mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
[Backend] Add YOLOv5、PPYOLOE and PP-Liteseg for RV1126 (#647)
* add yolov5 and ppyoloe for rk1126 * update code, rename rk1126 to rv1126 * add PP-Liteseg * update lite lib * updade doc for PPYOLOE * update doc * fix docs * fix doc and examples * update code * uodate doc * update doc Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
yeliang2258
6
README_CN.md
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6
README_CN.md
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@@ -68,8 +68,8 @@
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<div id="fastdeploy-quick-start-python"></div>
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<div id="fastdeploy-quick-start-python"></div>
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<details close>
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<details close>
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<summary><b>Python SDK快速开始（点开查看详情）</b></summary><div>
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<summary><b>Python SDK快速开始（点开查看详情）</b></summary><div>
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#### 快速安装
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#### 快速安装
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@@ -131,7 +131,7 @@ cv2.imwrite("vis_image.jpg", vis_im)
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<div id="fastdeploy-quick-start-cpp"></div>
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<div id="fastdeploy-quick-start-cpp"></div>
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<details close>
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<details close>
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<summary><b>C++ SDK快速开始（点开查看详情）</b></summary><div>
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<summary><b>C++ SDK快速开始（点开查看详情）</b></summary><div>
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0
README_EN.md
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0
README_EN.md
Normal file → Executable file
@@ -41,10 +41,12 @@ elseif(IOS)
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else()
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else()
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if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "aarch64")
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if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "aarch64")
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set(OPENCV_FILENAME "opencv-linux-aarch64-3.4.14")
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set(OPENCV_FILENAME "opencv-linux-aarch64-3.4.14")
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elseif(TARGET_ABI MATCHES "armhf")
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set(OPENCV_FILENAME "opencv-armv7hf")
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else()
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else()
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set(OPENCV_FILENAME "opencv-linux-x64-3.4.16")
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if(ENABLE_TIMVX)
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set(OPENCV_FILENAME "opencv-armv7hf")
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else()
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set(OPENCV_FILENAME "opencv-linux-x64-3.4.16")
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endif()
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endif()
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endif()
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if(ENABLE_OPENCV_CUDA)
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if(ENABLE_OPENCV_CUDA)
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if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "aarch64")
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if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "aarch64")
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@@ -57,7 +59,7 @@ endif()
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set(OPENCV_INSTALL_DIR ${THIRD_PARTY_PATH}/install/)
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set(OPENCV_INSTALL_DIR ${THIRD_PARTY_PATH}/install/)
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if(ANDROID)
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if(ANDROID)
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set(OPENCV_URL_PREFIX "https://bj.bcebos.com/fastdeploy/third_libs")
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set(OPENCV_URL_PREFIX "https://bj.bcebos.com/fastdeploy/third_libs")
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elseif(TARGET_ABI MATCHES "armhf")
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elseif(ENABLE_TIMVX)
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set(OPENCV_URL_PREFIX "https://bj.bcebos.com/fastdeploy/test")
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set(OPENCV_URL_PREFIX "https://bj.bcebos.com/fastdeploy/test")
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else() # TODO: use fastdeploy/third_libs instead.
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else() # TODO: use fastdeploy/third_libs instead.
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set(OPENCV_URL_PREFIX "https://bj.bcebos.com/paddle2onnx/libs")
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set(OPENCV_URL_PREFIX "https://bj.bcebos.com/paddle2onnx/libs")
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@@ -185,7 +187,7 @@ else()
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file(RENAME ${THIRD_PARTY_PATH}/install/${OPENCV_FILENAME}/ ${THIRD_PARTY_PATH}/install/opencv)
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file(RENAME ${THIRD_PARTY_PATH}/install/${OPENCV_FILENAME}/ ${THIRD_PARTY_PATH}/install/opencv)
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set(OPENCV_FILENAME opencv)
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set(OPENCV_FILENAME opencv)
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set(OpenCV_DIR ${THIRD_PARTY_PATH}/install/${OPENCV_FILENAME})
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set(OpenCV_DIR ${THIRD_PARTY_PATH}/install/${OPENCV_FILENAME})
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if(TARGET_ABI MATCHES "armhf")
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if(ENABLE_TIMVX)
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set(OpenCV_DIR ${OpenCV_DIR}/lib/cmake/opencv4)
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set(OpenCV_DIR ${OpenCV_DIR}/lib/cmake/opencv4)
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endif()
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endif()
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if (WIN32)
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if (WIN32)
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@@ -59,10 +59,12 @@ elseif(ANDROID)
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else() # Linux
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else() # Linux
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if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "aarch64")
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if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "aarch64")
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set(PADDLELITE_URL "${PADDLELITE_URL_PREFIX}/lite-linux-arm64-20220920.tgz")
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set(PADDLELITE_URL "${PADDLELITE_URL_PREFIX}/lite-linux-arm64-20220920.tgz")
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elseif(TARGET_ABI MATCHES "armhf")
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set(PADDLELITE_URL "https://bj.bcebos.com/fastdeploy/test/lite-linux_armhf_1101.tgz")
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else()
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else()
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message(FATAL_ERROR "Only support Linux aarch64 now, x64 is not supported with backend Paddle Lite.")
