bump version to 1.0.0rc2 (#1754)

* bump version * update cmake
open-mmlab · Mar 1, 2023 · 18db354 · 18db354
1 parent 339f7ae
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diff --git a/CMakeLists.txt b/CMakeLists.txt
@@ -5,7 +5,7 @@ endif ()
 message(STATUS "CMAKE_INSTALL_PREFIX: ${CMAKE_INSTALL_PREFIX}")
 
 cmake_minimum_required(VERSION 3.14)
-project(MMDeploy VERSION 0.12.0)
+project(MMDeploy VERSION 0.13.0)
 
 set(CMAKE_CXX_STANDARD 17)
 

diff --git a/csrc/mmdeploy/apis/csharp/README.md b/csrc/mmdeploy/apis/csharp/README.md
@@ -33,10 +33,10 @@ There are two methods to build the nuget package.
 
 (*option 1*) Use the command.
 
-If your environment is well prepared, you can just go to the `csrc\apis\csharp` folder, open a terminal and type the following command, the nupkg will be built in `csrc\apis\csharp\MMDeploy\bin\Release\MMDeployCSharp.1.0.0-rc1.nupkg`.
+If your environment is well prepared, you can just go to the `csrc\apis\csharp` folder, open a terminal and type the following command, the nupkg will be built in `csrc\apis\csharp\MMDeploy\bin\Release\MMDeployCSharp.1.0.0-rc2.nupkg`.
 
 ```shell
-dotnet build --configuration Release -p:Version=1.0.0-rc1
+dotnet build --configuration Release -p:Version=1.0.0-rc2
 ```
 
 (*option 2*) Open MMDeploy.sln && Build.

diff --git a/demo/csharp/image_classification/image_classification.csproj b/demo/csharp/image_classification/image_classification.csproj
@@ -14,7 +14,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc1" />
+    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc2" />
     <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
     <PackageReference Include="OpenCvSharp4.Extensions" Version="4.5.5.20211231" />
     <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />

diff --git a/demo/csharp/image_restorer/image_restorer.csproj b/demo/csharp/image_restorer/image_restorer.csproj
@@ -14,7 +14,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc1" />
+    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc2" />
     <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
     <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
   </ItemGroup>

diff --git a/demo/csharp/image_segmentation/image_segmentation.csproj b/demo/csharp/image_segmentation/image_segmentation.csproj
@@ -14,7 +14,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc1" />
+    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc2" />
     <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
     <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
   </ItemGroup>

diff --git a/demo/csharp/object_detection/object_detection.csproj b/demo/csharp/object_detection/object_detection.csproj
@@ -14,7 +14,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc1" />
+    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc2" />
     <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
     <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
   </ItemGroup>

diff --git a/demo/csharp/ocr_detection/ocr_detection.csproj b/demo/csharp/ocr_detection/ocr_detection.csproj
@@ -14,7 +14,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc1" />
+    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc2" />
     <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
     <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
   </ItemGroup>

diff --git a/demo/csharp/ocr_recognition/ocr_recognition.csproj b/demo/csharp/ocr_recognition/ocr_recognition.csproj
@@ -14,7 +14,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc1" />
+    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc2" />
     <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
     <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
   </ItemGroup>

diff --git a/demo/csharp/pose_detection/pose_detection.csproj b/demo/csharp/pose_detection/pose_detection.csproj
@@ -14,7 +14,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc1" />
+    <PackageReference Include="MMDeployCSharp" Version="1.0.0-rc2" />
     <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
     <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
   </ItemGroup>

diff --git a/docs/en/02-how-to-run/prebuilt_package_windows.md b/docs/en/02-how-to-run/prebuilt_package_windows.md
@@ -21,7 +21,7 @@
 
 ______________________________________________________________________
 
-This tutorial takes `mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1.zip` and `mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip` as examples to show how to use the prebuilt packages.
+This tutorial takes `mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1.zip` and `mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip` as examples to show how to use the prebuilt packages.
 
 The directory structure of the prebuilt package is as follows, where the `dist` folder is about model converter, and the `sdk` folder is related to model inference.
 
@@ -80,9 +80,9 @@ In order to use `ONNX Runtime` backend, you should also do the following steps.
 5. Install `mmdeploy` (Model Converter) and `mmdeploy_python` (SDK Python API).
 
    ```bash
-   # download mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1.zip
-   pip install .\mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-1.0.0rc1-py38-none-win_amd64.whl
-   pip install .\mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-1.0.0rc1-cp38-none-win_amd64.whl
+   # download mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1.zip
+   pip install .\mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-1.0.0rc2-py38-none-win_amd64.whl
+   pip install .\mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-1.0.0rc2-cp38-none-win_amd64.whl
    ```
 
    :point_right: If you have installed it before, please uninstall it first.
@@ -107,9 +107,9 @@ In order to use `TensorRT` backend, you should also do the following steps.
 5. Install `mmdeploy` (Model Converter) and `mmdeploy_python` (SDK Python API).
 
