cpp ) 6 | target_link_libraries( optimization lbfgs ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required (VERSION 2.8) 2 | project(denseCrf3d) 3 | 4 | FIND_PACKAGE ( ITK REQUIRED) 5 | INCLUDE( ${ITK_USE_FILE} ) 6 | 7 | set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PROJECT_SOURCE_DIR}/cmake) 8 | 9 | include_directories( include ) 10 | 11 | add_subdirectory (src) 12 | add_subdirectory (applicationAndExamples) 13 | add_subdirectory (external) 14 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/external/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | include_directories( liblbfgs/include ) 2 | add_library( lbfgs liblbfgs/lib/lbfgs.c ) 3 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/external/liblbfgs/AUTHORS: -------------------------------------------------------------------------------- 1 | Naoaki Okazaki 2 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/external/liblbfgs/Makefile.am: -------------------------------------------------------------------------------- 1 | # $Id$ 2 | 3 | SUBDIRS = lib sample 4 | 5 | docdir = $(prefix)/share/doc/@PACKAGE@ 6 | doc_DATA = README INSTALL COPYING AUTHORS ChangeLog NEWS 7 | 8 | EXTRA_DIST = \ 9 | autogen.sh \ 10 | lbfgs.sln 11 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/external/liblbfgs/NEWS: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/external/liblbfgs/NEWS -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/external/liblbfgs/autogen.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # $Id$ 3 | 4 | if [ "$1" = "--force" ]; 5 | then 6 | FORCE=--force 7 | NOFORCE= 8 | FORCE_MISSING=--force-missing 9 | else 10 | FORCE= 11 | NOFORCE=--no-force 12 | FORCE_MISSING= 13 | fi 14 | 15 | libtoolize --copy $FORCE 2>&1 | sed '/^You should/d' || { 16 | echo "libtoolize failed!" 17 | exit 1 18 | } 19 | 20 | aclocal $FORCE || { 21 | echo "aclocal failed!" 22 | exit 1 23 | } 24 | 25 | autoheader $FORCE || { 26 | echo "autoheader failed!" 27 | exit 1 28 | } 29 | 30 | automake -a -c $NOFORCE || { 31 | echo "automake failed!" 32 | exit 1 33 | } 34 | 35 | autoconf $FORCE || { 36 | echo "autoconf failed!" 37 | exit 1 38 | } 39 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/external/liblbfgs/lib/Makefile.am: -------------------------------------------------------------------------------- 1 | # $Id$ 2 | 3 | EXTRA_DIST = \ 4 | lib.vcxproj 5 | 6 | liblbfgsincludedir = $(includedir) 7 | liblbfgsinclude_HEADERS = \ 8 | ../include/lbfgs.h 9 | 10 | lib_LTLIBRARIES = liblbfgs.la 11 | 12 | liblbfgs_la_SOURCES = \ 13 | arithmetic_ansi.h \ 14 | arithmetic_sse_double.h \ 15 | arithmetic_sse_float.h \ 16 | ../include/lbfgs.h \ 17 | lbfgs.c 18 | 19 | liblbfgs_la_LDFLAGS = \ 20 | -no-undefined \ 21 | -release @VERSION@ 22 | 23 | AM_CFLAGS = @CFLAGS@ 24 | INCLUDES = @INCLUDES@ 25 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/external/liblbfgs/sample/Makefile.am: -------------------------------------------------------------------------------- 1 | # $Id$ 2 | 3 | EXTRA_DIST = \ 4 | sample.cpp \ 5 | sample.vcxproj 6 | 7 | noinst_PROGRAMS = sample 8 | 9 | sample_SOURCES = \ 10 | sample.c 11 | 12 | sample_LDADD = ../lib/liblbfgs.la 13 | 14 | AM_CFLAGS = @CFLAGS@ 15 | INCLUDES = @INCLUDES@ 16 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/Array: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_ARRAY_MODULE_H 2 | #define EIGEN_ARRAY_MODULE_H 3 | 4 | // include Core first to handle Eigen2 support macros 5 | #include "Core" 6 | 7 | #ifndef EIGEN2_SUPPORT 8 | #error The Eigen/Array header does no longer exist in Eigen3. All that functionality has moved to Eigen/Core. 9 | #endif 10 | 11 | #endif // EIGEN_ARRAY_MODULE_H 12 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | include(RegexUtils) 2 | test_escape_string_as_regex() 3 | 4 | file(GLOB Eigen_directory_files "*") 5 | 6 | escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}") 7 | 8 | foreach(f ${Eigen_directory_files}) 9 | if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/src") 10 | list(APPEND Eigen_directory_files_to_install ${f}) 11 | endif() 12 | endforeach(f ${Eigen_directory_files}) 13 | 14 | install(FILES 15 | ${Eigen_directory_files_to_install} 16 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel 17 | ) 18 | 19 | add_subdirectory(src) 20 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/Cholesky: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_CHOLESKY_MODULE_H 2 | #define EIGEN_CHOLESKY_MODULE_H 3 | 4 | #include "Core" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | /** \defgroup Cholesky_Module Cholesky module 9 | * 10 | * 11 | * 12 | * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices. 13 | * Those decompositions are accessible via the following MatrixBase methods: 14 | * - MatrixBase::llt(), 15 | * - MatrixBase::ldlt() 16 | * 17 | * \code 18 | * #include 19 | * \endcode 20 | */ 21 | 22 | #include "src/misc/Solve.h" 23 | #include "src/Cholesky/LLT.h" 24 | #include "src/Cholesky/LDLT.h" 25 | #ifdef EIGEN_USE_LAPACKE 26 | #include "src/Cholesky/LLT_MKL.h" 27 | #endif 28 | 29 | #include "src/Core/util/ReenableStupidWarnings.h" 30 | 31 | #endif // EIGEN_CHOLESKY_MODULE_H 32 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 33 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/Dense: -------------------------------------------------------------------------------- 1 | #include "Core" 2 | #include "LU" 3 | #include "Cholesky" 4 | #include "QR" 5 | #include "SVD" 6 | #include "Geometry" 7 | #include "Eigenvalues" 8 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/Eigen: -------------------------------------------------------------------------------- 1 | #include "Dense" 2 | //#include "Sparse" 3 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/Householder: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_HOUSEHOLDER_MODULE_H 2 | #define EIGEN_HOUSEHOLDER_MODULE_H 3 | 4 | #include "Core" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | /** \defgroup Householder_Module Householder module 9 | * This module provides Householder transformations. 