typos

documentation/common_questions.md

@@ -149,7 +149,7 @@ iterations each (250000 iterations). The training time thus scales approximately
 know what you are doing! Again, training times will be increased if you do this! 3) is a better way of increasing the 
 patch size.
 
-3) Run `nnUNet_plan_and_preprocess` with a larger GPU memory budget. This will make nnU-Net plan for larger batch sizes 
+3) Run `nnUNet_plan_and_preprocess` with a larger GPU memory budget. This will make nnU-Net plan for larger patch sizes 
 during experiment planning. Doing this can change the patch size, network topology, the batch size as well as the 
 presence of the U-Net cascade. To run with a different memory budget, you need to specify a different experiment planner, for example
 `nnUNet_plan_and_preprocess -t TASK_ID -pl2d None -pl3d ExperimentPlanner3D_v21_32GB` (note that `-pl2d None` will 

nnunet/experiment_planning/alternative_experiment_planning/target_spacing/experiment_planner_baseline_3DUNet_v21_noResampling.py

@@ -34,7 +34,7 @@ def plan_experiment(self):
         :return:
         """
         use_nonzero_mask_for_normalization = self.determine_whether_to_use_mask_for_norm()
-        print("Are we using the nonzero mask for normalizaion?", use_nonzero_mask_for_normalization)
+        print("Are we using the nonzero mask for normalization?", use_nonzero_mask_for_normalization)
         spacings = self.dataset_properties['all_spacings']
         sizes = self.dataset_properties['all_sizes']
 
@@ -132,7 +132,7 @@ def plan_experiment(self):
         :return:
         """
         use_nonzero_mask_for_normalization = self.determine_whether_to_use_mask_for_norm()
-        print("Are we using the nonzero mask for normalizaion?", use_nonzero_mask_for_normalization)
+        print("Are we using the nonzero mask for normalization?", use_nonzero_mask_for_normalization)
         spacings = self.dataset_properties['all_spacings']
         sizes = self.dataset_properties['all_sizes']
 

nnunet/experiment_planning/experiment_planner_baseline_2DUNet.py

@@ -89,7 +89,7 @@ def get_properties_for_stage(self, current_spacing, original_spacing, original_s
 
     def plan_experiment(self):
         use_nonzero_mask_for_normalization = self.determine_whether_to_use_mask_for_norm()
-        print("Are we using the nonzero mask for normalizaion?", use_nonzero_mask_for_normalization)
+        print("Are we using the nonzero mask for normalization?", use_nonzero_mask_for_normalization)
 
         spacings = self.dataset_properties['all_spacings']
         sizes = self.dataset_properties['all_sizes']

nnunet/experiment_planning/experiment_planner_baseline_3DUNet.py

@@ -246,7 +246,7 @@ def get_properties_for_stage(self, current_spacing, original_spacing, original_s
 
     def plan_experiment(self):
         use_nonzero_mask_for_normalization = self.determine_whether_to_use_mask_for_norm()
-        print("Are we using the nonzero mask for normalizaion?", use_nonzero_mask_for_normalization)
+        print("Are we using the nonzero mask for normalization?", use_nonzero_mask_for_normalization)
         spacings = self.dataset_properties['all_spacings']
         sizes = self.dataset_properties['all_sizes']
 

nnunet/training/network_training/nnUNetTrainerV2.py

@@ -170,7 +170,7 @@ def initialize_optimizer_and_scheduler(self):
     def run_online_evaluation(self, output, target):
         """
         due to deep supervision the return value and the reference are now lists of tensors. We only need the full
-        resolution output because this is what we are interested in the end. The others are ignored
+        resolution output because this is what we are interested in in the end. The others are ignored
         :param output:
         :param target:
         :return:

nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_softDeepSupervision.py

@@ -116,7 +116,7 @@ def initialize(self, training=True, force_load_plans=False):
     def run_online_evaluation(self, output, target):
         """
         due to deep supervision the return value and the reference are now lists of tensors. We only need the full
-        resolution output because this is what we are interested in the end. The others are ignored
+        resolution output because this is what we are interested in in the end. The others are ignored
         :param output:
         :param target:
         :return:

tests/test_steps_for_sliding_window_prediction.py

@@ -28,7 +28,7 @@ def _verify_steps(self, steps, patch_size, image_size, step_size):
                                                                                              str(patch_size), step_size)
         target_step_sizes_in_voxels = [i * step_size for i in patch_size]
 
-        # this code is copied from the current implementation. Not ideal, but I don't know hoe else to the the
+        # this code is copied from the current implementation. Not ideal, but I don't know how else to compute the
         # expected num_steps
         num_steps = [int(np.ceil((i - k) / j)) + 1 for i, j, k in zip(image_size, target_step_sizes_in_voxels,
                                                                       patch_size)]