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if(ENABLE_TIMVX)
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set(PADDLELITE_URL "https://bj.bcebos.com/fastdeploy/test/lite-linux_armhf_1130.tgz")
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else()
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message(FATAL_ERROR "Only support Linux aarch64 or ENABLE_TIMVX now, x64 is not supported with backend Paddle Lite.")
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endif()
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endif()
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endif()
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endif()
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endif()
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@@ -1,11 +1,10 @@
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if (NOT DEFINED TARGET_ABI)
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if (NOT DEFINED CMAKE_SYSTEM_PROCESSOR)
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set(CMAKE_SYSTEM_NAME Linux)
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set(CMAKE_SYSTEM_NAME Linux)
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set(CMAKE_SYSTEM_PROCESSOR arm)
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set(CMAKE_SYSTEM_PROCESSOR arm)
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set(CMAKE_C_COMPILER "arm-linux-gnueabihf-gcc")
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set(CMAKE_C_COMPILER "arm-linux-gnueabihf-gcc")
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set(CMAKE_CXX_COMPILER "arm-linux-gnueabihf-g++")
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set(CMAKE_CXX_COMPILER "arm-linux-gnueabihf-g++")
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set(CMAKE_CXX_FLAGS "-march=armv7-a -mfloat-abi=hard -mfpu=neon-vfpv4 ${CMAKE_CXX_FLAGS}")
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set(CMAKE_CXX_FLAGS "-march=armv7-a -mfloat-abi=hard -mfpu=neon-vfpv4 ${CMAKE_CXX_FLAGS}")
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set(CMAKE_C_FLAGS "-march=armv7-a -mfloat-abi=hard -mfpu=neon-vfpv4 ${CMAKE_C_FLAGS}" )
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set(CMAKE_C_FLAGS "-march=armv7-a -mfloat-abi=hard -mfpu=neon-vfpv4 ${CMAKE_C_FLAGS}" )
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set(TARGET_ABI armhf)
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set(CMAKE_BUILD_TYPE MinSizeRel)
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set(CMAKE_BUILD_TYPE MinSizeRel)
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else()
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else()
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if(NOT ${ENABLE_LITE_BACKEND})
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if(NOT ${ENABLE_LITE_BACKEND})
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@@ -8,10 +8,10 @@
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|||||||
## 自行编译安装
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## 自行编译安装
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||||||
- [GPU部署环境](gpu.md)
|
- [GPU部署环境](gpu.md)
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||||||
- [CPU部署环境](cpu.md)
|
- [CPU部署环境](cpu.md)
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||||||
- [CPU部署环境](ipu.md)
|
- [IPU部署环境](ipu.md)
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||||||
- [Jetson部署环境](jetson.md)
|
- [Jetson部署环境](jetson.md)
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||||||
- [Android平台部署环境](android.md)
|
- [Android平台部署环境](android.md)
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||||||
- [瑞芯微RK1126部署环境](rk1126.md)
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- [瑞芯微RV1126部署环境](rv1126.md)