10 | * 11 | * \code 12 | * #include 13 | * \endcode 14 | */ 15 | 16 | #include "src/Householder/Householder.h" 17 | #include "src/Householder/HouseholderSequence.h" 18 | #include "src/Householder/BlockHouseholder.h" 19 | 20 | #include "src/Core/util/ReenableStupidWarnings.h" 21 | 22 | #endif // EIGEN_HOUSEHOLDER_MODULE_H 23 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 24 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/Jacobi: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_JACOBI_MODULE_H 2 | #define EIGEN_JACOBI_MODULE_H 3 | 4 | #include "Core" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | /** \defgroup Jacobi_Module Jacobi module 9 | * This module provides Jacobi and Givens rotations. 10 | * 11 | * \code 12 | * #include 13 | * \endcode 14 | * 15 | * In addition to listed classes, it defines the two following MatrixBase methods to apply a Jacobi or Givens rotation: 16 | * - MatrixBase::applyOnTheLeft() 17 | * - MatrixBase::applyOnTheRight(). 18 | */ 19 | 20 | #include "src/Jacobi/Jacobi.h" 21 | 22 | #include "src/Core/util/ReenableStupidWarnings.h" 23 | 24 | #endif // EIGEN_JACOBI_MODULE_H 25 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 26 | 27 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/LU: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_LU_MODULE_H 2 | #define EIGEN_LU_MODULE_H 3 | 4 | #include "Core" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | /** \defgroup LU_Module LU module 9 | * This module includes %LU decomposition and related notions such as matrix inversion and determinant. 10 | * This module defines the following MatrixBase methods: 11 | * - MatrixBase::inverse() 12 | * - MatrixBase::determinant() 13 | * 14 | * \code 15 | * #include 16 | * \endcode 17 | */ 18 | 19 | #include "src/misc/Solve.h" 20 | #include "src/misc/Kernel.h" 21 | #include "src/misc/Image.h" 22 | #include "src/LU/FullPivLU.h" 23 | #include "src/LU/PartialPivLU.h" 24 | #ifdef EIGEN_USE_LAPACKE 25 | #include "src/LU/PartialPivLU_MKL.h" 26 | #endif 27 | #include "src/LU/Determinant.h" 28 | #include "src/LU/Inverse.h" 29 | 30 | #if defined EIGEN_VECTORIZE_SSE 31 | #include "src/LU/arch/Inverse_SSE.h" 32 | #endif 33 | 34 | #ifdef EIGEN2_SUPPORT 35 | #include "src/Eigen2Support/LU.h" 36 | #endif 37 | 38 | #include "src/Core/util/ReenableStupidWarnings.h" 39 | 40 | #endif // EIGEN_LU_MODULE_H 41 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 42 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/LeastSquares: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_REGRESSION_MODULE_H 2 | #define EIGEN_REGRESSION_MODULE_H 3 | 4 | #ifndef EIGEN2_SUPPORT 5 | #error LeastSquares is only available in Eigen2 support mode (define EIGEN2_SUPPORT) 6 | #endif 7 | 8 | // exclude from normal eigen3-only documentation 9 | #ifdef EIGEN2_SUPPORT 10 | 11 | #include "Core" 12 | 13 | #include "src/Core/util/DisableStupidWarnings.h" 14 | 15 | #include "Eigenvalues" 16 | #include "Geometry" 17 | 18 | /** \defgroup LeastSquares_Module LeastSquares module 19 | * This module provides linear regression and related features. 20 | * 21 | * \code 22 | * #include 23 | * \endcode 24 | */ 25 | 26 | #include "src/Eigen2Support/LeastSquares.h" 27 | 28 | #include "src/Core/util/ReenableStupidWarnings.h" 29 | 30 | #endif // EIGEN2_SUPPORT 31 | 32 | #endif // EIGEN_REGRESSION_MODULE_H 33 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/OrderingMethods: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_ORDERINGMETHODS_MODULE_H 2 | #define EIGEN_ORDERINGMETHODS_MODULE_H 3 | 4 | #include "SparseCore" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | /** \ingroup Sparse_modules 9 | * \defgroup OrderingMethods_Module OrderingMethods module 10 | * 11 | * This module is currently for internal use only. 12 | * 13 | * 14 | * \code 15 | * #include 16 | * \endcode 17 | */ 18 | 19 | #include "src/OrderingMethods/Amd.h" 20 | 21 | #include "src/Core/util/ReenableStupidWarnings.h" 22 | 23 | #endif // EIGEN_ORDERINGMETHODS_MODULE_H 24 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/PardisoSupport: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_PARDISOSUPPORT_MODULE_H 2 | #define EIGEN_PARDISOSUPPORT_MODULE_H 3 | 4 | #include "SparseCore" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | #include 9 | 10 | #include 11 | 12 | /** \ingroup Support_modules 13 | * \defgroup PardisoSupport_Module PardisoSupport module 14 | * 15 | * This module brings support for the Intel(R) MKL PARDISO direct sparse solvers. 16 | * 17 | * \code 18 | * #include 19 | * \endcode 20 | * 21 | * In order to use this module, the MKL headers must be accessible from the include paths, and your binary must be linked to the MKL library and its dependencies. 22 | * See this \ref TopicUsingIntelMKL "page" for more information on MKL-Eigen integration. 