Add icon

.vscode/c_cpp_properties.json

+{
+  "configurations": [
+    {
+      "name": "windows-gcc-x64",
+      "includePath": [
+        "${workspaceFolder}/**"
+      ],
+      "compilerPath": "gcc",
+      "cStandard": "${default}",
+      "cppStandard": "${default}",
+      "intelliSenseMode": "windows-gcc-x64",
+      "compilerArgs": [
+        ""
+      ]
+    }
+  ],
+  "version": 4
+}
\ No newline at end of file

.vscode/launch.json

+{
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "C/C++ Runner: Debug Session",
+      "type": "cppdbg",
+      "request": "launch",
+      "args": [],
+      "stopAtEntry": false,
+      "externalConsole": true,
+      "cwd": "d:/jobs_2024/modelzoo-icon/tvm_tune_resnet50-v2",
+      "program": "d:/jobs_2024/modelzoo-icon/tvm_tune_resnet50-v2/build/Debug/outDebug",
+      "MIMode": "gdb",
+      "miDebuggerPath": "gdb",
+      "setupCommands": [
+        {
+          "description": "Enable pretty-printing for gdb",
+          "text": "-enable-pretty-printing",
+          "ignoreFailures": true
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

.vscode/settings.json

+{
+  "C_Cpp_Runner.cCompilerPath": "gcc",
+  "C_Cpp_Runner.cppCompilerPath": "g++",
+  "C_Cpp_Runner.debuggerPath": "gdb",
+  "C_Cpp_Runner.cStandard": "",
+  "C_Cpp_Runner.cppStandard": "",
+  "C_Cpp_Runner.msvcBatchPath": "C:/Program Files/Microsoft Visual Studio/VR_NR/Community/VC/Auxiliary/Build/vcvarsall.bat",
+  "C_Cpp_Runner.useMsvc": false,
+  "C_Cpp_Runner.warnings": [
+    "-Wall",
+    "-Wextra",
+    "-Wpedantic",
+    "-Wshadow",
+    "-Wformat=2",
+    "-Wcast-align",
+    "-Wconversion",
+    "-Wsign-conversion",
+    "-Wnull-dereference"
+  ],
+  "C_Cpp_Runner.msvcWarnings": [
+    "/W4",
+    "/permissive-",
+    "/w14242",
+    "/w14287",
+    "/w14296",
+    "/w14311",
+    "/w14826",
+    "/w44062",
+    "/w44242",
+    "/w14905",
+    "/w14906",
+    "/w14263",
+    "/w44265",
+    "/w14928"
+  ],
+  "C_Cpp_Runner.enableWarnings": true,
+  "C_Cpp_Runner.warningsAsError": false,
+  "C_Cpp_Runner.compilerArgs": [],
+  "C_Cpp_Runner.linkerArgs": [],
+  "C_Cpp_Runner.includePaths": [],
+  "C_Cpp_Runner.includeSearch": [
+    "*",
+    "**/*"
+  ],
+  "C_Cpp_Runner.excludeSearch": [
+    "**/build",
+    "**/build/**",
+    "**/.*",
+    "**/.*/**",
+    "**/.vscode",
+    "**/.vscode/**"
+  ],
+  "C_Cpp_Runner.useAddressSanitizer": false,
+  "C_Cpp_Runner.useUndefinedSanitizer": false,
+  "C_Cpp_Runner.useLeakSanitizer": false,
+  "C_Cpp_Runner.showCompilationTime": false,
+  "C_Cpp_Runner.useLinkTimeOptimization": false,
+  "C_Cpp_Runner.msvcSecureNoWarnings": false
+}
\ No newline at end of file

README.md

@@ -2,10 +2,12 @@
 ## 论文
 `Deep Residual Learning for Image Recognition`
 - https://arxiv.org/abs/1512.03385
+
 ## 模型结构
 ResNet50网络中包含了49个卷积层、1个全连接层等
 
 ![image.png](./image.png)
+
 ## 算法原理
 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度爆炸问题，并使得网络可以向更深层发展。
 
@@ -14,87 +16,71 @@ 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
 ## 环境配置
 ### Docker（方法一）
 
