From e51c2aa4cc92ad5cdf9bbca630c54759b58f6487 Mon Sep 17 00:00:00 2001 From: RunningLeon Date: Mon, 25 Dec 2023 14:33:22 +0800 Subject: [PATCH] bump version to v1.13.1 --- CMakeLists.txt | 2 +- csrc/mmdeploy/apis/csharp/README.md | 6 ++--- .../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 | 26 +++++++++---------- docs/en/get_started.md | 12 ++++----- .../02-how-to-run/prebuilt_package_windows.md | 26 +++++++++---------- docs/zh_cn/get_started.md | 12 ++++----- mmdeploy/version.py | 2 +- .../packaging/mmdeploy_runtime/version.py | 2 +- 15 files changed, 51 insertions(+), 51 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 7e002a258e..a241d90674 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -9,7 +9,7 @@ if (NOT CMAKE_BUILD_TYPE) endif () cmake_minimum_required(VERSION 3.14) -project(MMDeploy VERSION 1.3.0) +project(MMDeploy VERSION 1.3.1) set(CMAKE_CXX_STANDARD 17) diff --git a/csrc/mmdeploy/apis/csharp/README.md b/csrc/mmdeploy/apis/csharp/README.md index 437a35b47b..9f58b0c01d 100644 --- a/csrc/mmdeploy/apis/csharp/README.md +++ b/csrc/mmdeploy/apis/csharp/README.md @@ -33,14 +33,14 @@ 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.1.3.0.nupkg`. +If your environment is well prepared, you can just go to the `csrc\apis\csharp` folder, open a terminal and type the following command, the nupkg will be built in `csrc\apis\csharp\MMDeploy\bin\Release\MMDeployCSharp.1.3.1.nupkg`. ```shell -dotnet build --configuration Release -p:Version=1.3.0 +dotnet build --configuration Release -p:Version=1.3.1 ``` (*option 2*) Open MMDeploy.sln && Build. -You can set the package-version through `Properties -> Package Version`. The default version is 1.3.0 if you don't set it. +You can set the package-version through `Properties -> Package Version`. The default version is 1.3.1 if you don't set it. If you encounter missing dependencies, follow the instructions for MSVC. diff --git a/demo/csharp/image_classification/image_classification.csproj b/demo/csharp/image_classification/image_classification.csproj index 717f0e391a..7899942480 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 aaa84f1387..fbd4f0683f 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 aaa84f1387..fbd4f0683f 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 ee5866f071..fe64dd0960 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 aaa84f1387..fbd4f0683f 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 aaa84f1387..fbd4f0683f 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 aaa84f1387..fbd4f0683f 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 08c5d14122..ec0018a52d 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-1.3.0-windows-amd64.zip` and `mmdeploy-1.3.0-windows-amd64-cuda11.8.zip` as examples to show how to use the prebuilt packages. The former support onnxruntime cpu inference, the latter support onnxruntime-gpu and tensorrt inference. +This tutorial takes `mmdeploy-1.3.1-windows-amd64.zip` and `mmdeploy-1.3.1-windows-amd64-cuda11.8.zip` as examples to show how to use the prebuilt packages. The former support onnxruntime cpu inference, the latter support onnxruntime-gpu and tensorrt inference. The directory structure of the prebuilt package is as follows, where the `dist` folder is about model converter, and the `sdk` folder is related to model inference. @@ -81,8 +81,8 @@ In order to use `ONNX Runtime` backend, you should also do the following steps. 