bump version to v1.1.0 (#2094)
RunningLeon
GitHub
parent
8670d838cb
commit
e47c6400b0
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@ -5,7 +5,7 @@ endif ()
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message(STATUS "CMAKE_INSTALL_PREFIX: ${CMAKE_INSTALL_PREFIX}")
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cmake_minimum_required(VERSION 3.14)
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project(MMDeploy VERSION 0.13.0)
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project(MMDeploy VERSION 1.1.0)
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set(CMAKE_CXX_STANDARD 17)
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@ -33,14 +33,14 @@ There are two methods to build the nuget package.
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(*option 1*) Use the command.
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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.nupkg`.
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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.1.1.0.nupkg`.
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```shell
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dotnet build --configuration Release -p:Version=1.0.0
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dotnet build --configuration Release -p:Version=1.1.0
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```
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(*option 2*) Open MMDeploy.sln && Build.
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You can set the package-version through `Properties -> Package Version`. The default version is 1.0.0 if you don't set it.
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You can set the package-version through `Properties -> Package Version`. The default version is 1.1.0 if you don't set it.
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If you encounter missing dependencies, follow the instructions for MSVC.
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@ -14,7 +14,7 @@
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="MMDeployCSharp" Version="1.0.0" />
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<PackageReference Include="MMDeployCSharp" Version="1.1.0" />
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<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
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<PackageReference Include="OpenCvSharp4.Extensions" Version="4.5.5.20211231" />
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<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
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@ -14,7 +14,7 @@
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="MMDeployCSharp" Version="1.0.0" />
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<PackageReference Include="MMDeployCSharp" Version="1.1.0" />
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<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
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<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
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</ItemGroup>
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@ -14,7 +14,7 @@
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="MMDeployCSharp" Version="1.0.0" />
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<PackageReference Include="MMDeployCSharp" Version="1.1.0" />
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<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
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<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
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</ItemGroup>
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@ -14,7 +14,7 @@
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="MMDeployCSharp" Version="1.0.0" />
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<PackageReference Include="MMDeployCSharp" Version="1.1.0" />
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<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
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<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
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</ItemGroup>
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@ -14,7 +14,7 @@
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="MMDeployCSharp" Version="1.0.0" />
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<PackageReference Include="MMDeployCSharp" Version="1.1.0" />
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<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
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<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
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</ItemGroup>
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@ -14,7 +14,7 @@
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="MMDeployCSharp" Version="1.0.0" />
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<PackageReference Include="MMDeployCSharp" Version="1.1.0" />
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<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
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<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
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</ItemGroup>
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@ -14,7 +14,7 @@
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="MMDeployCSharp" Version="1.0.0" />
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<PackageReference Include="MMDeployCSharp" Version="1.1.0" />
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<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
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<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
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</ItemGroup>
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@ -21,7 +21,7 @@
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______________________________________________________________________
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This tutorial takes `mmdeploy-1.0.0-windows-amd64.zip` and `mmdeploy-1.0.0-windows-amd64-cuda11.3.zip` as examples to show how to use the prebuilt packages. The former support onnxruntime cpu inference, the latter support onnxruntime-gpu and tensorrt inference.
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This tutorial takes `mmdeploy-1.1.0-windows-amd64.zip` and `mmdeploy-1.1.0-windows-amd64-cuda11.3.zip` as examples to show how to use the prebuilt packages. The former support onnxruntime cpu inference, the latter support onnxruntime-gpu and tensorrt inference.
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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.
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@ -81,8 +81,8 @@ In order to use `ONNX Runtime` backend, you should also do the following steps.
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5. Install `mmdeploy` (Model Converter) and `mmdeploy_runtime` (SDK Python API).
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```bash
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pip install mmdeploy==1.0.0
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pip install mmdeploy-runtime==1.0.0
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pip install mmdeploy==1.1.0
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pip install mmdeploy-runtime==1.1.0
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```
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:point_right: If you have installed it before, please uninstall it first.
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@ -100,7 +100,7 @@ In order to use `ONNX Runtime` backend, you should also do the following steps.
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:exclamation: Restart powershell to make the environment variables setting take effect. You can check whether the settings are in effect by `echo $env:PATH`.
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8. Download SDK C/cpp Library mmdeploy-1.0.0-windows-amd64.zip
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8. Download SDK C/cpp Library mmdeploy-1.1.0-windows-amd64.zip
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### TensorRT
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@ -109,8 +109,8 @@ In order to use `TensorRT` backend, you should also do the following steps.
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5. Install `mmdeploy` (Model Converter) and `mmdeploy_runtime` (SDK Python API).
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```bash
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pip install mmdeploy==1.0.0
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pip install mmdeploy-runtime-gpu==1.0.0
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pip install mmdeploy==1.1.0
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pip install mmdeploy-runtime-gpu==1.1.0
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```
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:point_right: If you have installed it before, please uninstall it first.
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@ -129,7 +129,7 @@ In order to use `TensorRT` backend, you should also do the following steps.
