SYMBOL INDEX (745 symbols across 119 files) FILE: documentation/competitions/FLARE24/Task_1/inference_flare_task1.py class FlarePredictor (line 25) | class FlarePredictor(nnUNetPredictor): method initialize_from_trained_model_folder (line 26) | def initialize_from_trained_model_folder(self, model_training_output_d... function convert_predicted_logits_to_segmentation_with_correct_shape (line 86) | def convert_predicted_logits_to_segmentation_with_correct_shape(predicte... function export_prediction_from_logits (line 129) | def export_prediction_from_logits(predicted_array_or_file: Union[np.ndar... function predict_flare (line 152) | def predict_flare(input_dir, output_dir, model_folder, folds=("all",)): FILE: documentation/competitions/FLARE24/Task_2/inference_flare_task2.py class FlarePredictor (line 36) | class FlarePredictor(nnUNetPredictor): method __init__ (line 37) | def __init__(self, method initialize_from_trained_model_folder (line 52) | def initialize_from_trained_model_folder(self, model_training_output_d... method predict_from_files (line 132) | def predict_from_files(self, method _internal_maybe_mirror_and_predict (line 162) | def _internal_maybe_mirror_and_predict(self, x: torch.Tensor) -> torch... method _internal_predict_sliding_window_return_logits (line 186) | def _internal_predict_sliding_window_return_logits(self, method predict_logits_from_preprocessed_data (line 243) | def predict_logits_from_preprocessed_data(self, data: torch.Tensor) ->... method predict_sliding_window_return_logits (line 283) | def predict_sliding_window_return_logits(self, input_image: torch.Tens... method convert_predicted_logits_to_segmentation_with_correct_shape (line 332) | def convert_predicted_logits_to_segmentation_with_correct_shape(self, ... method export_prediction_from_logits (line 367) | def export_prediction_from_logits(self, predicted_array_or_file: Union... method predict_single_npy_array (line 389) | def predict_single_npy_array(self, input_image: np.ndarray, image_prop... function predict_flare (line 425) | def predict_flare(input_dir, output_dir, model_folder, save_model): FILE: documentation/competitions/Toothfairy2/inference_script_semseg_only_customInf2.py class CustomPredictor (line 27) | class CustomPredictor(nnUNetPredictor): method predict_single_npy_array (line 28) | def predict_single_npy_array(self, input_image: np.ndarray, image_prop... method initialize_from_trained_model_folder (line 56) | def initialize_from_trained_model_folder(self, model_training_output_d... method predict_preprocessed_image (line 116) | def predict_preprocessed_image(self, image): method convert_predicted_logits_to_segmentation_with_correct_shape (line 159) | def convert_predicted_logits_to_segmentation_with_correct_shape(self, ... function predict_semseg (line 207) | def predict_semseg(im, prop, semseg_trained_model, semseg_folds): function map_labels_to_toothfairy (line 230) | def map_labels_to_toothfairy(predicted_seg: np.ndarray) -> np.ndarray: function postprocess (line 247) | def postprocess(prediction_npy, vol_per_voxel, verbose: bool = False): FILE: nnunetv2/batch_running/collect_results_custom_Decathlon.py function collect_results (line 12) | def collect_results(trainers: dict, datasets: List, output_file: str, function summarize (line 43) | def summarize(input_file, output_file, folds: Tuple[int, ...], configs: ... FILE: nnunetv2/batch_running/generate_lsf_runs_customDecathlon.py function merge (line 5) | def merge(dict1, dict2): FILE: nnunetv2/batch_running/release_trainings/nnunetv2_v1/collect_results.py function collect_results (line 12) | def collect_results(trainers: dict, datasets: List, output_file: str, function summarize (line 43) | def summarize(input_file, output_file, folds: Tuple[int, ...], configs: ... FILE: nnunetv2/batch_running/release_trainings/nnunetv2_v1/generate_lsf_commands.py function merge (line 5) | def merge(dict1, dict2): FILE: nnunetv2/dataset_conversion/Dataset027_ACDC.py function make_out_dirs (line 12) | def make_out_dirs(dataset_id: int, task_name="ACDC"): function create_ACDC_split (line 28) | def create_ACDC_split(labelsTr_folder: str, seed: int = 1234) -> List[di... function copy_files (line 44) | def copy_files(src_data_folder: Path, train_dir: Path, labels_dir: Path,... function convert_acdc (line 70) | def convert_acdc(src_data_folder: str, dataset_id=27): FILE: nnunetv2/dataset_conversion/Dataset042_BraTS18.py function copy_BraTS_segmentation_and_convert_labels_to_nnUNet (line 12) | def copy_BraTS_segmentation_and_convert_labels_to_nnUNet(in_file: str, o... function convert_labels_back_to_BraTS (line 32) | def convert_labels_back_to_BraTS(seg: np.ndarray): function load_convert_labels_back_to_BraTS (line 40) | def load_convert_labels_back_to_BraTS(filename, input_folder, output_fol... function convert_folder_with_preds_back_to_BraTS_labeling_convention (line 49) | def convert_folder_with_preds_back_to_BraTS_labeling_convention(input_fo... FILE: nnunetv2/dataset_conversion/Dataset043_BraTS19.py function copy_BraTS_segmentation_and_convert_labels_to_nnUNet (line 12) | def copy_BraTS_segmentation_and_convert_labels_to_nnUNet(in_file: str, o... function convert_labels_back_to_BraTS (line 32) | def convert_labels_back_to_BraTS(seg: np.ndarray): function load_convert_labels_back_to_BraTS (line 40) | def load_convert_labels_back_to_BraTS(filename, input_folder, output_fol... function convert_folder_with_preds_back_to_BraTS_labeling_convention (line 49) | def convert_folder_with_preds_back_to_BraTS_labeling_convention(input_fo... FILE: nnunetv2/dataset_conversion/Dataset114_MNMs.py function read_csv (line 14) | def read_csv(csv_file: str): function convert_mnms (line 38) | def convert_mnms(src_data_folder: Path, csv_file_name: str, dataset_id: ... function save_cardiac_phases (line 64) | def save_cardiac_phases( function save_extracted_nifti_slice (line 81) | def save_extracted_nifti_slice(image, ed_frame: int, es_frame: int, out_... function create_custom_splits (line 96) | def create_custom_splits(src_data_folder: Path, csv_file: str, dataset_i... function get_vendor_split (line 132) | def get_vendor_split(patients: list[str], num_val_patients: int): class RawTextArgumentDefaultsHelpFormatter (line 142) | class RawTextArgumentDefaultsHelpFormatter(argparse.ArgumentDefaultsHelp... FILE: nnunetv2/dataset_conversion/Dataset115_EMIDEC.py function copy_files (line 8) | def copy_files(src_data_dir: Path, src_test_dir: Path, train_dir: Path, ... function convert_emidec (line 28) | def convert_emidec(src_data_dir: str, src_test_dir: str, dataset_id=27): FILE: nnunetv2/dataset_conversion/Dataset119_ToothFairy2_All.py function mapping_DS119 (line 13) | def mapping_DS119() -> Dict[int, int]: function mapping_DS120 (line 23) | def mapping_DS120() -> Dict[int, int]: function mapping_DS121 (line 34) | def mapping_DS121() -> Dict[int, int]: function load_json (line 44) | def load_json(json_file: str) -> Any: function write_json (line 50) | def write_json(json_file: str, data: Any, indent: int = 4) -> None: function image_to_nifi (line 55) | def image_to_nifi(input_path: str, output_path: str) -> None: function label_mapping (line 60) | def label_mapping(input_path: str, output_path: str, mapping: Dict[int, ... function process_images (line 77) | def process_images(files: str, img_dir_in: str, img_dir_out: str, n_proc... function process_labels (line 93) | def process_labels( function process_ds (line 112) | def process_ds( FILE: nnunetv2/dataset_conversion/Dataset120_RoadSegmentation.py function load_and_convert_case (line 14) | def load_and_convert_case(input_image: str, input_seg: str, output_image... FILE: nnunetv2/dataset_conversion/Dataset137_BraTS21.py function copy_BraTS_segmentation_and_convert_labels_to_nnUNet (line 11) | def copy_BraTS_segmentation_and_convert_labels_to_nnUNet(in_file: str, o... function convert_labels_back_to_BraTS (line 31) | def convert_labels_back_to_BraTS(seg: np.ndarray): function load_convert_labels_back_to_BraTS (line 39) | def load_convert_labels_back_to_BraTS(filename, input_folder, output_fol... function convert_folder_with_preds_back_to_BraTS_labeling_convention (line 48) | def convert_folder_with_preds_back_to_BraTS_labeling_convention(input_fo... FILE: nnunetv2/dataset_conversion/Dataset218_Amos2022_task1.py function convert_amos_task1 (line 7) | def convert_amos_task1(amos_base_dir: str, nnunet_dataset_id: int = 218): FILE: nnunetv2/dataset_conversion/Dataset219_Amos2022_task2.py function convert_amos_task2 (line 7) | def convert_amos_task2(amos_base_dir: str, nnunet_dataset_id: int = 219): FILE: nnunetv2/dataset_conversion/Dataset220_KiTS2023.py function convert_kits2023 (line 7) | def convert_kits2023(kits_base_dir: str, nnunet_dataset_id: int = 220): FILE: nnunetv2/dataset_conversion/Dataset221_AutoPETII_2023.py function convert_autopet (line 7) | def convert_autopet(autopet_base_dir:str = '/media/isensee/My Book1/Auto... FILE: nnunetv2/dataset_conversion/convert_MSD_dataset.py function split_4d_nifti (line 13) | def split_4d_nifti(filename, output_folder): function convert_msd_dataset (line 40) | def convert_msd_dataset(source_folder: str, overwrite_target_id: Optiona... function entry_point (line 116) | def entry_point(): FILE: nnunetv2/dataset_conversion/convert_raw_dataset_from_old_nnunet_format.py function convert (line 8) | def convert(source_folder, target_dataset_name): function convert_entry_point (line 