23 | * 24 | */ 25 | 26 | #include "src/PardisoSupport/PardisoSupport.h" 27 | 28 | #include "src/Core/util/ReenableStupidWarnings.h" 29 | 30 | #endif // EIGEN_PARDISOSUPPORT_MODULE_H 31 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/QR: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_QR_MODULE_H 2 | #define EIGEN_QR_MODULE_H 3 | 4 | #include "Core" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | #include "Cholesky" 9 | #include "Jacobi" 10 | #include "Householder" 11 | 12 | /** \defgroup QR_Module QR module 13 | * 14 | * 15 | * 16 | * This module provides various QR decompositions 17 | * This module also provides some MatrixBase methods, including: 18 | * - MatrixBase::qr(), 19 | * 20 | * \code 21 | * #include 22 | * \endcode 23 | */ 24 | 25 | #include "src/misc/Solve.h" 26 | #include "src/QR/HouseholderQR.h" 27 | #include "src/QR/FullPivHouseholderQR.h" 28 | #include "src/QR/ColPivHouseholderQR.h" 29 | #ifdef EIGEN_USE_LAPACKE 30 | #include "src/QR/HouseholderQR_MKL.h" 31 | #include "src/QR/ColPivHouseholderQR_MKL.h" 32 | #endif 33 | 34 | #ifdef EIGEN2_SUPPORT 35 | #include "src/Eigen2Support/QR.h" 36 | #endif 37 | 38 | #include "src/Core/util/ReenableStupidWarnings.h" 39 | 40 | #ifdef EIGEN2_SUPPORT 41 | #include "Eigenvalues" 42 | #endif 43 | 44 | #endif // EIGEN_QR_MODULE_H 45 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 46 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/QtAlignedMalloc: -------------------------------------------------------------------------------- 1 | 2 | #ifndef EIGEN_QTMALLOC_MODULE_H 3 | #define EIGEN_QTMALLOC_MODULE_H 4 | 5 | #include "Core" 6 | 7 | #if (!EIGEN_MALLOC_ALREADY_ALIGNED) 8 | 9 | #include "src/Core/util/DisableStupidWarnings.h" 10 | 11 | void *qMalloc(size_t size) 12 | { 13 | return Eigen::internal::aligned_malloc(size); 14 | } 15 | 16 | void qFree(void *ptr) 17 | { 18 | Eigen::internal::aligned_free(ptr); 19 | } 20 | 21 | void *qRealloc(void *ptr, size_t size) 22 | { 23 | void* newPtr = Eigen::internal::aligned_malloc(size); 24 | memcpy(newPtr, ptr, size); 25 | Eigen::internal::aligned_free(ptr); 26 | return newPtr; 27 | } 28 | 29 | #include "src/Core/util/ReenableStupidWarnings.h" 30 | 31 | #endif 32 | 33 | #endif // EIGEN_QTMALLOC_MODULE_H 34 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 35 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/SVD: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_SVD_MODULE_H 2 | #define EIGEN_SVD_MODULE_H 3 | 4 | #include "QR" 5 | #include "Householder" 6 | #include "Jacobi" 7 | 8 | #include "src/Core/util/DisableStupidWarnings.h" 9 | 10 | /** \defgroup SVD_Module SVD module 11 | * 12 | * 13 | * 14 | * This module provides SVD decomposition for matrices (both real and complex). 15 | * This decomposition is accessible via the following MatrixBase method: 16 | * - MatrixBase::jacobiSvd() 17 | * 18 | * \code 19 | * #include 20 | * \endcode 21 | */ 22 | 23 | #include "src/misc/Solve.h" 24 | #include "src/SVD/JacobiSVD.h" 25 | #if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT) 26 | #include "src/SVD/JacobiSVD_MKL.h" 27 | #endif 28 | #include "src/SVD/UpperBidiagonalization.h" 29 | 30 | #ifdef EIGEN2_SUPPORT 31 | #include "src/Eigen2Support/SVD.h" 32 | #endif 33 | 34 | #include "src/Core/util/ReenableStupidWarnings.h" 35 | 36 | #endif // EIGEN_SVD_MODULE_H 37 | /* vim: set filetype=cpp et sw=2 ts=2 ai: */ 38 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/Sparse: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_SPARSE_MODULE_H 2 | #define EIGEN_SPARSE_MODULE_H 3 | 4 | /** \defgroup Sparse_modules Sparse modules 5 | * 6 | * Meta-module including all related modules: 7 | * - SparseCore 8 | * - OrderingMethods 9 | * - SparseCholesky 10 | * - IterativeLinearSolvers 11 | * 12 | * \code 13 | * #include 14 | * \endcode 15 | */ 16 | 17 | #include "SparseCore" 18 | #include "OrderingMethods" 19 | #include "SparseCholesky" 20 | #include "IterativeLinearSolvers" 21 | 22 | #endif // EIGEN_SPARSE_MODULE_H 23 | 24 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/SparseCholesky: -------------------------------------------------------------------------------- 1 | #ifndef EIGEN_SPARSECHOLESKY_MODULE_H 2 | #define EIGEN_SPARSECHOLESKY_MODULE_H 3 | 4 | #include "SparseCore" 5 | 6 | #include "src/Core/util/DisableStupidWarnings.h" 7 | 8 | /** \ingroup Sparse_modules 9 | * \defgroup SparseCholesky_Module SparseCholesky module 10 | * 11 | * This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian) matrices. 12 | * Those decompositions are accessible via the following classes: 13 | * - SimplicialLLt, 14 | * - SimplicialLDLt 15 | * 16 | * Such problems can also be solved using the ConjugateGradient solver from the IterativeLinearSolvers module. 17 | * 18 | * \code 19 | * #include 20 | * \endcode 21 | */ 22 | 23 | #include "src/misc/Solve.h" 24 | #include "src/misc/SparseSolve.h" 25 | 26 | #include "src/SparseCholesky/SimplicialCholesky.h" 27 | 28 | #include "src/Core/util/ReenableStupidWarnings.h" 29 | 30 | #endif // EIGEN_SPARSECHOLESKY_MODULE_H 31 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/StdDeque: -------------------------------------------------------------------------------- 1 | // This file is part of Eigen, a lightweight C++ template library 2 | // for linear algebra. 3 | // 4 | // Copyright (C) 2009 Gael Guennebaud 5 | // Copyright (C) 2009 Hauke Heibel 6 | // 7 | // This Source Code Form is subject to the terms of the Mozilla 8 | // Public License v. 2.0. If a copy of the MPL was not distributed 9 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 10 | 11 | #ifndef EIGEN_STDDEQUE_MODULE_H 12 | #define EIGEN_STDDEQUE_MODULE_H 13 | 14 | #include "Core" 15 | #include 16 | 17 | #if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */ 18 | 19 | #define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...) 20 | 21 | #else 22 | 23 | #include "src/StlSupport/StdDeque.h" 24 | 25 | #endif 26 | 27 | #endif // EIGEN_STDDEQUE_MODULE_H 28 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/StdList: -------------------------------------------------------------------------------- 1 | // This file is part of Eigen, a lightweight C++ template library 2 | // for linear algebra. 