bump version to v1.0.0rc0 (#1469)

* bump version to v1.0.0rc0 * fix typo * resolve comment * unchange cmakelists
2022-11-30 19:10:37 +08:00 · 2022-11-30 19:10:37 +08:00 · ab421f82d2
parent e52d7c42ca
commit ab421f82d2
13 changed files with 75 additions and 75 deletions
--- a/docs/en/01-how-to-build/android.md
+++ b/docs/en/01-how-to-build/android.md
@ -97,7 +97,7 @@ make -j$(nproc) install
  <tr>
  <td>OpenJDK </td>
  <td>It is necessary for building Java API.</br>
-  See <a href='https://github.com/open-mmlab/mmdeploy/blob/1.x/csrc/mmdeploy/apis/java/README.md'> Java API build </a> for building tutorials.
+  See <a href='https://github.com/open-mmlab/mmdeploy/blob/dev-1.x/csrc/mmdeploy/apis/java/README.md'> Java API build </a> for building tutorials.
  </td>
  </tr>
 </tbody>
--- a/docs/en/01-how-to-build/build_from_source.md
+++ b/docs/en/01-how-to-build/build_from_source.md
@ -26,7 +26,7 @@ Note:
 - If it fails when `git clone` via `SSH`, you can try the `HTTPS` protocol like this:

  ```shell
-  git clone -b 1.x https://github.com/open-mmlab/mmdeploy.git --recursive
+  git clone -b dev-1.x https://github.com/open-mmlab/mmdeploy.git --recursive
  ```

 ## Build
--- a/docs/en/01-how-to-build/jetsons.md
+++ b/docs/en/01-how-to-build/jetsons.md
@ -223,7 +223,7 @@ It takes about 15 minutes to install ppl.cv on a Jetson Nano. So, please be pati
 ## Install MMDeploy

 ```shell
-git clone -b 1.x --recursive https://github.com/open-mmlab/mmdeploy.git
+git clone -b dev-1.x --recursive https://github.com/open-mmlab/mmdeploy.git
 cd mmdeploy
 export MMDEPLOY_DIR=$(pwd)
 ```
--- 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-0.10.0-windows-amd64-onnxruntime1.8.1.zip` and `mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip` as examples to show how to use the prebuilt packages.
+This tutorial takes `mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1.zip` and `mmdeploy-1.0.0rc0-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.

@ -47,7 +47,7 @@ In order to use the prebuilt package, you need to install some third-party depen
 2. Clone the mmdeploy repository

   ```bash
-   git clone -b 1.x https://github.com/open-mmlab/mmdeploy.git
+   git clone -b dev-1.x https://github.com/open-mmlab/mmdeploy.git
   ```

   :point_right: The main purpose here is to use the configs, so there is no need to compile `mmdeploy`.
@ -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-0.10.0-windows-amd64-onnxruntime1.8.1.zip
-   pip install .\mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-0.10.0-py38-none-win_amd64.whl
-   pip install .\mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-0.10.0-cp38-none-win_amd64.whl
+   # download mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1.zip
+   pip install .\mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-1.0.0rc0-py38-none-win_amd64.whl
+   pip install .\mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-1.0.0rc0-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-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip
-   pip install .\mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-0.10.0-py38-none-win_amd64.whl
-   pip install .\mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-0.10.0-cp38-none-win_amd64.whl
+   # download mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip
+   pip install .\mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-1.0.0rc0-py38-none-win_amd64.whl
+   pip install .\mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-1.0.0rc0-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-0.10.0-windows-amd64-onnxruntime1.8.1
+|-- mmdeploy-1.0.0rc0-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-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
+|-- mmdeploy-1.0.0rc0-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-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
-|-- mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1
+|-- mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0
+|-- mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1
 |-- mmclassification
 |-- mmdeploy
 |-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -311,7 +311,7 @@ The following describes how to use the SDK's C API for inference

 1. Build examples

-   Under `mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1\sdk\example` directory
+   Under `mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\sdk\example` directory

   ```
   // Path should be modified according to the actual location
@ -319,7 +319,7 @@ The following describes how to use the SDK's C API for inference
   cd build
   cmake ..\cpp -A x64 -T v142 `
     -DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib `
-     -DMMDeploy_DIR=C:\workspace\mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy `
+     -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc0-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
@ -329,7 +329,7 @@ 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-0.10.0-windows-amd64-onnxruntime1.8.1\sdk\bin`) to the `PATH`.
+   If choose to add environment variables, add the runtime libraries path of `mmdeploy` (`mmdeploy-1.0.0rc0-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).

@ -337,7 +337,7 @@ The following describes how to use the SDK's C API for inference

   It is recommended to use `CMD` here.

-   Under `mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release` directory：
+   Under `mmdeploy-1.0.0rc0-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
@ -347,7 +347,7 @@ The following describes how to use the SDK's C API for inference

 1. Build examples

-   Under `mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example` directory
+   Under `mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example` directory

   ```
   // Path should be modified according to the actual location
@ -355,7 +355,7 @@ The following describes how to use the SDK's C API for inference
   cd build
   cmake ..\cpp -A x64 -T v142 `
     -DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib `
-     -DMMDeploy_DIR=C:\workspace\mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy `
+     -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc0-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
@ -365,7 +365,7 @@ 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-0.10.0-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-1.0.0rc0-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).

@ -373,7 +373,7 @@ The following describes how to use the SDK's C API for inference

   It is recommended to use `CMD` here.

-   Under `mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release` directory
+   Under `mmdeploy-1.0.0rc0-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
--- a/docs/en/04-supported-codebases/mmdet.md
+++ b/docs/en/04-supported-codebases/mmdet.md
@ -27,7 +27,7 @@ There are several methods to install mmdeploy, among which you can choose an app

 **Method I:** Install precompiled package

-> **TODO**. MMDeploy hasn't released based on 1.x branch.
+> **TODO**. MMDeploy hasn't released based on dev-1.x branch.

 **Method II:** Build using scripts

@ -35,7 +35,7 @@ If your target platform is **Ubuntu 18.04 or later version**, we encourage you t
 [scripts](../01-how-to-build/build_from_script.md). For example, the following commands install mmdeploy as well as inference engine - `ONNX Runtime`.

 ```shell
-git clone --recursive -b 1.x https://github.com/open-mmlab/mmdeploy.git
+git clone --recursive -b dev-1.x https://github.com/open-mmlab/mmdeploy.git
 cd mmdeploy
 python3 tools/scripts/build_ubuntu_x64_ort.py $(nproc)
 export PYTHONPATH=$(pwd)/build/lib:$PYTHONPATH
@ -48,7 +48,7 @@ If neither **I** nor **II** meets your requirements, [building mmdeploy from sou

 ## Convert model

-You can use [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/blob/1.x/tools/deploy.py) to convert mmdet models to the specified backend models. Its detailed usage can be learned from [here](../02-how-to-run/convert_model.md).
+You can use [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/blob/dev-1.x/tools/deploy.py) to convert mmdet models to the specified backend models. Its detailed usage can be learned from [here](../02-how-to-run/convert_model.md).

 The command below shows an example about converting `Faster R-CNN` model to onnx model that can be inferred by ONNX Runtime.

@ -68,7 +68,7 @@ python tools/deploy.py \
    --dump-info
 ```

-It is crucial to specify the correct deployment config during model conversion. We've already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/1.x/configs/mmdet) of all supported backends for mmdetection, under which the config file path follows the pattern:
+It is crucial to specify the correct deployment config during model conversion. We've already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/dev-1.x/configs/mmdet) of all supported backends for mmdetection, under which the config file path follows the pattern:

 ```
 {task}/{task}_{backend}-{precision}_{static | dynamic}_{shape}.py
@ -90,7 +90,7 @@ It is crucial to specify the correct deployment config during model conversion.

 - **{shape}:** input shape or shape range of a model

-Therefore, in the above example, you can also convert `faster r-cnn` to other backend models by changing the deployment config file `detection_onnxruntime_dynamic.py` to [others](https://github.com/open-mmlab/mmdeploy/tree/1.x/configs/mmdet/detection), e.g., converting to tensorrt-fp16 model by `detection_tensorrt-fp16_dynamic-320x320-1344x1344.py`.
+Therefore, in the above example, you can also convert `faster r-cnn` to other backend models by changing the deployment config file `detection_onnxruntime_dynamic.py` to [others](https://github.com/open-mmlab/mmdeploy/tree/dev-1.x/configs/mmdet/detection), e.g., converting to tensorrt-fp16 model by `detection_tensorrt-fp16_dynamic-320x320-1344x1344.py`.

