Merge pull request #1 from MIC-DKFZ/master

get newest commits from nnunet master

nnunetv2/dataset_conversion/Dataset220_KiTS2023.py

@@ -31,7 +31,7 @@ def convert_kits2023(kits_base_dir: str, nnunet_dataset_id: int = 220):
                           regions_class_order=(1, 3, 2),
                           num_training_cases=len(cases), file_ending='.nii.gz',
                           dataset_name=task_name, reference='none',
-                          release='prerelease',
+                          release='0.1.3',
                           overwrite_image_reader_writer='NibabelIOWithReorient',
                           description="KiTS2023")
 

nnunetv2/dataset_conversion/Dataset223_AMOS2022postChallenge.py

@@ -0,0 +1,59 @@
+import shutil
+
+from batchgenerators.utilities.file_and_folder_operations import *
+from nnunetv2.paths import nnUNet_raw
+from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json
+
+if __name__ == '__main__':
+    downloaded_amos_dir = '/home/isensee/amos22/amos22' # downloaded and extracted from https://zenodo.org/record/7155725#.Y0OOCOxBztM
+
+    target_dataset_id = 223
+    target_dataset_name = f'Dataset{target_dataset_id:3.0f}_AMOS2022postChallenge'
+
+    maybe_mkdir_p(join(nnUNet_raw, target_dataset_name))
+    imagesTr = join(nnUNet_raw, target_dataset_name, 'imagesTr')
+    imagesTs = join(nnUNet_raw, target_dataset_name, 'imagesTs')
+    labelsTr = join(nnUNet_raw, target_dataset_name, 'labelsTr')
+    maybe_mkdir_p(imagesTr)
+    maybe_mkdir_p(imagesTs)
+    maybe_mkdir_p(labelsTr)
+
+    train_identifiers = []
+    # copy images
+    source = join(downloaded_amos_dir, 'imagesTr')
+    source_files = nifti_files(source, join=False)
+    train_identifiers += source_files
+    for s in source_files:
+        shutil.copy(join(source, s), join(imagesTr, s[:-7] + '_0000.nii.gz'))
+
+    source = join(downloaded_amos_dir, 'imagesVa')
+    source_files = nifti_files(source, join=False)
+    train_identifiers += source_files
+    for s in source_files:
+        shutil.copy(join(source, s), join(imagesTr, s[:-7] + '_0000.nii.gz'))
+
+    source = join(downloaded_amos_dir, 'imagesTs')
+    source_files = nifti_files(source, join=False)
+    for s in source_files:
+        shutil.copy(join(source, s), join(imagesTs, s[:-7] + '_0000.nii.gz'))
+
+    # copy labels
+    source = join(downloaded_amos_dir, 'labelsTr')
+    source_files = nifti_files(source, join=False)
+    for s in source_files:
+        shutil.copy(join(source, s), join(labelsTr, s))
+
+    source = join(downloaded_amos_dir, 'labelsVa')
+    source_files = nifti_files(source, join=False)
+    for s in source_files:
+        shutil.copy(join(source, s), join(labelsTr, s))
+
+    old_dataset_json = load_json(join(downloaded_amos_dir, 'dataset.json'))
+    new_labels = {v: k for k, v in old_dataset_json['labels'].items()}
+
+    generate_dataset_json(join(nnUNet_raw, target_dataset_name), {0: 'nonCT'}, new_labels,
+                          num_training_cases=len(train_identifiers), file_ending='.nii.gz', regions_class_order=None,
+                          dataset_name=target_dataset_name, reference='https://zenodo.org/record/7155725#.Y0OOCOxBztM',
+                          license=old_dataset_json['licence'],  # typo in OG dataset.json
+                          description=old_dataset_json['description'],
+                          release=old_dataset_json['release'])

nnunetv2/experiment_planning/experiment_planners/default_experiment_planner.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 from batchgenerators.utilities.file_and_folder_operations import load_json, join, save_json, isfile, maybe_mkdir_p
-from dynamic_network_architectures.architectures.residual_unet import ResidualEncoderUNet
+from dynamic_network_architectures.architectures.unet import ResidualEncoderUNet
 from dynamic_network_architectures.architectures.unet import PlainConvUNet
 from dynamic_network_architectures.building_blocks.helper import convert_dim_to_conv_op, get_matching_instancenorm
 

nnunetv2/experiment_planning/experiment_planners/resencUNet_planner.py

@@ -1,9 +1,9 @@
 from typing import Union, List, Tuple
 
+from dynamic_network_architectures.architectures.unet import ResidualEncoderUNet
 from torch import nn
 
 from nnunetv2.experiment_planning.experiment_planners.default_experiment_planner import ExperimentPlanner
-from dynamic_network_architectures.architectures.residual_unet import ResidualEncoderUNet
 
 
 class ResEncUNetPlanner(ExperimentPlanner):

nnunetv2/training/nnUNetTrainer/variants/network_architecture/nnUNetTrainerBN.py

@@ -1,4 +1,4 @@
-from dynamic_network_architectures.architectures.residual_unet import ResidualEncoderUNet
+from dynamic_network_architectures.architectures.unet import ResidualEncoderUNet
 from dynamic_network_architectures.architectures.unet import PlainConvUNet
 from dynamic_network_architectures.building_blocks.helper import convert_dim_to_conv_op, get_matching_batchnorm
 from dynamic_network_architectures.initialization.weight_init import init_last_bn_before_add_to_0, InitWeights_He

nnunetv2/utilities/get_network_from_plans.py

@@ -1,5 +1,4 @@
-from dynamic_network_architectures.architectures.residual_unet import ResidualEncoderUNet
-from dynamic_network_architectures.architectures.unet import PlainConvUNet
+from dynamic_network_architectures.architectures.unet import PlainConvUNet, ResidualEncoderUNet
 from dynamic_network_architectures.building_blocks.helper import get_matching_instancenorm, convert_dim_to_conv_op
 from dynamic_network_architectures.initialization.weight_init import init_last_bn_before_add_to_0
 from nnunetv2.utilities.network_initialization import InitWeights_He