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bump version to v1.1.0 (#2094)

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2
CMakeLists.txt
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@ -5,7 +5,7 @@ endif ()
message(STATUS "CMAKE_INSTALL_PREFIX: ${CMAKE_INSTALL_PREFIX}") message(STATUS "CMAKE_INSTALL_PREFIX: ${CMAKE_INSTALL_PREFIX}")
cmake_minimum_required(VERSION 3.14) cmake_minimum_required(VERSION 3.14)
project(MMDeploy VERSION 0.13.0) project(MMDeploy VERSION 1.1.0)
set(CMAKE_CXX_STANDARD 17) set(CMAKE_CXX_STANDARD 17)

6
csrc/mmdeploy/apis/csharp/README.md
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@ -33,14 +33,14 @@ There are two methods to build the nuget package.
(*option 1*) Use the command. (*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.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.1.1.0.nupkg`.
```shell ```shell
dotnet build --configuration Release -p:Version=1.0.0 dotnet build --configuration Release -p:Version=1.1.0
``` ```
(*option 2*) Open MMDeploy.sln && Build. (*option 2*) Open MMDeploy.sln && Build.
You can set the package-version through `Properties -> Package Version`. The default version is 1.0.0 if you don't set it. You can set the package-version through `Properties -> Package Version`. The default version is 1.1.0 if you don't set it.
If you encounter missing dependencies, follow the instructions for MSVC. If you encounter missing dependencies, follow the instructions for MSVC.

2
demo/csharp/image_classification/image_classification.csproj
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@ -14,7 +14,7 @@
</PropertyGroup> </PropertyGroup>
<ItemGroup> <ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="1.0.0" /> <PackageReference Include="MMDeployCSharp" Version="1.1.0" />
<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.Extensions" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4.Extensions" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />

2
demo/csharp/image_restorer/image_restorer.csproj
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@ -14,7 +14,7 @@
</PropertyGroup> </PropertyGroup>
<ItemGroup> <ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="1.0.0" /> <PackageReference Include="MMDeployCSharp" Version="1.1.0" />
<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
</ItemGroup> </ItemGroup>

2
demo/csharp/image_segmentation/image_segmentation.csproj
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@ -14,7 +14,7 @@
</PropertyGroup> </PropertyGroup>
<ItemGroup> <ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="1.0.0" /> <PackageReference Include="MMDeployCSharp" Version="1.1.0" />
<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
</ItemGroup> </ItemGroup>

2
demo/csharp/object_detection/object_detection.csproj
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@ -14,7 +14,7 @@
</PropertyGroup> </PropertyGroup>
<ItemGroup> <ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="1.0.0" /> <PackageReference Include="MMDeployCSharp" Version="1.1.0" />
<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
</ItemGroup> </ItemGroup>

2
demo/csharp/ocr_detection/ocr_detection.csproj
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@ -14,7 +14,7 @@
</PropertyGroup> </PropertyGroup>
<ItemGroup> <ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="1.0.0" /> <PackageReference Include="MMDeployCSharp" Version="1.1.0" />
<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
</ItemGroup> </ItemGroup>

2
demo/csharp/ocr_recognition/ocr_recognition.csproj
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@ -14,7 +14,7 @@
</PropertyGroup> </PropertyGroup>
<ItemGroup> <ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="1.0.0" /> <PackageReference Include="MMDeployCSharp" Version="1.1.0" />
<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
</ItemGroup> </ItemGroup>

2
demo/csharp/pose_detection/pose_detection.csproj
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@ -14,7 +14,7 @@
</PropertyGroup> </PropertyGroup>
<ItemGroup> <ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="1.0.0" /> <PackageReference Include="MMDeployCSharp" Version="1.1.0" />
<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" /> <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
</ItemGroup> </ItemGroup>

26
docs/en/02-how-to-run/prebuilt_package_windows.md
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@ -21,7 +21,7 @@
______________________________________________________________________ ______________________________________________________________________
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. 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.
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. 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.
@ -81,8 +81,8 @@ In order to use `ONNX Runtime` backend, you should also do the following steps.
5. Install `mmdeploy` (Model Converter) and `mmdeploy_runtime` (SDK Python API). 5. Install `mmdeploy` (Model Converter) and `mmdeploy_runtime` (SDK Python API).
```bash ```bash
pip install mmdeploy==1.0.0 pip install mmdeploy==1.1.0
pip install mmdeploy-runtime==1.0.0 pip install mmdeploy-runtime==1.1.0
``` ```
:point_right: If you have installed it before, please uninstall it first. :point_right: If you have installed it before, please uninstall it first.
@ -100,7 +100,7 @@ In order to use `ONNX Runtime` backend, you should also do the following steps.
![sys-path](https://user-images.githubusercontent.com/16019484/181463801-1d7814a8-b256-46e9-86f2-c08de0bc150b.png) ![sys-path](https://user-images.githubusercontent.com/16019484/181463801-1d7814a8-b256-46e9-86f2-c08de0bc150b.png)
:exclamation: Restart powershell to make the environment variables setting take effect. You can check whether the settings are in effect by `echo $env:PATH`. :exclamation: Restart powershell to make the environment variables setting take effect. You can check whether the settings are in effect by `echo $env:PATH`.
8. Download SDK C/cpp Library mmdeploy-1.0.0-windows-amd64.zip 8. Download SDK C/cpp Library mmdeploy-1.1.0-windows-amd64.zip
### TensorRT ### TensorRT
@ -109,8 +109,8 @@ In order to use `TensorRT` backend, you should also do the following steps.
5. Install `mmdeploy` (Model Converter) and `mmdeploy_runtime` (SDK Python API). 5. Install `mmdeploy` (Model Converter) and `mmdeploy_runtime` (SDK Python API).
```bash ```bash
pip install mmdeploy==1.0.0 pip install mmdeploy==1.1.0
pip install mmdeploy-runtime-gpu==1.0.0 pip install mmdeploy-runtime-gpu==1.1.0
``` ```
:point_right: If you have installed it before, please uninstall it first. :point_right: If you have installed it before, please uninstall it first.
@ -129,7 +129,7 @@ In order to use `TensorRT` backend, you should also do the following steps.
7. Install pycuda by `pip install pycuda` 7. Install pycuda by `pip install pycuda`
8. Download SDK C/cpp Library mmdeploy-1.0.0-windows-amd64-cuda11.3.zip 8. Download SDK C/cpp Library mmdeploy-1.1.0-windows-amd64-cuda11.3.zip
## Model Convert ## Model Convert
@ -141,7 +141,7 @@ After preparation work, the structure of the current working directory should be
``` ```
.. ..
|-- mmdeploy-1.0.0-windows-amd64 |-- mmdeploy-1.1.0-windows-amd64
|-- mmpretrain |-- mmpretrain
|-- mmdeploy |-- mmdeploy
`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -189,7 +189,7 @@ After installation of mmdeploy-tensorrt prebuilt package, the structure of the c
``` ```
.. ..
|-- mmdeploy-1.0.0-windows-amd64-cuda11.3 |-- mmdeploy-1.1.0-windows-amd64-cuda11.3
|-- mmpretrain |-- mmpretrain
|-- mmdeploy |-- mmdeploy
`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -252,8 +252,8 @@ The structure of current working directory：
``` ```
. .
|-- mmdeploy-1.0.0-windows-amd64 |-- mmdeploy-1.1.0-windows-amd64
|-- mmdeploy-1.0.0-windows-amd64-cuda11.3 |-- mmdeploy-1.1.0-windows-amd64-cuda11.3
|-- mmpretrain |-- mmpretrain
|-- mmdeploy |-- mmdeploy
|-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth |-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -324,7 +324,7 @@ The following describes how to use the SDK's C API for inference
It is recommended to use `CMD` here. It is recommended to use `CMD` here.
Under `mmdeploy-1.0.0-windows-amd64\\example\\cpp\\build\\Release` directory： Under `mmdeploy-1.1.0-windows-amd64\\example\\cpp\\build\\Release` directory：
``` ```
.\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmpretrain\demo\demo.JPEG .\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmpretrain\demo\demo.JPEG
@ -344,7 +344,7 @@ The following describes how to use the SDK's C API for inference
It is recommended to use `CMD` here. It is recommended to use `CMD` here.
Under `mmdeploy-1.0.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release` directory Under `mmdeploy-1.1.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release` directory
``` ```
.\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmpretrain\demo\demo.JPEG .\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmpretrain\demo\demo.JPEG

12
docs/en/get_started.md
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@ -118,14 +118,14 @@ Take the latest precompiled package as example, you can install it as follows:
```shell ```shell
# 1. install MMDeploy model converter # 1. install MMDeploy model converter
pip install mmdeploy==1.0.0 pip install mmdeploy==1.1.0
# 2. install MMDeploy sdk inference # 2. install MMDeploy sdk inference
# you can install one to install according whether you need gpu inference # you can install one to install according whether you need gpu inference
# 2.1 support onnxruntime # 2.1 support onnxruntime
pip install mmdeploy-runtime==1.0.0 pip install mmdeploy-runtime==1.1.0
