TRAIN WORKS! + code clean, pep8 formated, debug coms deleted

BrainMapperUNet.py

@@ -123,7 +123,7 @@ class BrainMapperUNet3D(nn.Module):
             Y_bottleNeck, Y_np4)
 
         del Y_bottleNeck, Y_np4
-        
+
         Y_decoder_2 = self.decoderBlock2.forward(
             Y_decoder_1, Y_np3)
 
@@ -209,6 +209,7 @@ class BrainMapperUNet3D(nn.Module):
 
 # DEPRECATED ARCHITECTURES!
 
+
 class BrainMapperUNet(nn.Module):
     """Architecture class BrainMapper U-net.
 
@@ -293,7 +294,7 @@ class BrainMapperUNet(nn.Module):
             Y_bottleNeck, Y_np4, pool_indices4)
 
         del Y_bottleNeck, Y_np4, pool_indices4
-        
+
         Y_decoder_2 = self.decoderBlock2.forward(
             Y_decoder_1, Y_np3, pool_indices3)
 
@@ -377,6 +378,7 @@ class BrainMapperUNet(nn.Module):
 
         return prediction
 
+
 class BrainMapperUNet3D_Simple(nn.Module):
     """Architecture class BrainMapper 3D U-net.
 
@@ -462,7 +464,7 @@ class BrainMapperUNet3D_Simple(nn.Module):
             Y_bottleNeck, Y_np4, pool_indices4)
 
         del Y_bottleNeck, Y_np4, pool_indices4
-        
+
         Y_decoder_2 = self.decoderBlock2.forward(
             Y_decoder_1, Y_np3, pool_indices3)
 

run.py

@@ -131,7 +131,6 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
         pin_memory=True
     )
 
-
     if training_parameters['use_pre_trained']:
         BrainMapperModel = torch.load(training_parameters['pre_trained_path'])
     else:
@@ -216,17 +215,17 @@ def evaluate_score(training_parameters, network_parameters, misc_parameters, eva
                                           evaluation_parameters['saved_predictions_directory']
                                           )
 
-    average_dice_score = evaluations.evaluate_dice_score(trained_model_path=evaluation_parameters['trained_model_path'],
-                                                         number_of_classes=network_parameters['number_of_classes'],
-                                                         data_directory=evaluation_parameters['data_directory'],
-                                                         targets_directory=evaluation_parameters[
-                                                             'targets_directory'],
-                                                         data_list=evaluation_parameters['data_list'],
-                                                         orientation=evaluation_parameters['orientation'],
-                                                         prediction_output_path=prediction_output_path,
-                                                         device=misc_parameters['device'],
-                                                         LogWriter=logWriter
-                                                         )
+    _ = evaluations.evaluate_dice_score(trained_model_path=evaluation_parameters['trained_model_path'],
+                                        number_of_classes=network_parameters['number_of_classes'],
+                                        data_directory=evaluation_parameters['data_directory'],
+                                        targets_directory=evaluation_parameters[
+        'targets_directory'],
+        data_list=evaluation_parameters['data_list'],
+        orientation=evaluation_parameters['orientation'],
+        prediction_output_path=prediction_output_path,
+        device=misc_parameters['device'],
+        LogWriter=logWriter
+    )
 
     logWriter.close()
 
@@ -357,35 +356,39 @@ if __name__ == '__main__':
 
     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'])
+        data_test_train_validation_split(
+            data_parameters['data_directory'], data_parameters['train_percentage'], data_parameters['validation_percentage'])
         update_shuffling_flag('settings.ini')
         # TODO: This might also be a very good point to add cross-validation later
     else:
 
         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')
+                  network_parameters, misc_parameters)
+
+        # NOTE: THE EVAL FUNCTIONS HAVE NOT YET BEEN DEBUGGED (16/04/20)
+
+        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')

solver.py

@@ -117,7 +117,6 @@ class Solver():
         if use_last_checkpoint:
             self.load_checkpoint()
 
-
     def train(self, train_loader, test_loader):
         """Training Function
 
@@ -143,7 +142,8 @@ class Solver():
         print('=====================')
         print('Model Name: {}'.format(self.model_name))
         if torch.cuda.is_available():
-            print('Device Type: {}'.format(torch.cuda.get_device_name(self.device)))
+            print('Device Type: {}'.format(
+                torch.cuda.get_device_name(self.device)))
         else:
             print('Device Type: {}'.format(self.device))
         start_time = datetime.now()
@@ -159,8 +159,6 @@ class Solver():
                 print('-> Phase: {}'.format(phase))
 
                 losses = []
-                # outputs = []
-                # y_values = []
 
                 if phase == 'train':
                     model.train()
@@ -173,8 +171,8 @@ class Solver():
                     y = sampled_batch[1].type(torch.FloatTensor)
 