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7. Install pycuda by `pip install pycuda`
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8. Download SDK C/cpp Library mmdeploy-1.0.0-windows-amd64-cuda11.3.zip
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8. Download SDK C/cpp Library mmdeploy-1.1.0-windows-amd64-cuda11.3.zip
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## Model Convert
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@ -141,7 +141,7 @@ After preparation work, the structure of the current working directory should be
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```
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..
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|-- mmdeploy-1.0.0-windows-amd64
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|-- mmdeploy-1.1.0-windows-amd64
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|-- mmpretrain
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|-- mmdeploy
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`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
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@ -189,7 +189,7 @@ After installation of mmdeploy-tensorrt prebuilt package, the structure of the c
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```
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..
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|-- mmdeploy-1.0.0-windows-amd64-cuda11.3
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|-- mmdeploy-1.1.0-windows-amd64-cuda11.3
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|-- mmpretrain
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|-- mmdeploy
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`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
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@ -252,8 +252,8 @@ The structure of current working directory：
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```
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.
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|-- mmdeploy-1.0.0-windows-amd64
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|-- mmdeploy-1.0.0-windows-amd64-cuda11.3
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|-- mmdeploy-1.1.0-windows-amd64
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|-- mmdeploy-1.1.0-windows-amd64-cuda11.3
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|-- mmpretrain
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|-- mmdeploy
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|-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
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@ -324,7 +324,7 @@ The following describes how to use the SDK's C API for inference
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It is recommended to use `CMD` here.
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Under `mmdeploy-1.0.0-windows-amd64\\example\\cpp\\build\\Release` directory：
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Under `mmdeploy-1.1.0-windows-amd64\\example\\cpp\\build\\Release` directory：
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```
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.\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmpretrain\demo\demo.JPEG
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@ -344,7 +344,7 @@ The following describes how to use the SDK's C API for inference
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It is recommended to use `CMD` here.
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Under `mmdeploy-1.0.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release` directory
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Under `mmdeploy-1.1.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release` directory
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```
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.\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmpretrain\demo\demo.JPEG
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@ -118,14 +118,14 @@ Take the latest precompiled package as example, you can install it as follows:
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```shell
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# 1. install MMDeploy model converter
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pip install mmdeploy==1.0.0
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pip install mmdeploy==1.1.0
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# 2. install MMDeploy sdk inference
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# you can install one to install according whether you need gpu inference
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# 2.1 support onnxruntime
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pip install mmdeploy-runtime==1.0.0
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pip install mmdeploy-runtime==1.1.0
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# 2.2 support onnxruntime-gpu, tensorrt
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pip install mmdeploy-runtime-gpu==1.0.0
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pip install mmdeploy-runtime-gpu==1.1.0
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# 3. install inference engine
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# 3.1 install TensorRT
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@ -230,9 +230,9 @@ result = inference_model(
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You can directly run MMDeploy demo programs in the precompiled package to get inference results.
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```shell
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wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0/mmdeploy-1.0.0-linux-x86_64-cuda11.3.tar.gz
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tar xf mmdeploy-1.0.0-linux-x86_64-cuda11.3
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cd mmdeploy-1.0.0-linux-x86_64-cuda11.3
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wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.1.0/mmdeploy-1.1.0-linux-x86_64-cuda11.3.tar.gz
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tar xf mmdeploy-1.1.0-linux-x86_64-cuda11.3
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cd mmdeploy-1.1.0-linux-x86_64-cuda11.3
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# run python demo
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python example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg
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# run C/C++ demo
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@ -23,7 +23,7 @@ ______________________________________________________________________
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目前，`MMDeploy`在`Windows`平台下提供`cpu`以及`cuda`两种Device的预编译包，其中`cpu`版支持使用onnxruntime cpu进行推理，`cuda`版支持使用onnxruntime-gpu以及tensorrt进行推理，可以从[Releases](https://github.com/open-mmlab/mmdeploy/releases)获取。。
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本篇教程以`mmdeploy-1.0.0-windows-amd64.zip`和`mmdeploy-1.0.0-windows-amd64-cuda11.3.zip`为例，展示预编译包的使用方法。
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本篇教程以`mmdeploy-1.1.0-windows-amd64.zip`和`mmdeploy-1.1.0-windows-amd64-cuda11.3.zip`为例，展示预编译包的使用方法。
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为了方便使用者快速上手，本教程以分类模型(mmpretrain)为例，展示两种预编译包的使用方法。
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@ -89,8 +89,8 @@ ______________________________________________________________________
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5. 安装`mmdeploy`（模型转换）以及`mmdeploy_runtime`（模型推理Python API）的预编译包
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```bash
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pip install mmdeploy==1.0.0
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pip install mmdeploy-runtime==1.0.0
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pip install mmdeploy==1.1.0
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pip install mmdeploy-runtime==1.1.0
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```
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:point_right: 如果之前安装过，需要先卸载后再安装。
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@ -108,7 +108,7 @@ ______________________________________________________________________
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:exclamation: 重启powershell让环境变量生效，可以通过 echo $env:PATH 来检查是否设置成功。
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8. 下载 SDK C/cpp Library mmdeploy-1.0.0-windows-amd64.zip
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8. 下载 SDK C/cpp Library mmdeploy-1.1.0-windows-amd64.zip
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### TensorRT
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@ -117,8 +117,8 @@ ______________________________________________________________________
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5. 安装`mmdeploy`（模型转换）以及`mmdeploy_runtime`（模型推理Python API）的预编译包
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```bash
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pip install mmdeploy==1.0.0
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pip install mmdeploy-runtime-gpu==1.0.0
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pip install mmdeploy==1.1.0
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pip install mmdeploy-runtime-gpu==1.1.0
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```
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:point_right: 如果之前安装过，需要先卸载后再安装
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@ -137,7 +137,7 @@ ______________________________________________________________________
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7. 安装pycuda `pip install pycuda`
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8. 下载 SDK C/cpp Library mmdeploy-1.0.0-windows-amd64-cuda11.3.zip
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8. 下载 SDK C/cpp Library mmdeploy-1.1.0-windows-amd64-cuda11.3.zip
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## 模型转换
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@ -149,7 +149,7 @@ ______________________________________________________________________
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```
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..
