added flags for network transf blocks & weight init + ask for network name for custom settings

run.py

@@ -134,7 +134,6 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
             dataset=train_data,
             batch_size=training_parameters['training_batch_size'],
             shuffle=True,
-            num_workers=4,
             pin_memory=True
         )
 
@@ -142,7 +141,6 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
             dataset=validation_data,
             batch_size=training_parameters['validation_batch_size'],
             shuffle=False,
-            num_workers=4,
             pin_memory=True
         )
 
@@ -152,7 +150,9 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
         else:
             BrainMapperModel = BrainMapperAE3D(network_parameters)
 
-        BrainMapperModel.reset_parameters()
+        custom_weight_reset_flag = network_parameters['custom_weight_reset_flag']
+
+        BrainMapperModel.reset_parameters(custom_weight_reset_flag)
 
         optimizer = torch.optim.Adam
         # optimizer = torch.optim.AdamW
@@ -280,12 +280,15 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--mode', '-m', required=True,
                         help='run mode, valid values are train or evaluate')
-    parser.add_argument('--settings_path', '-sp', required=False,
-                        help='optional argument, set path to settings_evaluation.ini')
+    parser.add_argument('--model_name', '-n', required=True,
+                        help='model name, required for identifying the settings file modelName.ini & modelName_eval.ini')
 
     arguments = parser.parse_args()
 
-    settings = Settings('settings.ini')
+    settings_file_name = arguments.model_name + '.ini'
+    evaluation_settings_file_name = arguments.model_name + '_eval.ini'
+
+    settings = Settings(settings_file_name)
     data_parameters = settings['DATA']
     training_parameters = settings['TRAINING']
     network_parameters = settings['NETWORK']
@@ -300,10 +303,7 @@ if __name__ == '__main__':
 
     elif arguments.mode == 'evaluate-mapping':
         logging.basicConfig(filename='evaluate-mapping-error.log')
-        if arguments.settings_path is not None:
-            settings_evaluation = Settings(arguments.settings_path)
-        else:
-            settings_evaluation = Settings('settings_evaluation.ini')
+        settings_evaluation = Settings(evaluation_settings_file_name)
         mapping_evaluation_parameters = settings_evaluation['MAPPING']
         evaluate_mapping(mapping_evaluation_parameters)
     elif arguments.mode == 'clear-experiments':
@@ -317,10 +317,7 @@ if __name__ == '__main__':
         delete_files(misc_parameters['logs_directory'])
         print('Cleared the current experiments and logs directory successfully!')
     elif arguments.mode == 'train-and-evaluate-mapping':
-        if arguments.settings_path is not None:
-            settings_evaluation = Settings(arguments.settings_path)
-        else:
-            settings_evaluation = Settings('settings_evaluation.ini')
+        settings_evaluation = Settings(evaluation_settings_file_name)
         mapping_evaluation_parameters = settings_evaluation['MAPPING']
         train(data_parameters, training_parameters,
               network_parameters, misc_parameters)

solver.py

@@ -379,7 +379,7 @@ class Solver():
         self.model.load_state_dict(checkpoint['state_dict'])
         self.optimizer.load_state_dict(checkpoint['optimizer'])
 
-        for state in self.optimizer.state. ():
+        for state in self.optimizer.state.values():
             for key, value in state.items():
                 if torch.is_tensor(value):
                     state[key] = value.to(self.device)