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||||||
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||||||
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|
||||||
## FastDeploy编译选项说明
|
## FastDeploy编译选项说明
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||||||
@@ -22,6 +22,7 @@
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|||||||
| ENABLE_PADDLE_BACKEND | 默认OFF，是否编译集成Paddle Inference后端(CPU/GPU上推荐打开) |
|
| ENABLE_PADDLE_BACKEND | 默认OFF，是否编译集成Paddle Inference后端(CPU/GPU上推荐打开) |
|
||||||
| ENABLE_LITE_BACKEND | 默认OFF，是否编译集成Paddle Lite后端(编译Android库时需要设置为ON) |
|
| ENABLE_LITE_BACKEND | 默认OFF，是否编译集成Paddle Lite后端(编译Android库时需要设置为ON) |
|
||||||
| ENABLE_RKNPU2_BACKEND | 默认OFF，是否编译集成RKNPU2后端(RK3588/RK3568/RK3566上推荐打开) |
|
| ENABLE_RKNPU2_BACKEND | 默认OFF，是否编译集成RKNPU2后端(RK3588/RK3568/RK3566上推荐打开) |
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||||||
|
| ENABLE_TIMVX | 默认OFF，需要在RV1126/RV1109上部署时，需设置为ON |
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||||||
| ENABLE_TRT_BACKEND | 默认OFF，是否编译集成TensorRT后端(GPU上推荐打开) |
|
| ENABLE_TRT_BACKEND | 默认OFF，是否编译集成TensorRT后端(GPU上推荐打开) |
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||||||
| ENABLE_OPENVINO_BACKEND | 默认OFF，是否编译集成OpenVINO后端(CPU上推荐打开) |
|
| ENABLE_OPENVINO_BACKEND | 默认OFF，是否编译集成OpenVINO后端(CPU上推荐打开) |
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||||||
| ENABLE_VISION | 默认OFF，是否编译集成视觉模型的部署模块 |
|
| ENABLE_VISION | 默认OFF，是否编译集成视觉模型的部署模块 |
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@@ -1,63 +0,0 @@
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|||||||
# 瑞芯微 RK1126 部署环境编译安装
|
|
||||||
|
|
||||||
FastDeploy基于 Paddle-Lite 后端支持在瑞芯微（Rockchip）Soc 上进行部署推理。
|
|
||||||
更多详细的信息请参考：[PaddleLite部署示例](https://paddle-lite.readthedocs.io/zh/develop/demo_guides/verisilicon_timvx.html)。
|
|
||||||
|
|
||||||
本文档介绍如何编译基于 PaddleLite 的 C++ FastDeploy 交叉编译库。
|
|
||||||
|
|
||||||
相关编译选项说明如下：
|
|
||||||
|编译选项|默认值|说明|备注|
|
|
||||||
|:---|:---|:---|:---|
|
|
||||||
|ENABLE_LITE_BACKEND|OFF|编译RK库时需要设置为ON| - |
|
|
||||||
|
|
||||||
更多编译选项请参考[FastDeploy编译选项说明](./README.md)
|
|
||||||
|
|
||||||
## 交叉编译环境搭建
|
|
||||||
|
|
||||||
### 宿主机环境需求
|
|
||||||
- os：Ubuntu == 16.04
|
|
||||||
- cmake： version >= 3.10.0
|
|
||||||
|
|
||||||
### 环境搭建
|
|
||||||
```bash
|
|
||||||
# 1. Install basic software
|
|
||||||
apt update
|
|
||||||
apt-get install -y --no-install-recommends \
|
|
||||||
gcc g++ git make wget python unzip
|
|
||||||
|
|
||||||
# 2. Install arm gcc toolchains
|
|
||||||
apt-get install -y --no-install-recommends \
|
|
||||||
g++-arm-linux-gnueabi gcc-arm-linux-gnueabi \
|
|
||||||
g++-arm-linux-gnueabihf gcc-arm-linux-gnueabihf \
|
|
||||||
gcc-aarch64-linux-gnu g++-aarch64-linux-gnu
|
|
||||||
|
|
||||||
# 3. Install cmake 3.10 or above
|
|
||||||
wget -c https://mms-res.cdn.bcebos.com/cmake-3.10.3-Linux-x86_64.tar.gz && \
|
|
||||||
tar xzf cmake-3.10.3-Linux-x86_64.tar.gz && \
|
|
||||||
mv cmake-3.10.3-Linux-x86_64 /opt/cmake-3.10 && \
|
|
||||||
ln -s /opt/cmake-3.10/bin/cmake /usr/bin/cmake && \
|
|
||||||
ln -s /opt/cmake-3.10/bin/ccmake /usr/bin/ccmake
|
|
||||||
```
|
|
||||||
|
|
||||||
## 基于 PaddleLite 的 FastDeploy 交叉编译库编译
|
|
||||||
搭建好交叉编译环境之后，编译命令如下：
|
|
||||||
```bash
|
|
||||||
# Download the latest source code
|
|
||||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
|
||||||
cd FastDeploy
|
|
||||||
mkdir build && cd build
|
|
||||||
|
|
||||||
# CMake configuration with RK toolchain
|
|
||||||
cmake -DCMAKE_TOOLCHAIN_FILE=./../cmake/timvx.cmake \
|
|
||||||
-DENABLE_TIMVX=ON \
|
|
||||||
-DCMAKE_INSTALL_PREFIX=fastdeploy-tmivx \
|
|
||||||
-DENABLE_VISION=ON \ # 是否编译集成视觉模型的部署模块，可选择开启
|
|
||||||
-Wno-dev ..
