fix typo

Author: FabianIsensee

nnunetv2/inference/data_iterators.py

@@ -28,7 +28,7 @@ def preprocess_fromfiles_save_to_queue(list_of_lists: List[List[str]],
         label_manager = plans_manager.get_label_manager(dataset_json)
         preprocessor = configuration_manager.preprocessor_class(verbose=verbose)
         for idx in range(len(list_of_lists)):
-            data, seg, data_properites = preprocessor.run_case(list_of_lists[idx],
+            data, seg, data_properties = preprocessor.run_case(list_of_lists[idx],
                                                                list_of_segs_from_prev_stage_files[
                                                                    idx] if list_of_segs_from_prev_stage_files is not None else None,
                                                                plans_manager,
@@ -40,7 +40,7 @@ def preprocess_fromfiles_save_to_queue(list_of_lists: List[List[str]],
 
             data = torch.from_numpy(data).contiguous().float()
 
-            item = {'data': data, 'data_properites': data_properites,
+            item = {'data': data, 'data_properties': data_properties,
                     'ofile': output_filenames_truncated[idx] if output_filenames_truncated is not None else None}
             success = False
             while not success:
@@ -150,7 +150,7 @@ def generate_train_batch(self):
         # if we have a segmentation from the previous stage we have to process it together with the images so that we
         # can crop it appropriately (if needed). Otherwise it would just be resized to the shape of the data after
         # preprocessing and then there might be misalignments
-        data, seg, data_properites = self.preprocessor.run_case(files, seg_prev_stage, self.plans_manager,
+        data, seg, data_properties = self.preprocessor.run_case(files, seg_prev_stage, self.plans_manager,
                                                                 self.configuration_manager,
                                                                 self.dataset_json)
         if seg_prev_stage is not None:
@@ -159,7 +159,7 @@ def generate_train_batch(self):
 
         data = torch.from_numpy(data)
 
-        return {'data': data, 'data_properites': data_properites, 'ofile': ofile}
+        return {'data': data, 'data_properties': data_properties, 'ofile': ofile}
 
 
 class PreprocessAdapterFromNpy(DataLoader):
@@ -207,7 +207,7 @@ def generate_train_batch(self):
 
         data = torch.from_numpy(data)
 
-        return {'data': data, 'data_properites': props, 'ofile': ofname}
+        return {'data': data, 'data_properties': props, 'ofile': ofname}
 
 
 def preprocess_fromnpy_save_to_queue(list_of_images: List[np.ndarray],
@@ -238,7 +238,7 @@ def preprocess_fromnpy_save_to_queue(list_of_images: List[np.ndarray],
 
             data = torch.from_numpy(data).contiguous().float()
 
-            item = {'data': data, 'data_properites': list_of_image_properties[idx],
+            item = {'data': data, 'data_properties': list_of_image_properties[idx],
                     'ofile': truncated_ofnames[idx] if truncated_ofnames is not None else None}
             success = False
             while not success:

nnunetv2/inference/examples.py

@@ -80,7 +80,7 @@
     img4, props4 = SimpleITKIO().read_images([join(nnUNet_raw, 'Dataset003_Liver/imagesTs/liver_144_0000.nii.gz')])
 
 
-    # each element returned by data_iterator must be a dict with 'data', 'ofile' and 'data_properites' keys!
+    # each element returned by data_iterator must be a dict with 'data', 'ofile' and 'data_properties' keys!
     # If 'ofile' is None, the result will be returned instead of written to a file
     # the iterator is responsible for performing the correct preprocessing!
     # note how the iterator here does not use multiprocessing -> preprocessing will be done in the main thread!
@@ -95,7 +95,7 @@ def my_iterator(list_of_input_arrs, list_of_input_props):
                                                   predictor.plans_manager,
                                                   predictor.configuration_manager,
                                                   predictor.dataset_json)
-            yield {'data': torch.from_numpy(data).contiguous().pin_memory(), 'data_properites': p, 'ofile': None}
+            yield {'data': torch.from_numpy(data).contiguous().pin_memory(), 'data_properties': p, 'ofile': None}
 
 
     ret = predictor.predict_from_data_iterator(my_iterator([img, img2, img3, img4], [props, props2, props3, props4]),

nnunetv2/inference/predict_from_raw_data.py

@@ -333,7 +333,7 @@ def predict_from_data_iterator(self,
                                    save_probabilities: bool = False,
                                    num_processes_segmentation_export: int = default_num_processes):
         """
-        each element returned by data_iterator must be a dict with 'data', 'ofile' and 'data_properites' keys!
+        each element returned by data_iterator must be a dict with 'data', 'ofile' and 'data_properties' keys!
         If 'ofile' is None, the result will be returned instead of written to a file
         """
         with multiprocessing.get_context("spawn").Pool(num_processes_segmentation_export) as export_pool:
@@ -354,7 +354,7 @@ def predict_from_data_iterator(self,
 
                 print(f'perform_everything_on_gpu: {self.perform_everything_on_gpu}')
 
