From b793e5b3ec4a901e89316c957129f0698b470d61 Mon Sep 17 00:00:00 2001
From: wjj19950828 <wjjisloser@163.com>
Date: Mon, 17 Jan 2022 21:15:25 +0800
Subject: [PATCH] Add dynamic shape

---
 .../pytorch2paddle.md                         | 54 +++++++++++++++++--
 1 file changed, 51 insertions(+), 3 deletions(-)

diff --git a/docs/inference_model_convertor/pytorch2paddle.md b/docs/inference_model_convertor/pytorch2paddle.md
index 74976b5..f4840fb 100644
--- a/docs/inference_model_convertor/pytorch2paddle.md
+++ b/docs/inference_model_convertor/pytorch2paddle.md
@@ -14,7 +14,7 @@ treelib
 
 ## 使用方式
 
-``` python
+```python
 from x2paddle.convert import pytorch2paddle
 pytorch2paddle(module=torch_module,
                save_dir="./pd_model",
@@ -27,11 +27,14 @@ pytorch2paddle(module=torch_module,
 ```
 
 **注意：** 当jit_type为"trace"时，input_examples不可为None，转换后自动进行动转静；
-          当jit_type为"script"时"，input_examples不为None时，才可以进行动转静。
+
+          当jit_type为"script"时"，当input_examples为None时，只生成动态图代码；当input_examples不为None时，才能自动动转静。
 
 ## 使用示例
 
-``` python
+### Trace 模式
+
+```python
 import torch
 import numpy as np
 from torchvision.models import AlexNet
@@ -51,3 +54,48 @@ pytorch2paddle(torch_module,
                jit_type="trace",
                input_examples=[torch.tensor(input_data)])
 ```
+
+### Script 模式动态 shape 导出
+
+```python
+import torch
+import numpy as np
+from torchvision.models import AlexNet
+from torchvision.models.utils import load_state_dict_from_url
+
+# 获取PyTorch Module
+torch_module = AlexNet()
+torch_state_dict = load_state_dict_from_url('https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth')
+torch_module.load_state_dict(torch_state_dict)
+# 设置为eval模式
+torch_module.eval()
+# 进行转换
+from x2paddle.convert import pytorch2paddle
+pytorch2paddle(torch_module,
+               save_dir="pd_model_script",
+               jit_type="script",
+               input_examples=None)
+```
+
+在自动生成的x2paddle_code.py中添加如下代码：
+
+```python
+def main(x0):
+    # There are 0 inputs.
+    paddle.disable_static()
+    params = paddle.load('model.pdparams')
+    model = AlexNet()
+    model.set_dict(params)
+    model.eval()
+    ## convert to jit
+    sepc_list = list()
+    sepc_list.append(
+            paddle.static.InputSpec(
+                shape=[-1, 3, -1, -1], name="x0", dtype="float32"))
+    static_model = paddle.jit.to_static(model, input_spec=sepc_list)
+    paddle.jit.save(static_model, "pd_model_script/inference_model/model")
+    out = model(x0)
+    return out
+```
+
+运行main函数导出动态shape的静态图模型，若导出失败，可尝试动态shape导出onnx，再从onnx转到paddle，[相关文档](https://pytorch.org/docs/stable/onnx.html?highlight=onnx%20export#torch.onnx.export)
-- 
GitLab