lvhan028
GitHub
commit
034ba67556
|
|
@ -6,11 +6,7 @@ cd mmdeploy
|
|||
MMDEPLOY_DIR=$(pwd)
|
||||
mkdir -p build && cd build
|
||||
cmake .. -DMMDEPLOY_BUILD_SDK=ON -DMMDEPLOY_BUILD_TEST=ON -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
|
||||
-DMMDEPLOY_BUILD_SDK_CXX_API=ON -DMMDEPLOY_BUILD_SDK_CSHARP_API=ON \
|
||||
-DMMDEPLOY_BUILD_EXAMPLES=ON -DMMDEPLOY_BUILD_SDK_CXX_API=ON -DMMDEPLOY_BUILD_SDK_CSHARP_API=ON \
|
||||
-DMMDEPLOY_TARGET_DEVICES="$1" -DMMDEPLOY_TARGET_BACKENDS="$2" "${ARGS[@]:2}"
|
||||
|
||||
make -j$(nproc) && make install
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||||
cd install/example
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||||
mkdir -p build
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||||
cd build
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cmake ../cpp -DMMDeploy_DIR="$MMDEPLOY_DIR"/build/install/lib/cmake/MMDeploy "${ARGS[@]:2}" && make -j$(nproc)
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|
|
|
|||
|
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@ -1,3 +1,3 @@
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Invoke-WebRequest -Uri https://download.openmmlab.com/mmdeploy/library/opencv-4.5.5.zip -OutFile opencv.zip
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||||
Invoke-WebRequest -Uri https://github.com/irexyc/mmdeploy-ci-resource/releases/download/opencv/opencv-win-amd64-4.5.5-vc16.zip -OutFile opencv.zip
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Expand-Archive opencv.zip .
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Move-Item opencv-4.5.5 opencv
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|
|
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|||
|
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@ -192,19 +192,13 @@ jobs:
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-DMMDEPLOY_BUILD_SDK=ON `
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-DMMDEPLOY_BUILD_TEST=ON `
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-DMMDEPLOY_BUILD_SDK_PYTHON_API=ON `
|
||||
-DMMDEPLOY_BUILD_EXAMPLES=ON `
|
||||
-DMMDEPLOY_BUILD_SDK_CXX_API=ON `
|
||||
-DMMDEPLOY_BUILD_SDK_CSHARP_API=ON `
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-DMMDEPLOY_TARGET_BACKENDS="ort" `
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-DOpenCV_DIR="$env:OPENCV_PACKAGE_DIR"
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cmake --build . --config Release -- /m
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cmake --install . --config Release
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cd install/example
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mkdir build -ErrorAction SilentlyContinue
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cd build
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cmake ../cpp -G "Visual Studio 16 2019" -A x64 -T v142 `
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-DMMDeploy_DIR="$env:MMDEPLOY_DIR/build/install/lib/cmake/MMDeploy" `
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-DOpenCV_DIR="$env:OPENCV_PACKAGE_DIR"
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cmake --build . --config Release -- /m
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- install_mmdeploy
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- install_model_converter_req
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- perform_model_converter_ut
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@ -256,7 +250,7 @@ jobs:
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- run:
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name: Inference model by SDK
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command: |
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mmdeploy/build/install/example/build/image_classification cpu mmdeploy-models/mmcls/onnxruntime mmdeploy/tests/data/tiger.jpeg
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./mmdeploy/build/bin/image_classification cpu mmdeploy-models/mmcls/onnxruntime mmdeploy/demo/resources/cityscapes.png
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|
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# See: https://circleci.com/docs/2.0/configuration-reference/#workflows
|
||||
|
|
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|||
|
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@ -1,6 +1,7 @@
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|||
name: Bug report
|
||||
description: Create a report to help us improve
|
||||
|
||||
name: 🐞 Bug report
|
||||
description: Create a report to help us reproduce and fix the bug
|
||||
title: "[Bug] "
|
||||
labels: ['Bug']
|
||||
|
||||
body:
|
||||
- type: checkboxes
|
||||
|
|
@ -52,5 +53,3 @@ body:
|
|||
If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated!
|
||||
|
||||
Thanks for your bug report. We appreciate it a lot.
|
||||
|
||||
labels: ['Bug']
|
||||
|
|
@ -1,11 +1,15 @@
|
|||
name: Feature request
|
||||
name: 🚀 Feature request
|
||||
description: Suggest an idea for this project
|
||||
title: "[Feature] "
|
||||
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
## Describe the feature
|
||||
value: |
|
||||
We strongly appreciate you creating a PR to implement this feature [here](https://github.com/open-mmlab/mmdeploy/pulls)!
|
||||
If you need our help, please fill in as much of the following form as you're able to.
|
||||
|
||||
**The less clear the description, the longer it will take to solve it.**
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Motivation
|
||||
|
|
@ -0,0 +1,23 @@
|
|||
name: 📚 Documentation
|
||||
description: Report an issue related to the documentation.
|
||||
labels: "kind/doc,status/unconfirmed"
|
||||
title: "[Docs] "
|
||||
|
||||
body:
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: 📚 The doc issue
|
||||
description: >
|
||||
A clear and concise description the issue.
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Suggest a potential alternative/fix
|
||||
description: >
|
||||
Tell us how we could improve the documentation in this regard.
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
Thanks for contributing 🎉!
|
||||
|
|
@ -1,6 +1,12 @@
|
|||
blank_issues_enabled: false
|
||||
|
||||
contact_links:
|
||||
- name: Common Issues
|
||||
- name: 💥 FAQ
|
||||
url: https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/faq.md
|
||||
about: Check if your issue already has solutions
|
||||
- name: 💬 Forum
|
||||
url: https://github.com/open-mmlab/mmdeploy/discussions
|
||||
about: Ask general usage questions and discuss with other MMDeploy community members
|
||||
- name: 🌐 Explore OpenMMLab
|
||||
url: https://openmmlab.com/
|
||||
about: Get know more about OpenMMLab
|
||||
|
|
|
|||
|
|
@ -1,7 +0,0 @@
|
|||
---
|
||||
name: General questions
|
||||
about: Ask general questions to get help
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
---
|
||||
|
|
@ -89,6 +89,23 @@ jobs:
|
|||
ls -lah coverage.info
|
||||
cp coverage.info ../
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|
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cross_build_aarch64:
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||||
runs-on: ubuntu-20.04
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||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v3
|
||||
with:
|
||||
submodules: 'recursive'
|
||||
- name: update
|
||||
run: sudo apt update
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v2
|
||||
with:
|
||||
python-version: 3.8
|
||||
- name: gcc-multilib
|
||||
run: |
|
||||
sh -x tools/scripts/ubuntu_cross_build_aarch64.sh
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||||
|
||||
build_cuda102:
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||||
runs-on: ubuntu-18.04
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||||
container:
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||||
|
|
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|||
|
|
@ -0,0 +1,54 @@
|
|||
name: build_rknpu2_gcc
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||||
|
||||
on:
|
||||
push:
|
||||
paths:
|
||||
- "csrc/**"
|
||||
- "demo/csrc/**"
|
||||
- "CMakeLists.txt"
|
||||
|
||||
pull_request:
|
||||
paths-ignore:
|
||||
- "csrc/**"
|
||||
- "demo/csrc/**"
|
||||
- "CMakeLists.txt"
|
||||
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.ref }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
build_rknpu2_gcc:
|
||||
runs-on: ubuntu-18.04
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v3
|
||||
with:
|
||||
submodules: 'recursive'
|
||||
- name: rknpu2-gnu-toolchain
|
||||
run: |
|
||||
mkdir $GITHUB_WORKSPACE/rknpu2-gnu-toolchain
|
||||
cd $GITHUB_WORKSPACE/rknpu2-gnu-toolchain
|
||||
git clone https://github.com/Caesar-github/gcc-buildroot-9.3.0-2020.03-x86_64_aarch64-rockchip-linux-gnu.git
|
||||
- name: rknpu2
|
||||
run: |
|
||||
mkdir $GITHUB_WORKSPACE/rknpu2
|
||||
cd $GITHUB_WORKSPACE/rknpu2
|
||||
git clone https://github.com/rockchip-linux/rknpu2.git
|
||||
- name: build
|
||||
run: |
|
||||
export RKNN_TOOL_CHAIN=$GITHUB_WORKSPACE/rknpu2-gnu-toolchain/gcc-buildroot-9.3.0-2020.03-x86_64_aarch64-rockchip-linux-gnu/usr
|
||||
export LD_LIBRARY_PATH=$RKNN_TOOL_CHAIN/lib64:$LD_LIBRARY_PATH
|
||||
export RKNPU2_DEVICE_DIR=$GITHUB_WORKSPACE/rknpu2/rknpu2/runtime/RK3588
|
||||
mkdir build && cd build
|
||||
cmake .. \
|
||||
-DCMAKE_TOOLCHAIN_FILE=$(pwd)/../cmake/toolchains/rknpu2-linux-gnu.cmake \
|
||||
-DMMDEPLOY_BUILD_SDK=ON \
|
||||
-DMMDEPLOY_SHARED_LIBS=ON \
|
||||
-DMMDEPLOY_BUILD_EXAMPLES=ON \
|
||||
-DMMDEPLOY_TARGET_DEVICES="cpu" \
|
||||
-DMMDEPLOY_TARGET_BACKENDS="rknn" \
|
||||
-DMMDEPLOY_CODEBASES=all \
|
||||
-DOpenCV_DIR=$RKNPU2_DEVICE_DIR/../../examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV
|
||||
make -j$(nproc)
|
||||
make install
|
||||
|
|
@ -5,7 +5,7 @@ endif ()
|
|||
message(STATUS "CMAKE_INSTALL_PREFIX: ${CMAKE_INSTALL_PREFIX}")
|
||||
|
||||
cmake_minimum_required(VERSION 3.14)
|
||||
project(MMDeploy VERSION 0.9.0)
|
||||
project(MMDeploy VERSION 0.10.0)
|
||||
|
||||
set(CMAKE_CXX_STANDARD 17)
|
||||
|
||||
|
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@ -128,6 +128,7 @@ if (MMDEPLOY_BUILD_SDK)
|
|||
mmdeploy_add_deps(pplnn BACKENDS ${MMDEPLOY_TARGET_BACKENDS} DEPS pplnn)
|
||||
endif ()
|
||||
mmdeploy_add_deps(snpe BACKENDS ${MMDEPLOY_TARGET_BACKENDS} DEPS snpe)
|
||||
mmdeploy_add_deps(rknn BACKENDS ${MMDEPLOY_TARGET_BACKENDS} DEPS rknn)
|
||||
|
||||
include(CMakePackageConfigHelpers)
|
||||
# generate the config file that is includes the exports
|
||||
|
|
|
|||
|
|
@ -87,6 +87,7 @@ Please read [getting_started](docs/en/get_started.md) for the basic usage of MMD
|
|||
- [Build for Android](docs/en/01-how-to-build/android.md)
|
||||
- [Build for Jetson](docs/en/01-how-to-build/jetsons.md)
|
||||
- [Build for SNPE](docs/en/01-how-to-build/snpe.md)
|
||||
- [Cross Build for aarch64](docs/en/01-how-to-build/cross_build_ncnn_aarch64.md)
|
||||
- User Guide
|
||||
- [How to convert model](docs/en/02-how-to-run/convert_model.md)
|
||||
- [How to write config](docs/en/02-how-to-run/write_config.md)
|
||||
|
|
@ -148,6 +149,7 @@ This project is released under the [Apache 2.0 license](LICENSE).
|
|||
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
|
||||
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
|
||||
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
|
||||
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark
|
||||
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
|
||||
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
|
||||
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
|
||||
|
|
|
|||
|
|
@ -86,6 +86,7 @@ MMDeploy 是 [OpenMMLab](https://openmmlab.com/) 模型部署工具箱，**为
|
|||
- [Build for Android](docs/zh_cn/01-how-to-build/android.md)
|
||||
- [Build for Jetson](docs/zh_cn/01-how-to-build/jetsons.md)
|
||||
- [Build for SNPE](docs/zh_cn/01-how-to-build/snpe.md)
|
||||
- [Cross Build for aarch64](docs/zh_cn/01-how-to-build/cross_build_ncnn_aarch64.md)
|
||||
- 使用
|
||||
- [把模型转换到推理 Backend](docs/zh_cn/02-how-to-run/convert_model.md)
|
||||
- [配置转换参数](docs/zh_cn/02-how-to-run/write_config.md)
|
||||
|
|
@ -153,6 +154,7 @@ MMDeploy 是 [OpenMMLab](https://openmmlab.com/) 模型部署工具箱，**为
|
|||
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
|
||||
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
|
||||
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
|
||||
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱和基准测试
|
||||
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
|
||||
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
|
||||
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
|
||||
|
|
|
|||
|
|
@ -42,6 +42,7 @@ if (NOT CMAKE_CUDA_ARCHITECTURES)
|
|||
if (CUDA_VERSION_MAJOR VERSION_GREATER_EQUAL "8")
|
||||
set(_NVCC_FLAGS "${_NVCC_FLAGS} -gencode arch=compute_60,code=sm_60")
|
||||
set(_NVCC_FLAGS "${_NVCC_FLAGS} -gencode arch=compute_61,code=sm_61")
|
||||
set(_NVCC_FLAGS "${_NVCC_FLAGS} -gencode arch=compute_62,code=sm_62")
|
||||
endif ()
|
||||
if (CUDA_VERSION_MAJOR VERSION_GREATER_EQUAL "9")
|
||||
set(_NVCC_FLAGS "${_NVCC_FLAGS} -gencode arch=compute_70,code=sm_70")
|
||||
|
|
|
|||
|
|
@ -0,0 +1,17 @@
|
|||
set(CMAKE_SYSTEM_NAME Linux)
|
||||
set(CMAKE_SYSTEM_PROCESSOR aarch64)
|
||||
|
||||
set(CMAKE_C_COMPILER "aarch64-linux-gnu-gcc")
|
||||
set(CMAKE_CXX_COMPILER "aarch64-linux-gnu-g++")
|
||||
set(CMAKE_LINKER "aarch64-linux-gnu-ld")
|
||||
|
||||
set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
|
||||
set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
|
||||
set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
|
||||
|
||||
set(CMAKE_C_FLAGS "-march=armv8-a")
|
||||
set(CMAKE_CXX_FLAGS "-march=armv8-a")
|
||||
|
||||
# cache flags
|
||||
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}" CACHE STRING "c flags")
|
||||
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}" CACHE STRING "c++ flags")
|
||||
|
|
@ -0,0 +1,23 @@
|
|||
set(CMAKE_SYSTEM_NAME Linux)
|
||||
set(CMAKE_SYSTEM_PROCESSOR rockchip)
|
||||
|
||||
if(DEFINED ENV{RKNN_TOOL_CHAIN})
|
||||
file(TO_CMAKE_PATH $ENV{RKNN_TOOL_CHAIN} RKNN_TOOL_CHAIN)
|
||||
else()
|
||||
message(FATAL_ERROR "RKNN_TOOL_CHAIN env must be defined")
|
||||
endif()
|
||||
|
||||
set(CMAKE_C_COMPILER ${RKNN_TOOL_CHAIN}/bin/aarch64-rockchip-linux-gnu-gcc)
|
||||
set(CMAKE_CXX_COMPILER ${RKNN_TOOL_CHAIN}/bin/aarch64-rockchip-linux-gnu-g++)
|
||||
|
||||
set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
|
||||
set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
|
||||
set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
|
||||
set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY)
|
||||
|
||||
set(CMAKE_C_FLAGS "-Wl,--allow-shlib-undefined")
|
||||
set(CMAKE_CXX_FLAGS "-Wl,--allow-shlib-undefined")
|
||||
|
||||
# cache flags
|
||||
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}" CACHE STRING "c flags")
|
||||
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}" CACHE STRING "c++ flags")
|
||||
|
|
@ -53,6 +53,10 @@ inline Value to_value(T&& val) {
|
|||
return value;
|
||||
}
|
||||
|
||||
// fast path
|
||||
inline Value to_value(const Value& v) { return v; }
|
||||
inline Value to_value(Value&& v) { return std::move(v); }
|
||||
|
||||
template <typename T>
|
||||
void from_value(const Value& value, T&& x);
|
||||
|
||||
|
|
@ -107,6 +111,9 @@ void from_value(const Value& value, T&& x) {
|
|||
archive(std::forward<T>(x));
|
||||
}
|
||||
|
||||
// Required to avoid Value::Pointer being unwrapped by Value::get_to()
|
||||
inline void from_value(const Value& value, Value& x) { x = value; }
|
||||
|
||||
template <typename T>
|
||||
inline T from_value(const Value& value) {
|
||||
T x{};
|
||||
|
|
|
|||
|
|
@ -156,7 +156,7 @@ bool SubgraphMatcher::SubgraphMatcherImpl::matchAttributes(const Node* n1, Node*
|
|||
n1is = n1->is(attr_name);
|
||||
n2is = n2->is(attr_name);
|
||||
if (n1is.size() != n2is.size()) return false;
|
||||
for (int i = 0; i < n1is.size(); ++i) {
|
||||
for (size_t i = 0; i < n1is.size(); ++i) {
|
||||
if (n1is[i] != n2is[i]) return false;
|
||||
}
|
||||
break;
|
||||
|
|
@ -164,7 +164,7 @@ bool SubgraphMatcher::SubgraphMatcherImpl::matchAttributes(const Node* n1, Node*
|
|||
n1fs = n1->fs(attr_name);
|
||||
n2fs = n2->fs(attr_name);
|
||||
if (n1fs.size() != n2fs.size()) return false;
|
||||
for (int i = 0; i < n1fs.size(); ++i) {
|
||||
for (size_t i = 0; i < n1fs.size(); ++i) {
|
||||
if (n1fs[i] != n2fs[i]) return false;
|
||||
}
|
||||
break;
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ set(SRCS
|
|||
task.cpp
|
||||
static_router.cpp
|
||||
inference.cpp
|
||||
pipeline.cpp)
|
||||
pipeline.cpp
|
||||
cond.cpp)
|
||||
mmdeploy_add_module(${PROJECT_NAME} LIBRARY "${SRCS}")
|
||||
add_library(mmdeploy::graph ALIAS ${PROJECT_NAME})
|
||||
|
|
|
|||
|
|
@ -0,0 +1,124 @@
|
|||
// Copyright (c) OpenMMLab. All rights reserved.
|
||||
|
||||
#include "mmdeploy/graph/cond.h"
|
||||
|
||||
#include <algorithm>
|
||||
|
||||
namespace mmdeploy::graph {
|
||||
|
||||
namespace {
|
||||
|
||||
std::vector<int> get_predicates(const Value::Array& xs) {
|
||||
std::vector<int> ps;
|
||||
ps.reserve(xs.size());
|
||||
std::transform(std::begin(xs), std::end(xs), std::back_inserter(ps),
|
||||
[](const Value& x) { return static_cast<int>(x.get<bool>()); });
|
||||
return ps;
|
||||
}
|
||||
|
||||
std::pair<bool, int> choice(const std::vector<int>& xs) {
|
||||
auto count = std::count(std::begin(xs), std::end(xs), 1);
|
||||
if (count == 0 || count == xs.size()) {
|
||||
return std::make_pair(true, count == xs.size());
|
||||
}
|
||||
return std::make_pair(false, false);
|
||||
}
|
||||
|
||||
Value get_divergent_input(Value::Array& as, const std::vector<int>& ps) {
|
||||
Value::Array ts(as.size(), Value::kArray);
|
||||
for (size_t i = 0; i < ts.size(); ++i) {
|
||||
auto& t = ts[i].array();
|
||||
auto& a = as[i].array();
|
||||
for (size_t j = 0; j < ps.size(); ++j) {
|
||||
if (ps[j]) {
|
||||
t.push_back(std::move(a[j]));
|
||||
}
|
||||
}
|
||||
}
|
||||
return ts;
|
||||
}
|
||||
|
||||
Value get_divergent_output(Value::Array& rs, const vector<int>& ps) {
|
||||
Value::Array ys(rs.size(), Value::kArray);
|
||||
for (size_t i = 0; i < ys.size(); ++i) {
|
||||
auto& y = ys[i].array();
|
||||
auto& r = rs[i].array();
|
||||
size_t k = 0;
|
||||
for (const auto& p : ps) {
|
||||
y.push_back(p ? std::move(r[k++]) : nullptr);
|
||||
}
|
||||
}
|
||||
return ys;
|
||||
}
|
||||
|
||||
} // namespace
|
||||
|
||||
Sender<Value> Cond::Process(Sender<Value> input) {
|
||||
return LetValue(std::move(input), [this](Value& _input) -> Sender<Value> {
|
||||
assert(_input.is_array());
|
||||
auto& as = _input.array();
|
||||
auto ps = get_predicates(as.front().array());
|
||||
as.erase(as.begin());
|
||||
auto [coherent, branch] = choice(ps);
|
||||
if (coherent) {
|
||||
if (branch) {
|
||||
return node_->Process(Just(std::move(_input)));
|
||||
} else {
|
||||
Value::Array output(n_output_, Value::Array(ps.size(), nullptr));
|
||||
return Just(Value(std::move(output)));
|
||||
}
|
||||
} else {
|
||||
auto ts = get_divergent_input(as, ps);
|
||||
return node_->Process(Just(Value(std::move(ts)))) |
|
||||
Then([ps = std::move(ps)](Value rs) -> Value {
|
||||
return get_divergent_output(rs.array(), ps);
|
||||
});
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
CondBuilder::CondBuilder(Value config) : Builder(std::move(config)) {}
|
||||
|
||||
Result<unique_ptr<Node>> CondBuilder::BuildImpl() {
|
||||
try {
|
||||
auto cond = std::make_unique<Cond>();
|
||||
cond->n_output_ = static_cast<int>(config_["output"].size());
|
||||
|
||||
auto& body_config = config_["body"];
|
||||
auto inputs = config_["input"].array();
|
||||
inputs.erase(inputs.begin());
|
||||
|
||||
body_config["input"] = std::move(inputs);
|
||||
body_config["output"] = config_["output"];
|
||||
|
||||
// propagate context
|
||||
if (!body_config.contains("context")) {
|
||||
body_config["context"] = Value::Object();
|
||||
}
|
||||
if (config_.contains("context")) {
|
||||
update(body_config["context"].object(), config_["context"].object(), 2);
|
||||
}
|
||||
|
||||
if (auto builder = Builder::CreateFromConfig(body_config).value()) {
|
||||
if (auto node = builder->Build().value()) {
|
||||
cond->node_ = std::move(node);
|
||||
return std::move(cond);
|
||||
}
|
||||
}
|
||||
} catch (const std::exception& e) {
|
||||
MMDEPLOY_ERROR("error parsing config: {}", config_);
|
||||
}
|
||||
return Status(eFail);
|
||||
}
|
||||
|
||||
class CondCreator : public Creator<Builder> {
|
||||
public:
|
||||
const char* GetName() const override { return "Cond"; }
|
||||
unique_ptr<Builder> Create(const Value& config) override {
|
||||
return std::make_unique<CondBuilder>(config);
|
||||
}
|
||||
};
|
||||
|
||||
REGISTER_MODULE(Builder, CondCreator);
|
||||
|
||||
} // namespace mmdeploy::graph
|
||||
|
|
@ -0,0 +1,31 @@
|
|||
// Copyright (c) OpenMMLab. All rights reserved.
|
||||
|
||||
#ifndef MMDEPLOY_CSRC_MMDEPLOY_GRAPH_COND_H_
|
||||
#define MMDEPLOY_CSRC_MMDEPLOY_GRAPH_COND_H_
|
||||
|
||||
#include "mmdeploy/core/graph.h"
|
||||
|
||||
namespace mmdeploy::graph {
|
||||
|
||||
class Cond : public Node {
|
||||
friend class CondBuilder;
|
||||
|
||||
public:
|
||||
Sender<Value> Process(Sender<Value> input) override;
|
||||
|
||||
private:
|
||||
std::unique_ptr<Node> node_;
|
||||
int n_output_{0};
|
||||
};
|
||||
|
||||
class CondBuilder : public Builder {
|
||||
public:
|
||||
explicit CondBuilder(Value config);
|
||||
|
||||
protected:
|
||||
Result<unique_ptr<Node>> BuildImpl() override;
|
||||
};
|
||||
|
||||
} // namespace mmdeploy::graph
|
||||
|
||||
#endif // MMDEPLOY_CSRC_MMDEPLOY_GRAPH_COND_H_
|
||||
|
|
@ -38,5 +38,9 @@ if ("coreml" IN_LIST MMDEPLOY_TARGET_BACKENDS)
|
|||
add_subdirectory(coreml)
|
||||
endif ()
|
||||
|
||||
if ("rknn" IN_LIST MMDEPLOY_TARGET_BACKENDS)
|
||||
add_subdirectory(rknn)
|
||||
endif ()
|
||||
|
||||
mmdeploy_add_module(${PROJECT_NAME} net_module.cpp)
|
||||
add_library(mmdeploy::net_module ALIAS ${PROJECT_NAME})
|
||||
|
|
|
|||
|
|
@ -4,12 +4,8 @@ project(mmdeploy_ort_net)
|
|||
|
||||
include(${CMAKE_SOURCE_DIR}/cmake/modules/FindONNXRUNTIME.cmake)
|
||||
|
||||
if ("cpu" IN_LIST MMDEPLOY_TARGET_DEVICES)
|
||||
mmdeploy_add_module(${PROJECT_NAME} ort_net.cpp)
|
||||
target_include_directories(${PROJECT_NAME} PRIVATE ${ONNXRUNTIME_DIR}/include)
|
||||
target_link_libraries(${PROJECT_NAME} PRIVATE mmdeploy_onnxruntime_ops_obj)
|
||||
target_link_libraries(${PROJECT_NAME} PUBLIC onnxruntime)
|
||||
add_library(mmdeploy::ort_net ALIAS ${PROJECT_NAME})
|
||||
else ()
|
||||
message(ERROR "'ort_net' is NOT supported in target devices: ${MMDEPLOY_TARGET_DEVICES}")
|
||||
endif ()
|
||||
mmdeploy_add_module(${PROJECT_NAME} ort_net.cpp)
|
||||
target_include_directories(${PROJECT_NAME} PRIVATE ${ONNXRUNTIME_DIR}/include)
|
||||
target_link_libraries(${PROJECT_NAME} PRIVATE mmdeploy_onnxruntime_ops_obj)
|
||||
target_link_libraries(${PROJECT_NAME} PUBLIC onnxruntime)
|
||||
add_library(mmdeploy::ort_net ALIAS ${PROJECT_NAME})
|
||||
|
|
|
|||
|
|
@ -0,0 +1,20 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
|
||||
project(mmdeploy_rknn_net)
|
||||
|
||||
add_library(rknn SHARED IMPORTED)
|
||||
|
||||
if(DEFINED ENV{RKNPU2_DEVICE_DIR})
|
||||
file(TO_CMAKE_PATH $ENV{RKNPU2_DEVICE_DIR} RKNPU2_DEVICE_DIR)
|
||||
else()
|
||||
message(FATAL_ERROR "RKNPU2_DEVICE_DIR env must be defined")
|
||||
endif()
|
||||
|
||||
set_target_properties(rknn PROPERTIES
|
||||
IMPORTED_LOCATION "${RKNPU2_DEVICE_DIR}/Linux/librknn_api/aarch64/librknn_api.so"
|
||||
INTERFACE_INCLUDE_DIRECTORIES "${RKNPU2_DEVICE_DIR}/Linux/librknn_api/include"
|
||||
)
|
||||
|
||||
mmdeploy_add_module(${PROJECT_NAME} rknn_net.cpp)
|
||||
target_link_libraries(${PROJECT_NAME} PRIVATE rknn)
|
||||
add_library(mmdeploy::rknn_net ALIAS ${PROJECT_NAME})
|
||||
|
|
@ -0,0 +1,216 @@
|
|||
// Copyright (c) OpenMMLab. All rights reserved.
|
||||
#include "rknn_net.h"
|
||||
|
||||
#include <stdio.h>
|
||||
|
||||
#include <fstream>
|
||||
|
||||
#include "mmdeploy/core/logger.h"
|
||||
#include "mmdeploy/core/model.h"
|
||||
#include "mmdeploy/core/utils/filesystem.h"
|
||||
#include "mmdeploy/core/utils/formatter.h"
|
||||
|
||||
namespace mmdeploy::framework {
|
||||
|
||||
Result<rknn_tensor_type> GetRKNNDataType(DataType data_type) {
|
||||
switch (data_type) {
|
||||
case DataType::kFLOAT:
|
||||
return RKNN_TENSOR_FLOAT32;
|
||||
case DataType::kHALF:
|
||||
return RKNN_TENSOR_FLOAT16;
|
||||
case DataType::kINT8:
|
||||
return RKNN_TENSOR_INT8;
|
||||
case DataType::kINT32:
|
||||
return RKNN_TENSOR_INT32;
|
||||
case DataType::kINT64:
|
||||
return RKNN_TENSOR_INT64;
|
||||
default:
|
||||
return Status(eNotSupported);
|
||||
}
|
||||
}
|
||||
|
||||
Result<DataType> GetMMDeployDataType(rknn_tensor_type data_type) {
|
||||
switch (data_type) {
|
||||
case RKNN_TENSOR_FLOAT32:
|
||||
return DataType::kFLOAT;
|
||||
case RKNN_TENSOR_FLOAT16:
|
||||
return DataType::kHALF;
|
||||
case RKNN_TENSOR_INT8:
|
||||
return DataType::kINT8;
|
||||
case RKNN_TENSOR_INT32:
|
||||
return DataType::kINT32;
|
||||
case RKNN_TENSOR_INT64:
|
||||
return DataType::kINT64;
|
||||
default:
|
||||
return Status(eNotSupported);
|
||||
}
|
||||
}
|
||||
|
||||
RKNNNet::~RKNNNet() { rknn_destroy(ctx_); }
|
||||
|
||||
void RKNNNet::dump_tensor_attr(rknn_tensor_attr* attr) {
|
||||
MMDEPLOY_INFO(
|
||||
" index={}, name={}, n_dims={}, dims=[{}, {}, {}, {}], n_elems={}, size={}, fmt={}, "
|
||||
"type={}, qnt_type={}, "
|
||||
"zp={}, scale=%f\n",
|
||||
attr->index, attr->name, attr->n_dims, attr->dims[0], attr->dims[1], attr->dims[2],
|
||||
attr->dims[3], attr->n_elems, attr->size, get_format_string(attr->fmt),
|
||||
get_type_string(attr->type), get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale);
|
||||
}
|
||||
|
||||
Result<void> RKNNNet::Init(const Value& args) {
|
||||
auto& context = args["context"];
|
||||
device_ = context["device"].get<Device>();
|
||||
stream_ = context["stream"].get<Stream>();
|
||||
if (!device_.is_host()) {
|
||||
return Status(eNotSupported);
|
||||
}
|
||||
|
||||
auto name = args["name"].get<std::string>();
|
||||
auto model = context["model"].get<Model>();
|
||||
OUTCOME_TRY(auto config, model.GetModelConfig(name));
|
||||
|
||||
std::string content;
|
||||
OUTCOME_TRY(content, model.ReadFile(config.net));
|
||||
char* model_ptr = const_cast<char*>(content.data());
|
||||
int ret = rknn_init(&ctx_, model_ptr, content.size(), 0, NULL);
|
||||
if (ret != RKNN_SUCC) {
|
||||
MMDEPLOY_ERROR("Load .rknn failed! ret= {}", ret);
|
||||
return Status(eInvalidArgument);
|
||||
}
|
||||
|
||||
// Get Model Input Output Info
|
||||
rknn_input_output_num io_num;
|
||||
ret = rknn_query(ctx_, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num));
|
||||
if (ret != RKNN_SUCC) {
|
||||
MMDEPLOY_INFO("model input num: {}, output num: {}\n", io_num.n_input, io_num.n_output);
|
||||
MMDEPLOY_ERROR("rknn_query fail! ret= {}", ret);
|
||||
return Status(eFail);
|
||||
}
|
||||
|
||||
for (int i = 0; i < io_num.n_input; i++) {
|
||||
rknn_tensor_attr input_attr;
|
||||
input_attr.index = i;
|
||||
ret = rknn_query(ctx_, RKNN_QUERY_INPUT_ATTR, &(input_attr), sizeof(rknn_tensor_attr));
|
||||
if (ret != RKNN_SUCC) {
|
||||
MMDEPLOY_INFO("input tensors:\n");
|
||||
dump_tensor_attr(&(input_attr));
|
||||
MMDEPLOY_ERROR("rknn_query fail! ret= {}", ret);
|
||||
return Status(eFail);
|
||||
}
|
||||
input_attrs_.push_back(input_attr);
|
||||
OUTCOME_TRY(auto data_type, GetMMDeployDataType(input_attr.type));
|
||||
input_tensors_.emplace_back(TensorDesc{device_, data_type, {}, input_attr.name});
|
||||
}
|
||||
|
||||
for (int i = 0; i < io_num.n_output; i++) {
|
||||
rknn_tensor_attr output_attr;
|
||||
output_attr.index = i;
|
||||
ret = rknn_query(ctx_, RKNN_QUERY_OUTPUT_ATTR, &(output_attr), sizeof(rknn_tensor_attr));
|
||||
if (ret != RKNN_SUCC) {
|
||||
MMDEPLOY_INFO("output tensors:\n");
|
||||
dump_tensor_attr(&(output_attr));
|
||||
MMDEPLOY_ERROR("rknn_query fail! ret= {}", ret);
|
||||
return Status(eFail);
|
||||
}
|
||||
output_attrs_.push_back(output_attr);
|
||||
OUTCOME_TRY(auto data_type, GetMMDeployDataType(output_attr.type));
|
||||
output_tensors_.emplace_back(TensorDesc{device_, data_type, {}, output_attr.name});
|
||||
}
|
||||
|
||||
return success();
|
||||
}
|
||||
|
||||
Result<void> RKNNNet::ForwardAsync(Event* event) { return Status(eNotSupported); }
|
||||
|
||||
Result<void> RKNNNet::Deinit() { return success(); }
|
||||
|
||||
Result<Span<Tensor>> RKNNNet::GetInputTensors() { return input_tensors_; }
|
||||
|
||||
Result<Span<Tensor>> RKNNNet::GetOutputTensors() { return output_tensors_; }
|
||||
|
||||
Result<void> RKNNNet::Reshape(Span<TensorShape> input_shapes) {
|
||||
for (size_t i = 0; i < input_shapes.size(); ++i) {
|
||||
input_tensors_[i].Reshape(input_shapes[i]);
|
||||
}
|
||||
return success();
|
||||
}
|
||||
|
||||
Result<void> RKNNNet::Forward() {
|
||||
OUTCOME_TRY(stream_.Wait());
|
||||
|
||||
std::vector<rknn_input> inputs;
|
||||
for (int i = 0; i < input_tensors_.size(); i++) {
|
||||
rknn_input input;
|
||||
input.index = i;
|
||||
input.pass_through = 0;
|
||||
input.type = input_attrs_[i].type;
|
||||
input.fmt = input_attrs_[i].fmt;
|
||||
input.buf = input_tensors_[i].data<float>();
|
||||
input.size = input_attrs_[i].size;
|
||||
inputs.push_back(input);
|
||||
}
|
||||
|
||||
// Set input
|
||||
int ret = rknn_inputs_set(ctx_, input_tensors_.size(), inputs.data());
|
||||
if (ret < 0) {
|
||||
MMDEPLOY_ERROR("rknn_input_set fail! ret= {}", ret);
|
||||
return Status(eFail);
|
||||
}
|
||||
|
||||
// Get output
|
||||
std::vector<rknn_output> outputs;
|
||||
for (uint32_t i = 0; i < output_tensors_.size(); ++i) {
|
||||
rknn_output output;
|
||||
output.want_float = 1;
|
||||
output.index = i;
|
||||
output.is_prealloc = 0;
|
||||
outputs.push_back(output);
|
||||
}
|
||||
|
||||
ret = rknn_run(ctx_, NULL);
|
||||
if (ret < 0) {
|
||||
MMDEPLOY_ERROR("rknn_run fail! ret={}", ret);
|
||||
return Status(eFail);
|
||||
}
|
||||
|
||||
ret = rknn_outputs_get(ctx_, output_tensors_.size(), outputs.data(), NULL);
|
||||
if (ret < 0) {
|
||||
MMDEPLOY_ERROR("rknn_outputs_get fail! ret= {}", ret);
|
||||
return Status(eFail);
|
||||
}
|
||||
for (int i = 0; i < output_tensors_.size(); i++) {
|
||||
TensorShape tensor_shape;
|
||||
for (int j = 0; j < output_attrs_[i].n_dims; ++j) {
|
||||
tensor_shape.push_back(output_attrs_[i].dims[j]);
|
||||
}
|
||||
output_tensors_[i].Reshape(tensor_shape);
|
||||
memcpy(output_tensors_[i].data<float>(), (float*)outputs[i].buf, output_attrs_[i].size);
|
||||
}
|
||||
OUTCOME_TRY(stream_.Wait());
|
||||
return success();
|
||||
}
|
||||
|
||||
class RKNNNetCreator : public Creator<Net> {
|
||||
public:
|
||||
const char* GetName() const override { return "rknn"; }
|
||||
int GetVersion() const override { return 0; }
|
||||
std::unique_ptr<Net> Create(const Value& args) override {
|
||||
try {
|
||||
auto p = std::make_unique<RKNNNet>();
|
||||
if (auto r = p->Init(args)) {
|
||||
return p;
|
||||
} else {
|
||||
MMDEPLOY_ERROR("error creating RKNNNet: {}", r.error().message().c_str());
|
||||
return nullptr;
|
||||
}
|
||||
} catch (const std::exception& e) {
|
||||
MMDEPLOY_ERROR("unhandled exception when creating RKNNNet: {}", e.what());
|
||||
return nullptr;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
REGISTER_MODULE(Net, RKNNNetCreator);
|
||||
|
||||
} // namespace mmdeploy::framework
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
// Copyright (c) OpenMMLab. All rights reserved.
