修改readme及部分示例代码

Doc/Tutorial_Python.md

@@ -67,20 +67,19 @@ def Preprocessing(pathOfImage):
 ```
 def ort_seg_dcu(model_path,image):
     
-    #创建sess_options
-    sess_options = ort.SessionOptions()
+    # 选择migraphx后端推理
+    provider_options=[]
+    if staticInfer:
+        provider_options=[{'device_id':'0','migraphx_fp16_enable':'true','dynamic_model':'false'}]
 
-    #设置图优化
-    sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
+    if dynamicInfer:
+        provider_options=[{'device_id':'0','migraphx_fp16_enable':'true','dynamic_model':'true', 'migraphx_profile_max_shapes':'data:1x3x224x224'}]
 
-    #是否开启profiling
-    sess_options.enable_profiling = False
-    dcu_session = ort.InferenceSession(model_path,sess_options,providers=['ROCMExecutionProvider'],)
+    dcu_session = ort.InferenceSession(model_path, providers=['MIGraphXExecutionProvider'], provider_options=provider_options)
     input_name=dcu_session.get_inputs()[0].name
 
-    results = dcu_session.run(None, input_feed={input_name:image })
+    results = dcu_session.run(None, input_feed={input_name:image})
     scores=np.array(results[0])
-    # print("ort result.shape:",scores.shape)
 
     return scores
 ```

Python/Classifier.py

@@ -62,7 +62,7 @@ def ort_seg_dcu(model_path,image,staticInfer,dynamicInfer):
     
     provider_options=[]
     if staticInfer:
-        provider_options=[{'device_id':'0','migraphx_fp16_enable':'true','dynamic_model':'false'}]
+        provider_options=[{'device_id':'0','migraphx_fp16_enable':'true'}]
 
     if dynamicInfer:
         provider_options=[{'device_id':'0','migraphx_fp16_enable':'true','dynamic_model':'true', 'migraphx_profile_max_shapes':'data:1x3x224x224'}]

Python/Classifier_io_binding.py

@@ -60,7 +60,7 @@ def postprocess(scores,pathOfImage):
 
 def ort_seg_dcu(model_path,image):
     
-    provider_options=[{'device_id':'0','migraphx_fp16_enable':'true','dynamic_model':'false'}]
+    provider_options=[{'device_id':'0','migraphx_fp16_enable':'true'}]
     dcu_session = ort.InferenceSession(model_path, providers=['MIGraphXExecutionProvider'], provider_options=provider_options)
     output_data = np.empty(dcu_session.get_outputs()[0].shape).astype(np.float32)
 

Python/Classifier_run_with_ort.py

@@ -62,7 +62,7 @@ def ort_seg_dcu(model_path,image,staticInfer,dynamicInfer):
     
     provider_options=[]
     if staticInfer:
-        provider_options=[{'device_id':'0','migraphx_fp16_enable':'true','dynamic_model':'false'}]
+        provider_options=[{'device_id':'0','migraphx_fp16_enable':'true'}]
 
     if dynamicInfer:
         provider_options=[{'device_id':'0','migraphx_fp16_enable':'true','dynamic_model':'true', 'migraphx_profile_max_shapes':'data:1x3x224x224'}]

README.md

@@ -18,22 +18,16 @@ ResNet50使用了多个具有残差连接的残差块来解决梯度消失或梯
 ### Docker（方法一）
 拉取镜像：
 ```python
-docker pull image.sourcefind.cn:5000/dcu/admin/base/migraphx:4.3.0-ubuntu20.04-dtk24.04.1-py3.10
+
 ```
 
 创建并启动容器：
 ```
-docker run --shm-size 16g --network=host --name=resnet50_onnxruntime -v /opt/hyhal:/opt/hyhal:ro --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v $PWD/resnet50_onnxruntime:/home/resnet50_onnxruntime -it <Your Image ID> /bin/bash
 
-# 激活dtk
-source /opt/dtk/env.sh
 ```
 ### Dockerfile（方法二）
 ```
-cd ./docker
-docker build --no-cache -t resnet50_onnxruntime:2.0 .
 
-docker run --shm-size 16g --network=host --name=resnet50_onnxruntime -v /opt/hyhal:/opt/hyhal:ro --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v $PWD/resnet50_onnxruntime:/home/resnet50_onnxruntime -it <Your Image ID> /bin/bash
 ```
 
 ## 数据集
@@ -59,39 +53,54 @@ data
 -->
 ## 推理
 ### Python版本推理
-采用ONNXRuntime框架使用DCU进行推理，下面介绍如何运行python代码示例，Python示例的详细说明见Doc目录下的Tutorial_Python.md。
+采用ONNXRuntime框架使用DCU进行migraphx后端推理。
 #### 配置环境
 ```python
 # 进入resnet50 onnxruntime工程根目录
-cd <path_to_resnet50_onnxruntime> 
+cd <path_to_resnet50_onnxruntime_migraphx> 
 
 # 安装依赖
 pip install -r ./Python/requirements.txt
 ```
 #### 运行示例
+
+本示例程序中一共给出了三种推理方式：
+
 ```python
 # 进入resnet50 onnxruntime工程根目录
-cd <path_to_resnet50_onnxruntime> 
+cd <path_to_resnet50_onnxruntime_migraphx> 
 
 # 进入示例程序目录
 cd Python/
 
-# 运行示例
-python Classifier.py
+# 静态推理，输入输出为cpu数据
+python Classifier.py --staticInfer
+
+# 动态推理，输入输出为cpu数据
+python Classifier.py --dynamicInfer
+
+# 静态推理，输入为gpu数据，输出为cpu数据
+python Classifier_run_with_ort.py --staticInfer
+
+# 动态推理，输入为gpu数据，输出为cpu数据
+python Classifier_run_with_ort.py --dynamicInfer
+
+# 静态推理，输入输出为gpu数据
+python Classifier_io_binding.py
 ```
 
 ### C++版本推理
-采用ONNXRuntime框架使用DCU进行推理，下面介绍如何运行C++代码示例，C++示例的详细说明见Doc目录下的Tutorial_Cpp.md。
+采用ONNXRuntime框架使用DCU进行推理，下面介绍如何运行C++代码示例。
 #### 构建工程
 ```c++
-cd <path_to_resnet50_onnxruntime>
+cd <path_to_resnet50_onnxruntime_migraphx>
 
 rbuild build -d depend
 ```
 #### 设置环境变量
 将依赖库依赖加入环境变量LD_LIBRARY_PATH，在~/.bashrc中添加如下语句：
 ```c++
-export LD_LIBRARY_PATH=<path_to_resnet50_onnxruntime>/depend/lib64/:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=<path_to_resnet50_onnxruntime_migraphx>/depend/lib64/:$LD_LIBRARY_PATH
 ```
 然后执行:
 ```

Resource/Configuration.xml

@@ -3,7 +3,7 @@
 
 	<!--分类器-->
 	<Classifier>
-		<ModelPath>"../Resource/Models/resnet50-v2-7.onnx"</ModelPath>
+		<ModelPath>"../Resource/Models/resnet50_static.onnx"</ModelPath>
 		<UseInt8>0</UseInt8><!--是否使用int8,不支持-->
 		<UseFP16>0</UseFP16><!--是否使用FP16-->
 	</Classifier>