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bump version to v1.0.0rc1 (#1595)

pull/1597/head v1.0.0rc1
lvhan028 2022-12-30 18:25:24 +08:00 committed by GitHub
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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 0.10.0) project(MMDeploy VERSION 0.12.0)
set(CMAKE_CXX_STANDARD 17) set(CMAKE_CXX_STANDARD 17)

38
docs/en/02-how-to-run/prebuilt_package_windows.md
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@ -21,7 +21,7 @@
______________________________________________________________________ ______________________________________________________________________
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. This tutorial takes `mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1.zip` and `mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip` as examples to show how to use the prebuilt packages.
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.
@ -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). 5. Install `mmdeploy` (Model Converter) and `mmdeploy_python` (SDK Python API).
```bash ```bash
# download mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1.zip # download mmdeploy-1.0.0rc1-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-1.0.0rc1-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 pip install .\mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-1.0.0rc1-cp38-none-win_amd64.whl
``` ```
:point_right: If you have installed it before, please uninstall it first. :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). 5. Install `mmdeploy` (Model Converter) and `mmdeploy_python` (SDK Python API).
```bash ```bash
# download mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip # download mmdeploy-1.0.0rc1-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-1.0.0rc1-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 pip install .\mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-1.0.0rc1-cp38-none-win_amd64.whl
``` ```
:point_right: If you have installed it before, please uninstall it first. :point_right: If you have installed it before, please uninstall it first.
@ -138,7 +138,7 @@ After preparation work, the structure of the current working directory should be
``` ```
.. ..
|-- mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1 |-- mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1
|-- mmclassification |-- mmclassification
|-- mmdeploy |-- mmdeploy
`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -186,7 +186,7 @@ After installation of mmdeploy-tensorrt prebuilt package, the structure of the c
``` ```
.. ..
|-- mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0 |-- mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0
|-- mmclassification |-- mmclassification
|-- mmdeploy |-- mmdeploy
`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -249,8 +249,8 @@ The structure of current working directory：
``` ```
. .
|-- mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0 |-- mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0
|-- mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1 |-- mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1
|-- mmclassification |-- mmclassification
|-- mmdeploy |-- mmdeploy
|-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth |-- 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 1. Build examples
Under `mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\sdk\example` directory Under `mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\example` directory
``` ```
// Path should be modified according to the actual location // 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 cd build
cmake ..\cpp -A x64 -T v142 ` cmake ..\cpp -A x64 -T v142 `
-DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib ` -DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib `
-DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy ` -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy `
-DONNXRUNTIME_DIR=C:\Deps\onnxruntime\onnxruntime-win-gpu-x64-1.8.1 -DONNXRUNTIME_DIR=C:\Deps\onnxruntime\onnxruntime-win-gpu-x64-1.8.1
cmake --build . --config Release 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 :point_right: The purpose is to make the exe find the relevant dll
If choose to add environment variables, add the runtime libraries path of `mmdeploy` (`mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\sdk\bin`) to the `PATH`. If choose to add environment variables, add the runtime libraries path of `mmdeploy` (`mmdeploy-1.0.0rc1-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). 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. It is recommended to use `CMD` here.
Under `mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release` directory： Under `mmdeploy-1.0.0rc1-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 .\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 1. Build examples
Under `mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example` directory Under `mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example` directory
``` ```
// Path should be modified according to the actual location // 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 cd build
cmake ..\cpp -A x64 -T v142 ` cmake ..\cpp -A x64 -T v142 `
-DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib ` -DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib `
-DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy ` -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy `
-DTENSORRT_DIR=C:\Deps\tensorrt\TensorRT-8.2.3.0 ` -DTENSORRT_DIR=C:\Deps\tensorrt\TensorRT-8.2.3.0 `
-DCUDNN_DIR=C:\Deps\cudnn\8.2.1 -DCUDNN_DIR=C:\Deps\cudnn\8.2.1
cmake --build . --config Release 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 :point_right: The purpose is to make the exe find the relevant dll
If choose to add environment variables, add the runtime libraries path of `mmdeploy` (`mmdeploy-1.0.0rc0-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-1.0.0rc1-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). 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. It is recommended to use `CMD` here.
Under `mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release` directory Under `mmdeploy-1.0.0rc1-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 .\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmclassification\demo\demo.JPEG

22
docs/en/get_started.md
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@ -118,11 +118,11 @@ Take the latest precompiled package as example, you can install it as follows:
```shell ```shell
# install MMDeploy # install MMDeploy
wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc0/mmdeploy-1.0.0rc0-linux-x86_64-onnxruntime1.8.1.tar.gz wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc1/mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1.tar.gz
tar -zxvf mmdeploy-1.0.0rc0-linux-x86_64-onnxruntime1.8.1.tar.gz tar -zxvf mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1.tar.gz
cd mmdeploy-1.0.0rc0-linux-x86_64-onnxruntime1.8.1 cd mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1
pip install dist/mmdeploy-1.0.0rc0-py3-none-linux_x86_64.whl pip install dist/mmdeploy-1.0.0rc1-py3-none-linux_x86_64.whl
pip install sdk/python/mmdeploy_python-1.0.0rc0-cp38-none-linux_x86_64.whl pip install sdk/python/mmdeploy_python-1.0.0rc1-cp38-none-linux_x86_64.whl
cd .. cd ..
# install inference engine: ONNX Runtime # install inference engine: ONNX Runtime
pip install onnxruntime==1.8.1 pip install onnxruntime==1.8.1
@ -139,11 +139,11 @@ export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH
```shell ```shell
# install MMDeploy # install MMDeploy
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 wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc1/mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
tar -zxvf mmdeploy-1.0.0rc0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz tar -zxvf mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
cd mmdeploy-1.0.0rc0-linux-x86_64-cuda11.1-tensorrt8.2.3.0 cd mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0
pip install dist/mmdeploy-1.0.0rc0-py3-none-linux_x86_64.whl pip install dist/mmdeploy-1.0.0rc1-py3-none-linux_x86_64.whl
pip install sdk/python/mmdeploy_python-1.0.0rc0-cp38-none-linux_x86_64.whl pip install sdk/python/mmdeploy_python-1.0.0rc1-cp38-none-linux_x86_64.whl
cd .. cd ..
# install inference engine: TensorRT # install inference engine: TensorRT
# !!! Download TensorRT-8.2.3.0 CUDA 11.x tar package from NVIDIA, and extract it to the current directory # !!! 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. You can directly run MMDeploy demo programs in the precompiled package to get inference results.
```shell ```shell
cd mmdeploy-1.0.0rc0-linux-x86_64-cuda11.1-tensorrt8.2.3.0 cd mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0
# run python demo # run python demo
python sdk/example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg python sdk/example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg
# run C/C++ demo # run C/C++ demo

38
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`TensorRT`以及`ONNX Runtime`两种预编译包，可以从[Releases](https://github.com/open-mmlab/mmdeploy/releases)获取。 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-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。 本篇教程以`mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1.zip`和`mmdeploy-1.0.0rc1-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。 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 5. 安装`mmdeploy`（模型转换）以及`mmdeploy_python`（模型推理Python API）的预编译包
```bash ```bash
# 先下载 mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1.zip # 先下载 mmdeploy-1.0.0rc1-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-1.0.0rc1-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 pip install .\mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-1.0.0rc1-cp38-none-win_amd64.whl
``` ```
: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。
@ -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 5. 安装`mmdeploy`（模型转换）以及`mmdeploy_python`（模型推理Python API）的预编译包
```bash ```bash
# 先下载 mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip # 先下载 mmdeploy-1.0.0rc1-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-1.0.0rc1-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 pip install .\mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-1.0.0rc1-cp38-none-win_amd64.whl
``` ```
: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
@ -146,7 +146,7 @@ ______________________________________________________________________
``` ```
.. ..
|-- mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1 |-- mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1
|-- mmclassification |-- mmclassification
|-- mmdeploy |-- mmdeploy
`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -194,7 +194,7 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device)
``` ```
.. ..
|-- mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0 |-- mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0
|-- mmclassification |-- mmclassification
|-- mmdeploy |-- mmdeploy
`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@ -257,8 +257,8 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device)
``` ```
. .
|-- mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0 |-- mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0
|-- mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1 |-- mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1
|-- mmclassification |-- mmclassification
|-- mmdeploy |-- mmdeploy
|-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth |-- 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 1. 编译 examples
在`mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\sdk\example`目录下 在`mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\example`目录下
``` ```
// 部分路径根据实际位置进行修改 // 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 cd build
cmake ..\cpp -A x64 -T v142 ` cmake ..\cpp -A x64 -T v142 `
-DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib ` -DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib `
-DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc0-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy ` -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy `
-DONNXRUNTIME_DIR=C:\Deps\onnxruntime\onnxruntime-win-gpu-x64-1.8.1 -DONNXRUNTIME_DIR=C:\Deps\onnxruntime\onnxruntime-win-gpu-x64-1.8.1
cmake --build . --config Release 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 :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-1.0.0rc0-windows-amd64-onnxruntime1.8.1\sdk\bin`）添加到PATH，可参考onnxruntime的添加过程。 若选择添加环境变量，则将`mmdeploy`的运行时库路径（`mmdeploy-1.0.0rc1-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。 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 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-1.0.0rc0-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release目录下： 在mmdeploy-1.0.0rc1-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 .\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 1. 编译 examples
在mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example目录下 在mmdeploy-1.0.0rc1-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 // 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 cd build
cmake ..\cpp -A x64 -T v142 ` cmake ..\cpp -A x64 -T v142 `
-DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib ` -DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib `
-DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy ` -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy `
-DTENSORRT_DIR=C:\Deps\tensorrt\TensorRT-8.2.3.0 ` -DTENSORRT_DIR=C:\Deps\tensorrt\TensorRT-8.2.3.0 `
-DCUDNN_DIR=C:\Deps\cudnn\8.2.1 -DCUDNN_DIR=C:\Deps\cudnn\8.2.1
cmake --build . --config Release 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 :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-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。 若选择添加环境变量，则将`mmdeploy`的运行时库路径（`mmdeploy-1.0.0rc1-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。 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 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-1.0.0rc0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release目录下： 在mmdeploy-1.0.0rc1-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 .\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmclassification\demo\demo.JPEG

22
docs/zh_cn/get_started.md
View File

@ -113,11 +113,11 @@ mim install "mmcv>=2.0.0rc2"
```shell ```shell
# 安装 MMDeploy ONNX Runtime 自定义算子库和推理 SDK # 安装 MMDeploy ONNX Runtime 自定义算子库和推理 SDK
wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc0/mmdeploy-1.0.0rc0-linux-x86_64-onnxruntime1.8.1.tar.gz wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc1/mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1.tar.gz
tar -zxvf mmdeploy-1.0.0rc0-linux-x86_64-onnxruntime1.8.1.tar.gz tar -zxvf mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1.tar.gz
cd mmdeploy-1.0.0rc0-linux-x86_64-onnxruntime1.8.1 cd mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1
pip install dist/mmdeploy-1.0.0rc0-py3-none-linux_x86_64.whl pip install dist/mmdeploy-1.0.0rc1-py3-none-linux_x86_64.whl
pip install sdk/python/mmdeploy_python-1.0.0rc0-cp38-none-linux_x86_64.whl pip install sdk/python/mmdeploy_python-1.0.0rc1-cp38-none-linux_x86_64.whl
cd .. cd ..
# 安装推理引擎 ONNX Runtime # 安装推理引擎 ONNX Runtime
pip install onnxruntime==1.8.1 pip install onnxruntime==1.8.1
@ -134,11 +134,11 @@ export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH
```shell ```shell
# 安装 MMDeploy TensorRT 自定义算子库和推理 SDK # 安装 MMDeploy TensorRT 自定义算子库和推理 SDK
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 wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc1/mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
tar -zxvf mmdeploy-1.0.0rc0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz tar -zxvf mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
cd mmdeploy-1.0.0rc0-linux-x86_64-cuda11.1-tensorrt8.2.3.0 cd mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0
pip install dist/mmdeploy-1.0.0rc0-py3-none-linux_x86_64.whl pip install dist/mmdeploy-1.0.0rc1-py3-none-linux_x86_64.whl
pip install sdk/python/mmdeploy_python-1.0.0rc0-cp38-none-linux_x86_64.whl pip install sdk/python/mmdeploy_python-1.0.0rc1-cp38-none-linux_x86_64.whl
cd .. cd ..
# 安装推理引擎 TensorRT # 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 # !!! 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。 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
cd mmdeploy-1.0.0rc0-linux-x86_64-cuda11.1-tensorrt8.2.3.0 cd mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0
# 运行 python demo # 运行 python demo
python sdk/example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg python sdk/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__ = '1.0.0rc0' __version__ = '1.0.0rc1'
short_version = __version__ short_version = __version__

2
tools/package_tools/packaging/mmdeploy_python/version.py
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@ -1,2 +1,2 @@
# Copyright (c) OpenMMLab. All rights reserved. # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '1.0.0rc0' __version__ = '1.0.0rc1'