# 2.2 support onnxruntime-gpu, tensorrt # 2.2 support onnxruntime-gpu, tensorrt
pip install mmdeploy-runtime-gpu==1.0.0 pip install mmdeploy-runtime-gpu==1.1.0
# 3. install inference engine # 3. install inference engine
# 3.1 install TensorRT # 3.1 install TensorRT
@ -230,9 +230,9 @@ result = inference_model(
You can directly run MMDeploy demo programs in the precompiled package to get inference results. You can directly run MMDeploy demo programs in the precompiled package to get inference results.
```shell ```shell
wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0/mmdeploy-1.0.0-linux-x86_64-cuda11.3.tar.gz wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.1.0/mmdeploy-1.1.0-linux-x86_64-cuda11.3.tar.gz
tar xf mmdeploy-1.0.0-linux-x86_64-cuda11.3 tar xf mmdeploy-1.1.0-linux-x86_64-cuda11.3
cd mmdeploy-1.0.0-linux-x86_64-cuda11.3 cd mmdeploy-1.1.0-linux-x86_64-cuda11.3
# run python demo # run python demo
python example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg python example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg
# run C/C++ demo # run C/C++ demo

26
docs/zh_cn/02-how-to-run/prebuilt_package_windows.md
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@ -23,7 +23,7 @@ ______________________________________________________________________
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。。 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。。
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。 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。
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。 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。
@ -89,8 +89,8 @@ ______________________________________________________________________
5. 安装`mmdeploy`（模型转换）以及`mmdeploy_runtime`（模型推理Python API）的预编译包 5. 安装`mmdeploy`（模型转换）以及`mmdeploy_runtime`（模型推理Python API）的预编译包
```bash ```bash
pip install mmdeploy==1.0.0 pip install mmdeploy==1.1.0
pip install mmdeploy-runtime==1.0.0 pip install mmdeploy-runtime==1.1.0
``` ```
:point_right: 如果之前安装过，需要先卸载后再安装。 :point_right: 如果之前安装过，需要先卸载后再安装。
@ -108,7 +108,7 @@ ______________________________________________________________________
![sys-path](https://user-images.githubusercontent.com/16019484/181463801-1d7814a8-b256-46e9-86f2-c08de0bc150b.png) ![sys-path](https://user-images.githubusercontent.com/16019484/181463801-1d7814a8-b256-46e9-86f2-c08de0bc150b.png)
: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。 :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。
8. 下载 SDK C/cpp Library mmdeploy-1.0.0-windows-amd64.zip 8. 下载 SDK C/cpp Library mmdeploy-1.1.0-windows-amd64.zip
### TensorRT ### TensorRT
@ -117,8 +117,8 @@ ______________________________________________________________________
5. 安装`mmdeploy`（模型转换）以及`mmdeploy_runtime`（模型推理Python API）的预编译包 5. 安装`mmdeploy`（模型转换）以及`mmdeploy_runtime`（模型推理Python API）的预编译包
```bash ```bash
pip install mmdeploy==1.0.0 pip install mmdeploy==1.1.0
pip install mmdeploy-runtime-gpu==1.0.0 pip install mmdeploy-runtime-gpu==1.1.0
``` ```
:point_right: 如果之前安装过，需要先卸载后再安装 :point_right: 如果之前安装过，需要先卸载后再安装
@ -137,7 +137,7 @@ ______________________________________________________________________
7. 安装pycuda `pip install pycuda` 7. 安装pycuda `pip install pycuda`
8. 下载 SDK C/cpp Library mmdeploy-1.0.0-windows-amd64-cuda11.3.zip 8. 下载 SDK C/cpp Library mmdeploy-1.1.0-windows-amd64-cuda11.3.zip
## 模型转换 ## 模型转换
@ -149,7 +149,7 @@ ______________________________________________________________________
``` ```
.. ..
|-- mmdeploy-1.0.0-windows-amd64 |-- mmdeploy-1.1.0-windows-amd64
|-- mmpretrain |-- mmpretrain
|-- mmdeploy |-- mmdeploy
`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -197,7 +197,7 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device)
``` ```
.. ..
|-- mmdeploy-1.0.0-windows-amd64-cuda11.3 |-- mmdeploy-1.1.0-windows-amd64-cuda11.3
|-- mmpretrain |-- mmpretrain
|-- mmdeploy |-- mmdeploy
`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -260,8 +260,8 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device)
``` ```
. .
|-- mmdeploy-1.0.0-windows-amd64 |-- mmdeploy-1.1.0-windows-amd64
|-- mmdeploy-1.0.0-windows-amd64-cuda11.3 |-- mmdeploy-1.1.0-windows-amd64-cuda11.3
|-- mmpretrain |-- mmpretrain
|-- mmdeploy |-- mmdeploy
|-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth |-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -340,7 +340,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet
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 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
在mmdeploy-1.0.0-windows-amd64\\example\\cpp\\build\\Release目录下： 在mmdeploy-1.1.0-windows-amd64\\example\\cpp\\build\\Release目录下：
``` ```
.\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmpretrain\demo\demo.JPEG .\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmpretrain\demo\demo.JPEG
@ -360,7 +360,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet
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 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
在mmdeploy-1.0.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release目录下： 在mmdeploy-1.1.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release目录下：
``` ```
.\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmpretrain\demo\demo.JPEG .\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmpretrain\demo\demo.JPEG