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|-- mmdeploy-1.0.0-windows-amd64
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|-- mmdeploy-1.1.0-windows-amd64
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|-- mmpretrain
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|-- mmdeploy
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`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
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@ -197,7 +197,7 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device)
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```
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..
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|-- mmdeploy-1.0.0-windows-amd64-cuda11.3
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|-- mmdeploy-1.1.0-windows-amd64-cuda11.3
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|-- mmpretrain
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|-- mmdeploy
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`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
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@ -260,8 +260,8 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device)
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```
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.
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|-- mmdeploy-1.0.0-windows-amd64
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|-- mmdeploy-1.0.0-windows-amd64-cuda11.3
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|-- mmdeploy-1.1.0-windows-amd64
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|-- mmdeploy-1.1.0-windows-amd64-cuda11.3
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|-- mmpretrain
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|-- mmdeploy
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|-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
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@ -340,7 +340,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet
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这里建议使用cmd，这样如果exe运行时如果找不到相关的dll的话会有弹窗
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在mmdeploy-1.0.0-windows-amd64\\example\\cpp\\build\\Release目录下：
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在mmdeploy-1.1.0-windows-amd64\\example\\cpp\\build\\Release目录下：
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```
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.\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmpretrain\demo\demo.JPEG
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@ -360,7 +360,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet
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这里建议使用cmd，这样如果exe运行时如果找不到相关的dll的话会有弹窗
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在mmdeploy-1.0.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release目录下：
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在mmdeploy-1.1.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release目录下：
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```
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.\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmpretrain\demo\demo.JPEG
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@ -113,14 +113,14 @@ mim install "mmcv>=2.0.0rc2"
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```shell
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# 1. 安装 MMDeploy 模型转换工具（含trt/ort自定义算子）
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pip install mmdeploy==1.0.0
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pip install mmdeploy==1.1.0
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# 2. 安装 MMDeploy SDK推理工具
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# 根据是否需要GPU推理可任选其一进行下载安装
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# 2.1 支持 onnxruntime 推理
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pip install mmdeploy-runtime==1.0.0
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pip install mmdeploy-runtime==1.1.0
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# 2.2 支持 onnxruntime-gpu tensorrt 推理
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pip install mmdeploy-runtime-gpu==1.0.0
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pip install mmdeploy-runtime-gpu==1.1.0
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# 3. 安装推理引擎
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# 3.1 安装推理引擎 TensorRT
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@ -223,10 +223,10 @@ result = inference_model(
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你可以直接运行预编译包中的 demo 程序，输入 SDK Model 和图像，进行推理，并查看推理结果。
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```shell
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wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0/mmdeploy-1.0.0-linux-x86_64-cuda11.3.tar.gz
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tar xf mmdeploy-1.0.0-linux-x86_64-cuda11.3
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wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.1.0/mmdeploy-1.1.0-linux-x86_64-cuda11.3.tar.gz
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tar xf mmdeploy-1.1.0-linux-x86_64-cuda11.3
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cd mmdeploy-1.0.0-linux-x86_64-cuda11.3
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cd mmdeploy-1.1.0-linux-x86_64-cuda11.3
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# 运行 python demo
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python example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg
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# 运行 C/C++ demo
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@ -1,7 +1,7 @@
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# Copyright (c) OpenMMLab. All rights reserved.
|
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from typing import Tuple
|
||||
|
||||
__version__ = '1.0.0'
|
||||
__version__ = '1.1.0'
|
||||
short_version = __version__
|
||||
|
||||
|
||||
|
|
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|||
|
|
@ -1,2 +1,2 @@
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|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
__version__ = '1.0.0'
|
||||
__version__ = '1.1.0'
|
||||
|
|
|
|||
Loading…
Reference in New Issue