align imports with current main of dynamic network architectures

MIC-DKFZ · Feb 6, 2024 · 52cab35 · 52cab35
1 parent fa552d3
commit 52cab35
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Showing 6 changed files with 17 additions and 10 deletions.
diff --git a/nnunetv2/experiment_planning/experiment_planners/default_experiment_planner.py b/nnunetv2/experiment_planning/experiment_planners/default_experiment_planner.py
@@ -5,7 +5,8 @@
 
 import numpy as np
 from batchgenerators.utilities.file_and_folder_operations import load_json, join, save_json, isfile, maybe_mkdir_p
-from dynamic_network_architectures.architectures.unet import PlainConvUNet, ResidualEncoderUNet
+from dynamic_network_architectures.architectures.residual_unet import ResidualEncoderUNet
+from dynamic_network_architectures.architectures.unet import PlainConvUNet
 from dynamic_network_architectures.building_blocks.helper import convert_dim_to_conv_op, get_matching_instancenorm
 
 from nnunetv2.configuration import ANISO_THRESHOLD

diff --git a/nnunetv2/experiment_planning/experiment_planners/resencUNet_planner.py b/nnunetv2/experiment_planning/experiment_planners/resencUNet_planner.py
@@ -3,7 +3,7 @@
 from torch import nn
 
 from nnunetv2.experiment_planning.experiment_planners.default_experiment_planner import ExperimentPlanner
-from dynamic_network_architectures.architectures.unet import ResidualEncoderUNet
+from dynamic_network_architectures.architectures.residual_unet import ResidualEncoderUNet
 
 
 class ResEncUNetPlanner(ExperimentPlanner):

diff --git a/nnunetv2/inference/data_iterators.py b/nnunetv2/inference/data_iterators.py
@@ -53,6 +53,7 @@ def preprocess_fromfiles_save_to_queue(list_of_lists: List[List[str]],
                     pass
         done_event.set()
     except Exception as e:
+        # print(Exception, e)
         abort_event.set()
         raise e
 
@@ -99,6 +100,7 @@ def preprocessing_iterator_fromfiles(list_of_lists: List[List[str]],
 
     worker_ctr = 0
     while (not done_events[worker_ctr].is_set()) or (not target_queues[worker_ctr].empty()):
+        # import IPython;IPython.embed()
         if not target_queues[worker_ctr].empty():
             item = target_queues[worker_ctr].get()
             worker_ctr = (worker_ctr + 1) % num_processes

diff --git a/nnunetv2/inference/predict_from_raw_data.py b/nnunetv2/inference/predict_from_raw_data.py
@@ -368,8 +368,8 @@ def predict_from_data_iterator(self,
 
                 if ofile is not None:
                     # this needs to go into background processes
-                    # export_prediction_from_logits(prediction, properties, configuration_manager, plans_manager,
-                    #                               dataset_json, ofile, save_probabilities)
+                    # export_prediction_from_logits(prediction, properties, self.configuration_manager, self.plans_manager,
+                    #                               self.dataset_json, ofile, save_probabilities)
                     print('sending off prediction to background worker for resampling and export')
                     r.append(
                         export_pool.starmap_async(
@@ -379,10 +379,12 @@ def predict_from_data_iterator(self,
                         )
                     )
                 else:
-                    # convert_predicted_logits_to_segmentation_with_correct_shape(prediction, plans_manager,
-                    #                                                             configuration_manager, label_manager,
-                    #                                                             properties,
-                    #                                                             save_probabilities)
+                    # convert_predicted_logits_to_segmentation_with_correct_shape(
+                    #             prediction, self.plans_manager,
+                    #              self.configuration_manager, self.label_manager,
+                    #              properties,
+                    #              save_probabilities)
+
                     print('sending off prediction to background worker for resampling')
                     r.append(
                         export_pool.starmap_async(

diff --git a/nnunetv2/training/nnUNetTrainer/variants/network_architecture/nnUNetTrainerBN.py b/nnunetv2/training/nnUNetTrainer/variants/network_architecture/nnUNetTrainerBN.py
@@ -1,4 +1,5 @@
-from dynamic_network_architectures.architectures.unet import ResidualEncoderUNet, PlainConvUNet
+from dynamic_network_architectures.architectures.residual_unet import ResidualEncoderUNet
+from dynamic_network_architectures.architectures.unet import PlainConvUNet
 from dynamic_network_architectures.building_blocks.helper import convert_dim_to_conv_op, get_matching_batchnorm
 from dynamic_network_architectures.initialization.weight_init import init_last_bn_before_add_to_0, InitWeights_He
 from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer

diff --git a/nnunetv2/utilities/get_network_from_plans.py b/nnunetv2/utilities/get_network_from_plans.py
@@ -1,4 +1,5 @@
-from dynamic_network_architectures.architectures.unet import PlainConvUNet, ResidualEncoderUNet
+from dynamic_network_architectures.architectures.residual_unet import ResidualEncoderUNet
+from dynamic_network_architectures.architectures.unet import PlainConvUNet
 from dynamic_network_architectures.building_blocks.helper import get_matching_instancenorm, convert_dim_to_conv_op
 from dynamic_network_architectures.initialization.weight_init import init_last_bn_before_add_to_0
 from nnunetv2.utilities.network_initialization import InitWeights_He