5. Install `mmdeploy` (Model Converter) and `mmdeploy_runtime` (SDK Python API). ```bash - pip install mmdeploy==1.3.0 - pip install mmdeploy-runtime==1.3.0 + pip install mmdeploy==1.3.1 + pip install mmdeploy-runtime==1.3.1 ``` :point_right: If you have installed it before, please uninstall it first. @@ -100,7 +100,7 @@ In order to use `ONNX Runtime` backend, you should also do the following steps. ![sys-path](https://user-images.githubusercontent.com/16019484/181463801-1d7814a8-b256-46e9-86f2-c08de0bc150b.png) :exclamation: Restart powershell to make the environment variables setting take effect. You can check whether the settings are in effect by `echo $env:PATH`. -8. Download SDK C/cpp Library mmdeploy-1.3.0-windows-amd64.zip +8. Download SDK C/cpp Library mmdeploy-1.3.1-windows-amd64.zip ### TensorRT @@ -109,8 +109,8 @@ In order to use `TensorRT` backend, you should also do the following steps. 5. Install `mmdeploy` (Model Converter) and `mmdeploy_runtime` (SDK Python API). ```bash - pip install mmdeploy==1.3.0 - pip install mmdeploy-runtime-gpu==1.3.0 + pip install mmdeploy==1.3.1 + pip install mmdeploy-runtime-gpu==1.3.1 ``` :point_right: If you have installed it before, please uninstall it first. @@ -129,7 +129,7 @@ In order to use `TensorRT` backend, you should also do the following steps. 7. Install pycuda by `pip install pycuda` -8. Download SDK C/cpp Library mmdeploy-1.3.0-windows-amd64-cuda11.8.zip +8. Download SDK C/cpp Library mmdeploy-1.3.1-windows-amd64-cuda11.8.zip ## Model Convert @@ -141,7 +141,7 @@ After preparation work, the structure of the current working directory should be ``` .. -|-- mmdeploy-1.3.0-windows-amd64 +|-- mmdeploy-1.3.1-windows-amd64 |-- mmpretrain |-- mmdeploy `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth @@ -189,7 +189,7 @@ After installation of mmdeploy-tensorrt prebuilt package, the structure of the c ``` .. -|-- mmdeploy-1.3.0-windows-amd64-cuda11.8 +|-- mmdeploy-1.3.1-windows-amd64-cuda11.8 |-- mmpretrain |-- mmdeploy `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth @@ -252,8 +252,8 @@ The structure of current working directory： ``` . -|-- mmdeploy-1.3.0-windows-amd64 -|-- mmdeploy-1.3.0-windows-amd64-cuda11.8 +|-- mmdeploy-1.3.1-windows-amd64 +|-- mmdeploy-1.3.1-windows-amd64-cuda11.8 |-- mmpretrain |-- mmdeploy |-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth @@ -324,7 +324,7 @@ The following describes how to use the SDK's C API for inference It is recommended to use `CMD` here. - Under `mmdeploy-1.3.0-windows-amd64\\example\\cpp\\build\\Release` directory： + Under `mmdeploy-1.3.1-windows-amd64\\example\\cpp\\build\\Release` directory： ``` .\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmpretrain\demo\demo.JPEG @@ -344,7 +344,7 @@ The following describes how to use the SDK's C API for inference It is recommended to use `CMD` here. - Under `mmdeploy-1.3.0-windows-amd64-cuda11.8\\example\\cpp\\build\\Release` directory + Under `mmdeploy-1.3.1-windows-amd64-cuda11.8\\example\\cpp\\build\\Release` directory ``` .