diff --git a/README.md b/README.md
index b0f6dfe38f2fac0257f2b595645c3eee365e5b2d..df597287c478fa151e8de7bae7e97f7841a145d9 100644
--- a/README.md
+++ b/README.md
@@ -117,7 +117,7 @@ x2paddle --framework=caffe --prototxt=deploy.prototxt --weight=deploy.caffemodel
| --model | 当framework为tensorflow/onnx时,该参数指定tensorflow的pb模型文件或onnx模型路径 |
| --caffe_proto | **[可选]** 由caffe.proto编译成caffe_pb2.py文件的存放路径,当存在自定义Layer时使用,默认为None |
| --define_input_shape | **[可选]** For TensorFlow, 当指定该参数时,强制用户输入每个Placeholder的shape,见[文档Q2](./docs/inference_model_convertor/FAQ.md) |
-| --enable_code_optim | **[可选]** For PyTorch, 是否对生成代码进行优化,默认为True |
+| --enable_code_optim | **[可选]** For PyTorch, 是否对生成代码进行优化,默认为False |
| --to_lite | **[可选]** 是否使用opt工具转成Paddle-Lite支持格式,默认为False |
| --lite_valid_places | **[可选]** 指定转换类型,可以同时指定多个backend(以逗号分隔),opt将会自动选择最佳方式,默认为arm |
| --lite_model_type | **[可选]** 指定模型转化类型,目前支持两种类型:protobuf和naive_buffer,默认为naive_buffer |
diff --git a/docs/inference_model_convertor/pytorch2paddle.md b/docs/inference_model_convertor/pytorch2paddle.md
index 2fc49d348fe0aed6717f3764d6b254f31b1fa73d..d1f0248b1681e90d95944428e736ced8f4f24849 100644
--- a/docs/inference_model_convertor/pytorch2paddle.md
+++ b/docs/inference_model_convertor/pytorch2paddle.md
@@ -40,7 +40,7 @@ pytorch2paddle(module=torch_module,
import torch
import numpy as np
from torchvision.models import AlexNet
-from torchvision.models.utils import load_state_dict_from_url
+from torch.hub import load_state_dict_from_url
# 构建输入
input_data = np.random.rand(1, 3, 224, 224).astype("float32")
# 获取PyTorch Module
diff --git a/docs/inference_model_convertor/x2paddle_api.md b/docs/inference_model_convertor/x2paddle_api.md
index c6ee37d7724dfaa3cad22fc1eb81d8a9050c97e4..ff56f4a33b05e18bba5605d40dc3b7447ecf0d9e 100644
--- a/docs/inference_model_convertor/x2paddle_api.md
+++ b/docs/inference_model_convertor/x2paddle_api.md
@@ -63,7 +63,7 @@ x2paddle.convert.onnx2paddle(model_path, save_dir, convert_to_lite=False, lite_v
##
x2paddle.convert.pytorch2paddle
```python
-x2paddle.convert.pytorch2paddle(module, save_dir, jit_type="trace", input_examples=None, enable_code_optim=True, convert_to_lite=False, lite_valid_places="arm", lite_model_type="naive_buffer")
+x2paddle.convert.pytorch2paddle(module, save_dir, jit_type="trace", input_examples=None, enable_code_optim=False, convert_to_lite=False, lite_valid_places="arm", lite_model_type="naive_buffer")
```
> 转换Pytorch模型。
@@ -74,7 +74,7 @@ x2paddle.convert.pytorch2paddle(module, save_dir, jit_type="trace", input_exampl
> > - **save_dir** (str): 转换后模型保存路径
> > - **jit_type** (str): 转换方式。目前有两种:trace和script, 默认为trace
> > - **input_examples** (list[torch.tensor]): torch.nn. Module的输入示例,list的长度必须与输入的长度一致。默认为None
-> > - **enable_code_optim** (bool): 转换后的代码是否进行优化, 默认为True
+> > - **enable_code_optim** (bool): 转换后的代码是否进行优化, 默认为False
> > - **convert_to_lite** (bool): 是否使用opt工具转成Paddle-Lite支持格式,默认为False
> > - **lite_valid_places** (str): 指定转换类型,可以同时指定多个backend(以逗号分隔),opt将会自动选择最佳方式,默认为arm
> > - **lite_model_type** (str): 指定模型转化类型,目前支持两种类型:protobuf和naive_buffer,默认为naive_buffer
diff --git a/x2paddle/convert.py b/x2paddle/convert.py
index a14a0af1a98b96992ba3e773a3dd3889b3f86919..336feb36e32c70acb543edb9f193d47262750739 100644
--- a/x2paddle/convert.py
+++ b/x2paddle/convert.py
@@ -93,7 +93,7 @@ def arg_parser():
parser.add_argument(
"--enable_code_optim",
"-co",
- default=True,
+ default=False,
help="Turn on code optimization")
parser.add_argument(
"--enable_onnx_checker",
@@ -329,7 +329,7 @@ def pytorch2paddle(module,
save_dir,
jit_type="trace",
input_examples=None,
- enable_code_optim=True,
+ enable_code_optim=False,
convert_to_lite=False,
lite_valid_places="arm",
lite_model_type="naive_buffer",
diff --git a/x2paddle/core/program.py b/x2paddle/core/program.py
index 7d1ba96cc034bc3182fc820af2ed322c729c3130..7b88864cae222a52a774e3f20daf94372234a095 100755
--- a/x2paddle/core/program.py
+++ b/x2paddle/core/program.py
@@ -260,7 +260,7 @@ class PaddleGraph(object):
return update(self.layers)
- def gen_model(self, save_dir, jit_type=None, enable_code_optim=True):
+ def gen_model(self, save_dir, jit_type=None, enable_code_optim=False):
if not osp.exists(save_dir):
os.makedirs(save_dir)
if jit_type == "trace":