43) | def convert_entry_point(): FILE: nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset996_IntegrationTest_Hippocampus_regions_ignore.py function sparsify_segmentation (line 13) | def sparsify_segmentation(seg: np.ndarray, label_manager: LabelManager, ... FILE: nnunetv2/dataset_conversion/generate_dataset_json.py function generate_dataset_json (line 6) | def generate_dataset_json(output_folder: str, FILE: nnunetv2/ensembling/ensemble.py function average_probabilities (line 16) | def average_probabilities(list_of_files: List[str]) -> np.ndarray: function merge_files (line 31) | def merge_files(list_of_files, function ensemble_folders (line 48) | def ensemble_folders(list_of_input_folders: List[str], function entry_point_ensemble_folders (line 113) | def entry_point_ensemble_folders(): function ensemble_crossvalidations (line 127) | def ensemble_crossvalidations(list_of_trained_model_folders: List[str], FILE: nnunetv2/evaluation/accumulate_cv_results.py function accumulate_cv_results (line 12) | def accumulate_cv_results(trained_model_folder, FILE: nnunetv2/evaluation/evaluate_predictions.py function label_or_region_to_key (line 19) | def label_or_region_to_key(label_or_region: Union[int, Tuple[int]]): function key_to_label_or_region (line 23) | def key_to_label_or_region(key: str): function save_summary_json (line 33) | def save_summary_json(results: dict, output_file: str): function load_summary_json (line 50) | def load_summary_json(filename: str): function labels_to_list_of_regions (line 62) | def labels_to_list_of_regions(labels: List[int]): function region_or_label_to_mask (line 66) | def region_or_label_to_mask(segmentation: np.ndarray, region_or_label: U... function compute_tp_fp_fn_tn (line 76) | def compute_tp_fp_fn_tn(mask_ref: np.ndarray, mask_pred: np.ndarray, ign... function compute_metrics (line 88) | def compute_metrics(reference_file: str, prediction_file: str, image_rea... function compute_metrics_on_folder (line 121) | def compute_metrics_on_folder(folder_ref: str, folder_pred: str, output_... function compute_metrics_on_folder2 (line 177) | def compute_metrics_on_folder2(folder_ref: str, folder_pred: str, datase... function compute_metrics_on_folder_simple (line 199) | def compute_metrics_on_folder_simple(folder_ref: str, folder_pred: str, ... function evaluate_folder_entry_point (line 215) | def evaluate_folder_entry_point(): function evaluate_simple_entry_point (line 233) | def evaluate_simple_entry_point(): FILE: nnunetv2/evaluation/find_best_configuration.py function filter_available_models (line 27) | def filter_available_models(model_dict: Union[List[dict], Tuple[dict, ..... function generate_inference_command (line 52) | def generate_inference_command(dataset_name_or_id: Union[int, str], conf... function find_best_configuration (line 82) | def find_best_configuration(dataset_name_or_id, function print_inference_instructions (line 215) | def print_inference_instructions(inference_info_dict: dict, instructions... function dumb_trainer_config_plans_to_trained_models_dict (line 258) | def dumb_trainer_config_plans_to_trained_models_dict(trainers: List[str]... function find_best_configuration_entry_point (line 272) | def find_best_configuration_entry_point(): function accumulate_crossval_results_entry_point (line 301) | def accumulate_crossval_results_entry_point(): FILE: nnunetv2/experiment_planning/dataset_fingerprint/fingerprint_extractor.py class DatasetFingerprintExtractor (line 18) | class DatasetFingerprintExtractor(object): method __init__ (line 19) | def __init__(self, dataset_name_or_id: Union[str, int], num_processes:... method collect_foreground_intensities (line 43) | def collect_foreground_intensities(segmentation: np.ndarray, images: n... method analyze_case (line 91) | def analyze_case(image_files: List[str], segmentation_file: str, reade... method run (line 115) | def run(self, overwrite_existing: bool = False) -> dict: FILE: nnunetv2/experiment_planning/experiment_planners/default_experiment_planner.py class ExperimentPlanner (line 24) | class ExperimentPlanner(object): method __init__ (line 25) | def __init__(self, dataset_name_or_id: Union[str, int], method determine_reader_writer (line 89) | def determine_reader_writer(self): method static_estimate_VRAM_usage (line 94) | def static_estimate_VRAM_usage(patch_size: Tuple[int], method determine_resampling (line 113) | def determine_resampling(self, *args, **kwargs): method determine_segmentation_softmax_export_fn (line 137) | def determine_segmentation_softmax_export_fn(self, *args, **kwargs): method determine_fullres_target_spacing (line 155) | def determine_fullres_target_spacing(self) -> np.ndarray: method determine_normalization_scheme_and_whether_mask_is_used_for_norm (line 198) | def determine_normalization_scheme_and_whether_mask_is_used_for_norm(s... method determine_transpose (line 215) | def determine_transpose(self): method get_plans_for_configuration (line 228) | def get_plans_for_configuration(self, method plan_experiment (line 405) | def plan_experiment(self): method save_plans (line 543) | def save_plans(self, plans): method generate_data_identifier (line 562) | def generate_data_identifier(self, configuration_name: str) -> str: method load_plans (line 570) | def load_plans(self, fname: str): function _maybe_copy_splits_file (line 574) | def _maybe_copy_splits_file(splits_file: str, target_fname: str): FILE: nnunetv2/experiment_planning/experiment_planners/network_topology.py function get_shape_must_be_divisible_by (line 5) | def get_shape_must_be_divisible_by(net_numpool_per_axis): function pad_shape (line 9) | def pad_shape(shape, must_be_divisible_by): function get_pool_and_conv_props (line 30) | def get_pool_and_conv_props(spacing, patch_size, min_feature_map_size, m... FILE: nnunetv2/experiment_planning/experiment_planners/resampling/planners_no_resampling.py class nnUNetPlannerResEncL_noResampling (line 8) | class nnUNetPlannerResEncL_noResampling(nnUNetPlannerResEncL): method __init__ (line 13) | def __init__(self, dataset_name_or_id: Union[str, int], method generate_data_identifier (line 21) | def generate_data_identifier(self, configuration_name: str) -> str: method determine_resampling (line 29) | def determine_resampling(self, *args, **kwargs): method determine_segmentation_softmax_export_fn (line 43) | def determine_segmentation_softmax_export_fn(self, *args, **kwargs): FILE: nnunetv2/experiment_planning/experiment_planners/resampling/resample_with_torch.py class nnUNetPlannerResEncL_torchres (line 10) | class nnUNetPlannerResEncL_torchres(nnUNetPlannerResEncL): method __init__ (line 11) | def __init__(self, dataset_name_or_id: Union[str, int], method generate_data_identifier (line 19) | def generate_data_identifier(self, configuration_name: str) -> str: method determine_resampling (line 27) | def determine_resampling(self, *args, **kwargs): method determine_segmentation_softmax_export_fn (line 49) | def determine_segmentation_softmax_export_fn(self, *args, **kwargs): class nnUNetPlannerResEncL_torchres_sepz (line 67) | class nnUNetPlannerResEncL_torchres_sepz(nnUNetPlannerResEncL): method __init__ (line 68) | def __init__(self, dataset_name_or_id: Union[str, int], method generate_data_identifier (line 76) | def generate_data_identifier(self, configuration_name: str) -> str: method determine_resampling (line 84) | def determine_resampling(self, *args, **kwargs): method determine_segmentation_softmax_export_fn (line 108) | def determine_segmentation_softmax_export_fn(self, *args, **kwargs): class nnUNetPlanner_torchres (line 127) | class nnUNetPlanner_torchres(ExperimentPlanner): method __init__ (line 128) | def __init__(self, dataset_name_or_id: Union[str, int], method generate_data_identifier (line 136) | def generate_data_identifier(self, configuration_name: str) -> str: method determine_resampling (line 144) | def determine_resampling(self, *args, **kwargs): method determine_segmentation_softmax_export_fn (line 166) | def determine_segmentation_softmax_export_fn(self, *args, **kwargs): FILE: nnunetv2/experiment_planning/experiment_planners/resencUNet_planner.py class ResEncUNetPlanner (line 14) | class ResEncUNetPlanner(ExperimentPlanner): method __init__ (line 15) | def __init__(self, dataset_name_or_id: Union[str, int], method generate_data_identifier (line 30) | def generate_data_identifier(self, configuration_name: str) -> str: method get_plans_for_configuration (line 42) | def get_plans_for_configuration(self, FILE: nnunetv2/experiment_planning/experiment_planners/residual_unets/residual_encoder_unet_planners.py class ResEncUNetPlanner (line 17) | class ResEncUNetPlanner(ExperimentPlanner): method __init__ (line 18) | def __init__(self, dataset_name_or_id: Union[str, int], method generate_data_identifier (line 33) | def generate_data_identifier(self, configuration_name: str) -> str: method get_plans_for_configuration (line 45) | def get_plans_for_configuration(self, class nnUNetPlannerResEncM (line 221) | class nnUNetPlannerResEncM(ResEncUNetPlanner): method __init__ (line 225) | def __init__(self, dataset_name_or_id: Union[str, int], class nnUNetPlannerResEncL (line 247) | class nnUNetPlannerResEncL(ResEncUNetPlanner): method __init__ (line 251) | def __init__(self, dataset_name_or_id: Union[str, int], class nnUNetPlannerResEncXL (line 272) | class nnUNetPlannerResEncXL(ResEncUNetPlanner): method __init__ (line 276) | def __init__(self, dataset_name_or_id: