allow resampling with torch

nnunetv2/experiment_planning/experiment_planners/residual_unets/residual_encoder_unet_planners.py

@@ -294,63 +294,6 @@ def __init__(self, dataset_name_or_id: Union[str, int],
         self.max_dataset_covered = 1
 
 
-class nnUNetPlannerResEncL_torchres(nnUNetPlannerResEncL):
-    def __init__(self, dataset_name_or_id: Union[str, int],
-                 gpu_memory_target_in_gb: float = 24,
-                 preprocessor_name: str = 'DefaultPreprocessor', plans_name: str = 'nnUNetResEncUNetLPlans_torchres',
-                 overwrite_target_spacing: Union[List[float], Tuple[float, ...]] = None,
-                 suppress_transpose: bool = False):
-        super().__init__(dataset_name_or_id, gpu_memory_target_in_gb, preprocessor_name, plans_name,
-                         overwrite_target_spacing, suppress_transpose)
-
-    def generate_data_identifier(self, configuration_name: str) -> str:
-        """
-        configurations are unique within each plans file but different plans file can have configurations with the
-        same name. In order to distinguish the associated data we need a data identifier that reflects not just the
-        config but also the plans it originates from
-        """
-        return self.plans_identifier + '_' + configuration_name
-
-    def determine_resampling(self, *args, **kwargs):
-        """
-        returns what functions to use for resampling data and seg, respectively. Also returns kwargs
-        resampling function must be callable(data, current_spacing, new_spacing, **kwargs)
-
-        determine_resampling is called within get_plans_for_configuration to allow for different functions for each
-        configuration
-        """
-        resampling_data = resample_torch_fornnunet
-        resampling_data_kwargs = {
-            "is_seg": False,
-            'force_separate_z': False,
-            'memefficient_seg_resampling': False
-        }
-        resampling_seg = resample_torch_fornnunet
-        resampling_seg_kwargs = {
-            "is_seg": True,
-            'force_separate_z': False,
-            'memefficient_seg_resampling': False
-        }
-        return resampling_data, resampling_data_kwargs, resampling_seg, resampling_seg_kwargs
-
-    def determine_segmentation_softmax_export_fn(self, *args, **kwargs):
-        """
-        function must be callable(data, new_shape, current_spacing, new_spacing, **kwargs). The new_shape should be
-        used as target. current_spacing and new_spacing are merely there in case we want to use it somehow
-
-        determine_segmentation_softmax_export_fn is called within get_plans_for_configuration to allow for different
-        functions for each configuration
-
-        """
-        resampling_fn = resample_torch_fornnunet
-        resampling_fn_kwargs = {
-            "is_seg": False,
-            'force_separate_z': False,
-            'memefficient_seg_resampling': False
-        }
-        return resampling_fn, resampling_fn_kwargs
-
-
 if __name__ == '__main__':
     # we know both of these networks run with batch size 2 and 12 on ~8-10GB, respectively
     net = ResidualEncoderUNet(input_channels=1, n_stages=6, features_per_stage=(32, 64, 128, 256, 320, 320),

nnunetv2/preprocessing/preprocessors/default_preprocessor.py

@@ -260,7 +260,6 @@ def run(self, dataset_name_or_id: Union[int, str], configuration_name: str, plan
                         pbar.update()
                     remaining = [i for i in remaining if i not in done]
                     sleep(0.1)
-            _ = [i.get() for i in r]
 
     def modify_seg_fn(self, seg: np.ndarray, plans_manager: PlansManager, dataset_json: dict,
                       configuration_manager: ConfigurationManager) -> np.ndarray: