deal with comments

x2paddle/convert.py

@@ -88,7 +88,7 @@ def arg_parser():
         default=None,
         help="pretrain model file of pytorch model")
     parser.add_argument(
-        "--code_optimizer",
+        "--enable_code_optim",
         "-co",
         default=True,
         help="Turn on code optimization")
@@ -225,7 +225,7 @@ def pytorch2paddle(module,
                    save_dir,
                    jit_type="trace",
                    input_examples=None,
-                   code_optimizer=True,
+                   enable_code_optim=True,
                    convert_to_lite=False,
                    lite_valid_places="arm",
                    lite_model_type="naive_buffer"):
@@ -260,7 +260,7 @@ def pytorch2paddle(module,
     graph_opt.optimize(mapper.paddle_graph)
     print("Model optimized.")
     mapper.paddle_graph.gen_model(
-        save_dir, jit_type=jit_type, code_optimizer=code_optimizer)
+        save_dir, jit_type=jit_type, enable_code_optim=enable_code_optim)
     if convert_to_lite:
         convert2lite(save_dir, lite_valid_places, lite_model_type)
 

x2paddle/core/program.py

@@ -237,11 +237,11 @@ class PaddleGraph(object):
 
         return update(self.layers)
 
-    def gen_model(self, save_dir, jit_type=None, code_optimizer=True):
+    def gen_model(self, save_dir, jit_type=None, enable_code_optim=True):
         if not osp.exists(save_dir):
             os.makedirs(save_dir)
         if jit_type == "trace":
-            if not self.has_unpack and code_optimizer:
+            if not self.has_unpack and enable_code_optim:
                 from x2paddle.optimizer.pytorch_code_optimizer import HierarchicalTree
                 hierarchical_tree = HierarchicalTree(self)
                 for layer_id, layer in self.layers.items():
@@ -252,7 +252,7 @@ class PaddleGraph(object):
                 self.gen_code(save_dir)
                 self.dump_parameter(save_dir)
         else:
-            if self.source_type == "pytorch" and code_optimizer:
+            if self.source_type == "pytorch" and enable_code_optim:
                 from x2paddle.optimizer.pytorch_code_optimizer import ModuleGraph
                 module_graph = ModuleGraph(self)
                 module_graph.save_source_files(save_dir)