Union[str, int], FILE: nnunetv2/experiment_planning/plan_and_preprocess_api.py function extract_fingerprint_dataset (line 18) | def extract_fingerprint_dataset(dataset_id: int, function extract_fingerprints (line 39) | def extract_fingerprints(dataset_ids: List[int], fingerprint_extractor_c... function plan_experiment_dataset (line 55) | def plan_experiment_dataset(dataset_id: int, function plan_experiments (line 80) | def plan_experiments(dataset_ids: List[int], experiment_planner_class_na... function preprocess_dataset (line 104) | def preprocess_dataset(dataset_id: int, function preprocess (line 152) | def preprocess(dataset_ids: List[int], FILE: nnunetv2/experiment_planning/plan_and_preprocess_entrypoints.py function _add_logging_args (line 5) | def _add_logging_args(parser): function extract_fingerprint_entry (line 12) | def extract_fingerprint_entry(): function plan_experiment_entry (line 36) | def plan_experiment_entry(): function preprocess_entry (line 76) | def preprocess_entry(): function plan_and_preprocess_entry (line 111) | def plan_and_preprocess_entry(): FILE: nnunetv2/experiment_planning/plans_for_pretraining/move_plans_between_datasets.py function move_plans_between_datasets (line 12) | def move_plans_between_datasets( function entry_point_move_plans_between_datasets (line 64) | def entry_point_move_plans_between_datasets(): FILE: nnunetv2/experiment_planning/verify_dataset_integrity.py function verify_labels (line 29) | def verify_labels(label_file: str, readerclass: Type[BaseReaderWriter], ... function check_cases (line 44) | def check_cases(image_files: List[str], label_file: str, expected_num_ch... function verify_dataset_integrity (line 116) | def verify_dataset_integrity(folder: str, num_processes: int = 8) -> None: FILE: nnunetv2/imageio/base_reader_writer.py class BaseReaderWriter (line 21) | class BaseReaderWriter(ABC): method _check_all_same (line 23) | def _check_all_same(input_list): method _check_all_same_array (line 31) | def _check_all_same_array(input_list): method read_images (line 39) | def read_images(self, image_fnames: Union[List[str], Tuple[str, ...]])... method read_seg (line 72) | def read_seg(self, seg_fname: str) -> Tuple[np.ndarray, dict]: method write_seg (line 89) | def write_seg(self, seg: np.ndarray, output_fname: str, properties: di... FILE: nnunetv2/imageio/natural_image_reader_writer.py class NaturalImage2DIO (line 22) | class NaturalImage2DIO(BaseReaderWriter): method read_images (line 36) | def read_images(self, image_fnames: Union[List[str], Tuple[str, ...]])... method read_seg (line 61) | def read_seg(self, seg_fname: str) -> Tuple[np.ndarray, dict]: method write_seg (line 64) | def write_seg(self, seg: np.ndarray, output_fname: str, properties: di... FILE: nnunetv2/imageio/nibabel_reader_writer.py class NibabelIO (line 26) | class NibabelIO(BaseReaderWriter): method read_images (line 38) | def read_images(self, image_fnames: Union[List[str], Tuple[str, ...]])... method read_seg (line 91) | def read_seg(self, seg_fname: str) -> Tuple[np.ndarray, dict]: method write_seg (line 94) | def write_seg(self, seg: np.ndarray, output_fname: str, properties: di... class NibabelIOWithReorient (line 101) | class NibabelIOWithReorient(BaseReaderWriter): method read_images (line 115) | def read_images(self, image_fnames: Union[List[str], Tuple[str, ...]])... method read_seg (line 173) | def read_seg(self, seg_fname: str) -> Tuple[np.ndarray, dict]: method write_seg (line 176) | def write_seg(self, seg: np.ndarray, output_fname: str, properties: di... FILE: nnunetv2/imageio/reader_writer_registry.py function determine_reader_writer_from_dataset_json (line 23) | def determine_reader_writer_from_dataset_json(dataset_json_content: dict... function determine_reader_writer_from_file_ending (line 41) | def determine_reader_writer_from_file_ending(file_ending: str, example_f... function recursive_find_reader_writer_by_name (line 73) | def recursive_find_reader_writer_by_name(rw_class_name: str) -> Type[Bas... FILE: nnunetv2/imageio/simpleitk_reader_writer.py class SimpleITKIO (line 22) | class SimpleITKIO(BaseReaderWriter): method read_images (line 30) | def read_images(self, image_fnames: Union[List[str], Tuple[str, ...]])... method read_seg (line 114) | def read_seg(self, seg_fname: str) -> Tuple[np.ndarray, dict]: method write_seg (line 117) | def write_seg(self, seg: np.ndarray, output_fname: str, properties: di... class SimpleITKIOWithReorient (line 132) | class SimpleITKIOWithReorient(SimpleITKIO): method read_images (line 133) | def read_images(self, image_fnames: Union[List[str], Tuple[str, ...]],... method write_seg (line 221) | def write_seg(self, seg, output_fname, properties): FILE: nnunetv2/imageio/tif_reader_writer.py class Tiff3DIO (line 23) | class Tiff3DIO(BaseReaderWriter): method read_images (line 38) | def read_images(self, image_fnames: Union[List[str], Tuple[str, ...]])... method write_seg (line 71) | def write_seg(self, seg: np.ndarray, output_fname: str, properties: di... method read_seg (line 79) | def read_seg(self, seg_fname: str) -> Tuple[np.ndarray, dict]: FILE: nnunetv2/inference/JHU_inference.py function export_prediction_from_logits_singleFiles (line 21) | def export_prediction_from_logits_singleFiles( class JHUPredictor (line 67) | class JHUPredictor(nnUNetPredictor): method predict_from_data_iterator (line 68) | def predict_from_data_iterator(self, FILE: nnunetv2/inference/data_iterators.py function preprocess_fromfiles_save_to_queue (line 17) | def preprocess_fromfiles_save_to_queue(list_of_lists: List[List[str]], function preprocessing_iterator_fromfiles (line 61) | def preprocessing_iterator_fromfiles(list_of_lists: List[List[str]], class PreprocessAdapter (line 122) | class PreprocessAdapter(DataLoader): method __init__ (line 123) | def __init__(self, list_of_lists: List[List[str]], method generate_train_batch (line 148) | def generate_train_batch(self): class PreprocessAdapterFromNpy (line 166) | class PreprocessAdapterFromNpy(DataLoader): method __init__ (line 167) | def __init__(self, list_of_images: List[np.ndarray], method generate_train_batch (line 192) | def generate_train_batch(self): function preprocess_fromnpy_save_to_queue (line 211) | def preprocess_fromnpy_save_to_queue(list_of_images: List[np.ndarray], function preprocessing_iterator_fromnpy (line 257) | def preprocessing_iterator_fromnpy(list_of_images: List[np.ndarray], FILE: nnunetv2/inference/examples.py function my_iterator (line 89) | def my_iterator(list_of_input_arrs, list_of_input_props): FILE: nnunetv2/inference/export_prediction.py function convert_predicted_logits_to_segmentation_with_correct_shape (line 14) | def convert_predicted_logits_to_segmentation_with_correct_shape(predicte... function export_prediction_from_logits (line 74) | def export_prediction_from_logits(predicted_array_or_file: Union[np.ndar... function resample_and_save (line 113) | def resample_and_save(predicted: Union[torch.Tensor, np.ndarray], target... FILE: nnunetv2/inference/predict_from_raw_data.py class nnUNetPredictor (line 39) | class nnUNetPredictor(object): method __init__ (line 40) | def __init__(self, method initialize_from_trained_model_folder (line 67) | def initialize_from_trained_model_folder(self, model_training_output_d... method manual_initialization (line 131) | def manual_initialization(self, network: nn.Module, plans_manager: Pla... method auto_detect_available_folds (line 157) | def auto_detect_available_folds(model_training_output_dir, checkpoint_... method _manage_input_and_output_lists (line 166) | def _manage_input_and_output_lists(self, list_of_lists_or_source_folde... method predict_from_files (line 207) | def predict_from_files(self, method _internal_get_data_iterator_from_lists_of_filenames (line 270) | def _internal_get_data_iterator_from_lists_of_filenames(self, method get_data_iterator_from_raw_npy_data (line 293) | def get_data_iterator_from_raw_npy_data(self, method predict_from_list_of_npy_arrays (line 332) | def predict_from_list_of_npy_arrays(self, method predict_from_data_iterator (line 350) | def predict_from_data_iterator(self, method predict_single_npy_array (line 423) | def predict_single_npy_array(self, input_image: np.ndarray, image_prop... method predict_logits_from_preprocessed_data (line 471) | def predict_logits_from_preprocessed_data(self, data: torch.Tensor) ->... method _internal_get_sliding_window_slicers (line 506) | def _internal_get_sliding_window_slicers(self, image_size: Tuple[int, ... method _internal_maybe_mirror_and_predict (line 541) | def _internal_maybe_mirror_and_predict(self, x: torch.Tensor) -> torch... method _internal_predict_sliding_window_return_logits (line 560) | def _internal_predict_sliding_window_return_logits(self, method predict_sliding_window_return_logits (line 634) | def predict_sliding_window_return_logits(self, input_image: torch.Tens... method predict_from_files_sequential (line 682) | def predict_from_files_sequential(self, function _getDefaultValue (line 769) | def _getDefaultValue(env: str, dtype: type, default: any,) -> any: function predict_entry_point_modelfolder (line 776) | def predict_entry_point_modelfolder(): function predict_entry_point (line 873) | def predict_entry_point(): FILE: nnunetv2/inference/sliding_window_prediction.py function compute_gaussian (line 11) | def compute_gaussian(tile_size: Union[Tuple[int, ...], List[int]], sigma... function