compatible with python3

dface/train_net/train.py

@@ -254,7 +254,7 @@ def train_onet(model_store_path, end_epoch,imdb,
                 show4 = landmark_loss.data.tolist()[0]
                 show5 = all_loss.data.tolist()[0]
 
-                print "%s : Epoch: %d, Step: %d, accuracy: %s, det loss: %s, bbox loss: %s, landmark loss: %s, all_loss: %s, lr:%s "%(datetime.datetime.now(),cur_epoch,batch_idx, show1,show2,show3,show4,show5,base_lr)
+                print("%s : Epoch: %d, Step: %d, accuracy: %s, det loss: %s, bbox loss: %s, landmark loss: %s, all_loss: %s, lr:%s "%(datetime.datetime.now(),cur_epoch,batch_idx, show1,show2,show3,show4,show5,base_lr))
                 accuracy_list.append(accuracy)
                 cls_loss_list.append(cls_loss)
                 bbox_loss_list.append(box_offset_loss)

dface/train_net/train_o_net.py

@@ -43,7 +43,7 @@ def parse_args():
 if __name__ == '__main__':
     args = parse_args()
     print('train ONet argument:')
-    print args
+    print(args)
 
 
     train_net(annotation_file=args.annotation_file, model_store_path=args.model_store_path,

dface/train_net/train_p_net.py

@@ -43,7 +43,7 @@ def parse_args():
 if __name__ == '__main__':
     args = parse_args()
     print('train Pnet argument:')
-    print args
+    print(args)
 
     train_net(annotation_file=args.annotation_file, model_store_path=args.model_store_path,
                 end_epoch=args.end_epoch, frequent=args.frequent, lr=args.lr, batch_size=args.batch_size, use_cuda=args.use_cuda)

dface/train_net/train_r_net.py

@@ -43,7 +43,7 @@ def parse_args():
 if __name__ == '__main__':
     args = parse_args()
     print('train Rnet argument:')
-    print args
+    print(args)
 
 
     train_net(annotation_file=args.annotation_file, model_store_path=args.model_store_path,