3 | // 4 | // Copyright (C) 2009 Hauke Heibel 5 | // 6 | // This Source Code Form is subject to the terms of the Mozilla 7 | // Public License v. 2.0. If a copy of the MPL was not distributed 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9 | 10 | #ifndef EIGEN_STDLIST_MODULE_H 11 | #define EIGEN_STDLIST_MODULE_H 12 | 13 | #include "Core" 14 | #include 15 | 16 | #if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */ 17 | 18 | #define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...) 19 | 20 | #else 21 | 22 | #include "src/StlSupport/StdList.h" 23 | 24 | #endif 25 | 26 | #endif // EIGEN_STDLIST_MODULE_H 27 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/StdVector: -------------------------------------------------------------------------------- 1 | // This file is part of Eigen, a lightweight C++ template library 2 | // for linear algebra. 3 | // 4 | // Copyright (C) 2009 Gael Guennebaud 5 | // Copyright (C) 2009 Hauke Heibel 6 | // 7 | // This Source Code Form is subject to the terms of the Mozilla 8 | // Public License v. 2.0. If a copy of the MPL was not distributed 9 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 10 | 11 | #ifndef EIGEN_STDVECTOR_MODULE_H 12 | #define EIGEN_STDVECTOR_MODULE_H 13 | 14 | #include "Core" 15 | #include 16 | 17 | #if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */ 18 | 19 | #define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...) 20 | 21 | #else 22 | 23 | #include "src/StlSupport/StdVector.h" 24 | 25 | #endif 26 | 27 | #endif // EIGEN_STDVECTOR_MODULE_H 28 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | file(GLOB Eigen_src_subdirectories "*") 2 | escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}") 3 | foreach(f ${Eigen_src_subdirectories}) 4 | if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" ) 5 | add_subdirectory(${f}) 6 | endif() 7 | endforeach() 8 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Cholesky/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Cholesky_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Cholesky_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Cholesky COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/CholmodSupport/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_CholmodSupport_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_CholmodSupport_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/CholmodSupport COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Core/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core COMPONENT Devel 6 | ) 7 | 8 | ADD_SUBDIRECTORY(products) 9 | ADD_SUBDIRECTORY(util) 10 | ADD_SUBDIRECTORY(arch) 11 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Core/arch/AltiVec/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_arch_AltiVec_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_arch_AltiVec_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/AltiVec COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Core/arch/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | ADD_SUBDIRECTORY(SSE) 2 | ADD_SUBDIRECTORY(AltiVec) 3 | ADD_SUBDIRECTORY(NEON) 4 | ADD_SUBDIRECTORY(Default) 5 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Core/arch/Default/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_arch_Default_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_arch_Default_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/Default COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Core/arch/NEON/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_arch_NEON_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_arch_NEON_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/NEON COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Core/arch/SSE/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_arch_SSE_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_arch_SSE_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/SSE COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Core/products/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_Product_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_Product_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/products COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Core/util/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Core_util_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Core_util_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/util COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Core/util/NonMPL2.h: -------------------------------------------------------------------------------- 1 | #ifdef EIGEN_MPL2_ONLY 2 | #error Including non-MPL2 code in EIGEN_MPL2_ONLY mode 3 | #endif 4 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Core/util/ReenableStupidWarnings.h: -------------------------------------------------------------------------------- 1 | #ifdef EIGEN_WARNINGS_DISABLED 2 | #undef EIGEN_WARNINGS_DISABLED 3 | 4 | #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS 5 | #ifdef _MSC_VER 6 | #pragma warning( pop ) 7 | #elif defined __INTEL_COMPILER 8 | #pragma warning pop 9 | #elif defined __clang__ 10 | #pragma clang diagnostic pop 11 | #endif 12 | #endif 13 | 14 | #endif // EIGEN_WARNINGS_DISABLED 15 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Eigen2Support/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Eigen2Support_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Eigen2Support_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Eigen2Support COMPONENT Devel 6 | ) 7 | 8 | ADD_SUBDIRECTORY(Geometry) -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Eigen2Support/Geometry/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Eigen2Support_Geometry_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Eigen2Support_Geometry_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Eigen2Support/Geometry 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Eigen2Support/Macros.h: -------------------------------------------------------------------------------- 1 | // This file is part of Eigen, a lightweight C++ template library 2 | // for linear algebra. 