|
|
||||||
|
|
||||||
# Build FastDeploy RK1126 C++ SDK
|
|
||||||
make -j8
|
|
||||||
make install
|
|
||||||
```
|
|
||||||
编译完成之后，会生成 fastdeploy-tmivx 目录，表示基于 PadddleLite TIM-VX 的 FastDeploy 库编译完成。
|
|
||||||
|
|
||||||
RK1126 上部署 PaddleClas 分类模型请参考：[PaddleClas RK1126开发板 C++ 部署示例](../../../examples/vision/classification/paddleclas/rk1126/README.md)
|
|
||||||
100
docs/cn/build_and_install/rv1126.md
Executable file
100
docs/cn/build_and_install/rv1126.md
Executable file
@@ -0,0 +1,100 @@
|
|||||||
|
# 瑞芯微 RV1126 部署环境编译安装
|
||||||
|
|
||||||
|
FastDeploy基于 Paddle-Lite 后端支持在瑞芯微（Rockchip）Soc 上进行部署推理。
|
||||||
|
更多详细的信息请参考：[PaddleLite部署示例](https://www.paddlepaddle.org.cn/lite/develop/demo_guides/verisilicon_timvx.html)。
|
||||||
|
|
||||||
|
本文档介绍如何编译基于 PaddleLite 的 C++ FastDeploy 交叉编译库。
|
||||||
|
|
||||||
|
相关编译选项说明如下：
|
||||||
|
|编译选项|默认值|说明|备注|
|
||||||
|
|:---|:---|:---|:---|
|
||||||
|
|ENABLE_LITE_BACKEND|OFF|编译RK库时需要设置为ON| - |
|
||||||
|
|ENABLE_TIMVX|OFF|编译RK库时需要设置为ON| - |
|
||||||
|
|
||||||
|
更多编译选项请参考[FastDeploy编译选项说明](./README.md)
|
||||||
|
|
||||||
|
## 交叉编译环境搭建
|
||||||
|
|
||||||
|
### 宿主机环境需求
|
||||||
|
- os：Ubuntu == 16.04
|
||||||
|
- cmake： version >= 3.10.0
|
||||||
|
|
||||||
|
### 环境搭建
|
||||||
|
```bash
|
||||||
|
# 1. Install basic software
|
||||||
|
apt update
|
||||||
|
apt-get install -y --no-install-recommends \
|
||||||
|
gcc g++ git make wget python unzip
|
||||||
|
|
||||||
|
# 2. Install arm gcc toolchains
|
||||||
|
apt-get install -y --no-install-recommends \
|
||||||
|
g++-arm-linux-gnueabi gcc-arm-linux-gnueabi \
|
||||||
|
g++-arm-linux-gnueabihf gcc-arm-linux-gnueabihf \
|
||||||
|
gcc-aarch64-linux-gnu g++-aarch64-linux-gnu
|
||||||
|
|
||||||
|
# 3. Install cmake 3.10 or above
|
||||||
|
wget -c https://mms-res.cdn.bcebos.com/cmake-3.10.3-Linux-x86_64.tar.gz && \
|
||||||
|
tar xzf cmake-3.10.3-Linux-x86_64.tar.gz && \
|
||||||
|
mv cmake-3.10.3-Linux-x86_64 /opt/cmake-3.10 && \
|
||||||
|
ln -s /opt/cmake-3.10/bin/cmake /usr/bin/cmake && \
|
||||||
|
ln -s /opt/cmake-3.10/bin/ccmake /usr/bin/ccmake
|
||||||
|
```
|
||||||
|
|
||||||
|
## 基于 PaddleLite 的 FastDeploy 交叉编译库编译
|
||||||
|
搭建好交叉编译环境之后，编译命令如下：
|
||||||
|
```bash
|
||||||
|
# Download the latest source code
|
||||||
|
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||||
|
cd FastDeploy
|
||||||
|
mkdir build && cd build
|
||||||
|
|
||||||
|
# CMake configuration with RK toolchain
|
||||||
|
cmake -DCMAKE_TOOLCHAIN_FILE=./../cmake/timvx.cmake \
|
||||||
|
-DENABLE_TIMVX=ON \
|
||||||
|
-DCMAKE_INSTALL_PREFIX=fastdeploy-tmivx \
|
||||||
|
-DENABLE_VISION=ON \ # 是否编译集成视觉模型的部署模块，可选择开启
|
||||||
|
-Wno-dev ..
