Remove debugging statements which cause error (see v-sundaresan#9, v-sundaresan#10)

Author: pauldmccarthy

setup.py

@@ -3,7 +3,7 @@
     install_requires = [l.strip() for l in f.readlines()]
 
 setup(name='truenet',
-	version='1.0.0',
+	version='1.0.1',
 	description='DL method for WMH segmentation',
 	author='Vaanathi Sundaresan',
 	install_requires=install_requires,

truenet/true_net/truenet_test_function.py

@@ -32,10 +32,8 @@ def main(sub_name_dicts, eval_params, intermediate=False, model_dir=None,
     use_cpu = eval_params['Use_CPU']
     if use_cpu is True:
         device = torch.device("cpu")
-        print('testfunction:device used:' + device)
     else:
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        print('testfunction:device used:' + device)
     nclass = eval_params['Nclass']
     num_channels = eval_params['Numchannels']
 
@@ -82,72 +80,72 @@ def main(sub_name_dicts, eval_params, intermediate=False, model_dir=None,
     for sub in range(len(sub_name_dicts)):
         if verbose:
             print('Predicting output for subject ' + str(sub+1) + '...', flush=True)
-            
+
         test_sub_dict = [sub_name_dicts[sub]]
         basename = test_sub_dict[0]['basename']
-        
+
         probs_combined = []
         flair_path = test_sub_dict[0]['flair_path']
         flair_hdr = nib.load(flair_path).header
-        probs_axial = truenet_evaluate.evaluate_truenet(test_sub_dict, model_axial, eval_params, device, 
+        probs_axial = truenet_evaluate.evaluate_truenet(test_sub_dict, model_axial, eval_params, device,
                                                         mode='axial', verbose=verbose)
-        probs_axial = truenet_data_postprocessing.resize_to_original_size(probs_axial, test_sub_dict, 
+        probs_axial = truenet_data_postprocessing.resize_to_original_size(probs_axial, test_sub_dict,
                                                                           plane='axial')
         probs_combined.append(probs_axial)
-        
+
         if intermediate:
             save_path = os.path.join(output_dir,'Predicted_probmap_truenet_' + basename + '_axial.nii.gz')
             preds_axial = truenet_data_postprocessing.get_final_3dvolumes(probs_axial, test_sub_dict)
             if verbose:
                 print('Saving the intermediate Axial prediction ...', flush=True)
-            
+
             newhdr = flair_hdr.copy()
             newobj = nib.nifti1.Nifti1Image(preds_axial, None, header=newhdr)
-            nib.save(newobj, save_path)            
-        
-        probs_sagittal = truenet_evaluate.evaluate_truenet(test_sub_dict, model_sagittal, eval_params, device, 
+            nib.save(newobj, save_path)
+
+        probs_sagittal = truenet_evaluate.evaluate_truenet(test_sub_dict, model_sagittal, eval_params, device,
                                                         mode='sagittal', verbose=verbose)
-        probs_sagittal = truenet_data_postprocessing.resize_to_original_size(probs_sagittal, test_sub_dict, 
+        probs_sagittal = truenet_data_postprocessing.resize_to_original_size(probs_sagittal, test_sub_dict,
                                                                           plane='sagittal')
         probs_combined.append(probs_sagittal)
-        
+
         if intermediate:
             save_path = os.path.join(output_dir,'Predicted_probmap_truenet_' + basename + '_sagittal.nii.gz')
             preds_sagittal = truenet_data_postprocessing.get_final_3dvolumes(probs_sagittal, test_sub_dict)
             if verbose:
                 print('Saving the intermediate Sagittal prediction ...', flush=True)
-            
+
             newhdr = flair_hdr.copy()
             newobj = nib.nifti1.Nifti1Image(preds_sagittal, None, header=newhdr)
-            nib.save(newobj, save_path) 
-        
-        probs_coronal = truenet_evaluate.evaluate_truenet(test_sub_dict, model_coronal, eval_params, device, 
-                                                        mode='coronal', verbose=verbose)        
-        probs_coronal = truenet_data_postprocessing.resize_to_original_size(probs_coronal, test_sub_dict, 
+            nib.save(newobj, save_path)
+
+        probs_coronal = truenet_evaluate.evaluate_truenet(test_sub_dict, model_coronal, eval_params, device,
+                                                        mode='coronal', verbose=verbose)
+        probs_coronal = truenet_data_postprocessing.resize_to_original_size(probs_coronal, test_sub_dict,
                                                                           plane='coronal')
         probs_combined.append(probs_coronal)
-        
+
         if intermediate:
             save_path = os.path.join(output_dir,'Predicted_probmap_truenet_' + basename + '_coronal.nii.gz')
             preds_coronal = truenet_data_postprocessing.get_final_3dvolumes(probs_coronal, test_sub_dict)
             if verbose:
                 print('Saving the intermediate Coronal prediction ...', flush=True)
-            
+
             newhdr = flair_hdr.copy()
             newobj = nib.nifti1.Nifti1Image(preds_coronal, None, header=newhdr)
-            nib.save(newobj, save_path) 
-            
+            nib.save(newobj, save_path)
+
         probs_combined = np.array(probs_combined)
         prob_mean = np.mean(probs_combined,axis=0)
-        
+
         save_path = os.path.join(output_dir,'Predicted_probmap_truenet_' + basename + '.nii.gz')
         pred_mean = truenet_data_postprocessing.get_final_3dvolumes(prob_mean, test_sub_dict)
         if verbose:
             print('Saving the final prediction ...', flush=True)
 