3 | // 4 | // Copyright (C) 2011 Benoit Jacob 5 | // 6 | // This Source Code Form is subject to the terms of the Mozilla 7 | // Public License v. 2.0. If a copy of the MPL was not distributed 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9 | 10 | #ifndef EIGEN2_MACROS_H 11 | #define EIGEN2_MACROS_H 12 | 13 | #define ei_assert eigen_assert 14 | #define ei_internal_assert eigen_internal_assert 15 | 16 | #define EIGEN_ALIGN_128 EIGEN_ALIGN16 17 | 18 | #define EIGEN_ARCH_WANTS_ALIGNMENT EIGEN_ALIGN_STATICALLY 19 | 20 | #endif // EIGEN2_MACROS_H 21 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Eigenvalues/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_EIGENVALUES_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_EIGENVALUES_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Eigenvalues COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Geometry/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Geometry_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Geometry_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Geometry COMPONENT Devel 6 | ) 7 | 8 | ADD_SUBDIRECTORY(arch) 9 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Geometry/arch/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Geometry_arch_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Geometry_arch_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Geometry/arch COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Householder/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Householder_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Householder_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Householder COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/IterativeLinearSolvers/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_IterativeLinearSolvers_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_IterativeLinearSolvers_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/IterativeLinearSolvers COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/Jacobi/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_Jacobi_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_Jacobi_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Jacobi COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/LU/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_LU_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_LU_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/LU COMPONENT Devel 6 | ) 7 | 8 | ADD_SUBDIRECTORY(arch) 9 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/LU/arch/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_LU_arch_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_LU_arch_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/LU/arch COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/OrderingMethods/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_OrderingMethods_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_OrderingMethods_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/OrderingMethods COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/PaStiXSupport/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_PastixSupport_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_PastixSupport_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/PaStiXSupport COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/PardisoSupport/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_PardisoSupport_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_PardisoSupport_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/PardisoSupport COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/QR/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_QR_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_QR_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/QR COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/SVD/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_SVD_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_SVD_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SVD COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/SparseCholesky/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_SparseCholesky_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_SparseCholesky_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SparseCholesky COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/SparseCore/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_SparseCore_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_SparseCore_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SparseCore COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/SparseCore/SparseAssign.h: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/SparseCore/SparseAssign.h -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/SparseCore/SparseFuzzy.h: -------------------------------------------------------------------------------- 1 | // This file is part of Eigen, a lightweight C++ template library 2 | // for linear algebra. 