12
docs/zh_cn/get_started.md
View File

@ -113,14 +113,14 @@ mim install "mmcv>=2.0.0rc2"
```shell ```shell
# 1. 安装 MMDeploy 模型转换工具（含trt/ort自定义算子） # 1. 安装 MMDeploy 模型转换工具（含trt/ort自定义算子）
pip install mmdeploy==1.0.0 pip install mmdeploy==1.1.0
# 2. 安装 MMDeploy SDK推理工具 # 2. 安装 MMDeploy SDK推理工具
# 根据是否需要GPU推理可任选其一进行下载安装 # 根据是否需要GPU推理可任选其一进行下载安装
# 2.1 支持 onnxruntime 推理 # 2.1 支持 onnxruntime 推理
pip install mmdeploy-runtime==1.0.0 pip install mmdeploy-runtime==1.1.0
# 2.2 支持 onnxruntime-gpu tensorrt 推理 # 2.2 支持 onnxruntime-gpu tensorrt 推理
pip install mmdeploy-runtime-gpu==1.0.0 pip install mmdeploy-runtime-gpu==1.1.0
# 3. 安装推理引擎 # 3. 安装推理引擎
# 3.1 安装推理引擎 TensorRT # 3.1 安装推理引擎 TensorRT
@ -223,10 +223,10 @@ result = inference_model(
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。 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。
```shell ```shell
wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0/mmdeploy-1.0.0-linux-x86_64-cuda11.3.tar.gz wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.1.0/mmdeploy-1.1.0-linux-x86_64-cuda11.3.tar.gz
tar xf mmdeploy-1.0.0-linux-x86_64-cuda11.3 tar xf mmdeploy-1.1.0-linux-x86_64-cuda11.3
cd mmdeploy-1.0.0-linux-x86_64-cuda11.3 cd mmdeploy-1.1.0-linux-x86_64-cuda11.3
# 运行 python demo # 运行 python demo
python example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg python example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg
# 运行 C/C++ demo # 运行 C/C++ demo

2
mmdeploy/version.py
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@ -1,7 +1,7 @@
# Copyright (c) OpenMMLab. All rights reserved. # Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple from typing import Tuple
__version__ = '1.0.0' __version__ = '1.1.0'
short_version = __version__ short_version = __version__

2
tools/package_tools/packaging/mmdeploy_runtime/version.py
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# Copyright (c) OpenMMLab. All rights reserved. # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '1.0.0' __version__ = '1.1.0'