|
||||||
|
|
||||||
|
# Build FastDeploy RV1126 C++ SDK
|
||||||
|
make -j8
|
||||||
|
make install
|
||||||
|
```
|
||||||
|
编译完成之后，会生成 fastdeploy-tmivx 目录，表示基于 PadddleLite TIM-VX 的 FastDeploy 库编译完成。
|
||||||
|
|
||||||
|
## 准备设备运行环境
|
||||||
|
部署前要保证芯原 Linux Kernel NPU 驱动 galcore.so 版本及所适用的芯片型号与依赖库保持一致，在部署前，请登录开发板，并通过命令行输入以下命令查询 NPU 驱动版本，Rockchip建议的驱动版本为： 6.4.6.5
|
||||||
|
```bash
|
||||||
|
dmesg | grep Galcore
|
||||||
|
```
|
||||||
|
|
||||||
|
如果当前版本不符合上述，请用户仔细阅读以下内容，以保证底层 NPU 驱动环境正确。
|
||||||
|
|
||||||
|
有两种方式可以修改当前的 NPU 驱动版本：
|
||||||
|
1. 手动替换 NPU 驱动版本。（推荐）
|
||||||
|
2. 刷机，刷取 NPU 驱动版本符合要求的固件。
|
||||||
|
|
||||||
|
### 手动替换 NPU 驱动版本
|
||||||
|
1. 使用如下命令下载解压 PaddleLite demo，其中提供了现成的驱动文件
|
||||||
|
```bash
|
||||||
|
wget https://paddlelite-demo.bj.bcebos.com/devices/generic/PaddleLite-generic-demo.tar.gz
|
||||||
|
tar -xf PaddleLite-generic-demo.tar.gz
|
||||||
|
```
|
||||||
|
2. 使用 `uname -a` 查看 `Linux Kernel` 版本，确定为 `Linux` 系统 4.19.111 版本，
|
||||||
|
3. 将 `PaddleLite-generic-demo/libs/PaddleLite/linux/armhf/lib/verisilicon_timvx/viv_sdk_6_4_6_5/lib/1126/4.19.111/` 路径下的 `galcore.ko` 上传至开发板。
|
||||||
|
|
||||||
|
4. 登录开发板，命令行输入 `sudo rmmod galcore` 来卸载原始驱动，输入 `sudo insmod galcore.ko` 来加载传上设备的驱动。（是否需要 sudo 根据开发板实际情况，部分 adb 链接的设备请提前 adb root）。此步骤如果操作失败，请跳转至方法 2。
|
||||||
|
5. 在开发板中输入 `dmesg | grep Galcore` 查询 NPU 驱动版本，确定为：6.4.6.5
|
||||||
|
|
||||||
|
### 刷机
|
||||||
|
根据具体的开发板型号，向开发板卖家或官网客服索要 6.4.6.5 版本 NPU 驱动对应的固件和刷机方法。
|
||||||
|
|
||||||
|
更多细节请参考：[PaddleLite准备设备环境](https://www.paddlepaddle.org.cn/lite/develop/demo_guides/verisilicon_timvx.html#zhunbeishebeihuanjing)
|
||||||
|
|
||||||
|
## 基于 FastDeploy 在 RV1126 上的部署示例
|
||||||
|
1. RV1126 上部署 PaddleClas 分类模型请参考：[PaddleClas 分类模型在 RV1126 上的 C++ 部署示例](../../../examples/vision/classification/paddleclas/rv1126/README.md)
|
||||||
|
|
||||||
|
2. RV1126 上部署 PPYOLOE 检测模型请参考：[PPYOLOE 检测模型在 RV1126 上的 C++ 部署示例](../../../examples/vision/detection/paddledetection/rv1126/README.md)
|
||||||
|
|
||||||
|
3. RV1126 上部署 YOLOv5 检测模型请参考：[YOLOv5 检测模型在 RV1126 上的 C++ 部署示例](../../../examples/vision/detection/yolov5/rv1126/README.md)
|
||||||
|
|
||||||
|
4. RV1126 上部署 PP-LiteSeg 分割模型请参考：[PP-LiteSeg 分割模型在 RV1126 上的 C++ 部署示例](../../../examples/vision/segmentation/paddleseg/rv1126/README.md)
|
||||||
2
examples/vision/classification/paddleclas/quantize/README.md
Normal file → Executable file
2
examples/vision/classification/paddleclas/quantize/README.md
Normal file → Executable file
@@ -4,7 +4,7 @@ FastDeploy已支持部署量化模型,并提供一键模型自动化压缩的工
|
|||||||
|
|
||||||
## FastDeploy一键模型自动化压缩工具
|
## FastDeploy一键模型自动化压缩工具
|
||||||
FastDeploy 提供了一键模型自动化压缩工具, 能够简单地通过输入一个配置文件, 对模型进行量化.
