bug fixes for memory bleeds during cross validation

BrainMapperUNet.py

@@ -207,6 +207,24 @@ class BrainMapperUNet3D(nn.Module):
 
         return prediction
 
+    def reset_parameters(self):
+        """Parameter Initialization
+
+        This function (re)initializes the parameters of the defined network.
+        This function is a wrapper for the reset_parameters() function defined for each module. 
+        More information can be found here: https://discuss.pytorch.org/t/what-is-the-default-initialization-of-a-conv2d-layer-and-linear-layer/16055 + https://discuss.pytorch.org/t/how-to-reset-model-weights-to-effectively-implement-crossvalidation/53859 
+        An alternative (re)initialization method is described here: https://discuss.pytorch.org/t/how-to-reset-variables-values-in-nn-modules/32639 
+        """
+
+        print("Initializing network parameters...")
+
+        for _, module in self.named_children():
+            for _, submodule in module.named_children():
+                if isinstance(submodule, (torch.nn.PReLU, torch.nn.Dropout3d, torch.nn.MaxPool3d)) == False:
+                    submodule.reset_parameters()
+
+        print("Initialized network parameters!")
+
 # DEPRECATED ARCHITECTURES!
 
 
@@ -378,6 +396,23 @@ class BrainMapperUNet(nn.Module):
 
         return prediction
 
+    def reset_parameters(self):
+        """Parameter Initialization
+
+        This function (re)initializes the parameters of the defined network.
+        This function is a wrapper for the reset_parameters() function defined for each module. 
+        More information can be found here: https://discuss.pytorch.org/t/what-is-the-default-initialization-of-a-conv2d-layer-and-linear-layer/16055 + https://discuss.pytorch.org/t/how-to-reset-model-weights-to-effectively-implement-crossvalidation/53859 
+        An alternative (re)initialization method is described here: https://discuss.pytorch.org/t/how-to-reset-variables-values-in-nn-modules/32639 
+        """
+
+        print("Initializing network parameters...")
+
+        for _, module in self.named_children():
+            for _, submodule in module.named_children():
+                if isinstance(submodule, (torch.nn.PReLU, torch.nn.Dropout3d, torch.nn.MaxPool3d)) == False:
+                    submodule.reset_parameters()
+
+        print("Initialized network parameters!")
 
 class BrainMapperUNet3D_Simple(nn.Module):
     """Architecture class BrainMapper 3D U-net.
@@ -550,6 +585,24 @@ class BrainMapperUNet3D_Simple(nn.Module):
 
         return prediction
 
+    def reset_parameters(self):
+        """Parameter Initialization
+
+        This function (re)initializes the parameters of the defined network.
+        This function is a wrapper for the reset_parameters() function defined for each module. 
+        More information can be found here: https://discuss.pytorch.org/t/what-is-the-default-initialization-of-a-conv2d-layer-and-linear-layer/16055 + https://discuss.pytorch.org/t/how-to-reset-model-weights-to-effectively-implement-crossvalidation/53859 
+        An alternative (re)initialization method is described here: https://discuss.pytorch.org/t/how-to-reset-variables-values-in-nn-modules/32639 
+        """
+
+        print("Initializing network parameters...")
+
+        for _, module in self.named_children():
+            for _, submodule in module.named_children():
+                if isinstance(submodule, (torch.nn.PReLU, torch.nn.Dropout3d, torch.nn.MaxPool3d)) == False:
+                    submodule.reset_parameters()
+
+        print("Initialized network parameters!")
+
 # if __name__ == '__main__':
 
 #     # For debugging - To be deleted later! TODO

run.py

@@ -151,6 +151,8 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
         else:
             BrainMapperModel = BrainMapperUNet3D(network_parameters)
 
+        BrainMapperModel.reset_parameters()
+
         solver = Solver(model=BrainMapperModel,
                         device=misc_parameters['device'],
                         number_of_classes=network_parameters['number_of_classes'],
@@ -195,11 +197,11 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
 
     else:
         print("Training initiated using K-fold Cross Validation!")
-        for k in range(data_parameters['k_fold']):
+        k_fold_losses = []
 
-            print("K-fold Number: {}".format(k+1))
+        for k in range(data_parameters['k_fold']):
 
-            k_fold_losses = []
+            print("K-fold Number: {}".format(k+1))  
 
             data_parameters['train_list'] = os.path.join(
                 data_parameters['data_folder_name'], 'train' + str(k+1)+'.txt')

settings.py

@@ -35,7 +35,10 @@ class Settings(Mapping):
 
     def __init__(self, settings_file='settings.ini'):
         configurator = configparser.ConfigParser()
-        configurator.read(settings_file)
+        if not configurator.read(settings_file):
+            configurator.read('functionmapper/'+settings_file)
+        else:
+            configurator.read(settings_file)
         self.settings_dictionary = _parse_values(configurator)
 
     def __getitem__(self, key):