|
||||
|
||||
#ifndef MMDEPLOY_SRC_NET_RKNN_RKNN_NET_H_
|
||||
#define MMDEPLOY_SRC_NET_RKNN_RKNN_NET_H_
|
||||
|
||||
#include "mmdeploy/core/mpl/span.h"
|
||||
#include "mmdeploy/core/net.h"
|
||||
#include "rknn_api.h"
|
||||
|
||||
namespace mmdeploy::framework {
|
||||
|
||||
class RKNNNet : public Net {
|
||||
public:
|
||||
~RKNNNet() override;
|
||||
|
||||
Result<void> Init(const Value& args) override;
|
||||
|
||||
Result<void> Deinit() override;
|
||||
|
||||
Result<void> Reshape(Span<TensorShape> input_shapes) override;
|
||||
|
||||
Result<Span<Tensor> > GetInputTensors() override;
|
||||
|
||||
Result<Span<Tensor> > GetOutputTensors() override;
|
||||
|
||||
Result<void> Forward() override;
|
||||
|
||||
Result<void> ForwardAsync(Event* event) override;
|
||||
|
||||
private:
|
||||
void dump_tensor_attr(rknn_tensor_attr* attr);
|
||||
|
||||
Device device_;
|
||||
Stream stream_;
|
||||
rknn_context ctx_;
|
||||
std::vector<Tensor> input_tensors_;
|
||||
std::vector<Tensor> output_tensors_;
|
||||
std::vector<rknn_tensor_attr> input_attrs_;
|
||||
std::vector<rknn_tensor_attr> output_attrs_;
|
||||
static constexpr const auto kHost = Device(0);
|
||||
};
|
||||
|
||||
} // namespace mmdeploy::framework
|
||||
|
||||
#endif // MMDEPLOY_SRC_NET_RKNN_RKNN_NET_H_
|
||||
|
|
@ -30,7 +30,9 @@ function(add_example dep folder name)
|
|||
endfunction()
|
||||
|
||||
add_example(classifier c image_classification)
|
||||
add_example(classifier c batch_image_classification)
|
||||
add_example(detector c object_detection)
|
||||
add_example(detector c batch_object_detection)
|
||||
add_example(segmentor c image_segmentation)
|
||||
add_example(restorer c image_restorer)
|
||||
add_example(text_detector c ocr)
|
||||
|
|
@ -46,7 +48,8 @@ if (MMDEPLOY_BUILD_SDK_CXX_API)
|
|||
add_example(segmentor cpp segmentor)
|
||||
add_example(restorer cpp restorer)
|
||||
add_example(text_detector cpp text_ocr)
|
||||
add_example("" cpp text_det_recog)
|
||||
add_example(text_detector cpp text_det_recog)
|
||||
add_example(pose_detector cpp pose_detector)
|
||||
add_example(rotated_detector cpp rotated_detector)
|
||||
add_example(pose_detector cpp pose_tracker)
|
||||
endif ()
|
||||
|
|
|
|||
|
|
@ -0,0 +1,100 @@
|
|||
#include <fstream>
|
||||
#include <opencv2/imgcodecs/imgcodecs.hpp>
|
||||
#include <string>
|
||||
|
||||
#include "mmdeploy/classifier.h"
|
||||
|
||||
static int batch_inference(mmdeploy_classifier_t classifier,
|
||||
const std::vector<int>& image_ids,
|
||||
const std::vector<mmdeploy_mat_t>& mats);
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
if (argc < 5) {
|
||||
fprintf(stderr, "usage:\n image_classification device_name dump_model_directory "
|
||||
"imagelist.txt batch_size\n");
|
||||
return 1;
|
||||
}
|
||||
auto device_name = argv[1];
|
||||
auto model_path = argv[2];
|
||||
|
||||
mmdeploy_classifier_t classifier{};
|
||||
int status{};
|
||||
status = mmdeploy_classifier_create_by_path(model_path, device_name, 0, &classifier);
|
||||
if (status != MMDEPLOY_SUCCESS) {
|
||||
fprintf(stderr, "failed to create classifier, code: %d\n", (int)status);
|
||||
return 1;
|
||||
}
|
||||
|
||||
// `file_path` is the path of an image list file
|
||||
std::string file_path = argv[3];
|
||||
const int batch = std::stoi(argv[argc-1]);
|
||||
|
||||
// read image paths from the file
|
||||
std::ifstream ifs(file_path);
|
||||
std::string img_path;
|
||||
std::vector<std::string> img_paths;
|
||||
while (ifs >> img_path) {
|
||||
img_paths.emplace_back(std::move(img_path));
|
||||
}
|
||||
|
||||
// read images and process batch inference
|
||||
std::vector<cv::Mat> images;
|
||||
std::vector<int> image_ids;
|
||||
std::vector<mmdeploy_mat_t> mats;
|
||||
for (int i = 0; i < (int)img_paths.size(); ++i) {
|
||||
auto img = cv::imread(img_paths[i]);
|
||||
if (!img.data) {
|
||||
fprintf(stderr, "failed to load image: %s\n", img_paths[i].c_str());
|
||||
continue;
|
||||
}
|
||||
images.push_back(img);
|
||||
image_ids.push_back(i);
|
||||
mmdeploy_mat_t mat{
|
||||
img.data, img.rows, img.cols, 3, MMDEPLOY_PIXEL_FORMAT_BGR, MMDEPLOY_DATA_TYPE_UINT8};
|
||||
mats.push_back(mat);
|
||||
|
||||
// process batch inference
|
||||
if ((int)mats.size() == batch) {
|
||||
if (batch_inference(classifier, image_ids, mats) != 0) {
|
||||
continue;
|
||||
}
|
||||
// clear buffer for next batch
|
||||
mats.clear();
|
||||
image_ids.clear();
|
||||
images.clear();
|
||||
}
|
||||
}
|
||||
// process batch inference if there are still unhandled images
|
||||
if (!mats.empty()) {
|
||||
(void)batch_inference(classifier, image_ids, mats);
|
||||
}
|
||||
|
||||
mmdeploy_classifier_destroy(classifier);
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
||||
int batch_inference(mmdeploy_classifier_t classifier, const std::vector<int>& image_ids,
|
||||
const std::vector<mmdeploy_mat_t>& mats) {
|
||||
mmdeploy_classification_t* res{};
|
||||
int* res_count{};
|
||||
auto status = mmdeploy_classifier_apply(classifier, mats.data(), (int)mats.size(),
|
||||
&res, &res_count);
|
||||
if (status != MMDEPLOY_SUCCESS) {
|
||||
fprintf(stderr, "failed to apply classifier to batch images %d, code: %d\n",
|
||||
(int)mats.size(), (int)status);
|
||||
return 1;
|
||||
}
|
||||
// print the inference results
|
||||
auto res_ptr = res;
|
||||
for (int j = 0; j < (int)mats.size(); ++j) {
|
||||
fprintf(stderr, "results in the %d-th image:\n", image_ids[j]);
|
||||
for (int k = 0; k < res_count[j]; ++k, ++res_ptr) {
|
||||
fprintf(stderr, " label: %d, score: %.4f\n", res_ptr->label_id, res_ptr->score);
|
||||
}
|
||||
}
|
||||
// release results buffer
|
||||
mmdeploy_classifier_release_result(res, res_count, (int)mats.size());
|
||||
return 0;
|
||||
}
|
||||
|
|
@ -0,0 +1,147 @@
|
|||
#include <fstream>
|
||||
#include <opencv2/imgcodecs/imgcodecs.hpp>
|
||||
#include <opencv2/imgproc/imgproc.hpp>
|
||||
#include <string>
|
||||
|
||||
#include "mmdeploy/detector.h"
|
||||
|
||||
static int batch_inference(mmdeploy_detector_t detector, std::vector<cv::Mat>& images,
|
||||
const std::vector<int>& image_ids,
|
||||
const std::vector<mmdeploy_mat_t>& mats);
|
||||
|
||||
static void visualize_detection(const std::string& output_name, cv::Mat& image,
|
||||
const mmdeploy_detection_t* bboxes_ptr, int bboxes_num);
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
if (argc < 5) {
|
||||
fprintf(stderr, "usage:\n object_detection device_name sdk_model_path "
|
||||
"file_path batch_size\n");
|
||||
return 1;
|
||||
}
|
||||
auto device_name = argv[1];
|
||||
auto model_path = argv[2];
|
||||
|
||||
mmdeploy_detector_t detector{};
|
||||
int status{};
|
||||
status = mmdeploy_detector_create_by_path(model_path, device_name, 0, &detector);
|
||||
if (status != MMDEPLOY_SUCCESS) {
|
||||
fprintf(stderr, "failed to create detector, code: %d\n", (int)status);
|
||||
return 1;
|
||||
}
|
||||
|
||||
// file_path is the path of an image list file
|
||||
std::string file_path = argv[3];
|
||||
const int batch = std::stoi(argv[argc-1]);
|
||||
|
||||
// read image paths from the file
|
||||
std::ifstream ifs(file_path);
|
||||
std::string img_path;
|
||||
std::vector<std::string> img_paths;
|
||||
while (ifs >> img_path) {
|
||||
img_paths.emplace_back(std::move(img_path));
|
||||
}
|
||||
|
||||
|
||||
// read images and process batch inference
|
||||
std::vector<cv::Mat> images;
|
||||
std::vector<int> image_ids;
|
||||
std::vector<mmdeploy_mat_t> mats;
|
||||
for (int i = 0; i < (int)img_paths.size(); ++i) {
|
||||
auto img = cv::imread(img_paths[i]);
|
||||
if (!img.data) {
|
||||
fprintf(stderr, "failed to load image: %s\n", img_paths[i].c_str());
|
||||
continue;
|
||||
}
|
||||
images.push_back(img);
|
||||
image_ids.push_back(i);
|
||||
mmdeploy_mat_t mat{
|
||||
img.data, img.rows, img.cols, 3, MMDEPLOY_PIXEL_FORMAT_BGR, MMDEPLOY_DATA_TYPE_UINT8};
|
||||
mats.push_back(mat);
|
||||
|
||||
// process batch inference
|
||||
if ((int)mats.size() == batch) {
|
||||
if (batch_inference(detector, images, image_ids, mats) != 0) {
|
||||
continue;
|
||||
}
|
||||
// clear buffer for next batch
|
||||
mats.clear();
|
||||
image_ids.clear();
|
||||
images.clear();
|
||||
}
|
||||
}
|
||||
// process batch inference if there are still unhandled images
|
||||
if (!mats.empty()) {
|
||||
(void)batch_inference(detector, images, image_ids, mats);
|
||||
}
|
||||
|
||||
mmdeploy_detector_destroy(detector);
|
||||
return 0;
|
||||
}
|
||||
|
||||
int batch_inference(mmdeploy_detector_t detector, std::vector<cv::Mat>& images,
|
||||
const std::vector<int>& image_ids,
|
||||
const std::vector<mmdeploy_mat_t>& mats) {
|
||||
mmdeploy_detection_t* bboxes{};
|
||||
int* res_count{};
|
||||
auto status = mmdeploy_detector_apply(detector, mats.data(), mats.size(), &bboxes, &res_count);
|
||||
if (status != MMDEPLOY_SUCCESS) {
|
||||
fprintf(stderr, "failed to apply detector, code: %d\n", (int)status);
|
||||
return 1;
|
||||
}
|
||||
|
||||
mmdeploy_detection_t* bboxes_ptr = bboxes;
|
||||
for (int i = 0; i < (int)mats.size(); ++i) {
|
||||
fprintf(stdout, "results in the %d-th image:\n bbox_count=%d\n", image_ids[i], res_count[i]);
|
||||
const std::string output_name = "output_detection_" + std::to_string(image_ids[i]) + ".png";
|
||||
visualize_detection(output_name, images[i], bboxes_ptr, res_count[i]);
|
||||
bboxes_ptr = bboxes_ptr + res_count[i];
|
||||
}
|
||||
|
||||
mmdeploy_detector_release_result(bboxes, res_count, mats.size());
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
||||
void visualize_detection(const std::string& output_name, cv::Mat& image,
|
||||
const mmdeploy_detection_t* bboxes_ptr, int bbox_num) {
|
||||
for (int i = 0; i < bbox_num; ++i, ++bboxes_ptr) {
|
||||
const auto& box = bboxes_ptr->bbox;
|
||||
const auto& mask = bboxes_ptr->mask;
|
||||
|
||||
fprintf(stdout,
|
||||
" box %d, left=%.2f, top=%.2f, right=%.2f, bottom=%.2f, "
|
||||
"label=%d, score=%.4f\n",
|
||||
i, box.left, box.top, box.right, box.bottom, bboxes_ptr->label_id, bboxes_ptr->score);
|
||||
|
||||
// skip detections with invalid bbox size (bbox height or width < 1)
|
||||
if ((box.right - box.left) < 1 || (box.bottom - box.top) < 1) {
|
||||
continue;
|
||||
}
|
||||
|
||||
// skip detections less than specified score threshold
|
||||
if (bboxes_ptr->score < 0.3) {
|
||||
continue;
|
||||
}
|
||||
|
||||
// generate mask overlay if model exports masks
|
||||
if (mask != nullptr) {
|
||||
fprintf(stdout, "mask %d, height=%d, width=%d\n", i, mask->height, mask->width);
|
||||
|
||||
cv::Mat imgMask(mask->height, mask->width, CV_8UC1, &mask->data[0]);
|
||||
auto x0 = std::max(std::floor(box.left) - 1, 0.f);
|
||||
auto y0 = std::max(std::floor(box.top) - 1, 0.f);
|
||||
cv::Rect roi((int)x0, (int)y0, mask->width, mask->height);
|
||||
|
||||
// split the RGB channels, overlay mask to a specific color channel
|
||||
cv::Mat ch[3];
|
||||
split(image, ch);
|
||||
int col = 0;
|
||||
cv::bitwise_or(imgMask, ch[col](roi), ch[col](roi));
|
||||
merge(ch, 3, image);
|
||||
}
|
||||
|
||||
cv::rectangle(image, cv::Point{(int)box.left, (int)box.top},
|
||||
cv::Point{(int)box.right, (int)box.bottom}, cv::Scalar{0, 255, 0});
|
||||
}
|
||||
cv::imwrite(output_name, image);
|
||||
}
|
||||
|
|
@ -0,0 +1,427 @@
|
|||
|
||||
|
||||
#include "mmdeploy/archive/json_archive.h"
|
||||
#include "mmdeploy/archive/value_archive.h"
|
||||
#include "mmdeploy/common.hpp"
|
||||
#include "mmdeploy/core/mat.h"
|
||||
#include "mmdeploy/core/module.h"
|
||||
#include "mmdeploy/core/utils/formatter.h"
|
||||
#include "mmdeploy/experimental/module_adapter.h"
|
||||
#include "mmdeploy/pipeline.hpp"
|
||||
#include "opencv2/highgui.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include "opencv2/videoio.hpp"
|
||||
|
||||
const auto config_json = R"(
|
||||
{
|
||||
"type": "Pipeline",
|
||||
"input": ["data", "use_det", "state"],
|
||||
"output": "targets",
|
||||
"tasks": [
|
||||
{
|
||||
"type": "Cond",
|
||||
"input": ["use_det", "data"],
|
||||
"output": "dets",
|
||||
"body": {
|
||||
"name": "detection",
|
||||
"type": "Inference",
|
||||
"params": { "model": "detection" }
|
||||
}
|
||||
},
|
||||
{
|
||||
"type": "Task",
|
||||
"module": "ProcessBboxes",
|
||||
"input": ["dets", "data", "state"],
|
||||
"output": "rois"
|
||||
},
|
||||
{
|
||||
"input": "*rois",
|
||||
"output": "*keypoints",
|
||||
"name": "pose",
|
||||
"type": "Inference",
|
||||
"params": { "model": "pose" }
|
||||
},
|
||||
{
|
||||
"type": "Task",
|
||||
"module": "TrackPose",
|
||||
"scheduler": "pool",
|
||||
"input": ["keypoints", "state"],
|
||||
"output": "targets"
|
||||
}
|
||||
]
|
||||
}
|
||||
)"_json;
|
||||
|
||||
namespace mmdeploy {
|
||||
|
||||
#define REGISTER_SIMPLE_MODULE(name, fn) \
|
||||
class name##_Creator : public ::mmdeploy::Creator<Module> { \
|
||||
const char* GetName() const override { return #name; } \
|
||||
std::unique_ptr<Module> Create(const Value&) override { return CreateTask(fn); } \
|
||||
}; \
|
||||
REGISTER_MODULE(Module, name##_Creator)
|
||||
|
||||
std::optional<std::array<float, 4>> keypoints_to_bbox(const std::vector<cv::Point2f>& keypoints,
|
||||
const std::vector<float>& scores, float img_h,
|
||||
float img_w, float scale = 1.5,
|
||||
float kpt_thr = 0.3) {
|
||||
auto valid = false;
|
||||
auto x1 = static_cast<float>(img_w);
|
||||
auto y1 = static_cast<float>(img_h);
|
||||
auto x2 = 0.f;
|
||||
auto y2 = 0.f;
|
||||
for (size_t i = 0; i < keypoints.size(); ++i) {
|
||||
auto& kpt = keypoints[i];
|
||||