-拉取镜像：
-```
-    docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:tvm-0.11_fp32_cpp_dtk22.10_py38_centos-7.6-latest
-```
-创建并启动容器
 ```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:tvm-0.11_fp32_cpp_dtk22.10_py38_centos-7.6-latest
 docker run --shm-size 16g --network=host --name=ResNet50_v2 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined  -it <Your Image ID> /bin/bash
 
 # 激活dtk
 source /opt/dtk/env.sh
 ```
+
 ## 数据集
-  python 推理及调优代码使用的图片数据为：
-```
-    "https://s3.amazonaws.com/model-server/inputs/kitten.jpg"
-``` 
-  标签数据为：
-```
-    "https://s3.amazonaws.com/onnx-model-zoo/synset.txt"
-```
-   C++部署代码使用数据为：
-```
-    "https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip"
+python推理及调优代码使用的图片数据为：[kitten](./kitten.jpg)
 
-     coco128数据集的目录结构如下：
-     ├── images 
-     │   ├── train2017
-     ├── labels
-     │   ├── train2017
-     ├── LICENSE
-     ├── README.txt 
+标签数据为：[synset.txt](./synset.txt)
 
+C++部署代码使用数据为：[coco128](./coco128.zip)
+
+coco128数据集的目录结构如下：
 ```
-## 推理
-  模型文件下载地址： 
-```
-    "https://github.com/onnx/models/raw/main/vision/classification/resnet/model/resnet50-v2-7.onnx"
-```
-  下载模型文件后执行以下命令进行推理测试及调优测试：
-```
-    git clone http://developer.hpccube.com/codes/modelzoo/tvm_tune_resnet50-v2.git
-    cd tvm_tune_resnet50-v2
-    export HIP_VISIBLE_DEVICES=1 #此处为指定哪张加速卡进行推理的ID号
-    python tune_resnet50-v2.py   #根据实际修改模型路径
+├── images
+│   ├── train2017
+├── labels
+│   ├── train2017
+├── LICENSE
+├── README.txt
 ```
 
+## 推理
+下载模型文件[resnet50-v2-7.onnx](http://113.200.138.88:18080/aimodels/findsource-dependency/tvm_tune_resnet50-v2/-/blob/main/resnet50-v2-7.onnx)，执行以下命令进行推理测试及调优测试：
+```bash
+git clone http://developer.hpccube.com/codes/modelzoo/tvm_tune_resnet50-v2.git
+cd tvm_tune_resnet50-v2
+export HIP_VISIBLE_DEVICES=1 #此处为指定哪张加速卡进行推理的ID号
+python tune_resnet50-v2.py   #根据实际修改模型路径
+```
 
 ### 单卡部署推理测试
-  下载配置好镜像之后，cd /tvm-0.11-dev0/apps/ 进入该路径下，将代码下载放到该路径下，cd tvm_tune_resnet50-v2/ 进入该路径后，
-执行以下命令：
+下载配置好镜像之后，执行以下命令：
+```bash
+# 进入该路径下
+cd /tvm-0.11-dev0/apps/ # 将代码下载放到该路径下
+# 进入
+cd tvm_tune_resnet50-v2/
+# 执行
+mkdir -p lib
+export HIP_VISIBLE_DEVICES=1 #此处为指定哪张加速卡进行推理的ID号
+python prepare_test_libs.py  #根据实际修改模型路径
+sh run_example.sh
 ```
-    mkdir -p lib
-    export HIP_VISIBLE_DEVICES=1 #此处为指定哪张加速卡进行推理的ID号
-    python prepare_test_libs.py  #根据实际修改模型路径
-    sh run_example.sh
-```
-## result
 
+## result
 ![kitten-1.jpg](./kitten-1.jpg)
 
 ### 精度
-```
-    max_num:15.6692
-    max_iter:0x28cda14
-    max_num_index:345
-``` 
-## 应用场景
+| max_num | max_iter | max_num_index |
+| :------: | :------: | :------: |
+| 15.6692 | 0x28cda14 | 345 |
 
+## 应用场景
 ### 算法类别
-
 图像分类
 
 ### 热点应用行业
-
 制造,政府,医疗,科研
 
 ## 源码仓库及问题反馈
-
-  https://developer.hpccube.com/codes/modelzoo/tvm_tune_resnet50-v2
+- https://developer.hpccube.com/codes/modelzoo/tvm_tune_resnet50-v2
 
 ### 参考资料
-
-  https://tvm.apache.org/docs
-
+- https://tvm.apache.org/docs