Modify README.md

Signed-off-by: lijian <lijian6@sugon.com>

README.md

@@ -16,16 +16,16 @@ Vision Transformer先将图像用卷积进行分块以降低计算量，再对
 ## 环境配置
 ### Docker（方法一）
 ```
-docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:decode-ffmpeg-dtk23.04
+docker pull image.sourcefind.cn:5000/dcu/admin/base/migraphx:4.3.0-ubuntu20.04-dtk24.04.1-py3.8
 # <your IMAGE ID>用以上拉取的docker的镜像ID替换
-docker run --shm-size 10g --network=host --name=vit_migraphx --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v path_to_vit_migraphx:/home/vit_migraphx -it <your IMAGE ID> bash
+docker run --shm-size 10g --network=host --name=vit_migraphx --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v path_to_vit_migraphx:/home/vit_migraphx -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
 ```
 
 ### Dockerfile（方法二）
 ```
 cd vit_migraphx/docker
 docker build --no-cache -t vit_migraphx:test .
-docker run --rm --shm-size 10g --network=host --name=vit_migraphx --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v path_to_vit_migraphx:/home/vit_migraphx -it vit_migraphx:test bash
+docker run --rm --shm-size 10g --network=host --name=vit_migraphx --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v path_to_vit_migraphx:/home/vit_migraphx -v /opt/hyhal:/opt/hyhal:ro -it vit_migraphx:test bash
 ```
 
 ## 数据集
@@ -58,20 +58,19 @@ flower_photos
 ```
 
 ## 推理
-### 编译
-```
-git clone https://developer.hpccube.com/codes/modelzoo/vit_migraphx.git
-cd vit_migraphx
-make
-```
+
 ### 执行推理
 ```
-./ViT_MIGraphX
-```
-根据提示选择要运行的示例程序，比如执行
-```
-./ViT_MIGraphX --models=Models/model.onnx --input=flower_photos/daisy/
+cd vit_migraphx/Python/
+1、将flower_photos数据集解压到当前目录。
+
+2、使用migraphx推理：
+python infer_migraphx.py
+
+3、使用torch进行推理：
+python infer_pytroch.py ./flower_photos/daisy/
 ```
+
 运行ViT模型，对daisy图片进行分类
 
 ## result

docker/Dockerfile

-FROM image.sourcefind.cn:5000/dcu/admin/base/custom:decode-ffmpeg-dtk23.04
+FROM image.sourcefind.cn:5000/dcu/admin/base/migraphx:4.3.0-ubuntu20.04-dtk24.04.1-py3.8