 ```{tip}
 When converting mmdet models to tensorrt models, --device should be set to "cuda"
--- a/docs/en/get_started.md
+++ b/docs/en/get_started.md
@ -118,11 +118,11 @@ 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/v0.10.0/mmdeploy-0.10.0-linux-x86_64-onnxruntime1.8.1.tar.gz
-tar -zxvf mmdeploy-0.10.0-linux-x86_64-onnxruntime1.8.1.tar.gz
-cd mmdeploy-0.10.0-linux-x86_64-onnxruntime1.8.1
-pip install dist/mmdeploy-0.10.0-py3-none-linux_x86_64.whl
-pip install sdk/python/mmdeploy_python-0.10.0-cp38-none-linux_x86_64.whl
+wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc0/mmdeploy-1.0.0rc0-linux-x86_64-onnxruntime1.8.1.tar.gz
+tar -zxvf mmdeploy-1.0.0rc0-linux-x86_64-onnxruntime1.8.1.tar.gz
+cd mmdeploy-1.0.0rc0-linux-x86_64-onnxruntime1.8.1
+pip install dist/mmdeploy-1.0.0rc0-py3-none-linux_x86_64.whl
+pip install sdk/python/mmdeploy_python-1.0.0rc0-cp38-none-linux_x86_64.whl
 cd ..
 # install inference engine: ONNX Runtime
 pip install onnxruntime==1.8.1
@ -139,11 +139,11 @@ export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH

 ```shell
 # install MMDeploy
-wget https://github.com/open-mmlab/mmdeploy/releases/download/v0.10.0/mmdeploy-0.10.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
-tar -zxvf mmdeploy-0.10.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
-cd mmdeploy-0.10.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
-pip install dist/mmdeploy-0.10.0-py3-none-linux_x86_64.whl
-pip install sdk/python/mmdeploy_python-0.10.0-cp38-none-linux_x86_64.whl
+wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc0/mmdeploy-1.0.0rc0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
+tar -zxvf mmdeploy-1.0.0rc0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
+cd mmdeploy-1.0.0rc0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
+pip install dist/mmdeploy-1.0.0rc0-py3-none-linux_x86_64.whl
+pip install sdk/python/mmdeploy_python-1.0.0rc0-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
@ -232,7 +232,7 @@ result = inference_model(
 You can directly run MMDeploy demo programs in the precompiled package to get inference results.

 ```shell
-cd mmdeploy-0.10.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
+cd mmdeploy-1.0.0rc0-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
--- a/docs/zh_cn/01-how-to-build/android.md
+++ b/docs/zh_cn/01-how-to-build/android.md
@ -98,7 +98,7 @@ make -j$(nproc) install
    <tr>
  <td>OpenJDK </td>
  <td>编译Java API之前需要先准备OpenJDK开发环境</br>
-  请参考 <a href='https://github.com/open-mmlab/mmdeploy/blob/1.x/csrc/mmdeploy/apis/java/README.md'> Java API 编译 </a> 进行构建.
+  请参考 <a href='https://github.com/open-mmlab/mmdeploy/blob/dev-1.x/csrc/mmdeploy/apis/java/README.md'> Java API 编译 </a> 进行构建.
  </td>
  </tr>
 </tbody>
--- a/docs/zh_cn/01-how-to-build/build_from_source.md
+++ b/docs/zh_cn/01-how-to-build/build_from_source.md
@ -27,7 +27,7 @@ git clone -b master git@github.com:open-mmlab/mmdeploy.git --recursive
 - 如果以 `SSH` 方式 `git clone` 代码失败，您可以尝试使用 `HTTPS` 协议下载代码：

  ```bash
-  git clone -b 1.x https://github.com/open-mmlab/mmdeploy.git MMDeploy
+  git clone -b dev-1.x https://github.com/open-mmlab/mmdeploy.git MMDeploy
  cd MMDeploy
  git submodule update --init --recursive
  ```
--- a/docs/zh_cn/01-how-to-build/jetsons.md
+++ b/docs/zh_cn/01-how-to-build/jetsons.md
@ -199,7 +199,7 @@ conda activate mmdeploy
 ## 安装 MMDeploy

 ```shell
-git clone -b 1.x --recursive https://github.com/open-mmlab/mmdeploy.git
+git clone -b dev-1.x --recursive https://github.com/open-mmlab/mmdeploy.git
 cd mmdeploy
 export MMDEPLOY_DIR=$(pwd)
 ```
--- a/docs/zh_cn/02-how-to-run/prebuilt_package_windows.md
+++ b/docs/zh_cn/02-how-to-run/prebuilt_package_windows.md
@ -23,7 +23,7 @@ ______________________________________________________________________

