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bump version to v0.12.0 (#1594)

pull/1598/head v0.12.0
lvhan028 2022-12-30 16:17:37 +08:00 committed by GitHub
parent 20b2aff660
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14 changed files with 70 additions and 70 deletions
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2
CMakeLists.txt
View File

@ -5,7 +5,7 @@ endif ()
message(STATUS "CMAKE_INSTALL_PREFIX: ${CMAKE_INSTALL_PREFIX}")
cmake_minimum_required(VERSION 3.14)
project(MMDeploy VERSION 0.11.0)
project(MMDeploy VERSION 0.12.0)
set(CMAKE_CXX_STANDARD 17)

2
demo/csharp/image_classification/image_classification.csproj
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@ -14,7 +14,7 @@
</PropertyGroup>
<ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="0.11.0" />
<PackageReference Include="MMDeployCSharp" Version="0.12.0" />
<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" />

2
demo/csharp/image_restorer/image_restorer.csproj
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@ -14,7 +14,7 @@
</PropertyGroup>
<ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="0.11.0" />
<PackageReference Include="MMDeployCSharp" Version="0.12.0" />
<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
</ItemGroup>

2
demo/csharp/image_segmentation/image_segmentation.csproj
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@ -14,7 +14,7 @@
</PropertyGroup>
<ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="0.11.0" />
<PackageReference Include="MMDeployCSharp" Version="0.12.0" />
<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
</ItemGroup>

2
demo/csharp/object_detection/object_detection.csproj
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@ -14,7 +14,7 @@
</PropertyGroup>
<ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="0.11.0" />
<PackageReference Include="MMDeployCSharp" Version="0.12.0" />
<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
</ItemGroup>

2
demo/csharp/ocr_detection/ocr_detection.csproj
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@ -14,7 +14,7 @@
</PropertyGroup>
<ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="0.11.0" />
<PackageReference Include="MMDeployCSharp" Version="0.12.0" />
<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
</ItemGroup>

2
demo/csharp/ocr_recognition/ocr_recognition.csproj
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@ -14,7 +14,7 @@
</PropertyGroup>
<ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="0.11.0" />
<PackageReference Include="MMDeployCSharp" Version="0.12.0" />
<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
</ItemGroup>

2
demo/csharp/pose_detection/pose_detection.csproj
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@ -14,7 +14,7 @@
</PropertyGroup>
<ItemGroup>
<PackageReference Include="MMDeployCSharp" Version="0.11.0" />
<PackageReference Include="MMDeployCSharp" Version="0.12.0" />
<PackageReference Include="OpenCvSharp4" Version="4.5.5.20211231" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.5.5.20211231" />
</ItemGroup>

38
docs/en/02-how-to-run/prebuilt_package_windows.md
View File

@ -21,7 +21,7 @@
______________________________________________________________________
This tutorial takes `mmdeploy-0.11.0-windows-amd64-onnxruntime1.8.1.zip` and `mmdeploy-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip` as examples to show how to use the prebuilt packages.
This tutorial takes `mmdeploy-0.12.0-windows-amd64-onnxruntime1.8.1.zip` and `mmdeploy-0.12.0-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-0.11.0-windows-amd64-onnxruntime1.8.1.zip
pip install .\mmdeploy-0.11.0-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-0.11.0-py38-none-win_amd64.whl
pip install .\mmdeploy-0.11.0-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-0.11.0-cp38-none-win_amd64.whl
# download mmdeploy-0.12.0-windows-amd64-onnxruntime1.8.1.zip
pip install .\mmdeploy-0.12.0-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-0.12.0-py38-none-win_amd64.whl
pip install .\mmdeploy-0.12.0-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-0.12.0-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-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip
pip install .\mmdeploy-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-0.11.0-py38-none-win_amd64.whl
pip install .\mmdeploy-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-0.11.0-cp38-none-win_amd64.whl
# download mmdeploy-0.12.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip
pip install .\mmdeploy-0.12.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-0.12.0-py38-none-win_amd64.whl
pip install .\mmdeploy-0.12.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-0.12.0-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-0.11.0-windows-amd64-onnxruntime1.8.1
|-- mmdeploy-0.12.0-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-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
|-- mmdeploy-0.12.0-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-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
|-- mmdeploy-0.11.0-windows-amd64-onnxruntime1.8.1
|-- mmdeploy-0.12.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
|-- mmdeploy-0.12.0-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-0.11.0-windows-amd64-onnxruntime1.8.1\sdk\example` directory
Under `mmdeploy-0.12.0-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-0.11.0-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy `
-DMMDeploy_DIR=C:\workspace\mmdeploy-0.12.0-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-0.11.0-windows-amd64-onnxruntime1.8.1\sdk\bin`) to the `PATH`.
If choose to add environment variables, add the runtime libraries path of `mmdeploy` (`mmdeploy-0.12.0-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-0.11.0-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release` directory：
Under `mmdeploy-0.12.0-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-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example` directory
Under `mmdeploy-0.12.0-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-0.11.0-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy `
-DMMDeploy_DIR=C:\workspace\mmdeploy-0.12.0-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-0.11.0-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-0.12.0-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-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release` directory
Under `mmdeploy-0.12.0-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