if (scores[i] > kpt_thr) {
|
||||
x1 = std::min(x1, kpt.x);
|
||||
y1 = std::min(y1, kpt.y);
|
||||
x2 = std::max(x2, kpt.x);
|
||||
y2 = std::max(y2, kpt.y);
|
||||
valid = true;
|
||||
}
|
||||
}
|
||||
if (!valid) {
|
||||
return std::nullopt;
|
||||
}
|
||||
auto xc = .5f * (x1 + x2);
|
||||
auto yc = .5f * (y1 + y2);
|
||||
auto w = (x2 - x1) * scale;
|
||||
auto h = (y2 - y1) * scale;
|
||||
|
||||
return std::array<float, 4>{
|
||||
std::max(0.f, std::min(img_w, xc - .5f * w)),
|
||||
std::max(0.f, std::min(img_h, yc - .5f * h)),
|
||||
std::max(0.f, std::min(img_w, xc + .5f * w)),
|
||||
std::max(0.f, std::min(img_h, yc + .5f * h)),
|
||||
};
|
||||
}
|
||||
|
||||
struct Track {
|
||||
std::vector<std::vector<cv::Point2f>> keypoints;
|
||||
std::vector<std::vector<float>> scores;
|
||||
std::vector<std::array<float, 4>> bboxes;
|
||||
int64_t track_id{-1};
|
||||
};
|
||||
|
||||
struct TrackInfo {
|
||||
std::vector<Track> tracks;
|
||||
int64_t next_id{0};
|
||||
};
|
||||
|
||||
MMDEPLOY_REGISTER_TYPE_ID(TrackInfo, 0xcfe87980aa895d3a); // randomly generated type id
|
||||
|
||||
Value::Array GetObjectsByTracking(Value& state, int img_h, int img_w) {
|
||||
Value::Array objs;
|
||||
auto& track_info = state["track_info"].get_ref<TrackInfo&>();
|
||||
for (auto& track : track_info.tracks) {
|
||||
auto bbox = keypoints_to_bbox(track.keypoints.back(), track.scores.back(),
|
||||
static_cast<float>(img_h), static_cast<float>(img_w));
|
||||
if (bbox) {
|
||||
objs.push_back({{"bbox", to_value(*bbox)}});
|
||||
}
|
||||
}
|
||||
return objs;
|
||||
}
|
||||
|
||||
Value ProcessBboxes(const Value& detections, const Value& data, Value state) {
|
||||
assert(state.is_pointer());
|
||||
Value::Array bboxes;
|
||||
if (detections.is_array()) { // has detections
|
||||
auto& dets = detections.array();
|
||||
for (const auto& det : dets) {
|
||||
if (det["label_id"].get<int>() == 0 && det["score"].get<float>() >= .3f) {
|
||||
bboxes.push_back(det);
|
||||
}
|
||||
}
|
||||
MMDEPLOY_INFO("bboxes by detection: {}", bboxes.size());
|
||||
state["bboxes"] = bboxes;
|
||||
} else { // no detections, use tracked results
|
||||
auto img_h = state["img_shape"][0].get<int>();
|
||||
auto img_w = state["img_shape"][1].get<int>();
|
||||
bboxes = GetObjectsByTracking(state, img_h, img_w);
|
||||
MMDEPLOY_INFO("GetObjectsByTracking: {}", bboxes.size());
|
||||
}
|
||||
// attach bboxes to image data
|
||||
for (auto& bbox : bboxes) {
|
||||
auto img = data["ori_img"].get<framework::Mat>();
|
||||
auto box = from_value<std::array<float, 4>>(bbox["bbox"]);
|
||||
cv::Rect rect(cv::Rect2f(cv::Point2f(box[0], box[1]), cv::Point2f(box[2], box[3])));
|
||||
bbox = Value::Object{
|
||||
{"ori_img", img}, {"bbox", {rect.x, rect.y, rect.width, rect.height}}, {"rotation", 0.f}};
|
||||
};
|
||||
return bboxes;
|
||||
}
|
||||
REGISTER_SIMPLE_MODULE(ProcessBboxes, ProcessBboxes);
|
||||
|
||||
// xyxy format
|
||||
float ComputeIoU(const std::array<float, 4>& a, const std::array<float, 4>& b) {
|
||||
auto x1 = std::max(a[0], b[0]);
|
||||
auto y1 = std::max(a[1], b[1]);
|
||||
auto x2 = std::min(a[2], b[2]);
|
||||
auto y2 = std::min(a[3], b[3]);
|
||||
|
||||
auto inter_area = std::max(0.f, x2 - x1) * std::max(0.f, y2 - y1);
|
||||
|
||||
auto a_area = (a[2] - a[0]) * (a[3] - a[1]);
|
||||
auto b_area = (b[2] - b[0]) * (b[3] - b[1]);
|
||||
auto union_area = a_area + b_area - inter_area;
|
||||
|
||||
if (union_area == 0.f) {
|
||||
return 0;
|
||||
}
|
||||
|
||||
return inter_area / union_area;
|
||||
}
|
||||
|
||||
void UpdateTrack(Track& track, std::vector<cv::Point2f>& keypoints, std::vector<float>& score,
|
||||
const std::array<float, 4>& bbox, int n_history) {
|
||||
if (track.scores.size() == n_history) {
|
||||
std::rotate(track.keypoints.begin(), track.keypoints.begin() + 1, track.keypoints.end());
|
||||
std::rotate(track.scores.begin(), track.scores.begin() + 1, track.scores.end());
|
||||
std::rotate(track.bboxes.begin(), track.bboxes.begin() + 1, track.bboxes.end());
|
||||
track.keypoints.back() = std::move(keypoints);
|
||||
track.scores.back() = std::move(score);
|
||||
track.bboxes.back() = bbox;
|
||||
} else {
|
||||
track.keypoints.push_back(std::move(keypoints));
|
||||
track.scores.push_back(std::move(score));
|
||||
track.bboxes.push_back(bbox);
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<std::tuple<int, int, float>> GreedyAssignment(const std::vector<float>& scores,
|
||||
int n_rows, int n_cols, float thr) {
|
||||
std::vector<int> used_rows(n_rows);
|
||||
std::vector<int> used_cols(n_cols);
|
||||
std::vector<std::tuple<int, int, float>> assignment;
|
||||
assignment.reserve(std::max(n_rows, n_cols));
|
||||
while (true) {
|
||||
auto max_score = 0.f;
|
||||
int max_row = -1;
|
||||
int max_col = -1;
|
||||
for (int i = 0; i < n_rows; ++i) {
|
||||
if (!used_rows[i]) {
|
||||
for (int j = 0; j < n_cols; ++j) {
|
||||
if (!used_cols[j]) {
|
||||
if (scores[i * n_cols + j] > max_score) {
|
||||
max_score = scores[i * n_cols + j];
|
||||
max_row = i;
|
||||
max_col = j;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
if (max_score < thr) {
|
||||
break;
|
||||
}
|
||||
used_rows[max_row] = 1;
|
||||
used_cols[max_col] = 1;
|
||||
assignment.emplace_back(max_row, max_col, max_score);
|
||||
}
|
||||
return assignment;
|
||||
}
|
||||
|
||||
void TrackStep(std::vector<std::vector<cv::Point2f>>& keypoints,
|
||||
std::vector<std::vector<float>>& scores, TrackInfo& track_info, int img_h, int img_w,
|
||||
float iou_thr, int min_keypoints, int n_history) {
|
||||
auto& tracks = track_info.tracks;
|
||||
|
||||
std::vector<Track> new_tracks;
|
||||
new_tracks.reserve(tracks.size());
|
||||
|
||||
std::vector<std::array<float, 4>> bboxes;
|
||||
bboxes.reserve(keypoints.size());
|
||||
|
||||
std::vector<int> indices;
|
||||
indices.reserve(keypoints.size());
|
||||
|
||||
for (size_t i = 0; i < keypoints.size(); ++i) {
|
||||
if (auto bbox = keypoints_to_bbox(keypoints[i], scores[i], img_h, img_w, 1.f, 0.f)) {
|
||||
bboxes.push_back(*bbox);
|
||||
indices.push_back(i);
|
||||
}
|
||||
}
|
||||
|
||||
const auto n_rows = static_cast<int>(bboxes.size());
|
||||
const auto n_cols = static_cast<int>(tracks.size());
|
||||
|
||||
std::vector<float> similarities(n_rows * n_cols);
|
||||
for (size_t i = 0; i < n_rows; ++i) {
|
||||
for (size_t j = 0; j < n_cols; ++j) {
|
||||
similarities[i * n_cols + j] = ComputeIoU(bboxes[i], tracks[j].bboxes.back());
|
||||
}
|
||||
}
|
||||
|
||||
const auto assignment = GreedyAssignment(similarities, n_rows, n_cols, iou_thr);
|
||||
|
||||
std::vector<int> used(n_rows);
|
||||
for (auto [i, j, _] : assignment) {
|
||||
auto k = indices[i];
|
||||
UpdateTrack(tracks[j], keypoints[k], scores[k], bboxes[i], n_history);
|
||||
new_tracks.push_back(std::move(tracks[j]));
|
||||
used[i] = true;
|
||||
}
|
||||
|
||||
for (size_t i = 0; i < used.size(); ++i) {
|
||||
if (used[i] == 0) {
|
||||
auto k = indices[i];
|
||||
auto count = std::count_if(scores[k].begin(), scores[k].end(), [](auto x) { return x > 0; });
|
||||
if (count >= min_keypoints) {
|
||||
auto& track = new_tracks.emplace_back();
|
||||
track.track_id = track_info.next_id++;
|
||||
UpdateTrack(track, keypoints[k], scores[k], bboxes[i], n_history);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
tracks = std::move(new_tracks);
|
||||
}
|
||||
|
||||
Value TrackPose(const Value& result, Value state) {
|
||||
assert(state.is_pointer());
|
||||
assert(result.is_array());
|
||||
std::vector<std::vector<cv::Point2f>> keypoints;
|
||||
std::vector<std::vector<float>> scores;
|
||||
for (auto& output : result.array()) {
|
||||
auto& k = keypoints.emplace_back();
|
||||
auto& s = scores.emplace_back();
|
||||
for (auto& kpt : output["key_points"].array()) {
|
||||
k.push_back(cv::Point2f{kpt["bbox"][0].get<float>(), kpt["bbox"][1].get<float>()});
|
||||
s.push_back(kpt["score"].get<float>());
|
||||
}
|
||||
}
|
||||
auto& track_info = state["track_info"].get_ref<TrackInfo&>();
|
||||
auto img_h = state["img_shape"][0].get<int>();
|
||||
auto img_w = state["img_shape"][1].get<int>();
|
||||
auto iou_thr = state["iou_thr"].get<float>();
|
||||
auto min_keypoints = state["min_keypoints"].get<int>();
|
||||
auto n_history = state["n_history"].get<int>();
|
||||
TrackStep(keypoints, scores, track_info, img_h, img_w, iou_thr, min_keypoints, n_history);
|
||||
|
||||
Value::Array targets;
|
||||
for (const auto& track : track_info.tracks) {
|
||||
if (auto bbox = keypoints_to_bbox(track.keypoints.back(), track.scores.back(), img_h, img_w)) {
|
||||
Value::Array kpts;
|
||||
kpts.reserve(track.keypoints.back().size());
|
||||
for (const auto& kpt : track.keypoints.back()) {
|
||||
kpts.push_back(kpt.x);
|
||||
kpts.push_back(kpt.y);
|
||||
}
|
||||
targets.push_back({{"bbox", to_value(*bbox)}, {"keypoints", std::move(kpts)}});
|
||||
}
|
||||
}
|
||||
return targets;
|
||||
}
|
||||
REGISTER_SIMPLE_MODULE(TrackPose, TrackPose);
|
||||
|
||||
class PoseTracker {
|
||||
public:
|
||||
using State = Value;
|
||||
|
||||
public:
|
||||
PoseTracker(const Model& det_model, const Model& pose_model, Context context)
|
||||
: pipeline_([&] {
|
||||
context.Add("detection", det_model);
|
||||
context.Add("pose", pose_model);
|
||||
auto config = from_json<Value>(config_json);
|
||||
return Pipeline{config, context};
|
||||
}()) {}
|
||||
|
||||
State CreateState() { // NOLINT
|
||||
return make_pointer({{"frame_id", 0},
|
||||
{"n_history", 10},
|
||||
{"iou_thr", .3f},
|
||||
{"min_keypoints", 3},
|
||||
{"track_info", TrackInfo{}}});
|
||||
}
|
||||
|
||||
Value Track(const Mat& img, State& state, int use_detector = -1) {
|
||||
assert(state.is_pointer());
|
||||
framework::Mat mat(img.desc().height, img.desc().width,
|
||||
static_cast<PixelFormat>(img.desc().format),
|
||||
static_cast<DataType>(img.desc().type), {img.desc().data, [](void*) {}});
|
||||
// TODO: get_ref<int&> is not working
|
||||
auto frame_id = state["frame_id"].get<int>();
|
||||
if (use_detector < 0) {
|
||||
use_detector = frame_id % 10 == 0;
|
||||
if (use_detector) {
|
||||
MMDEPLOY_WARN("use detector");
|
||||
}
|
||||
}
|
||||
state["frame_id"] = frame_id + 1;
|
||||
state["img_shape"] = {mat.height(), mat.width()};
|
||||
Value::Object data{{"ori_img", mat}};
|
||||
Value input{{data}, {use_detector}, {state}};
|
||||
return pipeline_.Apply(input)[0][0];
|
||||
}
|
||||
|
||||
private:
|
||||
Pipeline pipeline_;
|
||||
};
|
||||
|
||||
} // namespace mmdeploy
|
||||
|
||||
using namespace mmdeploy;
|
||||
|
||||
void Visualize(cv::Mat& frame, const Value& result) {
|
||||
static std::vector<std::pair<int, int>> skeleton{
|
||||
{15, 13}, {13, 11}, {16, 14}, {14, 12}, {11, 12}, {5, 11}, {6, 12}, {5, 6}, {5, 7}, {6, 8},
|
||||
{7, 9}, {8, 10}, {1, 2}, {0, 1}, {0, 2}, {1, 3}, {2, 4}, {3, 5}, {4, 6}};
|
||||
const auto& targets = result.array();
|
||||
for (const auto& target : targets) {
|
||||
auto bbox = from_value<std::array<float, 4>>(target["bbox"]);
|
||||
auto kpts = from_value<std::vector<float>>(target["keypoints"]);
|
||||
cv::Point p1(bbox[0], bbox[1]);
|
||||
cv::Point p2(bbox[2], bbox[3]);
|
||||
cv::rectangle(frame, p1, p2, cv::Scalar(0, 255, 0));
|
||||
for (int i = 0; i < kpts.size(); i += 2) {
|
||||
cv::Point p(kpts[i], kpts[i + 1]);
|
||||
cv::circle(frame, p, 1, cv::Scalar(0, 255, 255), 2, cv::LINE_AA);
|
||||
}
|
||||
for (int i = 0; i < skeleton.size(); ++i) {
|
||||
auto [u, v] = skeleton[i];
|
||||
cv::Point p_u(kpts[u * 2], kpts[u * 2 + 1]);
|
||||
cv::Point p_v(kpts[v * 2], kpts[v * 2 + 1]);
|
||||
cv::line(frame, p_u, p_v, cv::Scalar(0, 255, 255), 1, cv::LINE_AA);
|
||||
}
|
||||
}
|
||||
cv::imshow("", frame);
|
||||
cv::waitKey(10);
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
const auto device_name = argv[1];
|
||||
const auto det_model_path = argv[2];
|
||||
const auto pose_model_path = argv[3];
|
||||
const auto video_path = argv[4];
|
||||
Device device(device_name);
|
||||
Context context(device);
|
||||
auto pool = Scheduler::ThreadPool(4);
|
||||
auto infer = Scheduler::Thread();
|
||||
context.Add("pool", pool);
|
||||
context.Add("infer", infer);
|
||||
PoseTracker tracker(Model(det_model_path), Model(pose_model_path), context);
|
||||
auto state = tracker.CreateState();
|
||||
|
||||
cv::Mat frame;
|
||||
std::chrono::duration<double, std::milli> dt{};
|
||||
|
||||
int frame_id{};
|
||||
|
||||
cv::VideoCapture video(video_path);
|
||||
while (true) {
|
||||
video >> frame;
|
||||
if (!frame.data) {
|
||||
break;
|
||||
}
|
||||
auto t0 = std::chrono::high_resolution_clock::now();
|
||||
auto result = tracker.Track(frame, state);
|
||||
auto t1 = std::chrono::high_resolution_clock::now();
|
||||
dt += t1 - t0;
|
||||
++frame_id;
|
||||
Visualize(frame, result);
|
||||
}
|
||||
|
||||
MMDEPLOY_INFO("frames: {}, time {} ms", frame_id, dt.count());
|
||||
}
|
||||
|
|
@ -2,7 +2,13 @@
|
|||
|
||||
Through user investigation, we know that most users are already familiar with python and torch before using mmdeploy. Therefore we provide scripts to simplify mmdeploy installation.
|
||||
|
||||
Assuming you have a python ready (whether `conda` or `pyenv`), run this script to install mmdeploy + ncnn backend, `nproc` is not compulsory.
|
||||
Assuming you already have
|
||||
|
||||
- python3 -m pip (`conda` or `pyenv`)
|
||||
- nvcc (depends on inference backend)
|
||||
- torch (not compulsory)
|
||||
|
||||
run this script to install mmdeploy + ncnn backend, `nproc` is not compulsory.