-                properties = preprocessed['data_properites']
+                properties = preprocessed['data_properties']
 
                 # let's not get into a runaway situation where the GPU predicts so fast that the disk has to b swamped with
                 # npy files
@@ -430,14 +430,14 @@ def predict_single_npy_array(self, input_image: np.ndarray, image_properties: di
         if self.verbose:
             print('resampling to original shape')
         if output_file_truncated is not None:
-            export_prediction_from_logits(predicted_logits, dct['data_properites'], self.configuration_manager,
+            export_prediction_from_logits(predicted_logits, dct['data_properties'], self.configuration_manager,
                                           self.plans_manager, self.dataset_json, output_file_truncated,
                                           save_or_return_probabilities)
         else:
             ret = convert_predicted_logits_to_segmentation_with_correct_shape(predicted_logits, self.plans_manager,
                                                                               self.configuration_manager,
                                                                               self.label_manager,
-                                                                              dct['data_properites'],
+                                                                              dct['data_properties'],
                                                                               return_probabilities=
                                                                               save_or_return_probabilities)
             if save_or_return_probabilities:

nnunetv2/inference/readme.md

@@ -184,7 +184,7 @@ cons:
     img2, props2 = SimpleITKIO().read_images([join(nnUNet_raw, 'Dataset003_Liver/imagesTs/liver_146_0000.nii.gz')])
     img3, props3 = SimpleITKIO().read_images([join(nnUNet_raw, 'Dataset003_Liver/imagesTs/liver_145_0000.nii.gz')])
     img4, props4 = SimpleITKIO().read_images([join(nnUNet_raw, 'Dataset003_Liver/imagesTs/liver_144_0000.nii.gz')])
-    # each element returned by data_iterator must be a dict with 'data', 'ofile' and 'data_properites' keys!
+    # each element returned by data_iterator must be a dict with 'data', 'ofile' and 'data_properties' keys!
     # If 'ofile' is None, the result will be returned instead of written to a file
     # the iterator is responsible for performing the correct preprocessing!
     # note how the iterator here does not use multiprocessing -> preprocessing will be done in the main thread!
@@ -199,7 +199,7 @@ cons:
                                                   predictor.plans_manager,
                                                   predictor.configuration_manager,
                                                   predictor.dataset_json)
-            yield {'data': torch.from_numpy(data).contiguous().pin_memory(), 'data_properites': p, 'ofile': None}
+            yield {'data': torch.from_numpy(data).contiguous().pin_memory(), 'data_properties': p, 'ofile': None}
     ret = predictor.predict_from_data_iterator(my_iterator([img, img2, img3, img4], [props, props2, props3, props4]),
                                                save_probabilities=False, num_processes_segmentation_export=3)
 ```

nnunetv2/preprocessing/preprocessors/default_preprocessor.py

@@ -128,17 +128,17 @@ def run_case(self, image_files: List[str], seg_file: Union[str, None], plans_man
         rw = plans_manager.image_reader_writer_class()
 
         # load image(s)
-        data, data_properites = rw.read_images(image_files)
+        data, data_properties = rw.read_images(image_files)
 
         # if possible, load seg
         if seg_file is not None:
             seg, _ = rw.read_seg(seg_file)
         else:
             seg = None
 
-        data, seg = self.run_case_npy(data, seg, data_properites, plans_manager, configuration_manager,
+        data, seg = self.run_case_npy(data, seg, data_properties, plans_manager, configuration_manager,
                                       dataset_json)
-        return data, seg, data_properites
+        return data, seg, data_properties
 
     def run_case_save(self, output_filename_truncated: str, image_files: List[str], seg_file: str,
                       plans_manager: PlansManager, configuration_manager: ConfigurationManager,