\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmpretrain\demo\demo.JPEG diff --git a/docs/en/get_started.md b/docs/en/get_started.md index fd806ae45a..6027df46f8 100644 --- a/docs/en/get_started.md +++ b/docs/en/get_started.md @@ -118,14 +118,14 @@ Take the latest precompiled package as example, you can install it as follows: ```shell # 1. install MMDeploy model converter -pip install mmdeploy==1.3.0 +pip install mmdeploy==1.3.1 # 2. install MMDeploy sdk inference # you can install one to install according whether you need gpu inference # 2.1 support onnxruntime -pip install mmdeploy-runtime==1.3.0 +pip install mmdeploy-runtime==1.3.1 # 2.2 support onnxruntime-gpu, tensorrt -pip install mmdeploy-runtime-gpu==1.3.0 +pip install mmdeploy-runtime-gpu==1.3.1 # 3. install inference engine # 3.1 install TensorRT @@ -230,9 +230,9 @@ result = inference_model( You can directly run MMDeploy demo programs in the precompiled package to get inference results. ```shell -wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.3.0/mmdeploy-1.3.0-linux-x86_64-cuda11.8.tar.gz -tar xf mmdeploy-1.3.0-linux-x86_64-cuda11.8 -cd mmdeploy-1.3.0-linux-x86_64-cuda11.8 +wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.3.1/mmdeploy-1.3.1-linux-x86_64-cuda11.8.tar.gz +tar xf mmdeploy-1.3.1-linux-x86_64-cuda11.8 +cd mmdeploy-1.3.1-linux-x86_64-cuda11.8 # run python demo python 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 074a0f468b..88ea63ceae 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`cpu`以及`cuda`两种Device的预编译包，其中`cpu`版支持使用onnxruntime cpu进行推理，`cuda`版支持使用onnxruntime-gpu以及tensorrt进行推理，可以从[Releases](https://github.com/open-mmlab/mmdeploy/releases)获取。。 -本篇教程以`mmdeploy-1.3.0-windows-amd64.zip`和`mmdeploy-1.3.0-windows-amd64-cuda11.8.zip`为例，展示预编译包的使用方法。 +本篇教程以`mmdeploy-1.3.1-windows-amd64.zip`和`mmdeploy-1.3.1-windows-amd64-cuda11.8.zip`为例，展示预编译包的使用方法。 为了方便使用者快速上手，本教程以分类模型(mmpretrain)为例，展示两种预编译包的使用方法。 @@ -89,8 +89,8 @@ ______________________________________________________________________ 5. 安装`mmdeploy`（模型转换）以及`mmdeploy_runtime`（模型推理Python API）的预编译包 ```bash - pip install mmdeploy==1.3.0 - pip install mmdeploy-runtime==1.3.0 + pip install mmdeploy==1.3.1 + pip install mmdeploy-runtime==1.3.1 ``` :point_right: 如果之前安装过，需要先卸载后再安装。 @@ -108,7 +108,7 @@ ______________________________________________________________________ ![sys-path](https://user-images.githubusercontent.com/16019484/181463801-1d7814a8-b256-46e9-86f2-c08de0bc150b.png) :exclamation: 重启powershell让环境变量生效，可以通过 echo $env:PATH 来检查是否设置成功。 -8. 下载 SDK C/cpp Library mmdeploy-1.3.0-windows-amd64.zip +8. 下载 SDK C/cpp Library mmdeploy-1.3.1-windows-amd64.zip ### TensorRT @@ -117,8 +117,8 @@ ______________________________________________________________________ 5. 安装`mmdeploy`（模型转换）以及`mmdeploy_runtime`（模型推理Python API）的预编译包 ```bash - pip install mmdeploy==1.3.0 - pip install mmdeploy-runtime-gpu==1.3.0 + pip install mmdeploy==1.3.1 + pip install mmdeploy-runtime-gpu==1.3.1 ``` :point_right: 如果之前安装过，需要先卸载后再安装 @@ -137,7 +137,7 @@ ______________________________________________________________________ 7. 安装pycuda `pip install pycuda` -8. 下载 SDK C/cpp Library mmdeploy-1.3.0-windows-amd64-cuda11.8.zip +8. 