creating save_model_directory if it does not exist

run.py

@@ -39,7 +39,7 @@ import torch.utils.data as data
 
 from solver import Solver
 from BrainMapperUNet import BrainMapperUNet3D
-from utils.data_utils import get_datasets, data_test_train_validation_split, update_shuffling_flag
+from utils.data_utils import get_datasets, data_test_train_validation_split, update_shuffling_flag, create_folder
 import utils.data_evaluation_utils as evaluations
 from utils.data_logging_utils import LogWriter
 
@@ -137,33 +137,36 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
     else:
         BrainMapperModel = BrainMapperUNet3D(network_parameters)
 
-    # solver = Solver(model=BrainMapperModel,
-    #                 device=misc_parameters['device'],
-    #                 number_of_classes=network_parameters['number_of_classes'],
-    #                 experiment_name=training_parameters['experiment_name'],
-    #                 optimizer_arguments={'lr': training_parameters['learning_rate'],
-    #                                      'betas': training_parameters['optimizer_beta'],
-    #                                      'eps': training_parameters['optimizer_epsilon'],
-    #                                      'weight_decay': training_parameters['optimizer_weigth_decay']
-    #                                      },
-    #                 model_name=misc_parameters['model_name'],
-    #                 number_epochs=training_parameters['number_of_epochs'],
-    #                 loss_log_period=training_parameters['loss_log_period'],
-    #                 learning_rate_scheduler_step_size=training_parameters[
-    #                     'learning_rate_scheduler_step_size'],
-    #                 learning_rate_scheduler_gamma=training_parameters['learning_rate_scheduler_gamma'],
-    #                 use_last_checkpoint=training_parameters['use_last_checkpoint'],
-    #                 experiment_directory=misc_parameters['experiments_directory'],
-    #                 logs_directory=misc_parameters['logs_directory']
-    #                 )
-
-    # solver.train(train_loader, validation_loader)
-
-    # model_output_path = os.path.join(
-    #     misc_parameters['save_model_directory'], training_parameters['final_model_output_file'])
-    # BrainMapperModel.save(model_output_path)
-
-    # print("Final Model Saved in: {}".format(model_output_path))
+    solver = Solver(model=BrainMapperModel,
+                    device=misc_parameters['device'],
+                    number_of_classes=network_parameters['number_of_classes'],
+                    experiment_name=training_parameters['experiment_name'],
+                    optimizer_arguments={'lr': training_parameters['learning_rate'],
+                                         'betas': training_parameters['optimizer_beta'],
+                                         'eps': training_parameters['optimizer_epsilon'],
+                                         'weight_decay': training_parameters['optimizer_weigth_decay']
+                                         },
+                    model_name=misc_parameters['model_name'],
+                    number_epochs=training_parameters['number_of_epochs'],
+                    loss_log_period=training_parameters['loss_log_period'],
+                    learning_rate_scheduler_step_size=training_parameters[
+                        'learning_rate_scheduler_step_size'],
+                    learning_rate_scheduler_gamma=training_parameters['learning_rate_scheduler_gamma'],
+                    use_last_checkpoint=training_parameters['use_last_checkpoint'],
+                    experiment_directory=misc_parameters['experiments_directory'],
+                    logs_directory=misc_parameters['logs_directory']
+                    )
+
+    solver.train(train_loader, validation_loader)
+
+    model_output_path = os.path.join(
+        misc_parameters['save_model_directory'], training_parameters['final_model_output_file'])
+
+    create_folder(misc_parameters['save_model_directory'])
+
+    BrainMapperModel.save(model_output_path)
+
+    print("Final Model Saved in: {}".format(model_output_path))
 
 
 def evaluate_score(training_parameters, network_parameters, misc_parameters, evaluation_parameters):