|
||||
|
||||
```bash
|
||||
$ cd /path/to/mmdeploy
|
||||
|
|
|
|||
|
|
@ -39,3 +39,4 @@ Please visit the following links to find out how to build MMDeploy according to
|
|||
- [NVIDIA Jetson](jetsons.md)
|
||||
- [SNPE](snpe.md)
|
||||
- [RISC-V](riscv.md)
|
||||
- [Rockchip](rockchip.md)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,108 @@
|
|||
# Ubuntu Cross Build aarch64
|
||||
|
||||
mmdeploy chose ncnn as the inference backend for aarch64 embedded linux devices. There are two parts:
|
||||
|
||||
Host
|
||||
|
||||
- model conversion
|
||||
- cross build SDK and demo for embedded devices
|
||||
|
||||
Device
|
||||
|
||||
- Run converted model
|
||||
|
||||
## 1. Model Convert on Host
|
||||
|
||||
Refer to the doc to install [mmdeploy](../01-how-to-build/) and [mmcls](https://github.com/open-mmlab/mmclassification), and convert resnet18 for model package
|
||||
|
||||
```bash
|
||||
export MODEL_CONFIG=/path/to/mmclassification/configs/resnet/resnet18_8xb32_in1k.py
|
||||
export MODEL_PATH=https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth
|
||||
|
||||
# Convert resnet18
|
||||
cd /path/to/mmdeploy
|
||||
python tools/deploy.py \
|
||||
configs/mmcls/classification_ncnn_static.py \
|
||||
$MODEL_CONFIG \
|
||||
$MODEL_PATH \
|
||||
tests/data/tiger.jpeg \
|
||||
--work-dir resnet18 \
|
||||
--device cpu \
|
||||
--dump-info
|
||||
```
|
||||
|
||||
## 2. Cross Build on Host
|
||||
|
||||
It is recommended to compile directly with the script
|
||||
|
||||
```bash
|
||||
sh -x tools/scripts/ubuntu_cross_build_aarch64.sh
|
||||
```
|
||||
|
||||
The following is the manual process corresponding to the script:
|
||||
|
||||
a) Install aarch64 build tools
|
||||
|
||||
```bash
|
||||
sudo apt install -y gcc-aarch64-linux-gnu g++-aarch64-linux-gnu
|
||||
```
|
||||
|
||||
b) Cross build opencv and install to /tmp/ocv-aarch64
|
||||
|
||||
```bash
|
||||
git clone https://github.com/opencv/opencv --depth=1 --branch=4.x --recursive
|
||||
cd opencv/platforms/linux/
|
||||
mkdir build && cd build
|
||||
cmake ../../.. \
|
||||
-DCMAKE_INSTALL_PREFIX=/tmp/ocv-aarch64 \
|
||||
-DCMAKE_TOOLCHAIN_FILE=../aarch64-gnu.toolchain.cmake
|
||||
make -j && make install
|
||||
ls -alh /tmp/ocv-aarch64
|
||||
..
|
||||
```
|
||||
|
||||
c) Cross build ncnn and install to /tmp/ncnn-aarch64
|
||||
|
||||
```bash
|
||||
git clone https://github.com/tencent/ncnn --branch 20220729 --depth=1
|
||||
mkdir build && cd build
|
||||
cmake .. \
|
||||
-DCMAKE_TOOLCHAIN_FILE=../toolchains/aarch64-linux-gnu.toolchain.cmake \
|
||||
-DCMAKE_INSTALL_PREFIX=/tmp/ncnn-aarch64
|
||||
make -j && make install
|
||||
ls -alh /tmp/ncnn-aarch64
|
||||
..
|
||||
```
|
||||
|
||||
d) Cross build mmdeploy
|
||||
|
||||
```bash
|
||||
git submodule init
|
||||
git submodule update
|
||||
mkdir build && cd build
|
||||
cmake .. \
|
||||
-DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/aarch64-linux-gnu.cmake \
|
||||
-DMMDEPLOY_TARGET_DEVICES="cpu" \
|
||||
-DMMDEPLOY_TARGET_BACKENDS="ncnn" \
|
||||
-Dncnn_DIR=/tmp/ncnn-aarch64/lib/cmake/ncnn \
|
||||
-DOpenCV_DIR=/tmp/ocv-aarch64/lib/cmake/opencv4
|
||||
make install
|
||||
ls -lah install/bin/*
|
||||
..
|
||||
```
|
||||
|
||||
## 3. Execute on Device
|
||||
|
||||
Make sure that `--dump-info` is used during model conversion, so that the `resnet18` directory contains the files required by the SDK such as `pipeline.json`.
|
||||
|
||||
Copy the model folder(resnet18), executable(image_classification) file, test image(tests/data/tiger.jpeg) and prebuilt OpenCV(/tmp/ocv-aarch64) to the device.
|
||||
|
||||
```bash
|
||||
./image_classification cpu ./resnet18 tiger.jpeg
|
||||
..
|
||||
label: 292, score: 0.9261
|
||||
label: 282, score: 0.0726
|
||||
label: 290, score: 0.0008
|
||||
label: 281, score: 0.0002
|
||||
label: 340, score: 0.0001
|
||||
```
|
||||
|
|
@ -0,0 +1,147 @@
|
|||
# Build for RKNN
|
||||
|
||||
This tutorial is based on Linux systems like Ubuntu-18.04 and Rockchip NPU like `rk3588`.
|
||||
|
||||
## Installation
|
||||
|
||||
It is recommended to create a virtual environment for the project.
|
||||
|
||||
1. get RKNN-Toolkit2 through:
|
||||
|
||||
```
|
||||
git clone git@github.com:rockchip-linux/rknn-toolkit2.git
|
||||
```
|
||||
|
||||
2. install RKNN python package following [official doc](https://github.com/rockchip-linux/rknn-toolkit2/tree/master/doc). In our testing, we used the rknn-toolkit2 1.2.0 with commit id `834ba0b0a1ab8ee27024443d77b02b5ba48b67fc`. When installing rknn-toolkit2, it is better to append `--no-deps` after the commands to avoid dependency conflicts. For example:
|
||||
|
||||
```
|
||||
pip install packages/rknn_toolkit2-1.2.0_f7bb160f-cp36-cp36m-linux_x86_64.whl --no-deps
|
||||
```
|
||||
|
||||
3. Install ONNX==1.8.0 before reinstall MMDeploy from source following the [instructions](../01-how-to-build/build_from_source.md). Note that there are conflicts between the pip dependencies of MMDeploy and RKNN. Here is the suggested packages versions for python 3.6:
|
||||
|
||||
```
|
||||
protobuf==3.19.4
|
||||
onnx==1.8.0
|
||||
onnxruntime==1.8.0
|
||||
torch==1.8.0
|
||||
torchvision==0.9.0
|
||||
```
|
||||
|
||||
4. Install torch and torchvision using conda. For example:
|
||||
|
||||
```
|
||||
conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c conda-forge
|
||||
```
|
||||
|
||||
To work with models from [MMClassification](https://mmclassification.readthedocs.io/en/latest/getting_started.html), you may need to install it additionally.
|
||||
|
||||
## Usage
|
||||
|
||||
Example:
|
||||
|
||||
```bash
|
||||
python tools/deploy.py \
|
||||
configs/mmcls/classification_rknn_static.py \
|
||||
/mmclassification_dir/configs/resnet/resnet50_8xb32_in1k.py \
|
||||
https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth \
|
||||
/mmclassification_dir/demo/demo.JPEG \
|
||||
--work-dir ../resnet50 \
|
||||
--device cpu
|
||||
```
|
||||
|
||||
## Deployment config
|
||||
|
||||
With the deployment config, you can modify the `backend_config` for your preference. An example `backend_config` of mmclassification is shown as below:
|
||||
|
||||
```python
|
||||
backend_config = dict(
|
||||
type='rknn',
|
||||
common_config=dict(
|
||||
mean_values=None,
|
||||
std_values=None,
|
||||
target_platform='rk3588',
|
||||
optimization_level=3),
|
||||
quantization_config=dict(do_quantization=False, dataset=None),
|
||||
input_size_list=[[3, 224, 224]])
|
||||
|
||||
```
|
||||
|
||||
The contents of `common_config` are for `rknn.config()`. The contents of `quantization_config` are used to control `rknn.build()`.
|
||||
|
||||
## Build SDK with Rockchip NPU
|
||||
|
||||
1. get rknpu2 through:
|
||||
|
||||
```
|
||||
git clone git@github.com:rockchip-linux/rknpu2.git
|
||||
```
|
||||
|
||||
2. for linux, download gcc cross compiler. The download link of the compiler from the official user guide of `rknpu2` was deprecated. You may use another verified [link](https://github.com/Caesar-github/gcc-buildroot-9.3.0-2020.03-x86_64_aarch64-rockchip-linux-gnu). After download and unzip the compiler, you may open the terminal, set `RKNN_TOOL_CHAIN` and `RKNPU2_DEVICE_DIR` by `export RKNN_TOOL_CHAIN=/path/to/gcc/usr;export RKNPU2_DEVICE_DIR=/path/to/rknpu2/runtime/RK3588`.
|
||||
|
||||
3. after the above preparition, run the following commands:
|
||||
|
||||
```shell
|
||||
cd /path/to/mmdeploy
|
||||
mkdir -p build && rm -rf build/CM* && cd build
|
||||
export LD_LIBRARY_PATH=$RKNN_TOOL_CHAIN/lib64:$LD_LIBRARY_PATH
|
||||
cmake \
|
||||
-DCMAKE_TOOLCHAIN_FILE=/path/to/mmdeploy/cmake/toolchains/rknpu2-linux-gnu.cmake \
|
||||
-DMMDEPLOY_BUILD_SDK=ON \
|
||||
-DCMAKE_BUILD_TYPE=Debug \
|
||||
-DOpenCV_DIR=${RKNPU2_DEVICE_DIR}/../../examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV \
|
||||
-DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
|
||||
-DMMDEPLOY_TARGET_DEVICES="cpu" \
|
||||
-DMMDEPLOY_TARGET_BACKENDS="rknn" \
|
||||
-DMMDEPLOY_CODEBASES=all \
|
||||
-DMMDEPLOY_BUILD_TEST=ON \
|
||||
-DMMDEPLOY_BUILD_EXAMPLES=ON \
|
||||
..
|
||||
make && make install
|
||||
```
|
||||
|
||||
## Run the demo with SDK
|
||||
|
||||
First make sure that`--dump-info`is used during convert model, so that the working directory has the files required by the SDK such as `pipeline.json`.
|
||||
|
||||
`adb push` the model directory, executable file and .so to the device.
|
||||
|
||||
```bash
|
||||
cd /path/to/mmdeploy
|
||||
adb push resnet50 /data/local/tmp/resnet50
|
||||
adb push /mmclassification_dir/demo/demo.JPEG /data/local/tmp/resnet50/demo.JPEG
|
||||
cd build
|
||||
adb push lib /data/local/tmp/lib
|
||||
adb push bin/image_classification /data/local/tmp/image_classification
|
||||
```
|
||||
|
||||
Set up environment variable and execute the sample.
|
||||
|
||||
```bash
|
||||
adb shell
|
||||
cd /data/local/tmp
|
||||
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/data/local/tmp/lib
|
||||
./image_classification cpu ./resnet50 ./resnet50/demo.JPEG
|
||||
..
|
||||
label: 65, score: 0.95
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
- Quantization fails.
|
||||
|
||||
Empirically, RKNN require the inputs not normalized if `do_quantization` is set to `True`. Please modify the settings of `Normalize` in the `model_cfg` from
|
||||
|
||||
```python
|
||||
img_norm_cfg = dict(
|
||||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
|
||||
```
|
||||
|
||||
to
|
||||
|
||||
```python
|
||||
img_norm_cfg = dict(
|
||||
mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True)
|
||||
```
|
||||
|
||||
Besides, the `mean_values` and `std_values` of deploy_cfg should be replaced with original normalization settings of `model_cfg`. Let `mean_values=[123.675, 116.28, 103.53]` and `std_values=[58.395, 57.12, 57.375]`.
|
||||
|
|
@ -21,7 +21,7 @@
|
|||
|
||||
______________________________________________________________________
|
||||
|
||||
This tutorial takes `mmdeploy-0.9.0-windows-amd64-onnxruntime1.8.1.zip` and `mmdeploy-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip` as examples to show how to use the prebuilt packages.
|
||||
This tutorial takes `mmdeploy-0.10.0-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.
|
||||
|
||||
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-0.9.0-windows-amd64-onnxruntime1.8.1.zip
|
||||
pip install .\mmdeploy-0.9.0-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-0.9.0-py38-none-win_amd64.whl
|
||||
pip install .\mmdeploy-0.9.0-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-0.9.0-cp38-none-win_amd64.whl
|
||||
# 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
|
||||
```
|
||||
|
||||
: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-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip
|
||||
pip install .\mmdeploy-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-0.9.0-py38-none-win_amd64.whl
|
||||
pip install .\mmdeploy-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-0.9.0-cp38-none-win_amd64.whl
|
||||
# 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
|
||||
```
|
||||
|
||||
: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-0.9.0-windows-amd64-onnxruntime1.8.1
|
||||
|-- mmdeploy-0.10.0-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-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
|
||||
|-- mmdeploy-0.10.0-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-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
|
||||
|-- mmdeploy-0.9.0-windows-amd64-onnxruntime1.8.1
|
||||
|-- mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
|
||||
|-- mmdeploy-0.10.0-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-0.9.0-windows-amd64-onnxruntime1.8.1\sdk\example` directory
|
||||
Under `mmdeploy-0.10.0-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-0.9.0-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy `
|
||||
-DMMDeploy_DIR=C:\workspace\mmdeploy-0.10.0-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-0.9.0-windows-amd64-onnxruntime1.8.1\sdk\bin`) to the `PATH`.
|
||||
If choose to add environment variables, add the runtime libraries path of `mmdeploy` (`mmdeploy-0.10.0-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-0.9.0-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release` directory：
|
||||
Under `mmdeploy-0.10.0-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-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example` directory
|
||||
Under `mmdeploy-0.10.0-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-0.9.0-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy `
|
||||
-DMMDeploy_DIR=C:\workspace\mmdeploy-0.10.0-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-0.9.0-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-0.10.0-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-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release` directory
|
||||
Under `mmdeploy-0.10.0-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
|
||||
|
|
|
|||
|
|
@ -1,80 +1,9 @@
|
|||
# RKNN support
|
||||
# Supported RKNN feature
|
||||
|
||||
This tutorial is based on Linux systems like Ubuntu-18.04 and Rockchip NPU like `rk3588`.
|
||||
Currently, MMDeploy only tests rk3588 with linux platform.
|
||||
|
||||
## Installation
|
||||
The following features cannot be automatically enabled by mmdeploy and you need to manually modify the configuration in MMDeploy like [here](https://github.com/open-mmlab/mmdeploy/blob/master/configs/_base_/backends/rknn.py).
|
||||
|
||||
It is recommended to create a virtual environment for the project.
|
||||
|
||||
1. get RKNN-Toolkit2 through:
|
||||
|
||||
```
|
||||
git clone https://github.com/rockchip-linux/rknn-toolkit2
|
||||
```
|
||||
|
||||
2. install RKNN python package following [official doc](https://github.com/rockchip-linux/rknn-toolkit2/tree/master/doc). In our testing, we used the rknn-toolkit 1.2.0 with commit id `834ba0b0a1ab8ee27024443d77b02b5ba48b67fc`.
|
||||
|
||||
3. reinstall MMDeploy from source following the [instructions](../01-how-to-build/build_from_source.md). Note that there are conflicts between the pip dependencies of MMDeploy and RKNN. Here is the suggested packages versions for python 3.6:
|
||||
|
||||
```
|
||||
protobuf==3.19.4
|
||||
onnx==1.8.0
|
||||
onnxruntime==1.8.0
|
||||
torch==1.8.0
|
||||
torchvision==0.9.0
|
||||
```
|
||||
|
||||
To work with models from [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/get_started.md), you may need to install it additionally.
|
||||
|
||||
## Usage
|
||||
|
||||
Example:
|
||||
|
||||
```bash
|
||||
python tools/deploy.py \
|
||||
configs/mmdet/detection/detection_rknn_static.py \
|
||||
/mmdetection_dir/mmdetection/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py \
|
||||
/tmp/snapshots/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth \
|
||||
tests/data/tiger.jpeg \
|
||||
--work-dir ../deploy_result \
|
||||
--device cpu
|
||||
```
|
||||
|
||||
## Deployment config
|
||||
|
||||
With the deployment config, you can modify the `backend_config` for your preference. An example `backend_config` of mmclassification is shown as below:
|
||||
|
||||
```python
|
||||
backend_config = dict(
|
||||
type='rknn',
|
||||
common_config=dict(
|
||||
mean_values=None,
|
||||
std_values=None,
|
||||
target_platform='rk3588',
|
||||
optimization_level=3),
|
||||
quantization_config=dict(do_quantization=False, dataset=None),
|
||||
input_size_list=[[3, 224, 224]])
|
||||
|
||||
```
|
||||
|
||||
The contents of `common_config` are for `rknn.config()`. The contents of `quantization_config` are used to control `rknn.build()`.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
- Quantization fails.
|
||||
|
||||
Empirically, RKNN require the inputs not normalized if `do_quantization` is set to `False`. Please modify the settings of `Normalize` in the `model_cfg` from
|
||||
|
||||
```python
|
||||
img_norm_cfg = dict(
|
||||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
|
||||
```
|
||||
|
||||
to
|
||||
|
||||
```python
|
||||
img_norm_cfg = dict(
|
||||
mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True)
|
||||
```
|
||||
|
||||
Besides, the `mean_values` and `std_values` of deploy_cfg should be replaced with original normalization settings of `model_cfg`. Let `mean_values=[123.675, 116.28, 103.53]` and `std_values=[58.395, 57.12, 57.375]`.
