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ModelZoo
ResNet50_onnxruntime
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@@ -25,7 +25,7 @@ export PYTHONPATH=/opt/dtk/lib:$PYTHONPATH
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@@ -25,7 +25,7 @@ export PYTHONPATH=/opt/dtk/lib:$PYTHONPATH
### 安装依赖
### 安装依赖
```
python
```
python
# 进入resnet50
migraphx
工程根目录
# 进入resnet50
ort
工程根目录
cd
<
path_to_resnet50_ort
>
cd
<
path_to_resnet50_ort
>
# 进入示例程序目录
# 进入示例程序目录
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@@ -43,7 +43,7 @@ python Classifier.py
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@@ -43,7 +43,7 @@ python Classifier.py
## C++版本推理
## C++版本推理
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,模型文件下载链接:https://github.com/onnx/models/blob/main/vision/classification/resnet/model/resnet50-v2-7.onnx ,并将resnet50-v2-7.onnx模型文件保存在Resource/文件夹下。
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。
### 下载镜像
### 下载镜像
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@@ -86,7 +86,7 @@ source ~/.bashrc
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@@ -86,7 +86,7 @@ source ~/.bashrc
### 运行示例
### 运行示例
```
python
```
python
# 进入resnet50
migraphx
工程根目录
# 进入resnet50
ort
工程根目录
cd
<
path_to_resnet50_ort
>
cd
<
path_to_resnet50_ort
>
# 进入build目录
# 进入build目录
...
@@ -95,13 +95,6 @@ cd build/
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@@ -95,13 +95,6 @@ cd build/
# 执行示例程序
# 执行示例程序
.
/
ResNet50
.
/
ResNet50
```
```
## 性能和准确率数据
测试数据使用的是ImageNet-1k,使用的加速卡是DCU Z100。
| Engine | Model Path| Model Format | accuracy (%) | Speed(qps) |
| :------: | :------: | :------: | :------: |:------: |
| ONNXRuntime | resnet50-v2-7.onnx | onnx | 74.83 | 149.83 |
## 源码仓库及问题反馈
## 源码仓库及问题反馈
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