 目前，`MMDeploy`在`Windows`平台下提供`TensorRT`以及`ONNX Runtime`两种预编译包，可以从[Releases](https://github.com/open-mmlab/mmdeploy/releases)获取。

-本篇教程以`mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1.zip`和`mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip`为例，展示预编译包的使用方法。
+本篇教程以`mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1.zip`和`mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0.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(mmclassification)为例，展示两种预编译包的使用方法。

@ -55,7 +55,7 @@ ______________________________________________________________________
 2. 克隆mmdeploy仓库

   ```bash
-   git clone -b 1.x https://github.com/open-mmlab/mmdeploy.git
+   git clone -b dev-1.x https://github.com/open-mmlab/mmdeploy.git
   ```

   :point_right: 这里主要为了使用configs文件，所以没有加`--recursive`来下载submodule，也不需要编译`mmdeploy`
@ -88,9 +88,9 @@ ______________________________________________________________________
 5. 安装`mmdeploy`（模型转换）以及`mmdeploy_python`（模型推理Python API）的预编译包

   ```bash
-   # 先下载 mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1.zip
-   pip install .\mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-0.10.0-py38-none-win_amd64.whl
-   pip install .\mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-0.10.0-cp38-none-win_amd64.whl
+   # 先下载 mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1.zip
+   pip install .\mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-1.0.0rc0-py38-none-win_amd64.whl
+   pip install .\mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-1.0.0rc0-cp38-none-win_amd64.whl
   ```

   :point_right: 如果之前安装过，需要先卸载后再安装。
@ -115,9 +115,9 @@ ______________________________________________________________________
 5. 安装`mmdeploy`（模型转换）以及`mmdeploy_python`（模型推理Python API）的预编译包

   ```bash
-   # 先下载 mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip
-   pip install .\mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-0.10.0-py38-none-win_amd64.whl
-   pip install .\mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-0.10.0-cp38-none-win_amd64.whl
+   # 先下载 mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip
+   pip install .\mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-1.0.0rc0-py38-none-win_amd64.whl
+   pip install .\mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-1.0.0rc0-cp38-none-win_amd64.whl
   ```

   :point_right: 如果之前安装过，需要先卸载后再安装
@ -146,7 +146,7 @@ ______________________________________________________________________

 ```
 ..
-|-- mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1
+|-- mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1
 |-- mmclassification
 |-- mmdeploy
 `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -194,7 +194,7 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device)

 ```
 ..
-|-- mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
+|-- mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0
 |-- mmclassification
 |-- mmdeploy
 `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -257,8 +257,8 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device)

 ```
 .
-|-- mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
-|-- mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1
+|-- mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0
+|-- mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1
 |-- mmclassification
 |-- mmdeploy
 |-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -327,7 +327,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet

 1. 编译 examples

-   在`mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1\sdk\example`目录下
+   在`mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\sdk\example`目录下

   ```
   // 部分路径根据实际位置进行修改
@ -335,7 +335,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet
   cd build
   cmake ..\cpp -A x64 -T v142 `
     -DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib `
-     -DMMDeploy_DIR=C:\workspace\mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy `
+     -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc0-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
@ -345,7 +345,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet

   :point_right: 目的是使exe运行时可以正确找到相关dll

-   若选择添加环境变量，则将`mmdeploy`的运行时库路径（`mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1\sdk\bin`）添加到PATH，可参考onnxruntime的添加过程。
+   若选择添加环境变量，则将`mmdeploy`的运行时库路径（`mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\sdk\bin`）添加到PATH，可参考onnxruntime的添加过程。

   若选择拷贝动态库，而将bin目录中的dll拷贝到刚才编译出的exe(build/Release)的同级目录下。

@ -353,7 +353,7 @@ 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

-   在mmdeploy-0.10.0-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release目录下：
+   在mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release目录下：