|
||||
- target_platform other than `3588`
|
||||
- quantization settings
|
||||
- optimization level other than 3
|
||||
|
|
|
|||
|
|
@ -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/v0.9.0/mmdeploy-0.9.0-linux-x86_64-onnxruntime1.8.1.tar.gz
|
||||
tar -zxvf mmdeploy-0.9.0-linux-x86_64-onnxruntime1.8.1.tar.gz
|
||||
cd mmdeploy-0.9.0-linux-x86_64-onnxruntime1.8.1
|
||||
pip install dist/mmdeploy-0.9.0-py3-none-linux_x86_64.whl
|
||||
pip install sdk/python/mmdeploy_python-0.9.0-cp38-none-linux_x86_64.whl
|
||||
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
|
||||
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/v0.9.0/mmdeploy-0.9.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
|
||||
tar -zxvf mmdeploy-0.9.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
|
||||
cd mmdeploy-0.9.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
|
||||
pip install dist/mmdeploy-0.9.0-py3-none-linux_x86_64.whl
|
||||
pip install sdk/python/mmdeploy_python-0.9.0-cp38-none-linux_x86_64.whl
|
||||
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
|
||||
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-0.9.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
|
||||
cd mmdeploy-0.10.0-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
|
||||
|
|
|
|||
|
|
@ -61,6 +61,7 @@ You can switch between Chinese and English documents in the lower-left corner of
|
|||
05-supported-backends/snpe.md
|
||||
05-supported-backends/tensorrt.md
|
||||
05-supported-backends/torchscript.md
|
||||
05-supported-backends/rknn.md
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
|
|
|||
|
|
@ -2,7 +2,13 @@
|
|||
|
||||
通过用户调研，我们得知多数使用者在了解 mmdeploy 前，已经熟知 python 和 torch 用法。因此我们提供脚本简化 mmdeploy 安装。
|
||||
|
||||
假设您已经准备好 Python3.6 pip 以上环境（无论 conda 或 pyenv），运行这个脚本来安装 mmdeploy + ncnn backend，`nproc` 可以不指定。
|
||||
假设您已经准备好
|
||||
|
||||
- python3 -m pip（必须，conda 或 pyenv 皆可）
|
||||
- nvcc（取决于推理后端）
|
||||
- torch（非必须，可延后安装）
|
||||
|
||||
运行这个脚本来安装 mmdeploy + ncnn backend，`nproc` 可以不指定。
|
||||
|
||||
```bash
|
||||
$ cd /path/to/mmdeploy
|
||||
|
|
|
|||
|
|
@ -42,3 +42,4 @@ git clone -b master git@github.com:open-mmlab/mmdeploy.git --recursive
|
|||
- [NVIDIA Jetson](jetsons.md)
|
||||
- [Qcom SNPE](snpe.md)
|
||||
- [RISC-V](riscv.md)
|
||||
- [Rockchip](rockchip.md)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,108 @@
|
|||
# ubuntu 交叉编译 aarch64
|
||||
|
||||
mmdeploy 选 ncnn 作为 aarch64 嵌入式 linux 设备的推理后端。 完整的部署分为两部分：
|
||||
|
||||
Host
|
||||
|
||||
- 模型转换
|
||||
- 交叉编译嵌入式设备所需 SDK 和 bin
|
||||
|
||||
Device
|
||||
|
||||
- 运行编译结果
|
||||
|
||||
## 1. Host 模型转换
|
||||
|
||||
参照文档安装 [mmdeploy](../01-how-to-build/) 和 [mmcls](https://github.com/open-mmlab/mmclassification)，转换 resnet18 对应模型包
|
||||
|
||||
```bash
|
||||
export MODEL_CONFIG=/path/to/mmclassification/configs/resnet/resnet18_8xb32_in1k.py
|
||||
export MODEL_PATH=https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth
|
||||
|
||||
# 模型转换
|
||||
cd /path/to/mmdeploy
|
||||
python tools/deploy.py \
|
||||
configs/mmcls/classification_ncnn_static.py \
|
||||
$MODEL_CONFIG \
|
||||
$MODEL_PATH \
|
||||
tests/data/tiger.jpeg \
|
||||
--work-dir resnet18 \
|
||||
--device cpu \
|
||||
--dump-info
|
||||
```
|
||||
|
||||
## 2. Host 交叉编译
|
||||
|
||||
建议直接用脚本编译
|
||||
|
||||
```bash
|
||||
sh -x tools/scripts/ubuntu_cross_build_aarch64.sh
|
||||
```
|
||||
|
||||
以下是脚本对应的手动过程
|
||||
|
||||
a) 安装 aarch64 交叉编译工具
|
||||
|
||||
```bash
|
||||
sudo apt install -y gcc-aarch64-linux-gnu g++-aarch64-linux-gnu
|
||||
```
|
||||
|
||||
b) 交叉编译 opencv 安装到 tmp 目录
|
||||
|
||||
```bash
|
||||
git clone https://github.com/opencv/opencv --depth=1 --branch=4.x --recursive
|
||||
cd opencv/platforms/linux/
|
||||
mkdir build && cd build
|
||||
cmake ../../.. \
|
||||
-DCMAKE_INSTALL_PREFIX=/tmp/ocv-aarch64 \
|
||||
-DCMAKE_TOOLCHAIN_FILE=../aarch64-gnu.toolchain.cmake
|
||||
make -j && make install
|
||||
ls -alh /tmp/ocv-aarch64
|
||||
..
|
||||
```
|
||||
|
||||
c) 交叉编译 ncnn 安装到 tmp 目录
|
||||
|
||||
```bash
|
||||
git clone https://github.com/tencent/ncnn --branch 20220729 --depth=1
|
||||
mkdir build && cd build
|
||||
cmake .. \
|
||||
-DCMAKE_TOOLCHAIN_FILE=../toolchains/aarch64-linux-gnu.toolchain.cmake \
|
||||
-DCMAKE_INSTALL_PREFIX=/tmp/ncnn-aarch64
|
||||
make -j && make install
|
||||
ls -alh /tmp/ncnn-aarch64
|
||||
..
|
||||
```
|
||||
|
||||
d) 交叉编译 mmdeploy，install/bin 目录是可执行文件
|
||||
|
||||
```bash
|
||||
git submodule init
|
||||
git submodule update
|
||||
mkdir build && cd build
|
||||
cmake .. \
|
||||
-DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/aarch64-linux-gnu.cmake \
|
||||
-DMMDEPLOY_TARGET_DEVICES="cpu" \
|
||||
-DMMDEPLOY_TARGET_BACKENDS="ncnn" \
|
||||
-Dncnn_DIR=/tmp/ncnn-aarch64/lib/cmake/ncnn \
|
||||
-DOpenCV_DIR=/tmp/ocv-aarch64/lib/cmake/opencv4
|
||||
make install
|
||||
ls -lah install/bin/*
|
||||
..
|
||||
```
|
||||
|
||||
## 3. Device 执行
|
||||
|
||||
确认转换模型用了 `--dump-info`，这样 `resnet18` 目录才有 `pipeline.json` 等 SDK 所需文件。
|
||||
|
||||
把 dump 好的模型目录(resnet18)、可执行文件(image_classification)、测试图片(tests/data/tiger.jpeg)、交叉编译的 OpenCV(/tmp/ocv-aarch64) 拷贝到设备中
|
||||
|
||||
```bash
|
||||
./image_classification cpu ./resnet18 tiger.jpeg
|
||||
..
|
||||
label: 292, score: 0.9261
|
||||
label: 282, score: 0.0726
|
||||
label: 290, score: 0.0008
|
||||
label: 281, score: 0.0002
|
||||
label: 340, score: 0.0001
|
||||
```
|
||||
|
|
@ -0,0 +1,147 @@
|
|||
# 支持 RKNN
|
||||
|
||||
本教程基于 Ubuntu-18.04 和 Rockchip `rk3588` NPU。
|
||||
|
||||
## 安装
|
||||
|
||||
建议为项目创建一个虚拟环境。
|
||||
|
||||
1. 获取 RKNN-Toolkit2:
|
||||
|
||||
```
|
||||
git clone git@github.com:rockchip-linux/rknn-toolkit2.git
|
||||
```
|
||||
|
||||
2. 通过 [官方文档](https://github.com/rockchip-linux/rknn-toolkit2/tree/master/doc)，安装 RKNN python 安装包. 在我们的测试中, 使用的 rknn-toolkit 版本是 1.2.0，commit id `834ba0b0a1ab8ee27024443d77b02b5ba48b67fc`。安装 rknn-toolkit2 时，最好在安装命令后添加`--no-deps`，以避免依赖包的冲突。比如:
|
||||
|
||||
```
|
||||
pip install packages/rknn_toolkit2-1.2.0_f7bb160f-cp36-cp36m-linux_x86_64.whl --no-deps
|
||||
```
|
||||
|
||||
3. 先安装onnx==1.8.0,跟着 [instructions](../01-how-to-build/build_from_source.md)，源码安装 MMDeploy。 需要注意的是， MMDeploy 和 RKNN 依赖的安装包间有冲突的内容. 这里提供建议在 python 3.6 环境中使用的安装包版本:
|
||||
|
||||
```
|
||||
protobuf==3.19.4
|
||||
onnx==1.8.0
|
||||
onnxruntime==1.8.0
|
||||
torch==1.8.0
|
||||
torchvision==0.9.0
|
||||
```
|
||||
|
||||
4. 使用 conda 安装 torch and torchvision，比如:
|
||||
|
||||
```
|
||||
conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c conda-forge
|
||||
```
|
||||
|
||||
如要使用 [MMClassification](https://mmclassification.readthedocs.io/en/latest/getting_started.html)， 需要用户自己安装使用。
|
||||
|
||||
## 使用
|
||||
|
||||
例子:
|
||||
|
||||
```bash
|
||||
python tools/deploy.py \
|
||||
configs/mmcls/classification_rknn_static.py \
|
||||
/mmclassification_dir/configs/resnet/resnet50_8xb32_in1k.py \
|
||||
https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth \
|
||||
/mmclassification_dir/demo/demo.JPEG \
|
||||
--work-dir ../resnet50 \
|
||||
--device cpu
|
||||
```
|
||||
|
||||
## 部署 config
|
||||
|
||||
部署 config，你可以根据需要修改 `backend_config` 字段. 一个 mmclassification 的 `backend_config`例子如下:
|
||||
|
||||
```python
|
||||
backend_config = dict(
|
||||
type='rknn',
|
||||
common_config=dict(
|
||||
mean_values=None,
|
||||
std_values=None,
|
||||
target_platform='rk3588',
|
||||
optimization_level=3),
|
||||
quantization_config=dict(do_quantization=False, dataset=None),
|
||||
input_size_list=[[3, 224, 224]])
|
||||
|
||||
```
|
||||
|
||||
`common_config` 的内容服务于 `rknn.config()`. `quantization_config` 的内容服务于 `rknn.build()`。
|
||||
|
||||
## 安装 SDK
|
||||
|
||||
1. 获取 rknpu2:
|
||||
|
||||
```
|
||||
git clone git@github.com:rockchip-linux/rknpu2.git
|
||||
```
|
||||
|
||||
2. 在 linux 系统, 下载 gcc 交叉编译器. `rknpu2` 的官方提供的下载链接无法使用了. 用户可以使用另一个 [链接](https://github.com/Caesar-github/gcc-buildroot-9.3.0-2020.03-x86_64_aarch64-rockchip-linux-gnu). 下载并解压完编译器, 打开终端, 设置 `RKNN_TOOL_CHAIN` 和 `RKNPU2_DEVICE_DIR` 为 `export RKNN_TOOL_CHAIN=/path/to/gcc/usr;export RKNPU2_DEVICE_DIR=/path/to/rknpu2/runtime/RK3588`。
|
||||
|
||||
3. 上述准备工作完成后, 运行如下指令安装:
|
||||
|
||||
```shell
|
||||
cd /path/to/mmdeploy
|
||||
mkdir -p build && rm -rf build/CM* && cd build
|
||||
export LD_LIBRARY_PATH=$RKNN_TOOL_CHAIN/lib64:$LD_LIBRARY_PATH
|
||||
cmake \
|
||||
-DCMAKE_TOOLCHAIN_FILE=/path/to/mmdeploy/cmake/toolchains/rknpu2-linux-gnu.cmake \
|
||||
-DMMDEPLOY_BUILD_SDK=ON \
|
||||
-DCMAKE_BUILD_TYPE=Debug \
|
||||
-DOpenCV_DIR=${RKNPU2_DEVICE_DIR}/../../examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV \
|
||||
-DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
|
||||
-DMMDEPLOY_TARGET_DEVICES="cpu" \
|
||||
-DMMDEPLOY_TARGET_BACKENDS="rknn" \
|
||||
-DMMDEPLOY_CODEBASES=all \
|
||||
-DMMDEPLOY_BUILD_TEST=ON \
|
||||
-DMMDEPLOY_BUILD_EXAMPLES=ON \
|
||||
..
|
||||
make && make install
|
||||
```
|
||||
|
||||
## 运行 SDK 的 demo
|
||||
|
||||
首先，确保`--dump-info`在转模型的时候调用了, 这样工作目录下包含 SDK 需要的配置文件 `pipeline.json`。
|
||||
|
||||
使用 `adb push` 将模型路径，执行文件和.so 文件传到板子上。
|
||||
|
||||
```bash
|
||||
cd /path/to/mmdeploy
|
||||
adb push resnet50 /data/local/tmp/resnet50
|
||||
adb push /mmclassification_dir/demo/demo.JPEG /data/local/tmp/resnet50/demo.JPEG
|
||||
cd build
|
||||
adb push lib /data/local/tmp/lib
|
||||
adb push bin/image_classification /data/local/tmp/image_classification
|
||||
```
|
||||
|
||||
设置环境变量，运行例子。
|
||||
|
||||
```bash
|
||||
adb shell
|
||||
cd /data/local/tmp
|
||||
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/data/local/tmp/lib
|
||||
./image_classification cpu ./resnet50 ./resnet50/demo.JPEG
|
||||
..
|
||||
label: 65, score: 0.95
|
||||
```
|
||||
|
||||
## 问题点
|
||||
|
||||
- 量化失败.
|
||||
|
||||
经验来说, 如果 `do_quantization` 被设置为 `True`，RKNN 需要的输入没有被归一化过。请修改 `Normalize` 在 `model_cfg` 的设置，如将
|
||||
|
||||
```python
|
||||
img_norm_cfg = dict(
|
||||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
|
||||
```
|
||||
|
||||
改为
|
||||
|
||||
```python
|
||||
img_norm_cfg = dict(
|
||||
mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True)
|
||||
```
|
||||
|
||||
此外, deploy_cfg 的 `mean_values` 和 `std_values` 应该被设置为 `model_cfg` 中归一化的设置. 使 `mean_values=[123.675, 116.28, 103.53]`， `std_values=[58.395, 57.12, 57.375]`。
|
||||
|
|
@ -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-0.9.0-windows-amd64-onnxruntime1.8.1.zip`和`mmdeploy-0.9.0-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-0.10.0-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。
|
||||
|
||||
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-0.9.0-windows-amd64-onnxruntime1.8.1.zip
|
||||
pip install .\mmdeploy-0.9.0-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-0.9.0-py38-none-win_amd64.whl
|
||||
pip install .\mmdeploy-0.9.0-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-0.9.0-cp38-none-win_amd64.whl
|
||||
# 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
|
||||
```
|
||||
|
||||
: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-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip
|
||||
pip install .\mmdeploy-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-0.9.0-py38-none-win_amd64.whl
|
||||
pip install .\mmdeploy-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-0.9.0-cp38-none-win_amd64.whl
|
||||
# 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
|
||||
```
|
||||
|
||||
:point_right: 如果之前安装过，需要先卸载后再安装
|
||||
|
|
@ -146,7 +146,7 @@ ______________________________________________________________________
|
|||
|
||||
```
|
||||
..
|
||||
|-- mmdeploy-0.9.0-windows-amd64-onnxruntime1.8.1
|
||||
|-- mmdeploy-0.10.0-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-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
|
||||
|-- mmdeploy-0.10.0-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-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
|
||||
|-- mmdeploy-0.9.0-windows-amd64-onnxruntime1.8.1
|
||||
|-- mmdeploy-0.10.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
|
||||
|-- mmdeploy-0.10.0-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-0.9.0-windows-amd64-onnxruntime1.8.1\sdk\example`目录下
|
||||
在`mmdeploy-0.10.0-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-0.9.0-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy `
|
||||
-DMMDeploy_DIR=C:\workspace\mmdeploy-0.10.0-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-0.9.0-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-0.10.0-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-0.9.0-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release目录下：
|
||||
在mmdeploy-0.10.0-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-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example目录下
|
||||
在mmdeploy-0.10.0-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-0.9.0-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy `
|
||||
-DMMDeploy_DIR=C:\workspace\mmdeploy-0.10.0-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-0.9.0-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-0.10.0-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-0.9.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release目录下：
|
||||
在mmdeploy-0.10.0-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
|
||||
|
|
|
|||
|
|
@ -0,0 +1,9 @@
|
|||
# 支持的 RKNN 特征
|
||||
|
||||
目前, MMDeploy 只在 rk3588 的 linux 平台上测试过.
|
||||
|
||||
以下特性需要手动在 MMDeploy 自行配置，如[这里](https://github.com/open-mmlab/mmdeploy/blob/master/configs/_base_/backends/rknn.py).
|
||||
|
||||
- target_platform ！= `3588`
|
||||
- quantization settings
|
||||
- optimization level ！= 3
|
||||
|
|
@ -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/v0.9.0/mmdeploy-0.9.0-linux-x86_64-onnxruntime1.8.1.tar.gz
|
||||
tar -zxvf mmdeploy-0.9.0-linux-x86_64-onnxruntime1.8.1.tar.gz
|
||||
cd mmdeploy-0.9.0-linux-x86_64-onnxruntime1.8.1
|
||||
pip install dist/mmdeploy-0.9.0-py3-none-linux_x86_64.whl
|
||||
pip install sdk/python/mmdeploy_python-0.9.0-cp38-none-linux_x86_64.whl
|
||||
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
|
||||
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/v0.9.0/mmdeploy-0.9.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
|
||||
tar -zxvf mmdeploy-0.9.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0.tar.gz
|
||||
cd mmdeploy-0.9.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
|
||||
pip install dist/mmdeploy-0.9.0-py3-none-linux_x86_64.whl
|
||||
pip install sdk/python/mmdeploy_python-0.9.0-cp38-none-linux_x86_64.whl
|
||||
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
|
||||
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-0.9.0-linux-x86_64-cuda11.1-tensorrt8.2.3.0
|
||||
cd mmdeploy-0.10.0-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
|
||||
|
|
|
|||
|
|
@ -58,6 +58,7 @@
|
|||
05-supported-backends/onnxruntime.md
|
||||
05-supported-backends/openvino.md
|
||||
05-supported-backends/pplnn.md
|
||||
05-supported-backends/rknn.md
|
||||
05-supported-backends/snpe.md
|
||||
05-supported-backends/tensorrt.md
|
||||
05-supported-backends/torchscript.md
|
||||
|
|
|
|||
|
|
@ -169,7 +169,12 @@ def from_onnx(onnx_model: Union[str, onnx.ModelProto],
|
|||
builder.max_workspace_size = max_workspace_size
|
||||
|
||||
config = builder.create_builder_config()
|
||||
config.max_workspace_size = max_workspace_size
|
||||
|
||||
if hasattr(config, 'set_memory_pool_limit'):
|
||||
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE,
|
||||
max_workspace_size)
|
||||
else:
|
||||
config.max_workspace_size = max_workspace_size
|
||||
|
||||
cuda_version = search_cuda_version()
|
||||
if cuda_version is not None:
|
||||
|
|
@ -187,14 +192,19 @@ def from_onnx(onnx_model: Union[str, onnx.ModelProto],
|
|||
opt_shape = param['opt_shape']
|
||||
max_shape = param['max_shape']
|
||||
profile.set_shape(input_name, min_shape, opt_shape, max_shape)
|
||||
config.add_optimization_profile(profile)
|
||||
if config.add_optimization_profile(profile) < 0:
|
||||
logger.warning(f'Invalid optimization profile {profile}.')
