eliminated val loss return, added new run argument modes

run.py

@@ -232,12 +232,14 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
                         crop_flag = data_parameters['crop_flag']
                         )
 
-        validation_loss = solver.train(train_loader, validation_loader)
+        # _ = solver.train(train_loader, validation_loader)
+
+        solver.train(train_loader, validation_loader)
 
         del train_data, validation_data, train_loader, validation_loader, BrainMapperModel, solver, optimizer
         torch.cuda.empty_cache()
 
-        return validation_loss
+        # return None
 
 
     if training_parameters['adam_w_flag'] == True:
@@ -250,7 +252,7 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
     # loss_function=torch.nn.CosineEmbeddingLoss()
 
     if network_parameters['cross_domain_flag'] == False:
-        _ = _train_runner(data_parameters, 
+        _train_runner(data_parameters, 
                     training_parameters, 
                     network_parameters, 
                     misc_parameters,
@@ -267,7 +269,7 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
 
             # loss_function = torch.nn.L1Loss()
 
-            _ = _train_runner(data_parameters, 
+            _train_runner(data_parameters, 
                         training_parameters, 
                         network_parameters, 
                         misc_parameters,
@@ -283,7 +285,7 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
 
             # loss_function = torch.nn.L1Loss()
 
-            _ = _train_runner(data_parameters, 
+            _train_runner(data_parameters, 
                         training_parameters, 
                         network_parameters, 
                         misc_parameters,
@@ -293,7 +295,7 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
 
         if network_parameters['cross_domain_x2y_flag'] == True:
 
-            _ = _train_runner(data_parameters, 
+            _train_runner(data_parameters, 
                         training_parameters, 
                         network_parameters, 
                         misc_parameters,
@@ -428,16 +430,43 @@ if __name__ == '__main__':
         settings_evaluation = Settings(evaluation_settings_file_name)
         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!')
+
+    elif arguments.mode == 'clear-checkpoints':
+        if network_parameters['cross_domain_flag'] == True:
+            if network_parameters['cross_domain_x2x_flag'] == True:
+                training_parameters['experiment_name'] = training_parameters['experiment_name'] + "_x2x"
+            if network_parameters['cross_domain_y2y_flag'] == True:
+                training_parameters['experiment_name'] = training_parameters['experiment_name'] + "_y2y"
+
+        shutil.rmtree(os.path.join(misc_parameters['experiments_directory'], training_parameters['experiment_name']))
+        print('Cleared the current experiment checkpoints successfully!')
+
+    elif arguments.mode == 'clear-logs':
+        if network_parameters['cross_domain_flag'] == True:
+            if network_parameters['cross_domain_x2x_flag'] == True:
+                training_parameters['experiment_name'] = training_parameters['experiment_name'] + "_x2x"
+            if network_parameters['cross_domain_y2y_flag'] == True:
+                training_parameters['experiment_name'] = training_parameters['experiment_name'] + "_y2y"
+
+        shutil.rmtree(os.path.join(misc_parameters['logs_directory'], training_parameters['experiment_name']))
+        print('Cleared the current experiment logs directory successfully!')
+
+    elif arguments.mode == 'clear-experiment':
+        if network_parameters['cross_domain_flag'] == True:
+            if network_parameters['cross_domain_x2x_flag'] == True:
+                training_parameters['experiment_name'] = training_parameters['experiment_name'] + "_x2x"
+            if network_parameters['cross_domain_y2y_flag'] == True:
+                training_parameters['experiment_name'] = training_parameters['experiment_name'] + "_y2y"
+
+        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 experiment checkpoints and logs directory successfully!')
+
+    # elif arguments.mode == 'clear-everything':
+    #     delete_files(misc_parameters['experiments_directory'])
+    #     delete_files(misc_parameters['logs_directory'])
+    #     print('Cleared the all the checkpoints and logs directory successfully!')
+
     elif arguments.mode == 'train-and-evaluate-mapping':
         settings_evaluation = Settings(evaluation_settings_file_name)
         mapping_evaluation_parameters = settings_evaluation['MAPPING']
@@ -445,6 +474,7 @@ if __name__ == '__main__':
               network_parameters, misc_parameters)
         logging.basicConfig(filename='evaluate-mapping-error.log')
         evaluate_mapping(mapping_evaluation_parameters)
+        
     else:
         raise ValueError(
-            'Invalid mode value! Only supports: train, evaluate-score, evaluate-mapping, train-and-evaluate-mapping, clear-experiments and clear-everything')
+            'Invalid mode value! Only supports: train, evaluate-score, evaluate-mapping, train-and-evaluate-mapping, clear-checkpoints, clear-logs,  clear-experiment and clear-everything (req uncomment for safety!)')