3 | // 4 | // Copyright (C) 2008 Gael Guennebaud 5 | // 6 | // This Source Code Form is subject to the terms of the Mozilla 7 | // Public License v. 2.0. If a copy of the MPL was not distributed 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9 | 10 | #ifndef EIGEN_SPARSE_FUZZY_H 11 | #define EIGEN_SPARSE_FUZZY_H 12 | 13 | // template 14 | // template 15 | // bool SparseMatrixBase::isApprox( 16 | // const OtherDerived& other, 17 | // typename NumTraits::Real prec 18 | // ) const 19 | // { 20 | // const typename internal::nested::type nested(derived()); 21 | // const typename internal::nested::type otherNested(other.derived()); 22 | // return (nested - otherNested).cwise().abs2().sum() 23 | // <= prec * prec * (std::min)(nested.cwise().abs2().sum(), otherNested.cwise().abs2().sum()); 24 | // } 25 | 26 | #endif // EIGEN_SPARSE_FUZZY_H 27 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/StlSupport/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_StlSupport_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_StlSupport_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/StlSupport COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/SuperLUSupport/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_SuperLUSupport_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_SuperLUSupport_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SuperLUSupport COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/UmfPackSupport/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_UmfPackSupport_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_UmfPackSupport_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/UmfPackSupport COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/misc/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_misc_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_misc_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/misc COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/include/Eigen/src/plugins/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | FILE(GLOB Eigen_plugins_SRCS "*.h") 2 | 3 | INSTALL(FILES 4 | ${Eigen_plugins_SRCS} 5 | DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/plugins COMPONENT Devel 6 | ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/densecrf3d/src/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | include_directories( ../external/liblbfgs/include ../external/ ) 2 | 3 | add_library( densecrf util.cpp permutohedral.cpp unary.cpp pairwise.cpp objective.cpp densecrf.cpp labelcompatibility.cpp ) 4 | 5 | add_library( optimization optimization.cpp ) 6 | target_link_libraries( optimization lbfgs ) 7 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dependency/maxflow-v3.0/instances.inc: -------------------------------------------------------------------------------- 1 | #include "graph.h" 2 | 3 | #ifdef _MSC_VER 4 | #pragma warning(disable: 4661) 5 | #endif 6 | 7 | // Instantiations: 8 | // IMPORTANT: 9 | // flowtype should be 'larger' than tcaptype 10 | // tcaptype should be 'larger' than captype 11 | 12 | template class Graph; 13 | template class Graph; 14 | template class Graph; 15 | template class Graph; 16 | 17 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dist/SimpleCRF-0.1.0-py3.7-linux-x86_64.egg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/SimpleCRF-master/dist/SimpleCRF-0.1.0-py3.7-linux-x86_64.egg -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/dist/SimpleCRF-0.1.0-py3.8-linux-x86_64.egg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/SimpleCRF-master/dist/SimpleCRF-0.1.0-py3.8-linux-x86_64.egg -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/maxflow_python/wrap_py2.cpp: -------------------------------------------------------------------------------- 1 | #include "maxflow.cpp" 2 | 3 | 4 | PyMODINIT_FUNC 5 | initmaxflow(void) { 6 | (void) Py_InitModule("maxflow", Methods); 7 | import_array(); 8 | } 9 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/maxflow_python/wrap_py3.cpp: -------------------------------------------------------------------------------- 1 | #include "maxflow.cpp" 2 | 3 | static struct PyModuleDef Maxflow = 4 | { 5 | PyModuleDef_HEAD_INIT, 6 | "maxflow", /* name of module */ 7 | "", /* module documentation, may be NULL */ 8 | -1, /* size of per-interpreter state of the module, or -1 if the module keeps state in global variables. */ 9 | Methods 10 | }; 11 | 12 | PyMODINIT_FUNC PyInit_maxflow(void) { 13 | import_array(); 14 | return PyModule_Create(&Maxflow); 15 | } 16 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/SimpleCRF-master/requirements.txt: -------------------------------------------------------------------------------- 1 | numpy>=1.19.1 2 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/WeaklySupervised-GrandChallenge-Logo.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/WeaklySupervised-GrandChallenge-Logo.jpeg -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/HIP_Logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/HIP_Logo.png -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/documentation/using_nnUNet_as_baseline.md: -------------------------------------------------------------------------------- 1 | (The U-Net is the current punching bag of methods development. nnU-Net is going to be that looking forward. That is 2 | cool (great, in fact!), but it should be done correctly. Here are tips on how to benchmark against nnU-Net) 3 | 4 | This is work in progress -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/__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 * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/configuration.