diff --git a/codes/train.py b/codes/train.py
index 2e737d3f..f18cb224 100644
--- a/codes/train.py
+++ b/codes/train.py
@@ -32,7 +32,7 @@ def init_dist(backend='nccl', **kwargs):
 def main():
     #### options
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_spsr3_gan.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_ssgr1.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
                         help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
@@ -179,6 +179,10 @@ def main():
                 print("Data fetch: %f" % (time() - _t))
                 _t = time()
 
+            #tb_logger.add_graph(model.netsG['generator'].module, [train_data['LQ'].to('cuda'),
+            #                                                      train_data['lq_fullsize_ref'].float().to('cuda'),
+            #                                                      train_data['lq_center'].to('cuda')])
+
             current_step += 1
             if current_step > total_iters:
                 break
@@ -214,6 +218,20 @@ def main():
                                 tb_logger.add_scalar(k, v, current_step)
                 if rank <= 0:
                     logger.info(message)
+
+            #### save models and training states
+            if current_step % opt['logger']['save_checkpoint_freq'] == 0:
+                if rank <= 0:
+                    logger.info('Saving models and training states.')
+                    model.save(current_step)
+                    model.save_training_state(epoch, current_step)
+                if 'alt_path' in opt['path'].keys():
+                    import shutil
+                    print("Synchronizing tb_logger to alt_path..")
+                    alt_tblogger = os.path.join(opt['path']['alt_path'], "tb_logger")
+                    shutil.rmtree(alt_tblogger, ignore_errors=True)
+                    shutil.copytree(tb_logger_path, alt_tblogger)
+
             #### validation
             if opt['datasets'].get('val', None) and current_step % opt['train']['val_freq'] == 0:
                 if opt['model'] in ['sr', 'srgan', 'corruptgan', 'spsrgan', 'extensibletrainer'] and rank <= 0:  # image restoration validation
@@ -272,19 +290,6 @@ def main():
                         #tb_logger.add_scalar('val_psnr', avg_psnr, current_step)
                         tb_logger.add_scalar('val_fea', avg_fea_loss, current_step)
 
-            #### save models and training states
-            if current_step % opt['logger']['save_checkpoint_freq'] == 0:
-                if rank <= 0:
-                    logger.info('Saving models and training states.')
-                    model.save(current_step)
-                    model.save_training_state(epoch, current_step)
-                if 'alt_path' in opt['path'].keys():
-                    import shutil
-                    print("Synchronizing tb_logger to alt_path..")
-                    alt_tblogger = os.path.join(opt['path']['alt_path'], "tb_logger")
-                    shutil.rmtree(alt_tblogger, ignore_errors=True)
-                    shutil.copytree(tb_logger_path, alt_tblogger)
-
     if rank <= 0:
         logger.info('Saving the final model.')
         model.save('latest')