From a3c056645163e269c096f01a9f47a74caa742cfa Mon Sep 17 00:00:00 2001 From: "q.yao" Date: Thu, 16 Feb 2023 14:20:33 +0800 Subject: [PATCH] bump version to 1.0.0rc2 (#1754) * bump version * update cmake --- CMakeLists.txt | 2 +- csrc/mmdeploy/apis/csharp/README.md | 4 +- .../image_classification.csproj | 2 +- .../image_restorer/image_restorer.csproj | 2 +- .../image_segmentation.csproj | 2 +- .../object_detection/object_detection.csproj | 2 +- .../csharp/ocr_detection/ocr_detection.csproj | 2 +- .../ocr_recognition/ocr_recognition.csproj | 2 +- .../pose_detection/pose_detection.csproj | 2 +- .../02-how-to-run/prebuilt_package_windows.md | 38 +++++++++---------- docs/en/get_started.md | 22 +++++------ .../02-how-to-run/prebuilt_package_windows.md | 38 +++++++++---------- docs/zh_cn/get_started.md | 22 +++++------ mmdeploy/version.py | 2 +- .../packaging/mmdeploy_python/version.py | 2 +- 15 files changed, 72 insertions(+), 72 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 89886b1d7c..83e8af1e46 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -5,7 +5,7 @@ endif () message(STATUS "CMAKE_INSTALL_PREFIX: ${CMAKE_INSTALL_PREFIX}") cmake_minimum_required(VERSION 3.14) -project(MMDeploy VERSION 0.12.0) +project(MMDeploy VERSION 0.13.0) set(CMAKE_CXX_STANDARD 17) diff --git a/csrc/mmdeploy/apis/csharp/README.md b/csrc/mmdeploy/apis/csharp/README.md index 16526cc1a9..16b345d8ff 100644 --- a/csrc/mmdeploy/apis/csharp/README.md +++ b/csrc/mmdeploy/apis/csharp/README.md @@ -33,10 +33,10 @@ There are two methods to build the nuget package. (*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-rc1.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.1.0.0-rc2.nupkg`. ```shell -dotnet build --configuration Release -p:Version=1.0.0-rc1 +dotnet build --configuration Release -p:Version=1.0.0-rc2 ``` (*option 2*) Open MMDeploy.sln && Build. diff --git a/demo/csharp/image_classification/image_classification.csproj b/demo/csharp/image_classification/image_classification.csproj index 88e059b63e..de0494c844 100644 --- a/demo/csharp/image_classification/image_classification.csproj +++ b/demo/csharp/image_classification/image_classification.csproj @@ -14,7 +14,7 @@ - + diff --git a/demo/csharp/image_restorer/image_restorer.csproj b/demo/csharp/image_restorer/image_restorer.csproj index d36f2e148c..15b887841a 100644 --- a/demo/csharp/image_restorer/image_restorer.csproj +++ b/demo/csharp/image_restorer/image_restorer.csproj @@ -14,7 +14,7 @@ - + diff --git a/demo/csharp/image_segmentation/image_segmentation.csproj b/demo/csharp/image_segmentation/image_segmentation.csproj index d36f2e148c..15b887841a 100644 --- a/demo/csharp/image_segmentation/image_segmentation.csproj +++ b/demo/csharp/image_segmentation/image_segmentation.csproj @@ -14,7 +14,7 @@ - + diff --git a/demo/csharp/object_detection/object_detection.csproj b/demo/csharp/object_detection/object_detection.csproj index 3d33080ffd..259e635c94 100644 --- a/demo/csharp/object_detection/object_detection.csproj +++ b/demo/csharp/object_detection/object_detection.csproj @@ -14,7 +14,7 @@ - + diff --git a/demo/csharp/ocr_detection/ocr_detection.csproj b/demo/csharp/ocr_detection/ocr_detection.csproj index d36f2e148c..15b887841a 100644 --- a/demo/csharp/ocr_detection/ocr_detection.csproj +++ b/demo/csharp/ocr_detection/ocr_detection.csproj @@ -14,7 +14,7 @@ - + diff --git a/demo/csharp/ocr_recognition/ocr_recognition.csproj b/demo/csharp/ocr_recognition/ocr_recognition.csproj index d36f2e148c..15b887841a 100644 --- a/demo/csharp/ocr_recognition/ocr_recognition.csproj +++ b/demo/csharp/ocr_recognition/ocr_recognition.csproj @@ -14,7 +14,7 @@ - + diff --git a/demo/csharp/pose_detection/pose_detection.csproj b/demo/csharp/pose_detection/pose_detection.csproj index d36f2e148c..15b887841a 100644 --- a/demo/csharp/pose_detection/pose_detection.csproj +++ b/demo/csharp/pose_detection/pose_detection.csproj @@ -14,7 +14,7 @@ - + diff --git a/docs/en/02-how-to-run/prebuilt_package_windows.md b/docs/en/02-how-to-run/prebuilt_package_windows.md index 4f25a1aab4..06f60ec62d 100644 --- 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-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. +This tutorial takes `mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1.zip` and `mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1.zip - pip install .\mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-1.0.0rc1-py38-none-win_amd64.whl - pip install .\mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-1.0.0rc1-cp38-none-win_amd64.whl + # download mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1.zip + pip install .\mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-1.0.0rc2-py38-none-win_amd64.whl + pip install .\mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-1.0.0rc2-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip - 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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-1.0.0rc1-cp38-none-win_amd64.whl + # download mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip + pip install .\mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-1.0.0rc2-py38-none-win_amd64.whl + pip install .\mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-1.0.0rc2-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1 +|-- mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0 +|-- mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0 -|-- mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1 +|-- mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0 +|-- mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\example` directory + Under `mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy ` + -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\bin`) to the `PATH`. + If choose to add environment variables, add the runtime libraries path of `mmdeploy` (`mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release` directory： + Under `mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example` directory + Under `mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy ` + -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc2-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-1.0.0rc1-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-1.0.0rc2-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release` directory + Under `mmdeploy-1.0.0rc2-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 diff --git a/docs/en/get_started.md b/docs/en/get_started.md index 3754b5fff9..9aa90fe742 100644 --- a/docs/en/get_started.md +++ b/docs/en/get_started.md @@ -119,11 +119,11 @@ 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/v1.0.0rc1/mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1.tar.gz -tar -zxvf mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1.tar.gz -cd mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1 -pip install dist/mmdeploy-1.0.0rc1-py3-none-linux_x86_64.whl -pip install sdk/python/mmdeploy_python-1.0.0rc1-cp38-none-linux_x86_64.whl +wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc2/mmdeploy-1.0.0rc2-linux-x86_64-onnxruntime1.8.1.tar.gz +tar -zxvf mmdeploy-1.0.0rc2-linux-x86_64-onnxruntime1.8.1.tar.gz +cd mmdeploy-1.0.0rc2-linux-x86_64-onnxruntime1.8.1 +pip install dist/mmdeploy-1.0.0rc2-py3-none-linux_x86_64.whl +pip install sdk/python/mmdeploy_python-1.0.0rc2-cp38-none-linux_x86_64.whl cd .. # install inference engine: ONNX Runtime pip install onnxruntime==1.8.1 @@ -140,11 +140,11 @@ export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH ```shell # install MMDeploy -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-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz -cd mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0 -pip install dist/mmdeploy-1.0.0rc1-py3-none-linux_x86_64.whl -pip install sdk/python/mmdeploy_python-1.0.0rc1-cp38-none-linux_x86_64.whl +wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc2/mmdeploy-1.0.0rc2-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz +tar -zxvf mmdeploy-1.0.0rc2-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz +cd mmdeploy-1.0.0rc2-linux-x86_64-cuda11.1-tensorrt8.2.3.0 +pip install dist/mmdeploy-1.0.0rc2-py3-none-linux_x86_64.whl +pip install sdk/python/mmdeploy_python-1.0.0rc2-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 @@ -233,7 +233,7 @@ result = inference_model( You can directly run MMDeploy demo programs in the precompiled package to get inference results. ```shell -cd mmdeploy-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0 +cd mmdeploy-1.0.0rc2-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 diff --git a/docs/zh_cn/02-how-to-run/prebuilt_package_windows.md b/docs/zh_cn/02-how-to-run/prebuilt_package_windows.md index 00eaabc21c..a4d2c8a5eb 100644 --- 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-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。 +本篇教程以`mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1.zip`和`mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1.zip - pip install .\mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-1.0.0rc1-py38-none-win_amd64.whl - pip install .\mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-1.0.0rc1-cp38-none-win_amd64.whl + # 先下载 mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1.zip + pip install .\mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-1.0.0rc2-py38-none-win_amd64.whl + pip install .\mmdeploy-1.0.0rc2-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-1.0.0rc2-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip - 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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-1.0.0rc1-cp38-none-win_amd64.whl + # 先下载 mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip + pip install .\mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-1.0.0rc2-py38-none-win_amd64.whl + pip install .\mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-1.0.0rc2-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1 +|-- mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0 +|-- mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0 -|-- mmdeploy-1.0.0rc1-windows-amd64-onnxruntime1.8.1 +|-- mmdeploy-1.0.0rc2-windows-amd64-cuda11.1-tensorrt8.2.3.0 +|-- mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\example`目录下 + 在`mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy ` + -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1\sdk\bin`）添加到PATH，可参考onnxruntime的添加过程。 + 若选择添加环境变量，则将`mmdeploy`的运行时库路径（`mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release目录下： + 在mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example目录下 + 在mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy ` + -DMMDeploy_DIR=C:\workspace\mmdeploy-1.0.0rc2-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-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。 + 若选择添加环境变量，则将`mmdeploy`的运行时库路径（`mmdeploy-1.0.0rc2-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-1.0.0rc1-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release目录下： + 在mmdeploy-1.0.0rc2-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 diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md index 0368dc1707..676dab7f47 100644 --- 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/v1.0.0rc1/mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1.tar.gz -tar -zxvf mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1.tar.gz -cd mmdeploy-1.0.0rc1-linux-x86_64-onnxruntime1.8.1 -pip install dist/mmdeploy-1.0.0rc1-py3-none-linux_x86_64.whl -pip install sdk/python/mmdeploy_python-1.0.0rc1-cp38-none-linux_x86_64.whl +wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc2/mmdeploy-1.0.0rc2-linux-x86_64-onnxruntime1.8.1.tar.gz +tar -zxvf mmdeploy-1.0.0rc2-linux-x86_64-onnxruntime1.8.1.tar.gz +cd mmdeploy-1.0.0rc2-linux-x86_64-onnxruntime1.8.1 +pip install dist/mmdeploy-1.0.0rc2-py3-none-linux_x86_64.whl +pip install sdk/python/mmdeploy_python-1.0.0rc2-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/v1.0.0rc1/mmdeploy-1.0.0rc1-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-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0 -pip install dist/mmdeploy-1.0.0rc1-py3-none-linux_x86_64.whl -pip install sdk/python/mmdeploy_python-1.0.0rc1-cp38-none-linux_x86_64.whl +wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0rc2/mmdeploy-1.0.0rc2-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz +tar -zxvf mmdeploy-1.0.0rc2-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz +cd mmdeploy-1.0.0rc2-linux-x86_64-cuda11.1-tensorrt8.2.3.0 +pip install dist/mmdeploy-1.0.0rc2-py3-none-linux_x86_64.whl +pip install sdk/python/mmdeploy_python-1.0.0rc2-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-1.0.0rc1-linux-x86_64-cuda11.1-tensorrt8.2.3.0 +cd mmdeploy-1.0.0rc2-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 diff --git a/mmdeploy/version.py b/mmdeploy/version.py index aaffb6f9cc..4e81e5295f 100644 --- a/mmdeploy/version.py +++ b/mmdeploy/version.py @@ -1,7 +1,7 @@ # Copyright (c) OpenMMLab. All rights reserved. from typing import Tuple -__version__ = '1.0.0rc1' +__version__ = '1.0.0rc2' short_version = __version__ diff --git a/tools/package_tools/packaging/mmdeploy_python/version.py b/tools/package_tools/packaging/mmdeploy_python/version.py index 5370902cd6..836d9b0907 100644 --- 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__ = '1.0.0rc1' +__version__ = '1.0.0rc2'