From e15ca80a44ffb8e2dcc63b824f78a60860e5a67c Mon Sep 17 00:00:00 2001
From: wjj19950828 <wjjisloser@163.com>
Date: Tue, 18 Jan 2022 11:21:23 +0800
Subject: [PATCH] Update readme

---
 .../pytorch2paddle.md                         | 39 ++++++++++++-------
 1 file changed, 26 insertions(+), 13 deletions(-)

diff --git a/docs/inference_model_convertor/pytorch2paddle.md b/docs/inference_model_convertor/pytorch2paddle.md
index 76aac39..2430886 100644
--- a/docs/inference_model_convertor/pytorch2paddle.md
+++ b/docs/inference_model_convertor/pytorch2paddle.md
@@ -26,7 +26,7 @@ pytorch2paddle(module=torch_module,
 # 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。
 ```
 
-**注意：**
+**注意:**
 - jit_type为"trace"时，input_examples不可为None，转换后自动进行动转静；
 - 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。
 
@@ -55,11 +55,12 @@ pytorch2paddle(torch_module,
                input_examples=[torch.tensor(input_data)])
 ```
 
-### Script 模式动态 shape 导出
+### 动态 shape 导出
+
+#### 方式一：PyTorch->ONNX->Paddle
 
 ```python
 import torch
-import numpy as np
 from torchvision.models import AlexNet
 from torchvision.models.utils import load_state_dict_from_url
 
@@ -69,15 +70,27 @@ torch_state_dict = load_state_dict_from_url('https://download.pytorch.org/models
 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)
+input_names = ["input_0"]
+output_names = ["output_0"]
+
+x = torch.randn((1, 3, 224, 224))
+y = torch.randn((1, 1000))
+
+torch.onnx.export(torch_module, x, 'model.onnx', opset_version=11, input_names=input_names,
+                  output_names=output_names, dynamic_axes={'input_0': [0], 'output_0': [0]})
+```
+
+导出 ONNX 动态 shape 模型，更多细节参考[相关文档](https://pytorch.org/docs/stable/onnx.html?highlight=onnx%20export#torch.onnx.export)
+
+然后通过 X2Paddle 命令导出 Paddle 模型
+
+```shell
+x2paddle --framework=onnx --model=model.onnx --save_dir=pd_model_dynamic
 ```
 
-在自动生成的x2paddle_code.py中添加如下代码：
+#### 方式二：手动动转静
+
+在自动生成的 x2paddle_code.py 中添加如下代码：
 
 ```python
 def main(x0):
@@ -91,11 +104,11 @@ def main(x0):
     sepc_list = list()
     sepc_list.append(
             paddle.static.InputSpec(
-                shape=[-1, 3, -1, -1], name="x0", dtype="float32"))
+                shape=[-1, 3, 224, 224], 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")
+    paddle.jit.save(static_model, "pd_model_trace/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)
+然后运行 main 函数导出动态 shape 模型
-- 
GitLab