remove unused imports, ensure data is float32, astype w/o copy

nnunetv2/preprocessing/preprocessors/default_preprocessor.py

@@ -14,20 +14,20 @@
 import multiprocessing
 import shutil
 from time import sleep
-from typing import Union, Tuple
+from typing import Tuple
 
-import nnunetv2
 import numpy as np
 from batchgenerators.utilities.file_and_folder_operations import *
+from tqdm import tqdm
+
+import nnunetv2
 from nnunetv2.paths import nnUNet_preprocessed, nnUNet_raw
 from nnunetv2.preprocessing.cropping.cropping import crop_to_nonzero
 from nnunetv2.preprocessing.resampling.default_resampling import compute_new_shape
 from nnunetv2.utilities.dataset_name_id_conversion import maybe_convert_to_dataset_name
 from nnunetv2.utilities.find_class_by_name import recursive_find_python_class
 from nnunetv2.utilities.plans_handling.plans_handler import PlansManager, ConfigurationManager
-from nnunetv2.utilities.utils import get_identifiers_from_splitted_dataset_folder, \
-    create_lists_from_splitted_dataset_folder, get_filenames_of_train_images_and_targets
-from tqdm import tqdm
+from nnunetv2.utilities.utils import get_filenames_of_train_images_and_targets
 
 
 class DefaultPreprocessor(object):
@@ -41,7 +41,7 @@ def run_case_npy(self, data: np.ndarray, seg: Union[np.ndarray, None], propertie
                      plans_manager: PlansManager, configuration_manager: ConfigurationManager,
                      dataset_json: Union[dict, str]):
         # let's not mess up the inputs!
-        data = np.copy(data)
+        data = data.astype(np.float32)  # this creates a copy
         if seg is not None:
             assert data.shape[1:] == seg.shape[1:], "Shape mismatch between image and segmentation. Please fix your dataset and make use of the --verify_dataset_integrity flag to ensure everything is correct"
             seg = np.copy(seg)

nnunetv2/preprocessing/resampling/default_resampling.py

@@ -151,7 +151,7 @@ def resample_data_or_seg(data: np.ndarray, new_shape: Union[Tuple[float, ...], L
         dtype_out = data.dtype
     reshaped_final = np.zeros((data.shape[0], *new_shape), dtype=dtype_out)
     if np.any(shape != new_shape):
-        data = data.astype(float)
+        data = data.astype(float, copy=False)
         if do_separate_z:
             # print("separate z, order in z is", order_z, "order inplane is", order)
             assert len(axis) == 1, "only one anisotropic axis supported"