|
||||
|
||||
if fp16_mode:
|
||||
if not getattr(builder, 'platform_has_fast_fp16', True):
|
||||
logger.warning('Platform does not has fast native fp16.')
|
||||
if version.parse(trt.__version__) < version.parse('8'):
|
||||
builder.fp16_mode = fp16_mode
|
||||
config.set_flag(trt.BuilderFlag.FP16)
|
||||
|
||||
if int8_mode:
|
||||
if not getattr(builder, 'platform_has_fast_int8', True):
|
||||
logger.warning('Platform does not has fast native int8.')
|
||||
from .calib_utils import HDF5Calibrator
|
||||
config.set_flag(trt.BuilderFlag.INT8)
|
||||
assert int8_param is not None
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
from .transformer import (MultiHeadAttentionop,
|
||||
multiheadattention__forward__ncnn)
|
||||
from . import conv2d_adaptive_padding # noqa: F401,F403
|
||||
from .transformer import MultiHeadAttentionop
|
||||
|
||||
__all__ = ['multiheadattention__forward__ncnn', 'MultiHeadAttentionop']
|
||||
__all__ = ['conv2d_adaptive_padding', 'MultiHeadAttentionop']
|
||||
|
|
|
|||
|
|
@ -0,0 +1,86 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
import math
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
from mmdeploy.core import FUNCTION_REWRITER
|
||||
from mmdeploy.utils import Backend, is_dynamic_batch, is_dynamic_shape
|
||||
|
||||
|
||||
def compute_padding(input_size, kernel_size, stride, dilation):
|
||||
"""Compute padding."""
|
||||
|
||||
input_h, input_w = input_size
|
||||
kernel_h, kernel_w = kernel_size
|
||||
stride_h, stride_w = stride
|
||||
dilation_h, dilation_w = dilation
|
||||
output_h = math.ceil(input_h / stride_h)
|
||||
output_w = math.ceil(input_w / stride_w)
|
||||
pad_h = max(
|
||||
(output_h - 1) * stride_h + (kernel_h - 1) * dilation_h + 1 - input_h,
|
||||
0)
|
||||
pad_w = max(
|
||||
(output_w - 1) * stride_w + (kernel_w - 1) * dilation_w + 1 - input_w,
|
||||
0)
|
||||
if pad_w > 0 or pad_h > 0:
|
||||
padded = [
|
||||
pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2
|
||||
]
|
||||
else:
|
||||
padded = None
|
||||
return padded
|
||||
|
||||
|
||||
class AdaptivePadOp(torch.autograd.Function):
|
||||
"""Dummy adaptive pad op."""
|
||||
|
||||
@staticmethod
|
||||
def forward(ctx, x, padded):
|
||||
if padded is not None:
|
||||
x = F.pad(x, padded)
|
||||
return x
|
||||
|
||||
@staticmethod
|
||||
def symbolic(g, x, padded):
|
||||
if padded is None:
|
||||
return g.op('Identity', x)
|
||||
padded = g.op(
|
||||
'Constant', value_t=torch.tensor(padded, dtype=torch.int64))
|
||||
constant_value = g.op(
|
||||
'Constant', value_t=torch.tensor(0, dtype=torch.int64))
|
||||
return g.op(
|
||||
'Pad', x, padded, constant_value, mode_s='constant', outputs=1)
|
||||
|
||||
|
||||
@FUNCTION_REWRITER.register_rewriter(
|
||||
func_name='mmcv.cnn.bricks.conv2d_adaptive_padding. \
|
||||
Conv2dAdaptivePadding.forward',
|
||||
backend=Backend.TENSORRT.value)
|
||||
def conv2d_adaptive_padding__forward__tensorrt(ctx, self, x):
|
||||
"""Rewrite `forward` of Conv2dAdaptivePadding used in EfficientNet for
|
||||
TensorRT backend. Main changes of this rewritten function is to separate
|
||||
the computation of padding and encapsulate it into another
|
||||
`torch.autograd.Function` so that the adaptive padding could be parsed as
|
||||
`Pad` ops in ONNX with the padding information computed in advance (Only
|
||||
for static shape configuration).
|
||||
|
||||
Args:
|
||||
x (Tensor): Input tensor of Conv2dAdaptivePadding ops
|
||||
Returns:
|
||||
Tensor: forward result of 2D convolution after padding
|
||||
"""
|
||||
|
||||
deploy_cfg = ctx.cfg
|
||||
is_dynamic_flag = is_dynamic_shape(deploy_cfg)
|
||||
if (not is_dynamic_flag) or is_dynamic_batch(deploy_cfg):
|
||||
padded = compute_padding(x.shape[2:], self.weight.shape[2:],
|
||||
self.stride, self.dilation)
|
||||
if padded is not None:
|
||||
padded = [int(_) for _ in padded]
|
||||
x = AdaptivePadOp.apply(x, padded)
|
||||
return F.conv2d(x, self.weight, self.bias, self.stride, self.padding,
|
||||
self.dilation, self.groups)
|
||||
else:
|
||||
x = ctx.origin_func(x)
|
||||
return x
|
||||
|
|
@ -1,7 +1,7 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
from typing import Tuple
|
||||
|
||||
__version__ = '0.9.0'
|
||||
__version__ = '0.10.0'
|
||||
short_version = __version__
|
||||
|
||||
|
||||
|
|
|
|||
Binary file not shown.
|
Before Width: | Height: | Size: 60 KiB After Width: | Height: | Size: 315 KiB |
|
|
@ -210,4 +210,13 @@ models:
|
|||
pipelines:
|
||||
- *pipeline_ts_fp32
|
||||
- *pipeline_ort_dynamic_fp32
|
||||
- *pipeline_trt_dynamic_fp32
|
||||
- *pipeline_trt_static_fp16
|
||||
|
||||
- name: EfficientNet
|
||||
metafile: configs/efficientnet/metafile.yml
|
||||
model_configs:
|
||||
- configs/efficientnet/efficientnet-b0_8xb32_in1k.py
|
||||
pipelines:
|
||||
- *pipeline_ort_static_fp32
|
||||
- convert_image: *convert_image
|
||||
deploy_config: configs/mmcls/classification_tensorrt_dynamic-224x224-224x224.py
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/core CORE_TC)
|
|||
aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/preprocess TRANSFORM_TC)
|
||||
aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/net NET_TC)
|
||||
aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/model MODEL_TC)
|
||||
aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/graph GRAPH_TC)
|
||||
|
||||
set(DEVICE_TC)
|
||||
foreach (DEVICE IN LISTS MMDEPLOY_TARGET_DEVICES)
|
||||
|
|
@ -58,7 +59,8 @@ set(TC_SRCS
|
|||
${MODEL_TC}
|
||||
${NET_TC}
|
||||
${DEVICE_TC}
|
||||
${CAPI_TC})
|
||||
${CAPI_TC}
|
||||
${GRAPH_TC})
|
||||
|
||||
add_executable(mmdeploy_tests ${TC_SRCS})
|
||||
target_include_directories(mmdeploy_tests
|
||||
|
|
|
|||
|
|
@ -0,0 +1,65 @@
|
|||
// Copyright (c) OpenMMLab. All rights reserved.
|
||||
|
||||
#include "catch.hpp"
|
||||
#include "mmdeploy/archive/json_archive.h"
|
||||
#include "mmdeploy/core/graph.h"
|
||||
#include "mmdeploy/core/registry.h"
|
||||
#include "mmdeploy/experimental/module_adapter.h"
|
||||
|
||||
using namespace mmdeploy;
|
||||
|
||||
namespace {
|
||||
|
||||
class PlusCreator : public Creator<Module> {
|
||||
public:
|
||||
const char* GetName() const override { return "Plus"; }
|
||||
std::unique_ptr<Module> Create(const Value&) override {
|
||||
return CreateTask([](int a, int b) { return a + b; });
|
||||
}
|
||||
};
|
||||
REGISTER_MODULE(Module, PlusCreator);
|
||||
|
||||
const auto json_config1 = R"(
|
||||
{
|
||||
"type": "Cond",
|
||||
"input": ["pred", "a", "b"],
|
||||
"output": "c",
|
||||
"body": {
|
||||
"type": "Task",
|
||||
"module": "Plus"
|
||||
}
|
||||
}
|
||||
)"_json;
|
||||
|
||||
} // namespace
|
||||
|
||||
TEST_CASE("test Cond node", "[graph]") {
|
||||
auto config = from_json<Value>(json_config1);
|
||||
auto builder = graph::Builder::CreateFromConfig(config).value();
|
||||
REQUIRE(builder);
|
||||
auto node = builder->Build().value();
|
||||
REQUIRE(node);
|
||||
{
|
||||
auto result = SyncWait(node->Process(Just(Value({{false}, {1}, {1}}))));
|
||||
MMDEPLOY_INFO("{}", result);
|
||||
}
|
||||
{
|
||||
auto result = SyncWait(node->Process(Just(Value({{true}, {1}, {1}}))));
|
||||
MMDEPLOY_INFO("{}", result);
|
||||
}
|
||||
{
|
||||
auto result = SyncWait(
|
||||
node->Process(Just(Value({{false, false, false, false}, {1, 2, 3, 4}, {1, 3, 5, 7}}))));
|
||||
MMDEPLOY_INFO("{}", result);
|
||||
}
|
||||
{
|
||||
auto result = SyncWait(
|
||||
node->Process(Just(Value({{true, true, true, true}, {1, 2, 3, 4}, {1, 3, 5, 7}}))));
|
||||
MMDEPLOY_INFO("{}", result);
|
||||
}
|
||||
{
|
||||
auto result = SyncWait(
|
||||
node->Process(Just(Value({{true, false, false, true}, {1, 2, 3, 4}, {1, 3, 5, 7}}))));
|
||||
MMDEPLOY_INFO("{}", result);
|
||||
}
|
||||
}
|
||||
|
|
@ -30,3 +30,30 @@ def test_multiheadattention_ncnn():
|
|||
else:
|
||||
assert torch.allclose(
|
||||
model_outputs, rewrite_outputs[0], rtol=1e-03, atol=1e-05)
|
||||
|
||||
|
||||
def test_conv2d_adaptive_padding_tensorrt():
|
||||
check_backend(Backend.TENSORRT)
|
||||
from mmcv.cnn.bricks.conv2d_adaptive_padding import Conv2dAdaptivePadding
|
||||
in_channels, out_channels = 3, 64
|
||||
kernel_sz = 3
|
||||
model = Conv2dAdaptivePadding(in_channels, out_channels, kernel_sz)
|
||||
dummy_input = torch.rand(1, 3, 256, 256)
|
||||
|
||||
deploy_cfg = Config(
|
||||
dict(
|
||||
onnx_config=dict(input_shape=None),
|
||||
backend_config=dict(type=Backend.TENSORRT.value),
|
||||
))
|
||||
model_outputs = model(dummy_input)
|
||||
rewrite_inputs = dict(x=dummy_input)
|
||||
rewrite_outputs, is_backend_output = get_rewrite_outputs(
|
||||
wrapped_model=model,
|
||||
model_inputs=rewrite_inputs,
|
||||
deploy_cfg=deploy_cfg,
|
||||
run_with_backend=True)
|
||||
if is_backend_output is None:
|
||||
assert rewrite_outputs is not None
|
||||
else:
|
||||
assert torch.allclose(
|
||||
model_outputs, rewrite_outputs[0], rtol=1e-03, atol=1e-05)
|
||||
|
|
|
|||
|
|
@ -1,2 +1,2 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
__version__ = '0.9.0'
|
||||
__version__ = '0.10.0'
|
||||
|
|
|
|||
|
|
@ -9,7 +9,8 @@ g_jobs = 2
|
|||
|
||||
|
||||
def install_protobuf(dep_dir) -> int:
|
||||
"""build and install protobuf.
|
||||
"""build and install protobuf. protobuf seems not support repeated install,
|
||||
so clean build first.
|
||||
|
||||
Args:
|
||||
wor_dir (_type_): _description_
|
||||
|
|
@ -29,11 +30,22 @@ def install_protobuf(dep_dir) -> int:
|
|||
os.chdir(os.path.join(dep_dir, 'protobuf-3.20.0'))
|
||||
|
||||
install_dir = os.path.join(dep_dir, 'pbinstall')
|
||||
if os.path.exists(install_dir):
|
||||
os.system('rm -rf {}'.format(install_dir))
|
||||
|
||||
os.system('make clean')
|
||||
os.system('./configure --prefix={}'.format(install_dir))
|
||||
os.system('make -j {} && make install'.format(g_jobs))
|
||||
protoc = os.path.join(dep_dir, 'pbinstall', 'bin', 'protoc')
|
||||
protoc = os.path.join(install_dir, 'bin', 'protoc')
|
||||
|
||||
print('protoc \t:{}'.format(cmd_result('{} --version'.format(protoc))))
|
||||
|
||||
os.system(""" echo 'export PATH={}:$PATH' >> ~/mmdeploy.env """.format(
|
||||
os.path.join(install_dir, 'bin')))
|
||||
os.system(
|
||||
""" echo 'export LD_LIBRARY_PATH={}:$LD_LIBRARY_PATH' >> ~/mmdeploy.env """ # noqa: E501
|
||||
.format(os.path.join(install_dir, 'lib')))
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
|
|
@ -60,6 +72,7 @@ def install_pyncnn(dep_dir):
|
|||
os.system('mkdir build')
|
||||
|
||||
os.chdir(os.path.join(ncnn_dir, 'build'))
|
||||
os.system('rm -rf CMakeCache.txt')
|
||||
pb_install = os.path.join(dep_dir, 'pbinstall')
|
||||
pb_bin = os.path.join(pb_install, 'bin', 'protoc')
|
||||
pb_lib = os.path.join(pb_install, 'lib', 'libprotobuf.so')
|
||||
|
|
@ -101,9 +114,9 @@ def install_mmdeploy(work_dir, dep_dir, ncnn_cmake_dir):
|
|||
pb_lib = os.path.join(pb_install, 'lib', 'libprotobuf.so')
|
||||
pb_include = os.path.join(pb_install, 'include')
|
||||
|
||||
os.system('rm -rf build/CMakeCache.txt')
|
||||
|
||||
cmd = 'cd build && cmake ..'
|
||||
cmd += ' -DCMAKE_C_COMPILER=gcc-7 '
|
||||
cmd += ' -DCMAKE_CXX_COMPILER=g++-7 '
|
||||
cmd += ' -DMMDEPLOY_BUILD_SDK=ON '
|
||||
cmd += ' -DMMDEPLOY_BUILD_EXAMPLES=ON '
|
||||
cmd += ' -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON '
|
||||
|
|
@ -117,7 +130,14 @@ def install_mmdeploy(work_dir, dep_dir, ncnn_cmake_dir):
|
|||
|
||||
os.system('cd build && make -j {} && make install'.format(g_jobs))
|
||||
os.system('python3 -m pip install -v -e .')
|
||||
os.system('python3 tools/check_env.py')
|
||||
os.system(""" echo 'export PATH={}:$PATH' >> ~/mmdeploy.env """.format(
|
||||
os.path.join(work_dir, 'mmdeploy', 'backend', 'ncnn')))
|
||||
try:
|
||||
import mmcv
|
||||
print(mmcv.__version__)
|
||||
os.system('python3 tools/check_env.py')
|
||||
except Exception:
|
||||
print('Please install torch & mmcv later.. ╮(╯▽╰)╭')
|
||||
return 0
|
||||
|
||||
|
||||
|
|
@ -143,7 +163,7 @@ def main():
|
|||
return -1
|
||||
os.mkdir(dep_dir)
|
||||
|
||||
success, envs = ensure_base_env(work_dir, dep_dir)
|
||||
success = ensure_base_env(work_dir, dep_dir)
|
||||
if success != 0:
|
||||
return -1
|
||||
|
||||
|
|
@ -155,12 +175,9 @@ def main():
|
|||
if install_mmdeploy(work_dir, dep_dir, ncnn_cmake_dir) != 0:
|
||||
return -1
|
||||
|
||||
if len(envs) > 0:
|
||||
print(
|
||||
'We recommend that you set the following environment variables:\n')
|
||||
for env in envs:
|
||||
print(env)
|
||||
print('\n')
|
||||
if os.path.exists('~/mmdeploy.env'):
|
||||
print('Please source ~/mmdeploy.env to setup your env !')
|
||||
os.system('cat ~/mmdeploy.env')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ def install_ort(dep_dir):
|
|||
# git clone
|
||||
if not os.path.exists('onnxruntime-linux-x64-1.8.1'):
|
||||
os.system(
|
||||
'wget https://github.com/microsoft/onnxruntime/releases/download/v1.8.1/onnxruntime-linux-x64-1.8.1.tgz' # noqa: E501
|
||||
'wget -q --show-progress https://github.com/microsoft/onnxruntime/releases/download/v1.8.1/onnxruntime-linux-x64-1.8.1.tgz' # noqa: E501
|
||||
)
|
||||
os.system('tar xvf onnxruntime-linux-x64-1.8.1.tgz')
|
||||
|
||||
|
|
@ -41,9 +41,9 @@ def install_mmdeploy(work_dir, ort_dir):
|
|||
if not os.path.exists('build'):
|
||||
os.system('mkdir build')
|
||||
|
||||
os.system('rm -rf build/CMakeCache.txt')
|
||||
|
||||
cmd = 'cd build && cmake ..'
|
||||
cmd += ' -DCMAKE_C_COMPILER=gcc-7 '
|
||||
cmd += ' -DCMAKE_CXX_COMPILER=g++-7 '
|
||||
cmd += ' -DMMDEPLOY_BUILD_SDK=ON '
|
||||
cmd += ' -DMMDEPLOY_BUILD_EXAMPLES=ON '
|
||||
cmd += ' -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON '
|
||||
|
|
@ -54,7 +54,12 @@ def install_mmdeploy(work_dir, ort_dir):
|
|||
|
||||
os.system('cd build && make -j {} && make install'.format(g_jobs))
|
||||
os.system('python3 -m pip install -e .')