   ```
   .\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmclassification\demo\demo.JPEG
@ -363,7 +363,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet

 1. 编译 examples

-   在mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example目录下
+   在mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example目录下

   ```
   // 部分路径根据所在硬盘的位置进行修改
@ -371,7 +371,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet
   cd build
   cmake ..\cpp -A x64 -T v142 `
     -DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib `
-     -DMMDeploy_DIR=C:\workspace\mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy `
+     -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc0-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
@ -381,7 +381,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet

   :point_right: 目的是使exe运行时可以正确找到相关dll

-   若选择添加环境变量，则将`mmdeploy`的运行时库路径（`mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\bin`）添加到PATH，可参考onnxruntime的添加过程。
+   若选择添加环境变量，则将`mmdeploy`的运行时库路径（`mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\bin`）添加到PATH，可参考onnxruntime的添加过程。

   若选择拷贝动态库，而将bin目录中的dll拷贝到刚才编译出的exe(build/Release)的同级目录下。

@ -389,7 +389,7 @@ 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

-   在mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release目录下：
+   在mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release目录下：

   ```
   .\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmclassification\demo\demo.JPEG
--- a/docs/zh_cn/get_started.md
+++ b/docs/zh_cn/get_started.md
@ -113,11 +113,11 @@ mim install "mmcv>=2.0.0rc2"

 ```shell
 # 安装 MMDeploy ONNX Runtime 自定义算子库和推理 SDK
-wget https://github.com/open-mmlab/mmdeploy/releases/download/v0.10.0/mmdeploy-0.10.0-linux-x86_64-onnxruntime1.8.1.tar.gz
-tar -zxvf mmdeploy-0.10.0-linux-x86_64-onnxruntime1.8.1.tar.gz
-cd mmdeploy-0.10.0-linux-x86_64-onnxruntime1.8.1
-pip install dist/mmdeploy-0.10.0-py3-none-linux_x86_64.whl
-pip install sdk/python/mmdeploy_python-0.10.0-cp38-none-linux_x86_64.whl
+wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc0/mmdeploy-1.0.0rc0-linux-x86_64-onnxruntime1.8.1.tar.gz
+tar -zxvf mmdeploy-1.0.0rc0-linux-x86_64-onnxruntime1.8.1.tar.gz
+cd mmdeploy-1.0.0rc0-linux-x86_64-onnxruntime1.8.1
+pip install dist/mmdeploy-1.0.0rc0-py3-none-linux_x86_64.whl
+pip install sdk/python/mmdeploy_python-1.0.0rc0-cp38-none-linux_x86_64.whl
 cd ..
 # 安装推理引擎 ONNX Runtime
 pip install onnxruntime==1.8.1
@ -134,11 +134,11 @@ export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH

 ```shell
 # 安装 MMDeploy TensorRT 自定义算子库和推理 SDK
-wget https://github.com/open-mmlab/mmdeploy/releases/download/v0.10.0/mmdeploy-0.10.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
-tar -zxvf mmdeploy-0.10.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
-cd mmdeploy-0.10.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
-pip install dist/mmdeploy-0.10.0-py3-none-linux_x86_64.whl
-pip install sdk/python/mmdeploy_python-0.10.0-cp38-none-linux_x86_64.whl
+wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc0/mmdeploy-1.0.0rc0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
+tar -zxvf mmdeploy-1.0.0rc0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
+cd mmdeploy-1.0.0rc0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
+pip install dist/mmdeploy-1.0.0rc0-py3-none-linux_x86_64.whl
+pip install sdk/python/mmdeploy_python-1.0.0rc0-cp38-none-linux_x86_64.whl
 cd ..
 # 安装推理引擎 TensorRT
 # !!! 从 NVIDIA 官网下载 TensorRT-8.2.3.0 CUDA 11.x 安装包并解压到当前目录
@ -226,7 +226,7 @@ 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。

 ```shell
-cd mmdeploy-0.10.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
+cd mmdeploy-1.0.0rc0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
 # 运行 python demo
 python sdk/example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg
 # 运行 C/C++ demo
--- a/mmdeploy/version.py
+++ b/mmdeploy/version.py
@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from typing import Tuple

-__version__ = '0.10.0'
+__version__ = '1.0.0rc0'
 short_version = __version__


--- a/tools/package_tools/packaging/mmdeploy_python/version.py
+++ b/tools/package_tools/packaging/mmdeploy_python/version.py
@ -1,2 +1,2 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-__version__ = '0.10.0'
+__version__ = '1.0.0rc0'