Add no_resampling function + remove unnecessary imports

nnunetv2/dataset_conversion/Dataset137_BraTS21.py

@@ -1,6 +1,5 @@
 import multiprocessing
 import shutil
-from multiprocessing import Pool
 
 import SimpleITK as sitk
 import numpy as np

nnunetv2/dataset_conversion/convert_MSD_dataset.py

@@ -1,7 +1,6 @@
 import argparse
 import multiprocessing
 import shutil
-from multiprocessing import Pool
 from typing import Optional
 import SimpleITK as sitk
 from batchgenerators.utilities.file_and_folder_operations import *

nnunetv2/ensembling/ensemble.py

@@ -2,7 +2,6 @@
 import multiprocessing
 import shutil
 from copy import deepcopy
-from multiprocessing import Pool
 from typing import List, Union, Tuple
 
 import numpy as np

nnunetv2/evaluation/evaluate_predictions.py

@@ -1,8 +1,7 @@
 import multiprocessing
 import os
 from copy import deepcopy
-from multiprocessing import Pool
-from typing import Tuple, List, Union, Optional
+from typing import Tuple, List, Union
 
 import numpy as np
 from batchgenerators.utilities.file_and_folder_operations import subfiles, join, save_json, load_json, \

nnunetv2/experiment_planning/plans_for_pretraining/move_plans_between_datasets.py

@@ -1,12 +1,11 @@
 import argparse
 from typing import Union
 
-from batchgenerators.utilities.file_and_folder_operations import join, isdir, isfile, load_json, subfiles, save_json
+from batchgenerators.utilities.file_and_folder_operations import join, isdir, isfile, load_json, save_json
 
 from nnunetv2.imageio.reader_writer_registry import determine_reader_writer_from_dataset_json
 from nnunetv2.paths import nnUNet_preprocessed, nnUNet_raw
 from nnunetv2.utilities.file_path_utilities import maybe_convert_to_dataset_name
-from nnunetv2.utilities.plans_handling.plans_handler import PlansManager
 from nnunetv2.utilities.utils import get_filenames_of_train_images_and_targets
 
 

nnunetv2/experiment_planning/verify_dataset_integrity.py

@@ -13,8 +13,6 @@
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
 import multiprocessing
-import re
-from multiprocessing import Pool
 from typing import Type
 
 import numpy as np
@@ -25,8 +23,7 @@
 from nnunetv2.imageio.reader_writer_registry import determine_reader_writer_from_dataset_json
 from nnunetv2.paths import nnUNet_raw
 from nnunetv2.utilities.label_handling.label_handling import LabelManager
-from nnunetv2.utilities.utils import get_identifiers_from_splitted_dataset_folder, \
-    get_filenames_of_train_images_and_targets
+from nnunetv2.utilities.utils import get_filenames_of_train_images_and_targets
 
 
 def verify_labels(label_file: str, readerclass: Type[BaseReaderWriter], expected_labels: List[int]) -> bool:

nnunetv2/inference/data_iterators.py

@@ -1,6 +1,6 @@
 import multiprocessing
 import queue
-from torch.multiprocessing import Event, Process, Queue, Manager
+from torch.multiprocessing import Event, Queue, Manager
 
 from time import sleep
 from typing import Union, List

nnunetv2/inference/export_prediction.py

@@ -1,5 +1,3 @@
-import os
-from copy import deepcopy
 from typing import Union, List
 
 import numpy as np
@@ -74,13 +72,6 @@ def export_prediction_from_logits(predicted_array_or_file: Union[np.ndarray, tor
                                   plans_manager: PlansManager,
                                   dataset_json_dict_or_file: Union[dict, str], output_file_truncated: str,
                                   save_probabilities: bool = False):
-    # if isinstance(predicted_array_or_file, str):
-    #     tmp = deepcopy(predicted_array_or_file)
-    #     if predicted_array_or_file.endswith('.npy'):
-    #         predicted_array_or_file = np.load(predicted_array_or_file)
-    #     elif predicted_array_or_file.endswith('.npz'):
-    #         predicted_array_or_file = np.load(predicted_array_or_file)['softmax']
-    #     os.remove(tmp)
 