|
||||
os.system('python3 tools/check_env.py')
|
||||
try:
|
||||
import mmcv
|
||||
print(mmcv.__version__)
|
||||
os.system('python3 tools/check_env.py')
|
||||
except Exception:
|
||||
print('Please install torch & mmcv later.. ⊙▽⊙')
|
||||
return 0
|
||||
|
||||
|
||||
|
|
@ -80,7 +85,7 @@ def main():
|
|||
return -1
|
||||
os.mkdir(dep_dir)
|
||||
|
||||
success, envs = ensure_base_env(work_dir, dep_dir)
|
||||
success = ensure_base_env(work_dir, dep_dir)
|
||||
if success != 0:
|
||||
return -1
|
||||
|
||||
|
|
@ -89,12 +94,9 @@ def main():
|
|||
if install_mmdeploy(work_dir, ort_dir) != 0:
|
||||
return -1
|
||||
|
||||
if len(envs) > 0:
|
||||
print(
|
||||
'We recommend that you set the following environment variables:\n')
|
||||
for env in envs:
|
||||
print(env)
|
||||
print('\n')
|
||||
if os.path.exists('~/mmdeploy.env'):
|
||||
print('Please source ~/mmdeploy.env to setup your env !')
|
||||
os.system('cat ~/mmdeploy.env')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
|||
|
|
@ -85,9 +85,9 @@ def install_mmdeploy(work_dir, pplnn_cmake_dir, pplcv_cmake_dir, build_cuda):
|
|||
if not os.path.exists('build'):
|
||||
os.system('mkdir build')
|
||||
|
||||
os.system('rm -rf build/CMakeCache.txt')
|
||||
|
||||
cmd = 'cd build && cmake ..'
|
||||
cmd += ' -DCMAKE_C_COMPILER=gcc-7 '
|
||||
cmd += ' -DCMAKE_CXX_COMPILER=g++-7 '
|
||||
cmd += ' -DMMDEPLOY_BUILD_SDK=ON '
|
||||
cmd += ' -DMMDEPLOY_BUILD_EXAMPLES=ON '
|
||||
cmd += ' -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON '
|
||||
|
|
@ -104,7 +104,12 @@ def install_mmdeploy(work_dir, pplnn_cmake_dir, pplcv_cmake_dir, build_cuda):
|
|||
|
||||
os.system('cd build && make -j {} && make install'.format(g_jobs))
|
||||
os.system('python3 -m pip install -e .')
|
||||
os.system('python3 tools/check_env.py')
|
||||
try:
|
||||
import mmcv
|
||||
print(mmcv.__version__)
|
||||
os.system('python3 tools/check_env.py')
|
||||
except Exception:
|
||||
print('Please install torch & mmcv later.. ∩▽∩')
|
||||
return 0
|
||||
|
||||
|
||||
|
|
@ -130,23 +135,10 @@ def main():
|
|||
return -1
|
||||
os.mkdir(dep_dir)
|
||||
|
||||
success, envs = ensure_base_env(work_dir, dep_dir)
|
||||
success = ensure_base_env(work_dir, dep_dir)
|
||||
if success != 0:
|
||||
return -1
|
||||
|
||||
# enable g++ and gcc
|
||||
gplus = cmd_result('which g++')
|
||||
if gplus is None or len(gplus) < 1:
|
||||
sudo = 'sudo'
|
||||
if 'root' in cmd_result('whoami'):
|
||||
sudo = ''
|
||||
os.system(
|
||||
'{} update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-7 200' # noqa: E501
|
||||
.format(sudo))
|
||||
os.system(
|
||||
'{} update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-7 200' # noqa: E501
|
||||
.format(sudo))
|
||||
|
||||
# install pplcv and pplnn
|
||||
nvcc = cmd_result('which nvcc')
|
||||
build_cuda = False
|
||||
|
|
@ -158,12 +150,9 @@ def main():
|
|||
build_cuda) != 0:
|
||||
return -1
|
||||
|
||||
if len(envs) > 0:
|
||||
print(
|
||||
'We recommend that you set the following environment variables:\n')
|
||||
for env in envs:
|
||||
print(env)
|
||||
print('\n')
|
||||
if os.path.exists('~/mmdeploy.env'):
|
||||
print('Please source ~/mmdeploy.env to setup your env !')
|
||||
os.system('cat ~/mmdeploy.env')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
|||
|
|
@ -3,7 +3,8 @@ import os
|
|||
import sys
|
||||
import time
|
||||
|
||||
from ubuntu_utils import cmd_result, cu_version_name, ensure_base_env, get_job
|
||||
from ubuntu_utils import (cmd_result, cu_version_name, ensure_base_env,
|
||||
get_job, pytorch_version)
|
||||
|
||||
g_jobs = 2
|
||||
|
||||
|
|
@ -17,15 +18,9 @@ def install_libtorch(dep_dir):
|
|||
if os.path.exists(unzipped_name):
|
||||
return os.path.join(dep_dir, unzipped_name)
|
||||
|
||||
torch_version = None
|
||||
try:
|
||||
import torch
|
||||
torch_version = torch.__version__
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
torch_version = pytorch_version()
|
||||
if torch_version is None:
|
||||
print('torch version is None, use 1.11.0')
|
||||
print('torch version is None, try 1.11.0')
|
||||
torch_version = '1.11.0'
|
||||
|
||||
version_name = None
|
||||
|
|
@ -46,7 +41,7 @@ def install_libtorch(dep_dir):
|
|||
torch_version, version_name)
|
||||
url = 'https://download.pytorch.org/libtorch/{}/{}'.format(
|
||||
version_name, filename)
|
||||
os.system('wget {} -O libtorch.zip'.format(url))
|
||||
os.system('wget -q --show-progress {} -O libtorch.zip'.format(url))
|
||||
os.system('unzip libtorch.zip')
|
||||
if not os.path.exists(unzipped_name):
|
||||
print(
|
||||
|
|
@ -67,9 +62,9 @@ def install_mmdeploy(work_dir, libtorch_dir):
|
|||
if not os.path.exists('build'):
|
||||
os.system('mkdir build')
|
||||
|
||||
os.system('rm -rf build/CMakeCache.txt')
|
||||
|
||||
cmd = 'cd build && Torch_DIR={} cmake ..'.format(libtorch_dir)
|
||||
cmd += ' -DCMAKE_C_COMPILER=gcc-7 '
|
||||
cmd += ' -DCMAKE_CXX_COMPILER=g++-7 '
|
||||
cmd += ' -DMMDEPLOY_BUILD_SDK=ON '
|
||||
cmd += ' -DMMDEPLOY_BUILD_EXAMPLES=ON '
|
||||
cmd += ' -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON '
|
||||
|
|
@ -80,7 +75,12 @@ def install_mmdeploy(work_dir, libtorch_dir):
|
|||
|
||||
os.system('cd build && make -j {} && make install'.format(g_jobs))
|
||||
os.system('python3 -m pip install -e .')
|
||||
os.system('python3 tools/check_env.py')
|
||||
try:
|
||||
import mmcv
|
||||
print(mmcv.__version__)
|
||||
os.system('python3 tools/check_env.py')
|
||||
except Exception:
|
||||
print('Please install torch & mmcv later.. ≥▽≤')
|
||||
return 0
|
||||
|
||||
|
||||
|
|
@ -106,7 +106,7 @@ def main():
|
|||
return -1
|
||||
os.mkdir(dep_dir)
|
||||
|
||||
success, envs = ensure_base_env(work_dir, dep_dir)
|
||||
success = ensure_base_env(work_dir, dep_dir)
|
||||
if success != 0:
|
||||
return -1
|
||||
|
||||
|
|
@ -118,12 +118,9 @@ def main():
|
|||
if install_mmdeploy(work_dir, libtorch_dir) != 0:
|
||||
return -1
|
||||
|
||||
if len(envs) > 0:
|
||||
print(
|
||||
'We recommend that you set the following environment variables:\n')
|
||||
for env in envs:
|
||||
print(env)
|
||||
print('\n')
|
||||
if os.path.exists('~/mmdeploy.env'):
|
||||
print('Please source ~/mmdeploy.env to setup your env !')
|
||||
os.system('cat ~/mmdeploy.env')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
|||
|
|
@ -0,0 +1,105 @@
|
|||
#!/bin/bash
|
||||
# set -ex
|
||||
# get appropriate proc number: max(1, nproc-3)
|
||||
good_nproc() {
|
||||
num=`nproc`
|
||||
num=`expr $num - 3`
|
||||
if [ $num -lt 1 ];then
|
||||
return 1
|
||||
fi
|
||||
return ${num}
|
||||
}
|
||||
|
||||
install_tools() {
|
||||
sudo apt install -y gcc-aarch64-linux-gnu g++-aarch64-linux-gnu
|
||||
aarch64-linux-gnu-g++ --version
|
||||
aarch64-linux-gnu-gcc --version
|
||||
aarch64-linux-gnu-ld --version
|
||||
|
||||
sudo apt install wget git git-lfs
|
||||
|
||||
python3 -m pip install cmake==3.22.0
|
||||
|
||||
echo 'export PATH=~/.local/bin:${PATH}' >> ~/mmdeploy.env
|
||||
export PATH=~/.local/bin:${PATH}
|
||||
}
|
||||
|
||||
build_ocv() {
|
||||
if [ ! -e "opencv" ];then
|
||||
git clone https://github.com/opencv/opencv --depth=1 --branch=4.6.0 --recursive
|
||||
fi
|
||||
if [ ! -e "opencv/platforms/linux/cross_build_aarch64" ];then
|
||||
mkdir opencv/platforms/linux/cross_build_aarch64
|
||||
fi
|
||||
cd opencv/platforms/linux/cross_build_aarch64
|
||||
rm -rf CMakeCache.txt
|
||||
cmake ../../.. -DCMAKE_INSTALL_PREFIX=/tmp/ocv-aarch64 -DCMAKE_TOOLCHAIN_FILE=../aarch64-gnu.toolchain.cmake
|
||||
good_nproc
|
||||
jobs=$?
|
||||
make -j${jobs}
|
||||
make install
|
||||
cd -
|
||||
}
|
||||
|
||||
build_ncnn() {
|
||||
if [ ! -e "ncnn" ];then
|
||||
git clone https://github.com/tencent/ncnn --branch 20220729 --depth=1
|
||||
fi
|
||||
if [ ! -e "ncnn/build_aarch64" ];then
|
||||
mkdir -p ncnn/build_aarch64
|
||||
fi
|
||||
cd ncnn/build_aarch64
|
||||
rm -rf CMakeCache.txt
|
||||
cmake .. \
|
||||
-DCMAKE_TOOLCHAIN_FILE=../toolchains/aarch64-linux-gnu.toolchain.cmake \
|
||||
-DCMAKE_INSTALL_PREFIX=/tmp/ncnn-aarch64
|
||||
good_nproc
|
||||
jobs=$?
|
||||
make -j${jobs}
|
||||
make install
|
||||
cd -
|
||||
}
|
||||
|
||||
build_mmdeploy() {
|
||||
git submodule init
|
||||
git submodule update
|
||||
|
||||
if [ ! -e "build_aarch64" ];then
|
||||
mkdir build_aarch64
|
||||
fi
|
||||
cd build_aarch64
|
||||
|
||||
rm -rf CMakeCache.txt
|
||||
cmake .. \
|
||||
-DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/aarch64-linux-gnu.cmake \
|
||||
-DMMDEPLOY_TARGET_DEVICES="cpu" \
|
||||
-DMMDEPLOY_TARGET_BACKENDS="ncnn" \
|
||||
-Dncnn_DIR=/tmp/ncnn-aarch64/lib/cmake/ncnn \
|
||||
-DOpenCV_DIR=/tmp/ocv-aarch64/lib/cmake/opencv4
|
||||
|
||||
good_nproc
|
||||
jobs=$?
|
||||
make -j${jobs}
|
||||
make install
|
||||
|
||||
ls -lah install/bin/*
|
||||
}
|
||||
|
||||
print_success() {
|
||||
echo "----------------------------------------------------------------------"
|
||||
echo "Cross build finished, PLS copy bin/model/test_data to the device.. QVQ"
|
||||
echo "----------------------------------------------------------------------"
|
||||
}
|
||||
|
||||
if [ ! -e "../mmdeploy-dep" ];then
|
||||
mkdir ../mmdeploy-dep
|
||||
fi
|
||||
cd ../mmdeploy-dep
|
||||
|
||||
install_tools
|
||||
build_ocv
|
||||
build_ncnn
|
||||
|
||||
cd ../mmdeploy
|
||||
build_mmdeploy
|
||||
print_success
|
||||
|
|
@ -4,21 +4,33 @@ import re
|
|||
import time
|
||||
|
||||
|
||||
def pytorch_version():
|
||||
version = None
|
||||
try:
|
||||
import torch
|
||||
raw = torch.__version__
|
||||
pattern = re.compile(r'[0-9]+\.[0-9]+\.[0-9]+')
|
||||
version = pattern.findall(raw)[0]
|
||||
except Exception:
|
||||
pass
|
||||
return version
|
||||
|
||||
|
||||
def cmd_result(txt: str):
|
||||
cmd = os.popen(txt)
|
||||
return cmd.read().rstrip().lstrip()
|
||||
|
||||
|
||||
def get_job(argv) -> int:
|
||||
# get nprocs, if user not specified, use max(2, nproc-1)
|
||||
# get nprocs, if user not specified, use max(1, nproc-2)
|
||||
job = 2
|
||||
if len(argv) <= 1:
|
||||
print('your can use `python3 {} N` to set make -j [N]'.format(argv[0]))
|
||||
nproc = cmd_result('nproc')
|
||||
if nproc is not None and len(nproc) > 0:
|
||||
job = max(int(nproc) - 1, 2)
|
||||
job = max(int(nproc) - 2, 1)
|
||||
else:
|
||||
job = 2
|
||||
job = 1
|
||||
else:
|
||||
job = int(argv[1])
|
||||
return job
|
||||
|
|
@ -55,7 +67,7 @@ def ensure_base_env(work_dir, dep_dir):
|
|||
check python, root, pytorch version, auto install these binary:
|
||||
|
||||
* make
|
||||
* g++-7
|
||||
* g++
|
||||
* git
|
||||
* wget
|
||||
* unzip
|
||||
|
|
@ -63,7 +75,6 @@ def ensure_base_env(work_dir, dep_dir):
|
|||
* mmcv (not compulsory)
|
||||
"""
|
||||
|
||||
envs = []
|
||||
print('-' * 10 + 'ensure base env' + '-' * 10)
|
||||
print(description)
|
||||
|
||||
|
|
@ -83,18 +94,18 @@ def ensure_base_env(work_dir, dep_dir):
|
|||
cmake = cmd_result('which cmake')
|
||||
if cmake is None or len(cmake) < 1:
|
||||
print('cmake not found, try install cmake ..', end='')
|
||||
os.system('python3 -m pip install cmake>=3.14.0')
|
||||
os.system('python3 -m pip install cmake')
|
||||
|
||||
cmake = cmd_result('which cmake')
|
||||
if cmake is None or len(cmake) < 1:
|
||||
env = 'export PATH=${PATH}:~/.local/bin'
|
||||
os.system(env)
|
||||
envs.append(env)
|
||||
os.system(""" echo '{}' >> ~/mmdeploy.env """.format(env))
|
||||
|
||||
cmake = cmd_result('which cmake')
|
||||
if cmake is None or len(cmake) < 1:
|
||||
print('Check cmake failed.')
|
||||
return -1, envs
|
||||
return -1
|
||||
print('success')
|
||||
|
||||
# check make
|
||||
|
|
@ -109,14 +120,14 @@ def ensure_base_env(work_dir, dep_dir):
|
|||
make = cmd_result('which make')
|
||||
if make is None or len(make) < 1:
|
||||
print('Check make failed.')
|
||||
return -1, envs
|
||||
return -1
|
||||
print('success')
|
||||
|
||||
# check g++ version
|
||||
gplus = cmd_result('which g++-7')
|
||||
gplus = cmd_result('which g++')
|
||||
if gplus is None or len(gplus) < 1:
|
||||
# install g++
|
||||
print('g++-7 not found, try install g++-7 ..', end='')
|
||||
print('g++ not found, try install g++ ..', end='')
|
||||
os.system(
|
||||
'{} DEBIAN_FRONTEND="noninteractive" apt install software-properties-common -y' # noqa: E501
|
||||
.format(sudo)) # noqa: E501
|
||||
|
|
@ -125,18 +136,12 @@ def ensure_base_env(work_dir, dep_dir):
|
|||
os.system(
|
||||
'{} add-apt-repository ppa:ubuntu-toolchain-r/test -y'.format(
|
||||
sudo))
|
||||
os.system('{} apt install gcc-7 g++-7 -y'.format(sudo))
|
||||
os.system('{} apt install gcc g++ -y'.format(sudo))
|
||||
|
||||
gplus = cmd_result('which g++-7')
|
||||
gplus = cmd_result('which g++')
|
||||
if gplus is None or len(gplus) < 1:
|
||||
print('Check g++-7 failed.')
|
||||
return -1, envs
|
||||
os.system(
|
||||
'{} update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-7 200' # noqa: E501
|
||||
.format(sudo))
|
||||
os.system(
|
||||
'{} update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-7 200' # noqa: E501
|
||||
.format(sudo))
|
||||
print('Check g++ failed.')
|
||||
return -1
|
||||
print('success')
|
||||
|
||||
# wget
|
||||
|
|
@ -197,7 +202,7 @@ def ensure_base_env(work_dir, dep_dir):
|
|||
ocv = cmd_result('which opencv_version')
|
||||
if ocv is None or len(ocv) < 1:
|
||||
print('Check ocv failed.')
|
||||
return -1, envs
|
||||
return -1
|
||||
print('success')
|
||||
|
||||
# print all
|
||||
|
|
@ -217,11 +222,11 @@ def ensure_base_env(work_dir, dep_dir):
|
|||
cmd_result(" make --version | head -n 1 | awk '{print $3}' ")))
|
||||
|
||||
print('wget bin\t:{}'.format(wget))
|
||||
print('g++-7 bin\t:{}'.format(gplus))
|
||||
print('g++ bin\t:{}'.format(gplus))
|
||||
|
||||
print('mmcv version\t:{}'.format(mmcv_version))
|
||||
if mmcv_version is None:
|
||||
print('\t please install an mm serials algorithm later.')
|
||||
print('\t please install mmcv later.')
|
||||
time.sleep(2)
|
||||
|
||||
print('torch version\t:{}'.format(torch_version))
|
||||
|
|
@ -241,4 +246,4 @@ def ensure_base_env(work_dir, dep_dir):
|
|||
print('dep dir \t:{}'.format(dep_dir))
|
||||
|
||||
print('\n')
|
||||
return 0, envs
|
||||
return 0
|
||||
|
|
|
|||
Loading…
Reference in New Issue