22
docs/en/get_started.md
View File

@ -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/v0.11.0/mmdeploy-0.11.0-linux-x86_64-onnxruntime1.8.1.tar.gz
tar -zxvf mmdeploy-0.11.0-linux-x86_64-onnxruntime1.8.1.tar.gz
cd mmdeploy-0.11.0-linux-x86_64-onnxruntime1.8.1
pip install dist/mmdeploy-0.11.0-py3-none-linux_x86_64.whl
pip install sdk/python/mmdeploy_python-0.11.0-cp38-none-linux_x86_64.whl
wget https://github.com/open-mmlab/mmdeploy/releases/download/v0.12.0/mmdeploy-0.12.0-linux-x86_64-onnxruntime1.8.1.tar.gz
tar -zxvf mmdeploy-0.12.0-linux-x86_64-onnxruntime1.8.1.tar.gz
cd mmdeploy-0.12.0-linux-x86_64-onnxruntime1.8.1
pip install dist/mmdeploy-0.12.0-py3-none-linux_x86_64.whl
pip install sdk/python/mmdeploy_python-0.12.0-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/v0.11.0/mmdeploy-0.11.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
tar -zxvf mmdeploy-0.11.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
cd mmdeploy-0.11.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
pip install dist/mmdeploy-0.11.0-py3-none-linux_x86_64.whl
pip install sdk/python/mmdeploy_python-0.11.0-cp38-none-linux_x86_64.whl
wget https://github.com/open-mmlab/mmdeploy/releases/download/v0.12.0/mmdeploy-0.12.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
tar -zxvf mmdeploy-0.12.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
cd mmdeploy-0.12.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
pip install dist/mmdeploy-0.12.0-py3-none-linux_x86_64.whl
pip install sdk/python/mmdeploy_python-0.12.0-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-0.11.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
cd mmdeploy-0.12.0-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

38
docs/zh_cn/02-how-to-run/prebuilt_package_windows.md
View File

@ -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-0.11.0-windows-amd64-onnxruntime1.8.1.zip`和`mmdeploy-0.11.0-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-0.12.0-windows-amd64-onnxruntime1.8.1.zip`和`mmdeploy-0.12.0-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。
@ -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-0.11.0-windows-amd64-onnxruntime1.8.1.zip
pip install .\mmdeploy-0.11.0-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-0.11.0-py38-none-win_amd64.whl
pip install .\mmdeploy-0.11.0-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-0.11.0-cp38-none-win_amd64.whl
# 先下载 mmdeploy-0.12.0-windows-amd64-onnxruntime1.8.1.zip
pip install .\mmdeploy-0.12.0-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-0.12.0-py38-none-win_amd64.whl
pip install .\mmdeploy-0.12.0-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-0.12.0-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-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip
pip install .\mmdeploy-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-0.11.0-py38-none-win_amd64.whl
pip install .\mmdeploy-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-0.11.0-cp38-none-win_amd64.whl
# 先下载 mmdeploy-0.12.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip
pip install .\mmdeploy-0.12.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-0.12.0-py38-none-win_amd64.whl
pip install .\mmdeploy-0.12.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-0.12.0-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-0.11.0-windows-amd64-onnxruntime1.8.1
|-- mmdeploy-0.12.0-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-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
|-- mmdeploy-0.12.0-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-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
|-- mmdeploy-0.11.0-windows-amd64-onnxruntime1.8.1
|-- mmdeploy-0.12.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
|-- mmdeploy-0.12.0-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-0.11.0-windows-amd64-onnxruntime1.8.1\sdk\example`目录下
在`mmdeploy-0.12.0-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-0.11.0-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy `
-DMMDeploy_DIR=C:\workspace\mmdeploy-0.12.0-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-0.11.0-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-0.12.0-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-0.11.0-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release目录下：
在mmdeploy-0.12.0-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-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example目录下
在mmdeploy-0.12.0-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-0.11.0-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy `
-DMMDeploy_DIR=C:\workspace\mmdeploy-0.12.0-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-0.11.0-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-0.12.0-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-0.11.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release目录下：
在mmdeploy-0.12.0-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

22
docs/zh_cn/get_started.md
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@ -113,11 +113,11 @@ mim install mmcv-full
```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/v0.11.0/mmdeploy-0.11.0-linux-x86_64-onnxruntime1.8.1.tar.gz
tar -zxvf mmdeploy-0.11.0-linux-x86_64-onnxruntime1.8.1.tar.gz
cd mmdeploy-0.11.0-linux-x86_64-onnxruntime1.8.1
pip install dist/mmdeploy-0.11.0-py3-none-linux_x86_64.whl
pip install sdk/python/mmdeploy_python-0.11.0-cp38-none-linux_x86_64.whl
wget https://github.com/open-mmlab/mmdeploy/releases/download/v0.12.0/mmdeploy-0.12.0-linux-x86_64-onnxruntime1.8.1.tar.gz
tar -zxvf mmdeploy-0.12.0-linux-x86_64-onnxruntime1.8.1.tar.gz
cd mmdeploy-0.12.0-linux-x86_64-onnxruntime1.8.1
pip install dist/mmdeploy-0.12.0-py3-none-linux_x86_64.whl
pip install sdk/python/mmdeploy_python-0.12.0-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/v0.11.0/mmdeploy-0.11.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
tar -zxvf mmdeploy-0.11.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
cd mmdeploy-0.11.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
pip install dist/mmdeploy-0.11.0-py3-none-linux_x86_64.whl
pip install sdk/python/mmdeploy_python-0.11.0-cp38-none-linux_x86_64.whl
wget https://github.com/open-mmlab/mmdeploy/releases/download/v0.12.0/mmdeploy-0.12.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
tar -zxvf mmdeploy-0.12.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
cd mmdeploy-0.12.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
pip install dist/mmdeploy-0.12.0-py3-none-linux_x86_64.whl
pip install sdk/python/mmdeploy_python-0.12.0-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-0.11.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
cd mmdeploy-0.12.0-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

2
mmdeploy/version.py
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@ -1,7 +1,7 @@
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
__version__ = '0.11.0'
__version__ = '0.12.0'
short_version = __version__

2
tools/package_tools/packaging/mmdeploy_python/version.py
View File

@ -1,2 +1,2 @@
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.11.0'
__version__ = '0.12.0'