compute_steps_for_sliding_window (line 30) | def compute_steps_for_sliding_window(image_size: Tuple[int, ...], tile_s... FILE: nnunetv2/model_sharing/entry_points.py function print_license_warning (line 6) | def print_license_warning(): function download_by_url (line 18) | def download_by_url(): function install_from_zip_entry_point (line 31) | def install_from_zip_entry_point(): function export_pretrained_model_entry (line 41) | def export_pretrained_model_entry(): FILE: nnunetv2/model_sharing/model_download.py function download_and_install_from_url (line 11) | def download_and_install_from_url(url): function download_file (line 37) | def download_file(url: str, local_filename: str, chunk_size: Optional[in... FILE: nnunetv2/model_sharing/model_export.py function export_pretrained_model (line 6) | def export_pretrained_model(dataset_name_or_id: Union[int, str], output_... FILE: nnunetv2/model_sharing/model_import.py function install_model_from_zip_file (line 6) | def install_model_from_zip_file(zip_file: str): FILE: nnunetv2/postprocessing/remove_connected_components.py function remove_all_but_largest_component_from_segmentation (line 21) | def remove_all_but_largest_component_from_segmentation(segmentation: np.... function apply_postprocessing (line 36) | def apply_postprocessing(segmentation: np.ndarray, pp_fns: List[Callable... function load_postprocess_save (line 42) | def load_postprocess_save(segmentation_file: str, function determine_postprocessing (line 52) | def determine_postprocessing(folder_predictions: str, function apply_postprocessing_to_folder (line 247) | def apply_postprocessing_to_folder(input_folder: str, function entry_point_determine_postprocessing_folder (line 297) | def entry_point_determine_postprocessing_folder(): function entry_point_apply_postprocessing (line 317) | def entry_point_apply_postprocessing(): FILE: nnunetv2/preprocessing/cropping/cropping.py function create_nonzero_mask (line 6) | def create_nonzero_mask(data): function crop_to_nonzero (line 19) | def crop_to_nonzero(data, seg=None, nonzero_label=-1): FILE: nnunetv2/preprocessing/normalization/default_normalization_schemes.py class ImageNormalization (line 8) | class ImageNormalization(ABC): method __init__ (line 11) | def __init__(self, use_mask_for_norm: bool = None, intensityproperties... method run (line 20) | def run(self, image: np.ndarray, seg: np.ndarray = None) -> np.ndarray: class ZScoreNormalization (line 27) | class ZScoreNormalization(ImageNormalization): method run (line 30) | def run(self, image: np.ndarray, seg: np.ndarray = None) -> np.ndarray: class CTNormalization (line 58) | class CTNormalization(ImageNormalization): method run (line 61) | def run(self, image: np.ndarray, seg: np.ndarray = None) -> np.ndarray: class NoNormalization (line 76) | class NoNormalization(ImageNormalization): method run (line 79) | def run(self, image: np.ndarray, seg: np.ndarray = None) -> np.ndarray: class RescaleTo01Normalization (line 83) | class RescaleTo01Normalization(ImageNormalization): method run (line 86) | def run(self, image: np.ndarray, seg: np.ndarray = None) -> np.ndarray: class RGBTo01Normalization (line 94) | class RGBTo01Normalization(ImageNormalization): method run (line 97) | def run(self, image: np.ndarray, seg: np.ndarray = None) -> np.ndarray: FILE: nnunetv2/preprocessing/normalization/map_channel_name_to_normalization.py function get_normalization_scheme (line 15) | def get_normalization_scheme(channel_name: str) -> Type[ImageNormalizati... FILE: nnunetv2/preprocessing/preprocessors/default_preprocessor.py class DefaultPreprocessor (line 36) | class DefaultPreprocessor(object): method __init__ (line 37) | def __init__(self, verbose: bool = True): method run_case_npy (line 44) | def run_case_npy(self, data: np.ndarray, seg: Union[np.ndarray, None],... method run_case (line 119) | def run_case(self, image_files: List[str], seg_file: Union[str, None],... method run_case_save (line 149) | def run_case_save(self, output_filename_truncated: str, image_files: L... method _sample_foreground_locations (line 170) | def _sample_foreground_locations( method _normalize (line 335) | def _normalize(self, data: np.ndarray, seg: np.ndarray, configuration_... method run (line 349) | def run(self, dataset_name_or_id: Union[int, str], configuration_name:... method modify_seg_fn (line 412) | def modify_seg_fn(self, seg: np.ndarray, plans_manager: PlansManager, ... function example_test_case_preprocessing (line 420) | def example_test_case_preprocessing(): function _verify_class_locations (line 441) | def _verify_class_locations(shape, outfile, class_locs): FILE: nnunetv2/preprocessing/resampling/default_resampling.py function get_do_separate_z (line 14) | def get_do_separate_z(spacing: Union[Tuple[float, ...], List[float], np.... function get_lowres_axis (line 19) | def get_lowres_axis(new_spacing: Union[Tuple[float, ...], List[float], n... function compute_new_shape (line 24) | def compute_new_shape(old_shape: Union[Tuple[int, ...], List[int], np.nd... function determine_do_sep_z_and_axis (line 33) | def determine_do_sep_z_and_axis( function resample_data_or_seg_to_spacing (line 69) | def resample_data_or_seg_to_spacing(data: np.ndarray, function resample_data_or_seg_to_shape (line 89) | def resample_data_or_seg_to_shape(data: Union[torch.Tensor, np.ndarray], function resample_data_or_seg (line 113) | def resample_data_or_seg(data: np.ndarray, new_shape: Union[Tuple[float,... FILE: nnunetv2/preprocessing/resampling/no_resampling.py function no_resampling_hack (line 7) | def no_resampling_hack( FILE: nnunetv2/preprocessing/resampling/resample_torch.py function resample_torch_simple (line 14) | def resample_torch_simple( function resample_torch_fornnunet (line 96) | def resample_torch_fornnunet( FILE: nnunetv2/preprocessing/resampling/utils.py function recursive_find_resampling_fn_by_name (line 8) | def recursive_find_resampling_fn_by_name(resampling_fn: str) -> Callable: FILE: nnunetv2/run/load_pretrained_weights.py function load_pretrained_weights (line 7) | def load_pretrained_weights(network, fname, verbose=False): FILE: nnunetv2/run/run_training.py function find_free_network_port (line 19) | def find_free_network_port() -> int: function get_trainer_from_args (line 32) | def get_trainer_from_args(dataset_name_or_id: Union[int, str], function maybe_load_checkpoint (line 72) | def maybe_load_checkpoint(nnunet_trainer: nnUNetTrainer, continue_traini... function setup_ddp (line 103) | def setup_ddp(rank, world_size): function cleanup_ddp (line 108) | def cleanup_ddp(): function run_ddp (line 112) | def run_ddp(rank, dataset_name_or_id, configuration, fold, tr, p, disabl... function run_training (line 139) | def run_training(dataset_name_or_id: Union[str, int], function run_training_entry (line 216) | def run_training_entry(): FILE: nnunetv2/tests/integration_tests/run_nnunet_inference.py function dice_score (line 10) | def dice_score(y_true, y_pred): function run_tests_and_exit_on_failure (line 17) | def run_tests_and_exit_on_failure(): FILE: nnunetv2/training/data_augmentation/compute_initial_patch_size.py function get_patch_size (line 4) | def get_patch_size(final_patch_size, rot_x, rot_y, rot_z, scale_range): FILE: nnunetv2/training/data_augmentation/custom_transforms/cascade_transforms.py class MoveSegAsOneHotToData (line 10) | class MoveSegAsOneHotToData(AbstractTransform): method __init__ (line 11) | def __init__(self, index_in_origin: int, all_labels: Union[Tuple[int, ... method __call__ (line 23) | def __call__(self, **data_dict): class RemoveRandomConnectedComponentFromOneHotEncodingTransform (line 40) | class RemoveRandomConnectedComponentFromOneHotEncodingTransform(Abstract... method __init__ (line 41) | def __init__(self, channel_idx: Union[int, List[int]], key: str = "dat... method __call__ (line 58) | def __call__(self, **data_dict): class ApplyRandomBinaryOperatorTransform (line 88) | class ApplyRandomBinaryOperatorTransform(AbstractTransform): method __init__ (line 89) | def __init__(self, method __call__ (line 111) | def __call__(self, **data_dict): FILE: nnunetv2/training/data_augmentation/custom_transforms/deep_supervision_donwsampling.py class DownsampleSegForDSTransform2 (line 8) | class DownsampleSegForDSTransform2(AbstractTransform): method __init__ (line 12) | def __init__(self, ds_scales: Union[List, Tuple], method __call__ (line 27) | def __call__(self, **data_dict): FILE: nnunetv2/training/data_augmentation/custom_transforms/masking.py class MaskTransform (line 6) | class MaskTransform(AbstractTransform): method __init__ (line 7) | def __init__(self, apply_to_channels: List[int], mask_idx_in_seg: int ... method __call__ (line 18) | def __call__(self, **data_dict): FILE: nnunetv2/training/data_augmentation/custom_transforms/region_based_training.py class ConvertSegmentationToRegionsTransform (line 7) | class ConvertSegmentationToRegionsTransform(AbstractTransform): method __init__ (line 8) | def __init__(self, regions: Union[List, Tuple], method __call__ (line 23) | def __call__(self, **data_dict): FILE: nnunetv2/training/data_augmentation/custom_transforms/transforms_for_dummy_2d.py class Convert3DTo2DTransform (line 6) | class Convert3DTo2DTransform(AbstractTransform): method __init__ (line 7) | def __init__(self, apply_to_keys: Union[List[str], Tuple[str]] = ('dat... method __call__ (line 13) | def __call__(self, **data_dict): class Convert2DTo3DTransform (line 