|
FastDeploy 提供了一键模型自动化压缩工具, 能够简单地通过输入一个配置文件, 对模型进行量化.
|
||||||
详细教程请见: [一键模型自动化压缩工具](../../../../../tools/auto_compression/)
|
详细教程请见: [一键模型自动化压缩工具](../../../../../tools/common_tools/auto_compression/)
|
||||||
注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可。
|
注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可。
|
||||||
|
|
||||||
## 下载量化完成的PaddleClas模型
|
## 下载量化完成的PaddleClas模型
|
||||||
|
|||||||
2
examples/vision/classification/paddleclas/quantize/cpp/README.md
Normal file → Executable file
2
examples/vision/classification/paddleclas/quantize/cpp/README.md
Normal file → Executable file
@@ -8,7 +8,7 @@
|
|||||||
|
|
||||||
### 量化模型准备
|
### 量化模型准备
|
||||||
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
||||||
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
|
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
|
||||||
|
|
||||||
## 以量化后的ResNet50_Vd模型为例, 进行部署，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
## 以量化后的ResNet50_Vd模型为例, 进行部署，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
||||||
在本目录执行如下命令即可完成编译,以及量化模型部署.
|
在本目录执行如下命令即可完成编译,以及量化模型部署.
|
||||||
|
|||||||
2
examples/vision/classification/paddleclas/quantize/python/README.md
Normal file → Executable file
2
examples/vision/classification/paddleclas/quantize/python/README.md
Normal file → Executable file
@@ -8,7 +8,7 @@
|
|||||||
|
|
||||||
### 量化模型准备
|
### 量化模型准备
|
||||||
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
||||||
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
|
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
|
||||||
|
|
||||||
|
|
||||||
## 以量化后的ResNet50_Vd模型为例, 进行部署
|
## 以量化后的ResNet50_Vd模型为例, 进行部署
|
||||||
|
|||||||
@@ -1,11 +1,11 @@
|
|||||||
# PaddleClas 量化模型在 RK1126 上的部署
|
# PaddleClas 量化模型在 RV1126 上的部署
|
||||||
目前 FastDeploy 已经支持基于 PaddleLite 部署 PaddleClas 量化模型到 RK1126 上。
|
目前 FastDeploy 已经支持基于 PaddleLite 部署 PaddleClas 量化模型到 RV1126 上。
|
||||||
|
|
||||||
模型的量化和量化模型的下载请参考：[模型量化](../quantize/README.md)
|
模型的量化和量化模型的下载请参考：[模型量化](../quantize/README.md)
|
||||||
|
|
||||||
|
|
||||||
## 详细部署文档
|
## 详细部署文档
|
||||||
|
|
||||||
在 RK1126 上只支持 C++ 的部署。
|
在 RV1126 上只支持 C++ 的部署。
|
||||||
|
|
||||||
- [C++部署](cpp)
|
- [C++部署](cpp)
|
||||||
@@ -1,22 +1,22 @@
|
|||||||
# PaddleClas RK1126开发板 C++ 部署示例
|
# PaddleClas RV1126 开发板 C++ 部署示例
|
||||||
本目录下提供的 `infer.cc`，可以帮助用户快速完成 PaddleClas 量化模型在 RK1126 上的部署推理加速。
|
本目录下提供的 `infer.cc`，可以帮助用户快速完成 PaddleClas 量化模型在 RV1126 上的部署推理加速。