下载 SDK C/cpp Library mmdeploy-1.3.1-windows-amd64-cuda11.8.zip ## 模型转换 @@ -149,7 +149,7 @@ ______________________________________________________________________ ``` .. -|-- mmdeploy-1.3.0-windows-amd64 +|-- mmdeploy-1.3.1-windows-amd64 |-- mmpretrain |-- mmdeploy `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth @@ -197,7 +197,7 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device) ``` .. -|-- mmdeploy-1.3.0-windows-amd64-cuda11.8 +|-- mmdeploy-1.3.1-windows-amd64-cuda11.8 |-- mmpretrain |-- mmdeploy `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth @@ -260,8 +260,8 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device) ``` . -|-- mmdeploy-1.3.0-windows-amd64 -|-- mmdeploy-1.3.0-windows-amd64-cuda11.8 +|-- mmdeploy-1.3.1-windows-amd64 +|-- mmdeploy-1.3.1-windows-amd64-cuda11.8 |-- mmpretrain |-- mmdeploy |-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth @@ -340,7 +340,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet 这里建议使用cmd，这样如果exe运行时如果找不到相关的dll的话会有弹窗 - 在mmdeploy-1.3.0-windows-amd64\\example\\cpp\\build\\Release目录下： + 在mmdeploy-1.3.1-windows-amd64\\example\\cpp\\build\\Release目录下： ``` .\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmpretrain\demo\demo.JPEG @@ -360,7 +360,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet 这里建议使用cmd，这样如果exe运行时如果找不到相关的dll的话会有弹窗 - 在mmdeploy-1.3.0-windows-amd64-cuda11.8\\example\\cpp\\build\\Release目录下： + 在mmdeploy-1.3.1-windows-amd64-cuda11.8\\example\\cpp\\build\\Release目录下： ``` .\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmpretrain\demo\demo.JPEG diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md index 05ef2b0c27..4168888869 100644 --- a/docs/zh_cn/get_started.md +++ b/docs/zh_cn/get_started.md @@ -113,14 +113,14 @@ mim install "mmcv>=2.0.0rc2" ```shell # 1. 安装 MMDeploy 模型转换工具（含trt/ort自定义算子） -pip install mmdeploy==1.3.0 +pip install mmdeploy==1.3.1 # 2. 安装 MMDeploy SDK推理工具 # 根据是否需要GPU推理可任选其一进行下载安装 # 2.1 支持 onnxruntime 推理 -pip install mmdeploy-runtime==1.3.0 +pip install mmdeploy-runtime==1.3.1 # 2.2 支持 onnxruntime-gpu tensorrt 推理 -pip install mmdeploy-runtime-gpu==1.3.0 +pip install mmdeploy-runtime-gpu==1.3.1 # 3. 安装推理引擎 # 3.1 安装推理引擎 TensorRT @@ -223,10 +223,10 @@ result = inference_model( 你可以直接运行预编译包中的 demo 程序，输入 SDK Model 和图像，进行推理，并查看推理结果。 ```shell -wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.3.0/mmdeploy-1.3.0-linux-x86_64-cuda11.8.tar.gz -tar xf mmdeploy-1.3.0-linux-x86_64-cuda11.8 +wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.3.1/mmdeploy-1.3.1-linux-x86_64-cuda11.8.tar.gz +tar xf mmdeploy-1.3.1-linux-x86_64-cuda11.8 -cd mmdeploy-1.3.0-linux-x86_64-cuda11.8 +cd mmdeploy-1.3.1-linux-x86_64-cuda11.8 # 运行 python demo python 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 bdf914fb98..e0f9e6e520 100644 --- a/mmdeploy/version.py +++ b/mmdeploy/version.py @@ -1,7 +1,7 @@ # Copyright (c) OpenMMLab. All rights reserved. from typing import Tuple -__version__ = '1.3.0' +__version__ = '1.3.1' short_version = __version__ diff --git a/tools/package_tools/packaging/mmdeploy_runtime/version.py b/tools/package_tools/packaging/mmdeploy_runtime/version.py index 535755a831..ee719d4563 100644 --- a/tools/package_tools/packaging/mmdeploy_runtime/version.py +++ b/tools/package_tools/packaging/mmdeploy_runtime/version.py @@ -1,2 +1,2 @@ # Copyright (c) OpenMMLab. All rights reserved. -__version__ = '1.3.0' +__version__ = '1.3.1'