         newhdr = flair_hdr.copy()
         newobj = nib.nifti1.Nifti1Image(pred_mean, None, header=newhdr)
-        nib.save(newobj, save_path) 
-        
+        nib.save(newobj, save_path)
+
     if verbose:
         print('Testing complete for all subjects!', flush=True)

truenet/utils/truenet_utils.py

@@ -22,7 +22,7 @@ def select_train_val_names(data_path,val_numbers):
     :return:
     '''
     val_ids = random.choices(list(np.arange(len(data_path))),k=val_numbers)
-    train_ids = np.setdiff1d(np.arange(len(data_path)),val_ids)    
+    train_ids = np.setdiff1d(np.arange(len(data_path)),val_ids)
     data_path_train = [data_path[ind] for ind in train_ids]
     data_path_val = [data_path[ind] for ind in val_ids]
     return data_path_train,data_path_val,val_ids
@@ -40,7 +40,7 @@ def freeze_layer_for_finetuning(model, layer_to_ft, verbose=False):
     model_layers_tobe_ftd = []
     for layer_id in layer_to_ft:
             model_layers_tobe_ftd.append(model_layer_names[layer_id-1])
-            
+
     for name, child in model.module.named_children():
         if name in model_layers_tobe_ftd:
             if verbose:
@@ -54,24 +54,20 @@ def freeze_layer_for_finetuning(model, layer_to_ft, verbose=False):
                 print(name + ' is frozen', flush=True)
             for param in child.parameters():
                 param.requires_grad = False
-                
+
     return model
 
 
 def loading_model(model_name, model, device, mode='weights'):
     if mode == 'weights':
         if device == 'cpu':
-            print('utils:device used:' + device)
             axial_state_dict = torch.load(model_name, map_location='cpu')
         else:
-            print('utils:device used:' + device)
             axial_state_dict = torch.load(model_name)
     else:
         if device == 'cpu':
-            print('utils:device used:' + device)
             ckpt = torch.load(model_name, map_location='cpu')
         else:
-            print('utils:device used:' + device)
             ckpt = torch.load(model_name)
         axial_state_dict = ckpt['model_state_dict']
 
@@ -175,10 +171,3 @@ def save_checkpoint(self, val_loss, val_acc, best_val_acc, model, epoch, optimiz
         else:
             if self.verbose:
                 print('Validation loss increased; Exiting without saving the model ...')
-
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