     if isinstance(dataset_json_dict_or_file, str):
         dataset_json_dict_or_file = load_json(dataset_json_dict_or_file)
@@ -111,13 +102,7 @@ def resample_and_save(predicted: Union[torch.Tensor, np.ndarray], target_shape:
                       plans_manager: PlansManager, configuration_manager: ConfigurationManager, properties_dict: dict,
                       dataset_json_dict_or_file: Union[dict, str], num_threads_torch: int = default_num_processes) \
         -> None:
-    # # needed for cascade
-    # if isinstance(predicted, str):
-    #     assert isfile(predicted), "If isinstance(segmentation_softmax, str) then " \
-    #                               "isfile(segmentation_softmax) must be True"
-    #     del_file = deepcopy(predicted)
-    #     predicted = np.load(predicted)
-    #     os.remove(del_file)
+
     old_threads = torch.get_num_threads()
     torch.set_num_threads(num_threads_torch)
 

nnunetv2/postprocessing/remove_connected_components.py

@@ -1,7 +1,6 @@
 import argparse
 import multiprocessing
 import shutil
-from multiprocessing import Pool
 from typing import Union, Tuple, List, Callable
 
 import numpy as np

nnunetv2/preprocessing/resampling/default_resampling.py

@@ -4,7 +4,6 @@
 
 import numpy as np
 import pandas as pd
-import sklearn
 import torch
 from batchgenerators.augmentations.utils import resize_segmentation
 from scipy.ndimage import map_coordinates
@@ -143,7 +142,6 @@ def resample_data_or_seg(data: np.ndarray, new_shape: Union[Tuple[float, ...], L
     if np.any(shape != new_shape):
         data = data.astype(float, copy=False)
         if do_separate_z:
-            # print("separate z, order in z is", order_z, "order inplane is", order)
             assert axis is not None, 'If do_separate_z, we need to know what axis is anisotropic'
             if axis == 0:
                 new_shape_2d = new_shape[1:]
@@ -191,13 +189,27 @@ def resample_data_or_seg(data: np.ndarray, new_shape: Union[Tuple[float, ...], L
                 else:
                     reshaped_final[c] = reshaped_here
         else:
-            # print("no separate z, order", order)
             for c in range(data.shape[0]):
                 reshaped_final[c] = resize_fn(data[c], new_shape, order, **kwargs)
         return reshaped_final
     else:
         # print("no resampling necessary")
         return data
+
+
+def no_resampling_data_or_seg_to_shape(data: Union[torch.Tensor, np.ndarray],
+                                       new_shape: Union[Tuple[int, ...], List[int], np.ndarray],
+                                       current_spacing: Union[Tuple[float, ...], List[float], np.ndarray],
+                                       new_spacing: Union[Tuple[float, ...], List[float], np.ndarray],
+                                       is_seg: bool = False,
+                                       order: int = 3, order_z: int = 0,
+                                       force_separate_z: Union[bool, None] = False,
+                                       separate_z_anisotropy_threshold: float = ANISO_THRESHOLD):
+    """
+    A simplified resampling function that bypasses actual resampling. 
+    This approach makes it much easier to implement no-resampling training and inference.
+    """
+    return data
 
 
 if __name__ == '__main__':

nnunetv2/run/run_training.py

@@ -261,7 +261,6 @@ def run_training_entry():
     assert args.device in ['cpu', 'cuda', 'mps'], f'-device must be either cpu, mps or cuda. Other devices are not tested/supported. Got: {args.device}.'
     if args.device == 'cpu':
         # let's allow torch to use hella threads
-        import multiprocessing
         torch.set_num_threads(multiprocessing.cpu_count())
         device = torch.device('cpu')
     elif args.device == 'cuda':

nnunetv2/training/loss/deep_supervision.py

@@ -1,4 +1,3 @@
-import torch
 from torch import nn