                     # We add an extra dimension (~ number of channels) for the 3D convolutions.
-                    X = torch.unsqueeze(X, dim= 1)
-                    y = torch.unsqueeze(y, dim= 1)
+                    X = torch.unsqueeze(X, dim=1)
+                    y = torch.unsqueeze(y, dim=1)
 
                     if model.test_if_cuda:
                         X = X.cuda(self.device, non_blocking=True)
@@ -196,11 +194,7 @@ class Solver():
 
                         iteration += 1
 
-                    
-
                     losses.append(loss.item())
-                    # outputs.append(torch.max(y_hat, dim=1)[1].cpu())
-                    # y_values.append(y.cpu())
 
                     # Clear the memory
 
@@ -214,26 +208,9 @@ class Solver():
                             print("100%", flush=True)
 
                 with torch.no_grad():
-                    # output_array, y_array = torch.cat(
-                    #     outputs), torch.cat(y_values)
 
                     self.LogWriter.loss_per_epoch(losses, phase, epoch)
 
-                    # dice_score_mean = self.LogWriter.dice_score_per_epoch(
-                    #     phase, output_array, y_array, epoch)
-
-                    # if phase == 'test':
-                    #     if dice_score_mean > self.best_mean_score:
-                    #         self.best_mean_score = dice_score_mean
-                    #         self.best_mean_score_epoch = epoch
-
-                    # index = np.random.choice(
-                    #     len(dataloaders[phase].dataset.X), size=3, replace=False)
-                    # self.LogWriter.sample_image_per_epoch(prediction=model.predict(dataloaders[phase].dataset.X[index], self.device),
-                    #                                       ground_truth=dataloaders[phase].dataset.y[index],
-                    #                                       phase=phase,
-                    #                                       epoch=epoch)
-
             print("Epoch {}/{} DONE!".format(epoch, self.number_epochs))
 
             self.save_checkpoint(state={'epoch': epoch + 1,

utils/data_logging_utils.py

@@ -65,7 +65,7 @@ class LogWriter():
         self.log_writer = {
             'train': SummaryWriter(logdir=training_logs_directory),
             'test': SummaryWriter(logdir=testing_logs_directory)
-            }
+        }
 
         self.confusion_matrix_color_map = confusion_matrix_cmap
 

utils/data_utils.py

@@ -560,13 +560,3 @@ def get_datasetsHDF5(data_parameters):
                        training_labels['label'][()]),
         DataMapperHDF5(testing_data['data'][()], testing_labels['label'][()])
     )
-
-
-if __name__ == "__main__":
-    pass
-
-    # folder_location = "../well/win-biobank/projects/imaging/data/data3/subjectsAll/"
-    # # data_test_train_validation_split(folder_location, 90, 5)
-    # subDirectoryList = directory_reader(folder_location, write_txt=True)
-    # print(subDirectoryList)
-    # tract_sum_generator(folder_location)
\ No newline at end of file

utils/losses.py

@@ -78,9 +78,10 @@ class MSELoss(_WeightedLoss):
         return self.loss(X, y)
 
 # DEPRECATED LOSSES
-# NOTE: THESE LOSSES ARE USUALLY USED FOR CLASSIFICATION TASKS. 
+# NOTE: THESE LOSSES ARE USUALLY USED FOR CLASSIFICATION TASKS.
 # THIS IS NOT A CLASSIFICATION TASK. THUS, THESE ARE IGNORED FOR NOW!
 
+
 class CrossEntropyLoss(_WeightedLoss):
     """Cross Entropy Loss
 

utils/modules.py

@@ -500,7 +500,7 @@ class DecoderBlock3D(ConvolutionalBlock3D):
             Y (torch.tensor): Output forward passed tensor through the decoder block
         """
 
-        # ATTENTION: As of this code version, only "upconv" works! Debugging is ongoing for upconv and upsample! 
+        # ATTENTION: As of this code version, only "upconv" works! Debugging is ongoing for upconv and upsample!
         # It seems that errors are generated by variable filter sizes and the unorthodox input sizes 91x109x91.
 
         if self.up_mode == 'unpool':