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | default_num_threads = 4 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) -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/dataset_conversion/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/evaluation/model_selection/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/experiment_planning/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/normalization/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/normalization/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/patch_size/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/patch_size/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/pooling_and_convs/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/pooling_and_convs/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/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. -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/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) -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/experiment_planning/old/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/experiment_planning/old/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/inference/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/inference/pretrained_models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/inference/pretrained_models/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/network_architecture/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/network_architecture/custom_modules/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/network_architecture/custom_modules/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/postprocessing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/postprocessing/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/preprocessing/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/preprocessing/custom_preprocessors/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/preprocessing/custom_preprocessors/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/run/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/cascade_stuff/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/data_augmentation/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/dataloading/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/learning_rate/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/learning_rate/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/learning_rate/poly_lr.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 | def poly_lr(epoch, max_epochs, initial_lr, exponent=0.9): 17 | return initial_lr * (1 - epoch / max_epochs)**exponent 18 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/loss_functions/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/loss_functions/crossentropy.py: -------------------------------------------------------------------------------- 1 | from torch import nn, Tensor 2 | 3 | 4 | class RobustCrossEntropyLoss(nn.CrossEntropyLoss): 5 | """ 6 | this is just a compatibility layer because my target tensor is float and has an extra dimension 7 | """ 8 | def forward(self, input: Tensor, target: Tensor) -> Tensor: 9 | if len(target.shape) == len(input.shape): 10 | assert target.shape[1] == 1 11 | target = target[:, 0] 12 | return super().forward(input, target.long()) -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/competitions_with_custom_Trainers/BraTS2020/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/competitions_with_custom_Trainers/BraTS2020/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/competitions_with_custom_Trainers/MMS/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/competitions_with_custom_Trainers/MMS/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/competitions_with_custom_Trainers/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/competitions_with_custom_Trainers/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/architectural_variants/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/architectural_variants/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/benchmarking/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/benchmarking/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/cascade/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/cascade/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/copies/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/copies/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/data_augmentation/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/data_augmentation/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/loss_function/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/loss_function/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/miscellaneous/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/miscellaneous/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/resampling/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/resampling/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/training/optimizer/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/3-WeaklySupervisedLearning/nnUNet/nnunet/training/optimizer/__init__.py -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/utilities/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /3-WeaklySupervisedLearning/nnUNet/setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | description-file = readme.md -------------------------------------------------------------------------------- /4-ContinualLearning/Continual-Learning-GrandChallenge-Logo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/Continual-Learning-GrandChallenge-Logo.jpg -------------------------------------------------------------------------------- /4-ContinualLearning/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM ashishsingh21/nnunet-gpu:0.1.0 2 | 3 | RUN mkdir -p /workspace/datasets/nnUNet/3d_fullres/Task444_Subtask1/ 4 | COPY . /workspace/datasets/nnUNet/3d_fullres/Task444_Subtask1/ 5 | 6 | COPY predict.sh /workspace/ 7 | -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/HIP_Logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/HIP_Logo.png -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/documentation/using_nnUNet_as_baseline.md: -------------------------------------------------------------------------------- 1 | (The U-Net is the current punching bag of methods development. nnU-Net is going to be that looking forward. That is 2 | cool (great, in fact!), but it should be done correctly. Here are tips on how to benchmark against nnU-Net) 3 | 4 | This is work in progress -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/__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 * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/configuration.