26) | class Convert2DTo3DTransform(AbstractTransform): method __init__ (line 27) | def __init__(self, apply_to_keys: Union[List[str], Tuple[str]] = ('dat... method __call__ (line 33) | def __call__(self, **data_dict): FILE: nnunetv2/training/dataloading/data_loader.py class nnUNetDataLoader (line 19) | class nnUNetDataLoader(DataLoader): method __init__ (line 20) | def __init__(self, method _oversample_last_XX_percent (line 69) | def _oversample_last_XX_percent(self, sample_idx: int) -> bool: method _probabilistic_oversampling (line 75) | def _probabilistic_oversampling(self, sample_idx: int) -> bool: method determine_shapes (line 79) | def determine_shapes(self): method get_bbox (line 96) | def get_bbox(self, data_shape: np.ndarray, force_fg: bool, class_locat... method generate_train_batch (line 172) | def generate_train_batch(self): FILE: nnunetv2/training/dataloading/nnunet_dataset.py class nnUNetBaseDataset (line 19) | class nnUNetBaseDataset(ABC): method __init__ (line 23) | def __init__(self, folder: str, identifiers: List[str] = None, method __getitem__ (line 35) | def __getitem__(self, identifier): method load_case (line 39) | def load_case(self, identifier): method save_case (line 44) | def save_case( method get_identifiers (line 54) | def get_identifiers(folder: str) -> List[str]: method unpack_dataset (line 58) | def unpack_dataset(folder: str, overwrite_existing: bool = False, class nnUNetDatasetNumpy (line 64) | class nnUNetDatasetNumpy(nnUNetBaseDataset): method load_case (line 65) | def load_case(self, identifier): method save_case (line 91) | def save_case( method save_seg (line 101) | def save_seg( method get_identifiers (line 108) | def get_identifiers(folder: str) -> List[str]: method unpack_dataset (line 116) | def unpack_dataset(folder: str, overwrite_existing: bool = False, class nnUNetDatasetBlosc2 (line 122) | class nnUNetDatasetBlosc2(nnUNetBaseDataset): method __init__ (line 123) | def __init__(self, folder: str, identifiers: List[str] = None, method __getitem__ (line 128) | def __getitem__(self, identifier): method load_case (line 131) | def load_case(self, identifier): method save_case (line 154) | def save_case( method save_seg (line 186) | def save_seg( method get_identifiers (line 195) | def get_identifiers(folder: str) -> List[str]: method unpack_dataset (line 203) | def unpack_dataset(folder: str, overwrite_existing: bool = False, method comp_blosc2_params (line 209) | def comp_blosc2_params( function infer_dataset_class (line 307) | def infer_dataset_class(folder: str) -> Union[Type[nnUNetDatasetBlosc2],... FILE: nnunetv2/training/dataloading/utils.py function _convert_to_npy (line 13) | def _convert_to_npy(npz_file: str, unpack_segmentation: bool = True, ove... function unpack_dataset (line 58) | def unpack_dataset(folder: str, unpack_segmentation: bool = True, overwr... FILE: nnunetv2/training/logging/nnunet_logger.py function get_cluster_job_id (line 18) | def get_cluster_job_id(): class MetaLogger (line 27) | class MetaLogger(object): method __init__ (line 34) | def __init__(self, output_folder, resume, verbose: bool = False): method update_config (line 49) | def update_config(self, config: dict): method log (line 58) | def log(self, key: str, value: Any, step: int): method log_summary (line 81) | def log_summary(self, key: str, value: Any): method get_value (line 92) | def get_value(self, key: str, step: Any): method plot_progress_png (line 104) | def plot_progress_png(self, output_folder: str): method get_checkpoint (line 112) | def get_checkpoint(self): method load_checkpoint (line 120) | def load_checkpoint(self, checkpoint: dict): method _is_logger_enabled (line 128) | def _is_logger_enabled(self, env_var): class LocalLogger (line 138) | class LocalLogger: method __init__ (line 146) | def __init__(self, verbose: bool = False): method log (line 160) | def log(self, key, value, epoch: int): method get_value (line 177) | def get_value(self, key, step): method plot_progress_png (line 183) | def plot_progress_png(self, output_folder): method get_checkpoint (line 228) | def get_checkpoint(self): method load_checkpoint (line 231) | def load_checkpoint(self, checkpoint: dict): class WandbLogger (line 235) | class WandbLogger: method __init__ (line 244) | def __init__(self, output_folder, resume): method update_config (line 274) | def update_config(self, config: dict): method log (line 282) | def log(self, key, value, step: int): method log_summary (line 295) | def log_summary(self, key, value): FILE: nnunetv2/training/loss/compound_losses.py class DC_and_CE_loss (line 8) | class DC_and_CE_loss(nn.Module): method __init__ (line 9) | def __init__(self, soft_dice_kwargs, ce_kwargs, weight_ce=1, weight_di... method forward (line 31) | def forward(self, net_output: torch.Tensor, target: torch.Tensor): class DC_and_BCE_loss (line 59) | class DC_and_BCE_loss(nn.Module): method __init__ (line 60) | def __init__(self, bce_kwargs, soft_dice_kwargs, weight_ce=1, weight_d... method forward (line 83) | def forward(self, net_output: torch.Tensor, target: torch.Tensor): class DC_and_topk_loss (line 108) | class DC_and_topk_loss(nn.Module): method __init__ (line 109) | def __init__(self, soft_dice_kwargs, ce_kwargs, weight_ce=1, weight_di... method forward (line 130) | def forward(self, net_output: torch.Tensor, target: torch.Tensor): FILE: nnunetv2/training/loss/deep_supervision.py class DeepSupervisionWrapper (line 4) | class DeepSupervisionWrapper(nn.Module): method __init__ (line 5) | def __init__(self, loss, weight_factors=None): method forward (line 18) | def forward(self, *args): FILE: nnunetv2/training/loss/dice.py class SoftDiceLoss (line 8) | class SoftDiceLoss(nn.Module): method __init__ (line 9) | def __init__(self, apply_nonlin: Callable = None, batch_dice: bool = F... method forward (line 22) | def forward(self, x, y, loss_mask=None): class MemoryEfficientSoftDiceLoss (line 58) | class MemoryEfficientSoftDiceLoss(nn.Module): method __init__ (line 59) | def __init__(self, apply_nonlin: Callable = None, batch_dice: bool = F... method forward (line 72) | def forward(self, x, y, loss_mask=None): function get_tp_fp_fn_tn (line 122) | def get_tp_fp_fn_tn(net_output, gt, axes=None, mask=None, square=False): FILE: nnunetv2/training/loss/robust_ce_loss.py class RobustCrossEntropyLoss (line 6) | class RobustCrossEntropyLoss(nn.CrossEntropyLoss): method forward (line 12) | def forward(self, input: Tensor, target: Tensor) -> Tensor: class TopKLoss (line 19) | class TopKLoss(RobustCrossEntropyLoss): method __init__ (line 23) | def __init__(self, weight=None, ignore_index: int = -100, k: float = 1... method forward (line 27) | def forward(self, inp, target): FILE: nnunetv2/training/lr_scheduler/polylr.py class PolyLRScheduler (line 4) | class PolyLRScheduler(_LRScheduler): method __init__ (line 5) | def __init__(self, optimizer, initial_lr: float, max_steps: int, expon... method step (line 13) | def step(self, current_step=None): method get_last_lr (line 25) | def get_last_lr(self): FILE: nnunetv2/training/lr_scheduler/warmup.py class Lin_incr_LRScheduler (line 10) | class Lin_incr_LRScheduler(_LRScheduler): method __init__ (line 11) | def __init__(self, optimizer, max_lr: float, max_steps: int, current_s... method step (line 18) | def step(self, current_step=None): class Lin_incr_offset_LRScheduler (line 28) | class Lin_incr_offset_LRScheduler(_LRScheduler): method __init__ (line 29) | def __init__(self, optimizer, max_lr: float, max_steps: int, start_ste... method step (line 37) | def step(self, current_step=None): class PolyLRScheduler_offset (line 47) | class PolyLRScheduler_offset(_LRScheduler): method __init__ (line 48) | def __init__( method step (line 65) | def step(self, current_step=None): class CosineAnnealingLR_offset (line 79) | class CosineAnnealingLR_offset(CosineAnnealingLR): method __init__ (line 80) | def __init__( method _get_closed_form_lr (line 92) | def _get_closed_form_lr(self): method step (line 101) | def step(self, epoch: Optional[int] = None): FILE: nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py class nnUNetTrainer (line 70) | class nnUNetTrainer(object): method __init__ (line 71) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... method initialize (line 203) | def initialize(self): method _do_i_compile (line 256) | def _do_i_compile(self): method _save_debug_information (line 284) | def _save_debug_information(self): method build_network_architecture (line 326) | def build_network_architecture(architecture_class_name: str, method _get_deep_supervision_scales (line 360) | def _get_deep_supervision_scales(self): method _set_batch_size_and_oversample (line 368) | def _set_batch_size_and_oversample(self): method _build_loss (line 413) | def _build_loss(self): method configure_rotation_dummyDA_mirroring_and_inital_patch_size (line 449) | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): method print_to_log_file (line 492) | def print_to_log_file(self, *args, also_print_to_console=True, add_tim... method print_plans (line 520) | def print_plans(self): method configure_optimizers (line 529) | def configure_optimizers(self): method plot_network_architecture (line 535) | def plot_network_architecture(self): method do_split (line 573) | def do_split(self): method get_tr_and_val_datasets (line 633) | def get_tr_and_val_datasets(self): method get_dataloaders (line 645) | def get_dataloaders(self): method get_training_transforms (line 716) | def get_training_transforms( method get_validation_transforms (line 867) | def get_validation_transforms( method set_deep_supervision_enabled (line 901) | def set_deep_supervision_enabled(self, enabled: bool): method on_train_start (line 915) | def on_train_start(self): method on_train_end (line 958) | def on_train_end(self): method on_train_epoch_start (line 984) | def on_train_epoch_start(self): method train_step (line 994) | def train_step(self, batch: dict) -> dict: method on_train_epoch_end (line 1026) | def on_train_epoch_end(self, train_outputs: List[dict]): method on_validation_epoch_start (line 1038) | def on_validation_epoch_start(self): method validation_step (line 1041) | def validation_step(self, batch: dict) -> dict: method on_validation_epoch_end (line 1108) | def on_validation_epoch_end(self, val_outputs: List[dict]): method on_epoch_start (line 1141) | def on_epoch_start(self): method on_epoch_end (line 1144) | def on_epoch_end(self): method save_checkpoint (line 1170) | def save_checkpoint(self, filename: str) -> None: method load_checkpoint (line 1195) | def load_checkpoint(self, filename_or_checkpoint: Union[dict, str]) ->... method perform_actual_validation (line 1233) | def perform_actual_validation(self, save_probabilities: bool = False): method run_training (line 1392) | def run_training(self): FILE: nnunetv2/training/nnUNetTrainer/primus/primus_trainers.py class AbstractPrimus (line 18) | class AbstractPrimus(nnUNetTrainer_warmup): method __init__ (line 19) | def __init__( method build_network_architecture (line 33) | def build_network_architecture( method configure_optimizers (line 44) | def configure_optimizers(self, stage: str = "warmup_all"): method train_step (line 85) | def train_step(self, batch: dict) -> dict: method set_deep_supervision_enabled (line 117) | def set_deep_supervision_enabled(self, enabled: bool): class nnUNet_Primus_S_Trainer (line 121) | class nnUNet_Primus_S_Trainer(AbstractPrimus): method build_network_architecture (line 123) | def build_network_architecture( class nnUNet_Primus_B_Trainer (line 148) | class nnUNet_Primus_B_Trainer(AbstractPrimus): method build_network_architecture (line 150) | def build_network_architecture( class nnUNet_Primus_M_Trainer (line 175) | class nnUNet_Primus_M_Trainer(AbstractPrimus): method build_network_architecture (line 177) | def build_network_architecture( class nnUNet_Primus_M_Trainer_BS8 (line 202) | class nnUNet_Primus_M_Trainer_BS8(nnUNet_Primus_M_Trainer): method __init__ (line 204) | def __init__( class nnUNet_Primus_M_Trainer_BS8_2e4 (line 216) | class nnUNet_Primus_M_Trainer_BS8_2e4(nnUNet_Primus_M_Trainer): method __init__ (line 218) | def __init__( class nnUNet_Trainer_BS8 (line 231) | class nnUNet_Trainer_BS8(nnUNetTrainer): method __init__ (line 233) | def __init__( class nnUNet_Primus_L_Trainer (line 245) | class nnUNet_Primus_L_Trainer(AbstractPrimus): method build_network_architecture (line 247) | def build_network_architecture( class _Primus_S_96_BS1 (line 272) | class _Primus_S_96_BS1(nnUNet_Primus_S_Trainer): method __init__ (line 273) | def __init__( class _Primus_B_96_BS1 (line 286) | class _Primus_B_96_BS1(nnUNet_Primus_B_Trainer): method __init__ (line 287) | def __init__( class _Primus_M_96_BS1 (line 300) | class _Primus_M_96_BS1(nnUNet_Primus_M_Trainer): method __init__ (line 301) | def __init__( class _Primus_L_48_BS1 (line 314) | class _Primus_L_48_BS1(nnUNet_Primus_L_Trainer): method __init__ (line 315) | def __init__( FILE: nnunetv2/training/nnUNetTrainer/variants/benchmarking/nnUNetTrainerBenchmark_5epochs.py class nnUNetTrainerBenchmark_5epochs (line 10) | class nnUNetTrainerBenchmark_5epochs(nnUNetTrainer): method __init__ (line 11) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... method perform_actual_validation (line 20) | def perform_actual_validation(self, save_probabilities: bool = False): method save_checkpoint (line 23) | def save_checkpoint(self, filename: str) -> None: method run_training (line 27) | def run_training(self): method on_train_end (line 34) | def on_train_end(self): FILE: nnunetv2/training/nnUNetTrainer/variants/benchmarking/nnUNetTrainerBenchmark_5epochs_noDataLoading.py class nnUNetTrainerBenchmark_5epochs_noDataLoading (line 9) | class nnUNetTrainerBenchmark_5epochs_noDataLoading(nnUNetTrainerBenchmar... method __init__ (line 10) | def __init__( method get_dataloaders (line 37) | def get_dataloaders(self): method run_training (line 40) | def run_training(self): FILE: nnunetv2/training/nnUNetTrainer/variants/competitions/aortaseg24.py class nnUNetTrainer_onlyMirror01_DA5 (line 4) | class nnUNetTrainer_onlyMirror01_DA5(nnUNetTrainer_onlyMirror01, nnUNetT... FILE: nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerDA5.py class TensorToNumpy (line 40) | class TensorToNumpy(AbstractTransform): method __init__ (line 41) | def __init__(self, keys=None, cast_to=None): method cast (line 54) | def cast(self, array: np.ndarray): method _to_numpy (line 62) | def _to_numpy(self, tensor): method __call__ (line 71) | def __call__(self, **data_dict): class nnUNetTrainerDA5 (line 91) | class nnUNetTrainerDA5(nnUNetTrainer): method configure_rotation_dummyDA_mirroring_and_inital_patch_size (line 92) | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): method get_training_transforms (line 133) | def get_training_transforms( method get_validation_transforms (line 348) | def get_validation_transforms( method get_dataloaders (line 380) | def get_dataloaders(self): method validation_step (line 448) | def validation_step(self, batch: dict) -> dict: class nnUNetTrainerDA5ord0 (line 516) | class nnUNetTrainerDA5ord0(nnUNetTrainerDA5): method get_training_transforms (line 518) | def get_training_transforms( function _brightnessadditive_localgamma_transform_scale (line 732) | def _brightnessadditive_localgamma_transform_scale(x, y): function _brightness_gradient_additive_max_strength (line 736) | def _brightness_gradient_additive_max_strength(_x, _y): function _local_gamma_gamma (line 740) | def _local_gamma_gamma(): class nnUNetTrainerDA5Segord0 (line 744) | class nnUNetTrainerDA5Segord0(nnUNetTrainerDA5): method get_training_transforms (line 746) | def get_training_transforms( class nnUNetTrainerDA5_10epochs (line 960) | class nnUNetTrainerDA5_10epochs(nnUNetTrainerDA5): method __init__ (line 961) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... FILE: nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerDAOrd0.py class nnUNetTrainer_DASegOrd0 (line 33) | class nnUNetTrainer_DASegOrd0(nnUNetTrainer): method get_training_transforms (line 35) | def get_training_transforms( class nnUNetTrainer_DASegOrd0_NoMirroring (line 186) | class nnUNetTrainer_DASegOrd0_NoMirroring(nnUNetTrainer_DASegOrd0): method configure_rotation_dummyDA_mirroring_and_inital_patch_size (line 187) | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): FILE: nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerNoDA.py class nnUNetTrainerNoDA (line 10) | class nnUNetTrainerNoDA(nnUNetTrainer): method get_training_transforms (line 12) | def get_training_transforms( method configure_rotation_dummyDA_mirroring_and_inital_patch_size (line 27) | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): FILE: nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerNoMirroring.py class nnUNetTrainerNoMirroring (line 29) | class nnUNetTrainerNoMirroring(nnUNetTrainer): method configure_rotation_dummyDA_mirroring_and_inital_patch_size (line 30) | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): class nnUNetTrainer_onlyMirror01 (line 38) | class nnUNetTrainer_onlyMirror01(nnUNetTrainer): method configure_rotation_dummyDA_mirroring_and_inital_patch_size (line 42) | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): class nnUNetTrainer_onlyMirror01_1500ep (line 55) | class nnUNetTrainer_onlyMirror01_1500ep(nnUNetTrainer_onlyMirror01): method __init__ (line 56) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainer_onlyMirror01_DASegOrd0 (line 62) | class nnUNetTrainer_onlyMirror01_DASegOrd0(nnUNetTrainer_onlyMirror01): method get_training_transforms (line 64) | def get_training_transforms( FILE: nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainer_noDummy2DDA.py class nnUNetTrainer_noDummy2DDA (line 6) | class nnUNetTrainer_noDummy2DDA(nnUNetTrainer): method configure_rotation_dummyDA_mirroring_and_inital_patch_size (line 7) | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): FILE: nnunetv2/training/nnUNetTrainer/variants/loss/nnUNetTrainerCELoss.py class nnUNetTrainerCELoss (line 8) | class nnUNetTrainerCELoss(nnUNetTrainer): method _build_loss (line 9) | def _build_loss(self): class nnUNetTrainerCELoss_5epochs (line 29) | class nnUNetTrainerCELoss_5epochs(nnUNetTrainerCELoss): method __init__ (line 30) | def __init__( FILE: nnunetv2/training/nnUNetTrainer/variants/loss/nnUNetTrainerDiceLoss.py class nnUNetTrainerDiceLoss (line 11) | class nnUNetTrainerDiceLoss(nnUNetTrainer): method _build_loss (line 12) | def _build_loss(self): class nnUNetTrainerDiceCELoss_noSmooth (line 32) | class nnUNetTrainerDiceCELoss_noSmooth(nnUNetTrainer): method _build_loss (line 33) | def _build_loss(self): FILE: nnunetv2/training/nnUNetTrainer/variants/loss/nnUNetTrainerTopkLoss.py class nnUNetTrainerTopk10Loss (line 8) | class nnUNetTrainerTopk10Loss(nnUNetTrainer): method _build_loss (line 9) | def _build_loss(self): class nnUNetTrainerTopk10LossLS01 (line 30) | class nnUNetTrainerTopk10LossLS01(nnUNetTrainer): method _build_loss (line 31) | def _build_loss(self): class nnUNetTrainerDiceTopK10Loss (line 54) | class nnUNetTrainerDiceTopK10Loss(nnUNetTrainer): method _build_loss (line 55) | def _build_loss(self): FILE: nnunetv2/training/nnUNetTrainer/variants/lr_schedule/nnUNetTrainerCosAnneal.py class nnUNetTrainerCosAnneal (line 7) | class nnUNetTrainerCosAnneal(nnUNetTrainer): method configure_optimizers (line 8) | def configure_optimizers(self): FILE: nnunetv2/training/nnUNetTrainer/variants/lr_schedule/nnUNetTrainer_warmup.py class nnUNetTrainer_warmup (line 13) | class nnUNetTrainer_warmup(nnUNetTrainer): method __init__ (line 19) | def __init__( method configure_optimizers (line 33) | def configure_optimizers(self, stage: str = "warmup_all"): method on_train_epoch_start (line 69) | def on_train_epoch_start(self): method load_checkpoint (line 77) | def load_checkpoint(self, filename_or_checkpoint: Union[dict, str]) ->... FILE: nnunetv2/training/nnUNetTrainer/variants/network_architecture/nnUNetTrainerBN.py class nnUNetTrainerBN (line 8) | class nnUNetTrainerBN(nnUNetTrainer): method build_network_architecture (line 10) | def build_network_architecture(architecture_class_name: str, FILE: nnunetv2/training/nnUNetTrainer/variants/network_architecture/nnUNetTrainerNoDeepSupervision.py class nnUNetTrainerNoDeepSupervision (line 5) | class nnUNetTrainerNoDeepSupervision(nnUNetTrainer): method __init__ (line 6) | def __init__( FILE: nnunetv2/training/nnUNetTrainer/variants/optimizer/nnUNetTrainerAdam.py class nnUNetTrainerAdam (line 8) | class nnUNetTrainerAdam(nnUNetTrainer): method configure_optimizers (line 9) | def configure_optimizers(self): class nnUNetTrainerVanillaAdam (line 20) | class nnUNetTrainerVanillaAdam(nnUNetTrainer): method configure_optimizers (line 21) | def configure_optimizers(self): class nnUNetTrainerVanillaAdam1en3 (line 31) | class nnUNetTrainerVanillaAdam1en3(nnUNetTrainerVanillaAdam): method __init__ (line 32) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainerVanillaAdam3en4 (line 38) | class nnUNetTrainerVanillaAdam3en4(nnUNetTrainerVanillaAdam): method __init__ (line 40) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainerAdam1en3 (line 46) | class nnUNetTrainerAdam1en3(nnUNetTrainerAdam): method __init__ (line 47) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainerAdam3en4 (line 53) | class nnUNetTrainerAdam3en4(nnUNetTrainerAdam): method __init__ (line 55) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... FILE: nnunetv2/training/nnUNetTrainer/variants/optimizer/nnUNetTrainerAdan.py class nnUNetTrainerAdan (line 12) | class nnUNetTrainerAdan(nnUNetTrainer): method configure_optimizers (line 13) | def configure_optimizers(self): class nnUNetTrainerAdan1en3 (line 26) | class nnUNetTrainerAdan1en3(nnUNetTrainerAdan): method __init__ (line 27) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainerAdan3en4 (line 33) | class nnUNetTrainerAdan3en4(nnUNetTrainerAdan): method __init__ (line 35) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainerAdan1en1 (line 41) | class nnUNetTrainerAdan1en1(nnUNetTrainerAdan): method __init__ (line 43) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainerAdanCosAnneal (line 49) | class nnUNetTrainerAdanCosAnneal(nnUNetTrainerAdan): method configure_optimizers (line 55) | def configure_optimizers(self): FILE: nnunetv2/training/nnUNetTrainer/variants/sampling/nnUNetTrainer_probabilisticOversampling.py class nnUNetTrainer_probabilisticOversampling (line 8) | class nnUNetTrainer_probabilisticOversampling(nnUNetTrainer): method __init__ (line 16) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... method _set_batch_size_and_oversample (line 25) | def _set_batch_size_and_oversample(self): class nnUNetTrainer_probabilisticOversampling_033 (line 51) | class nnUNetTrainer_probabilisticOversampling_033(nnUNetTrainer_probabil... method __init__ (line 52) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainer_probabilisticOversampling_010 (line 58) | class nnUNetTrainer_probabilisticOversampling_010(nnUNetTrainer_probabil... method __init__ (line 59) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... FILE: nnunetv2/training/nnUNetTrainer/variants/training_length/nnUNetTrainer_Xepochs.py class nnUNetTrainer_5epochs (line 6) | class nnUNetTrainer_5epochs(nnUNetTrainer): method __init__ (line 7) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainer_1epoch (line 14) | class nnUNetTrainer_1epoch(nnUNetTrainer): method __init__ (line 15) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainer_10epochs (line 22) | class nnUNetTrainer_10epochs(nnUNetTrainer): method __init__ (line 23) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainer_20epochs (line 30) | class nnUNetTrainer_20epochs(nnUNetTrainer): method __init__ (line 31) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainer_50epochs (line 37) | class nnUNetTrainer_50epochs(nnUNetTrainer): method __init__ (line 38) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainer_100epochs (line 44) | class nnUNetTrainer_100epochs(nnUNetTrainer): method __init__ (line 45) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainer_250epochs (line 51) | class nnUNetTrainer_250epochs(nnUNetTrainer): method __init__ (line 52) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainer_500epochs (line 58) | class nnUNetTrainer_500epochs(nnUNetTrainer): method __init__ (line 59) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainer_750epochs (line 65) | class nnUNetTrainer_750epochs(nnUNetTrainer): method __init__ (line 66) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainer_2000epochs (line 72) | class nnUNetTrainer_2000epochs(nnUNetTrainer): method __init__ (line 73) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainer_4000epochs (line 79) | class nnUNetTrainer_4000epochs(nnUNetTrainer): method __init__ (line 80) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... class nnUNetTrainer_8000epochs (line 86) | class nnUNetTrainer_8000epochs(nnUNetTrainer): method __init__ (line 87) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... FILE: nnunetv2/training/nnUNetTrainer/variants/training_length/nnUNetTrainer_Xepochs_NoMirroring.py class nnUNetTrainer_250epochs_NoMirroring (line 6) | class nnUNetTrainer_250epochs_NoMirroring(nnUNetTrainer): method __init__ (line 7) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... method configure_rotation_dummyDA_mirroring_and_inital_patch_size (line 12) | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): class nnUNetTrainer_2000epochs_NoMirroring (line 20) | class nnUNetTrainer_2000epochs_NoMirroring(nnUNetTrainer): method __init__ (line 21) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... method configure_rotation_dummyDA_mirroring_and_inital_patch_size (line 26) | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): class nnUNetTrainer_4000epochs_NoMirroring (line 34) | class nnUNetTrainer_4000epochs_NoMirroring(nnUNetTrainer): method __init__ (line 35) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... method configure_rotation_dummyDA_mirroring_and_inital_patch_size (line 40) | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): class nnUNetTrainer_8000epochs_NoMirroring (line 48) | class nnUNetTrainer_8000epochs_NoMirroring(nnUNetTrainer): method __init__ (line 49) | def __init__(self, plans: dict, configuration: str, fold: int, dataset... method configure_rotation_dummyDA_mirroring_and_inital_patch_size (line 54) | def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self): FILE: nnunetv2/utilities/collate_outputs.py function collate_outputs (line 6) | def collate_outputs(outputs: List[dict]): FILE: nnunetv2/utilities/crossval_split.py function generate_crossval_split (line 7) | def generate_crossval_split(train_identifiers: List[str], seed=12345, n_... FILE: nnunetv2/utilities/dataset_name_id_conversion.py function find_candidate_datasets (line 21) | def find_candidate_datasets(dataset_id: int): function convert_id_to_dataset_name (line 42) | def convert_id_to_dataset_name(dataset_id: int): function convert_dataset_name_to_id (line 58) | def convert_dataset_name_to_id(dataset_name: str): function maybe_convert_to_dataset_name (line 64) | def maybe_convert_to_dataset_name(dataset_name_or_id: Union[int, str]) -... FILE: nnunetv2/utilities/ddp_allgather.py function print_if_rank0 (line 20) | def print_if_rank0(*args): class AllGatherGrad (line 25) | class AllGatherGrad(torch.autograd.Function): method forward (line 28) | def forward( method backward (line 43) | def backward(ctx: Any, *grad_output: torch.Tensor) -> Tuple[torch.Tens... FILE: nnunetv2/utilities/default_n_proc_DA.py function get_allowed_n_proc_DA (line 5) | def get_allowed_n_proc_DA(): FILE: nnunetv2/utilities/file_path_utilities.py function convert_trainer_plans_config_to_identifier (line 11) | def convert_trainer_plans_config_to_identifier(trainer_name, plans_ident... function convert_identifier_to_trainer_plans_config (line 15) | def convert_identifier_to_trainer_plans_config(identifier: str): function get_output_folder (line 19) | def get_output_folder(dataset_name_or_id: Union[str, int], trainer_name:... function parse_dataset_trainer_plans_configuration_from_path (line 29) | def parse_dataset_trainer_plans_configuration_from_path(path: str): function get_ensemble_name (line 60) | def get_ensemble_name(model1_folder, model2_folder, folds: Tuple[int, ..... function get_ensemble_name_from_d_tr_c (line 66) | def get_ensemble_name_from_d_tr_c(dataset, tr1, p1, c1, tr2, p2, c2, fol... function convert_ensemble_folder_to_model_identifiers_and_folds (line 73) | def convert_ensemble_folder_to_model_identifiers_and_folds(ensemble_fold... function folds_tuple_to_string (line 78) | def folds_tuple_to_string(folds: Union[List[int], Tuple[int, ...]]): function folds_string_to_tuple (line 85) | def folds_string_to_tuple(folds_string: str): function check_workers_alive_and_busy (line 96) | def check_workers_alive_and_busy(export_pool: Pool, worker_list: List, r... FILE: nnunetv2/utilities/find_class_by_name.py function recursive_find_python_class (line 7) | def recursive_find_python_class(folder: str, class_name: str, current_mo... FILE: nnunetv2/utilities/get_network_from_plans.py function get_network_from_plans (line 9) | def get_network_from_plans(arch_class_name, arch_kwargs, arch_kwargs_req... FILE: nnunetv2/utilities/helpers.py function softmax_helper_dim0 (line 4) | def softmax_helper_dim0(x: torch.Tensor) -> torch.Tensor: function softmax_helper_dim1 (line 8) | def softmax_helper_dim1(x: torch.Tensor) -> torch.Tensor: function empty_cache (line 12) | def empty_cache(device: torch.device): class dummy_context (line 22) | class dummy_context(object): method __enter__ (line 23) | def __enter__(self): method __exit__ (line 26) | def __exit__(self, exc_type, exc_val, exc_tb): FILE: nnunetv2/utilities/json_export.py function recursive_fix_for_json_export (line 7) | def recursive_fix_for_json_export(my_dict: dict): function fix_types_iterable (line 40) | def fix_types_iterable(iterable, output_type): FILE: nnunetv2/utilities/label_handling/label_handling.py class LabelManager (line 21) | class LabelManager(object): method __init__ (line 22) | def __init__(self, label_dict: dict, regions_class_order: Union[List[i... method _sanity_check (line 51) | def _sanity_check(self, label_dict: dict): method _get_all_labels (line 62) | def _get_all_labels(self) -> List[int]: method _get_regions (line 77) | def _get_regions(self) -> Union[None, List[Union[int, Tuple[int, ...]]]]: method _determine_ignore_label (line 101) | def _determine_ignore_label(self) -> Union[None, int]: method has_regions (line 109) | def has_regions(self) -> bool: method has_ignore_label (line 113) | def has_ignore_label(self) -> bool: method all_regions (line 117) | def all_regions(self) -> Union[None, List[Union[int, Tuple[int, ...]]]]: method all_labels (line 121) | def all_labels(self) -> List[int]: method ignore_label (line 125) | def ignore_label(self) -> Union[None, int]: method apply_inference_nonlin (line 128) | def apply_inference_nonlin(self, logits: Union[np.ndarray, torch.Tenso... method convert_probabilities_to_segmentation (line 144) | def convert_probabilities_to_segmentation(self, predicted_probabilitie... method convert_logits_to_segmentation (line 185) | def convert_logits_to_segmentation(self, predicted_logits: Union[np.nd... method revert_cropping_on_probabilities (line 197) | def revert_cropping_on_probabilities(self, predicted_probabilities: Un... method filter_background (line 223) | def filter_background(classes_or_regions: Union[List[int], List[Union[... method foreground_regions (line 233) | def foreground_regions(self): method foreground_labels (line 237) | def foreground_labels(self): method num_segmentation_heads (line 241) | def num_segmentation_heads(self): function get_labelmanager_class_from_plans (line 248) | def get_labelmanager_class_from_plans(plans: dict) -> Type[LabelManager]: function convert_labelmap_to_one_hot (line 259) | def convert_labelmap_to_one_hot(segmentation: Union[np.ndarray, torch.Te... function determine_num_input_channels (line 294) | def determine_num_input_channels(plans_manager: PlansManager, FILE: nnunetv2/utilities/network_initialization.py class InitWeights_He (line 4) | class InitWeights_He(object): method __init__ (line 5) | def __init__(self, neg_slope=1e-2): method __call__ (line 8) | def __call__(self, module): FILE: nnunetv2/utilities/overlay_plots.py function hex_to_rgb (line 50) | def hex_to_rgb(hex: str): function generate_overlay (line 55) | def generate_overlay(input_image: np.ndarray, segmentation: np.ndarray, ... function select_slice_to_plot (line 99) | def select_slice_to_plot(image: np.ndarray, segmentation: np.ndarray) ->... function select_slice_to_plot2 (line 113) | def select_slice_to_plot2(image: np.ndarray, segmentation: np.ndarray) -... function plot_overlay (line 132) | def plot_overlay(image_file: str, segmentation_file: str, image_reader_w... function plot_overlay_preprocessed (line 154) | def plot_overlay_preprocessed(dataset: nnUNetBaseDataset, k: str, output... function multiprocessing_plot_overlay (line 171) | def multiprocessing_plot_overlay(list_of_image_files, list_of_seg_files,... function multiprocessing_plot_overlay_preprocessed (line 182) | def multiprocessing_plot_overlay_preprocessed(dataset: nnUNetBaseDataset... function generate_overlays_from_raw (line 196) | def generate_overlays_from_raw(dataset_name_or_id: Union[int, str], outp... function generate_overlays_from_preprocessed (line 216) | def generate_overlays_from_preprocessed(dataset_name_or_id: Union[int, s... function entry_point_generate_overlay (line 247) | def entry_point_generate_overlay(): FILE: nnunetv2/utilities/plans_handling/plans_handler.py class ConfigurationManager (line 31) | class ConfigurationManager(object): method __init__ (line 32) | def __init__(self, configuration_dict: dict): method __repr__ (line 99) | def __repr__(self): method data_identifier (line 103) | def data_identifier(self) -> str: method preprocessor_name (line 107) | def preprocessor_name(self) -> str: method preprocessor_class (line 112) | def preprocessor_class(self) -> Type[DefaultPreprocessor]: method batch_size (line 119) | def batch_size(self) -> int: method patch_size (line 123) | def patch_size(self) -> List[int]: method median_image_size_in_voxels (line 127) | def median_image_size_in_voxels(self) -> List[int]: method spacing (line 131) | def spacing(self) -> List[float]: method normalization_schemes (line 135) | def normalization_schemes(self) -> List[str]: method use_mask_for_norm (line 139) | def use_mask_for_norm(self) -> List[bool]: method network_arch_class_name (line 143) | def network_arch_class_name(self) -> str: method network_arch_init_kwargs (line 147) | def network_arch_init_kwargs(self) -> dict: method network_arch_init_kwargs_req_import (line 151) | def network_arch_init_kwargs_req_import(self) -> Union[Tuple[str, ...]... method pool_op_kernel_sizes (line 155) | def pool_op_kernel_sizes(self) -> Tuple[Tuple[int, ...], ...]: method resampling_fn_data (line 160) | def resampling_fn_data(self) -> Callable[ method resampling_fn_probabilities (line 173) | def resampling_fn_probabilities(self) -> Callable[ method resampling_fn_seg (line 186) | def resampling_fn_seg(self) -> Callable[ method batch_dice (line 198) | def batch_dice(self) -> bool: method next_stage_names (line 202) | def next_stage_names(self) -> Union[List[str], None]: method previous_stage_name (line 210) | def previous_stage_name(self) -> Union[str, None]: class PlansManager (line 214) | class PlansManager(object): method __init__ (line 215) | def __init__(self, plans_file_or_dict: Union[str, dict]): method __repr__ (line 228) | def __repr__(self): method _internal_resolve_configuration_inheritance (line 231) | def _internal_resolve_configuration_inheritance(self, configuration_na... method get_configuration (line 256) | def get_configuration(self, configuration_name: str): method dataset_name (line 265) | def dataset_name(self) -> str: method plans_name (line 269) | def plans_name(self) -> str: method original_median_spacing_after_transp (line 273) | def original_median_spacing_after_transp(self) -> List[float]: method original_median_shape_after_transp (line 277) | def original_median_shape_after_transp(self) -> List[float]: method image_reader_writer_class (line 282) | def image_reader_writer_class(self) -> Type[BaseReaderWriter]: method transpose_forward (line 286) | def transpose_forward(self) -> List[int]: method transpose_backward (line 290) | def transpose_backward(self) -> List[int]: method available_configurations (line 294) | def available_configurations(self) -> List[str]: method experiment_planner_class (line 299) | def experiment_planner_class(self) -> Type[ExperimentPlanner]: method experiment_planner_name (line 307) | def experiment_planner_name(self) -> str: method label_manager_class (line 312) | def label_manager_class(self) -> Type[LabelManager]: method get_label_manager (line 315) | def get_label_manager(self, dataset_json: dict, **kwargs) -> LabelMana... method foreground_intensity_properties_per_channel (line 321) | def foreground_intensity_properties_per_channel(self) -> dict: FILE: nnunetv2/utilities/utils.py function get_identifiers_from_splitted_dataset_folder (line 27) | def get_identifiers_from_splitted_dataset_folder(folder: str, file_endin... function create_paths_fn (line 37) | def create_paths_fn(folder, files, file_ending, f): function create_lists_from_splitted_dataset_folder (line 42) | def create_lists_from_splitted_dataset_folder(folder: str, file_ending: ... function get_filenames_of_train_images_and_targets (line 59) | def get_filenames_of_train_images_and_targets(raw_dataset_folder: str, d...