bug fixes

run.py

@@ -159,7 +159,7 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
                         device=misc_parameters['device'],
                         number_of_classes=network_parameters['number_of_classes'],
                         experiment_name=training_parameters['experiment_name'],
-                        optimizer= optimizer,
+                        optimizer=optimizer,
                         optimizer_arguments={'lr': training_parameters['learning_rate'],
                                              'betas': training_parameters['optimizer_beta'],
                                              'eps': training_parameters['optimizer_epsilon'],
@@ -204,7 +204,7 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
 
         for k in range(data_parameters['k_fold']):
 
-            print("K-fold Number: {}".format(k+1))  
+            print("K-fold Number: {}".format(k+1))
 
             data_parameters['train_list'] = os.path.join(
                 data_parameters['data_folder_name'], 'train' + str(k+1)+'.txt')
@@ -314,12 +314,14 @@ def evaluate_mapping(mapping_evaluation_parameters):
     exit_on_error = mapping_evaluation_parameters['exit_on_error']
 
     evaluations.evaluate_mapping(trained_model_path,
-                                data_directory,
-                                mapping_data_file,
-                                data_list,
-                                prediction_output_path,
-                                batch_size,
-                                device=device,
+                                 data_directory,
+                                 mapping_data_file,
+                                 data_list,
+                                 prediction_output_path,
+                                 batch_size,
+                                 device=device,
+                                 exit_on_error=exit_on_error)
+
 
 def delete_files(folder):
     """ Clear Folder Contents

solver.py

@@ -71,7 +71,7 @@ class Solver():
                  use_last_checkpoint=True,
                  experiment_directory='experiments',
                  logs_directory='logs',
-                 checkpoint_directory = 'checkpoints'
+                 checkpoint_directory='checkpoints'
                  ):
 
         self.model = model
@@ -203,14 +203,14 @@ class Solver():
 
                         iteration += 1
 
-                    losses.append(loss.item())                 
+                    losses.append(loss.item())
 
                     # Clear the memory
 
                     del X, y, y_hat, loss
                     torch.cuda.empty_cache()
 
-                    if phase == 'validation':                
+                    if phase == 'validation':
 
                         if batch_index != len(dataloaders[phase]) - 1:
                             print("#", end='', flush=True)
@@ -222,7 +222,8 @@ class Solver():
                     self.LogWriter.loss_per_epoch(losses, phase, epoch)
 
                     if phase == 'validation':
-                        early_stop, save_checkpoint = self.EarlyStopping(np.mean(losses))
+                        early_stop, save_checkpoint = self.EarlyStopping(
+                            np.mean(losses))
                         self.early_stop = early_stop
                         if save_checkpoint == True:
                             validation_loss = np.mean(losses)
@@ -233,11 +234,12 @@ class Solver():
                                                         'optimizer': optimizer.state_dict(),
                                                         'scheduler': learning_rate_scheduler.state_dict()
                                                         },
-                                                filename=os.path.join(self.experiment_directory_path, self.checkpoint_directory,
-                                                                    'checkpoint_epoch_' + str(epoch) + '.' + checkpoint_extension)
-                                                )
+                                                 filename=os.path.join(self.experiment_directory_path, self.checkpoint_directory,
+                                                                       'checkpoint_epoch_' + str(epoch) + '.' + checkpoint_extension)
+                                                 )
                             if epoch != self.start_epoch:
-                                os.remove(os.path.join(self.experiment_directory_path, self.checkpoint_directory, 'checkpoint_epoch_' + str(epoch-1) + '.' + checkpoint_extension)) 
+                                os.remove(os.path.join(self.experiment_directory_path, self.checkpoint_directory,
+                                                       'checkpoint_epoch_' + str(epoch-1) + '.' + checkpoint_extension))
 
                 if phase == 'train':
                     learning_rate_scheduler.step()

utils/data_evaluation_utils.py

@@ -202,14 +202,14 @@ def evaluate_dice_score(trained_model_path,
 
 
 def evaluate_mapping(trained_model_path,
-                    data_directory,
-                    mapping_data_file,
-                    data_list,
-                    prediction_output_path,
-                    batch_size,
-                    device=0,
-                    mode='evaluate',
-                    exit_on_error=False):
+                     data_directory,
+                     mapping_data_file,
+                     data_list,
+                     prediction_output_path,
+                     batch_size,
+                     device=0,
+                     mode='evaluate',
+                     exit_on_error=False):
     """Model Evaluator
 