    ```bash
-   # download mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip
-   pip install .\mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-1.0.0rc1-py38-none-win_amd64.whl
-   pip install .\mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-1.0.0rc1-cp38-none-win_amd64.whl
+   # download mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip
+   pip install .\mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-1.0.0rc2-py38-none-win_amd64.whl
+   pip install .\mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-1.0.0rc2-cp38-none-win_amd64.whl
    ```
 
    :point_right: If you have installed it before, please uninstall it first.
@@ -138,7 +138,7 @@ After preparation work, the structure of the current working directory should be
 
 ```
 ..
-|-- mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1
+|-- mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1
 |-- mmclassification
 |-- mmdeploy
 `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@@ -186,7 +186,7 @@ After installation of mmdeploy-tensorrt prebuilt package, the structure of the c
 
 ```
 ..
-|-- mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0
+|-- mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0
 |-- mmclassification
 |-- mmdeploy
 `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@@ -249,8 +249,8 @@ The structure of current working directory：
 
 ```
 .
-|-- mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0
-|-- mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1
+|-- mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0
+|-- mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1
 |-- mmclassification
 |-- mmdeploy
 |-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@@ -311,15 +311,15 @@ The following describes how to use the SDK's C API for inference
 
 1. Build examples
 
-   Under `mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\example` directory
+   Under `mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1\sdk\example` directory
 
    ```
    // Path should be modified according to the actual location
    mkdir build
    cd build
    cmake ..\cpp -A x64 -T v142 `
      -DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib `
-     -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy `
+     -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy `
      -DONNXRUNTIME_DIR=C:\Deps\onnxruntime\onnxruntime-win-gpu-x64-1.8.1
 
    cmake --build . --config Release
@@ -329,15 +329,15 @@ The following describes how to use the SDK's C API for inference
 
    :point_right: The purpose is to make the exe find the relevant dll
 
-   If choose to add environment variables, add the runtime libraries path of `mmdeploy` (`mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\bin`) to the `PATH`.
+   If choose to add environment variables, add the runtime libraries path of `mmdeploy` (`mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1\sdk\bin`) to the `PATH`.
 
    If choose to copy the dynamic libraries, copy the dll in the bin directory to the same level directory of the just compiled exe (build/Release).
 
 3. Inference：
 
    It is recommended to use `CMD` here.
 
-   Under `mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release` directory：
+   Under `mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release` directory：
 
    ```
    .\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmclassification\demo\demo.JPEG
@@ -347,15 +347,15 @@ The following describes how to use the SDK's C API for inference
 
 1. Build examples
 
-   Under `mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example` directory
+   Under `mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example` directory
 
    ```
    // Path should be modified according to the actual location
    mkdir build
    cd build
    cmake ..\cpp -A x64 -T v142 `
      -DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib `
-     -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy `
+     -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy `
      -DTENSORRT_DIR=C:\Deps\tensorrt\TensorRT-8.2.3.0 `
      -DCUDNN_DIR=C:\Deps\cudnn\8.2.1
    cmake --build . --config Release
@@ -365,15 +365,15 @@ The following describes how to use the SDK's C API for inference
 
    :point_right: The purpose is to make the exe find the relevant dll
 
-   If choose to add environment variables, add the runtime libraries path of `mmdeploy` (`mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\bin`) to the `PATH`.
+   If choose to add environment variables, add the runtime libraries path of `mmdeploy` (`mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\bin`) to the `PATH`.
 
    If choose to copy the dynamic libraries, copy the dll in the bin directory to the same level directory of the just compiled exe (build/Release).
 
 3. Inference
 
    It is recommended to use `CMD` here.
 
-   Under `mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release` directory
+   Under `mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release` directory
 
    ```
    .\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmclassification\demo\demo.JPEG

diff --git a/docs/en/get_started.md b/docs/en/get_started.md
@@ -119,11 +119,11 @@ Take the latest precompiled package as example, you can install it as follows:
 
 ```shell
 # install MMDeploy
-wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc1/mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1.tar.gz
-tar -zxvf mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1.tar.gz
-cd mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1
-pip install dist/mmdeploy-1.0.0rc1-py3-none-linux_x86_64.whl
-pip install sdk/python/mmdeploy_python-1.0.0rc1-cp38-none-linux_x86_64.whl
+wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc2/mmdeploy-1.0.0rc2-linux-x86_64-onnxruntime1.8.1.tar.gz
+tar -zxvf mmdeploy-1.0.0rc2-linux-x86_64-onnxruntime1.8.1.tar.gz
+cd mmdeploy-1.0.0rc2-linux-x86_64-onnxruntime1.8.1
+pip install dist/mmdeploy-1.0.0rc2-py3-none-linux_x86_64.whl
+pip install sdk/python/mmdeploy_python-1.0.0rc2-cp38-none-linux_x86_64.whl
 cd ..
 # install inference engine: ONNX Runtime
 pip install onnxruntime==1.8.1
@@ -140,11 +140,11 @@ export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH
 
 ```shell
 # install MMDeploy
-wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc1/mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
-tar -zxvf mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
-cd mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0
-pip install dist/mmdeploy-1.0.0rc1-py3-none-linux_x86_64.whl
-pip install sdk/python/mmdeploy_python-1.0.0rc1-cp38-none-linux_x86_64.whl
+wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc2/mmdeploy-1.0.0rc2-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
+tar -zxvf mmdeploy-1.0.0rc2-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
+cd mmdeploy-1.0.0rc2-linux-x86_64-cuda11.1-tensorrt8.2.3.0
+pip install dist/mmdeploy-1.0.0rc2-py3-none-linux_x86_64.whl
+pip install sdk/python/mmdeploy_python-1.0.0rc2-cp38-none-linux_x86_64.whl
 cd ..
 # install inference engine: TensorRT
 # !!! Download TensorRT-8.2.3.0 CUDA 11.x tar package from NVIDIA, and extract it to the current directory
@@ -233,7 +233,7 @@ result = inference_model(
 You can directly run MMDeploy demo programs in the precompiled package to get inference results.
 
 ```shell
-cd mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0
+cd mmdeploy-1.0.0rc2-linux-x86_64-cuda11.1-tensorrt8.2.3.0
 # run python demo
 python sdk/example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg
 # run C/C++ demo