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | default_num_threads = 4 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) -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/dataset_conversion/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/evaluation/model_selection/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/experiment_planning/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/normalization/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/normalization/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/patch_size/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/patch_size/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/pooling_and_convs/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/pooling_and_convs/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/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. -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/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) -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/experiment_planning/old/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/experiment_planning/old/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/inference/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/inference/pretrained_models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/inference/pretrained_models/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/network_architecture/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/network_architecture/custom_modules/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/network_architecture/custom_modules/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/postprocessing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/postprocessing/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/preprocessing/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/preprocessing/custom_preprocessors/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/preprocessing/custom_preprocessors/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/run/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/training/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/training/cascade_stuff/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/training/data_augmentation/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/training/dataloading/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/training/learning_rate/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/training/learning_rate/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/training/learning_rate/poly_lr.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 | def poly_lr(epoch, max_epochs, initial_lr, exponent=0.9): 17 | return initial_lr * (1 - epoch / max_epochs)**exponent 18 | -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/training/loss_functions/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/training/loss_functions/crossentropy.py: -------------------------------------------------------------------------------- 1 | from torch import nn, Tensor 2 | 3 | 4 | class RobustCrossEntropyLoss(nn.CrossEntropyLoss): 5 | """ 6 | this is just a compatibility layer because my target tensor is float and has an extra dimension 7 | """ 8 | def forward(self, input: Tensor, target: Tensor) -> Tensor: 9 | if len(target.shape) == len(input.shape): 10 | assert target.shape[1] == 1 11 | target = target[:, 0] 12 | return super().forward(input, target.long()) -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/training/network_training/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/training/network_training/competitions_with_custom_Trainers/BraTS2020/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/training/network_training/competitions_with_custom_Trainers/BraTS2020/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/training/network_training/competitions_with_custom_Trainers/MMS/__init__.py: -------------------------------------------------------------------------------- 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/4-ContinualLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/data_augmentation/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/data_augmentation/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/training/network_training/nnUNet_variants/loss_function/__init__.py: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/4-ContinualLearning/nnUNet/nnunet/training/optimizer/__init__.py -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/utilities/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/nnunet/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 | -------------------------------------------------------------------------------- /4-ContinualLearning/nnUNet/setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | description-file = readme.md -------------------------------------------------------------------------------- /Abdomen1K-supp.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JunMa11/AbdomenCT-1K/54c5e084dab5288185a97c378862f0561f3f6a3c/Abdomen1K-supp.pdf --------------------------------------------------------------------------------