added call for test/train/val split

run.py

@@ -39,7 +39,7 @@ import torch.utils.data as data
 
 from solver import Solver
 from BrainMapperUNet import BrainMapperUNet
-from utils.data_utils import get_datasets
+from utils.data_utils import get_datasets, data_test_train_validation_split
 import utils.data_evaluation_utils as evaluations
 from utils.data_logging_utils import LogWriter
 
@@ -201,6 +201,8 @@ def evaluate_score(training_parameters, network_parameters, misc_parameters, eva
         }
     """
 
+    ################# TODO - NEED TO UPDATE THE DATA FUNCTIONS!
+
     logWriter = LogWriter(number_of_classes=network_parameters['number_of_classes'],
                           logs_directory=misc_parameters['logs_directory'],
                           experiment_name=training_parameters['experiment_name']
@@ -348,30 +350,39 @@ if __name__ == '__main__':
     misc_parameters = settings['MISC']
     evaluation_parameters = settings['EVALUATION']
 
-    if arguments.mode == 'train':
-        train(data_parameters, training_parameters,
-              network_parameters, misc_parameters)
-    elif arguments.mode == 'evaluate-score':
-        evaluate_score(training_parameters,
-                       network_parameters, misc_parameters, evaluation_parameters)
-    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')
-        mapping_evaluation_parameters = settings_evaluation['MAPPING']
-        evaluate_mapping(mapping_evaluation_parameters)
-    elif arguments.mode == 'clear-experiments':
-        shutil.rmtree(os.path.join(
-            misc_parameters['experiments_directory'], training_parameters['experiment_name']))
-        shutil.rmtree(os.path.join(
-            misc_parameters['logs_directory'], training_parameters['experiment_name']))
-        print('Cleared the current experiments and logs directory successfully!')
-    elif arguments.mode == 'clear-everything':
-        delete_files(misc_parameters['experiments_directory'])
-        delete_files(misc_parameters['logs_directory'])
-        print('Cleared the current experiments and logs directory successfully!')
+    data_shuffling_flag = data_parameters['data_split_flag']
+
+    if data_shuffling_flag == True:
+        # Here we shuffle the data!
+        data_test_train_validation_split(data_parameters['data_directory'], data_parameters['train_percentage'], data_parameters['validation_percentage'])
+
+        # TODO: This might also be a very good point to add cross-validation later
     else:
-        raise ValueError(
-            'Invalid mode value! Only supports: train, evaluate-score, evaluate-mapping, clear-experiments and clear-everything')
+
+        if arguments.mode == 'train':
+            train(data_parameters, training_parameters,
+                network_parameters, misc_parameters)
+        elif arguments.mode == 'evaluate-score':
+            evaluate_score(training_parameters,
+                        network_parameters, misc_parameters, evaluation_parameters)
+        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')
+            mapping_evaluation_parameters = settings_evaluation['MAPPING']
+            evaluate_mapping(mapping_evaluation_parameters)
+        elif arguments.mode == 'clear-experiments':
+            shutil.rmtree(os.path.join(
+                misc_parameters['experiments_directory'], training_parameters['experiment_name']))
+            shutil.rmtree(os.path.join(
+                misc_parameters['logs_directory'], training_parameters['experiment_name']))
+            print('Cleared the current experiments and logs directory successfully!')
+        elif arguments.mode == 'clear-everything':
+            delete_files(misc_parameters['experiments_directory'])
+            delete_files(misc_parameters['logs_directory'])
+            print('Cleared the current experiments and logs directory successfully!')
+        else:
+            raise ValueError(
+                'Invalid mode value! Only supports: train, evaluate-score, evaluate-mapping, clear-experiments and clear-everything')