     This function generates the rsfMRI map for an input running on on a single axis or path
@@ -262,21 +262,24 @@ def evaluate_mapping(trained_model_path,
     # Initiate the evaluation
 
     log.info("rsfMRI Generation Started")
-    file_paths = data_utils.load_file_paths(data_directory, data_list, mapping_data_file)
+    file_paths = data_utils.load_file_paths(
+        data_directory, data_list, mapping_data_file)
 
     with torch.no_grad():
 
         for volume_index, file_path in enumerate(file_paths):
             try:
                 # Generate volume & header
-                _, predicted_volume, header, xform = _generate_volume_map(file_path, model, batch_size, device, cuda_available)
+                _, predicted_volume, header, xform = _generate_volume_map(
+                    file_path, model, batch_size, device, cuda_available)
 
                 # Generate New Header Affine
 
                 header_affines = np.array(
                     [header['srow_x'], header['srow_y'], header['srow_z'], [0, 0, 0, 1]])
 
-                output_nifti_image = Image(predicted_volume, header=header, xform=xform)
+                output_nifti_image = Image(
+                    predicted_volume, header=header, xform=xform)
 
                 output_nifti_path = os.path.join(
                     prediction_output_path, volumes_to_be_used[volume_index])
@@ -321,7 +324,8 @@ def _generate_volume_map(file_path, model, batch_size, device, cuda_available):
         header (class): 'nibabel.nifti1.Nifti1Header' class object, containing volume metadata
     """
 
-    volume, header, xform = data_utils.load_and_preprocess_evaluation(file_path)
+    volume, header, xform = data_utils.load_and_preprocess_evaluation(
+        file_path)
 
     if len(volume.shape) == 4:
         volume = volume
@@ -797,4 +801,4 @@ def _generate_volume(file_path, model, orientation, batch_size, device, cuda_ava
         predicted_volume = predicted_volume
         output_volume = output_volume
 
-    return output_volume, predicted_volume, header
+    return output_volume, predicted_volume, header
\ No newline at end of file

utils/data_utils.py

@@ -109,7 +109,8 @@ def data_test_train_validation_split(data_folder_name, test_percentage, subject_
     """
 
     if data_file is not None:
-        subDirectoryList = data_file_reader(data_file, data_directory, subject_number)
+        subDirectoryList = data_file_reader(
+            data_file, data_directory, subject_number)
     else:
         subDirectoryList = directory_reader(data_directory, subject_number)
 
@@ -353,7 +354,7 @@ def load_file_paths(data_directory, data_list, mapping_data_file, targets_direct
         file_paths = [[os.path.join(data_directory, volume, mapping_data_file)]
                       for volume in volumes_to_be_used]
     else:
-        file_paths = [[os.path.join(data_directory, volume, mapping_data_file), os.join.path(
+        file_paths = [[os.path.join(data_directory, volume, mapping_data_file), os.path.join(
             targets_directory, volume, )] for volume in volumes_to_be_used]
 
     return file_paths
@@ -469,11 +470,9 @@ def load_and_preprocess_evaluation(file_path, min_max=False):
     return volume, header, xform
 
 
-
 # Deprecated Functions & Classes & Methods:
 
 
-
 def tract_sum_generator(folder_path):
     """Sums the tracts of different dMRI files
 
@@ -646,4 +645,4 @@ def load_and_preprocess_evaluation2D(file_path, orientation, min_max=True):
         raise ValueError(
             "Orientation value is invalid. It must be either >>coronal<<, >>axial<< or >>sagital<< ")
 
-    return volume, header
+    return volume, header
\ No newline at end of file