|
||||||
|
|
||||||
## 部署准备
|
## 部署准备
|
||||||
### FastDeploy 交叉编译环境准备
|
### FastDeploy 交叉编译环境准备
|
||||||
- 1. 软硬件环境满足要求，以及交叉编译环境的准备，请参考：[FastDeploy 交叉编译环境准备](../../../../../../docs/cn/build_and_install/rk1126.md#交叉编译环境搭建)
|
- 1. 软硬件环境满足要求，以及交叉编译环境的准备，请参考：[FastDeploy 交叉编译环境准备](../../../../../../docs/cn/build_and_install/rv1126.md#交叉编译环境搭建)
|
||||||
|
|
||||||
### 量化模型准备
|
### 量化模型准备
|
||||||
- 1. 用户可以直接使用由 FastDeploy 提供的量化模型进行部署。
|
- 1. 用户可以直接使用由 FastDeploy 提供的量化模型进行部署。
|
||||||
- 2. 用户可以使用 FastDeploy 提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/)，自行进行模型量化, 并使用产出的量化模型进行部署。(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此 yaml 文件, 用户从 FP32 模型文件夹下复制此 yaml 文件到量化后的模型文件夹内即可.)
|
- 2. 用户可以使用 FastDeploy 提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/)，自行进行模型量化, 并使用产出的量化模型进行部署。(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此 yaml 文件, 用户从 FP32 模型文件夹下复制此 yaml 文件到量化后的模型文件夹内即可.)
|
||||||
- 更多量化相关相关信息可查阅[模型量化](../../quantize/README.md)
|
- 更多量化相关相关信息可查阅[模型量化](../../quantize/README.md)
|
||||||
|
|
||||||
## 在 RK1126 上部署量化后的 ResNet50_Vd 分类模型
|
## 在 RV1126 上部署量化后的 ResNet50_Vd 分类模型
|
||||||
请按照以下步骤完成在 RK1126 上部署 ResNet50_Vd 量化模型：
|
请按照以下步骤完成在 RV1126 上部署 ResNet50_Vd 量化模型：
|
||||||
1. 交叉编译编译 FastDeploy 库，具体请参考：[交叉编译 FastDeploy](../../../../../../docs/cn/build_and_install/rk1126.md#基于-paddlelite-的-fastdeploy-交叉编译库编译)
|
1. 交叉编译编译 FastDeploy 库，具体请参考：[交叉编译 FastDeploy](../../../../../../docs/cn/build_and_install/rv1126.md#基于-paddlelite-的-fastdeploy-交叉编译库编译)
|
||||||
|
|
||||||
2. 将编译后的库拷贝到当前目录，可使用如下命令：
|
2. 将编译后的库拷贝到当前目录，可使用如下命令：
|
||||||
```bash
|
```bash
|
||||||
cp -r FastDeploy/build/fastdeploy-tmivx/ FastDeploy/examples/vision/classification/paddleclas/rk1126/cpp/
|
cp -r FastDeploy/build/fastdeploy-tmivx/ FastDeploy/examples/vision/classification/paddleclas/rv1126/cpp/
|
||||||
```
|
```
|
||||||
|
|
||||||
3. 在当前路径下载部署所需的模型和示例图片：
|
3. 在当前路径下载部署所需的模型和示例图片：
|
||||||
@@ -32,7 +32,7 @@ cp -r ILSVRC2012_val_00000010.jpeg images
|
|||||||
4. 编译部署示例，可使入如下命令：
|
4. 编译部署示例，可使入如下命令：
|
||||||
```bash
|
```bash
|
||||||
mkdir build && cd build
|
mkdir build && cd build
|
||||||
cmake -DCMAKE_TOOLCHAIN_FILE=../fastdeploy-tmivx/timvx.cmake -DFASTDEPLOY_INSTALL_DIR=fastdeploy-tmivx ..
|
cmake -DCMAKE_TOOLCHAIN_FILE=${PWD}/../fastdeploy-tmivx/timvx.cmake -DFASTDEPLOY_INSTALL_DIR=${PWD}/../fastdeploy-tmivx ..
|
||||||
make -j8
|
make -j8
|
||||||
make install
|
make install
|
||||||
# 成功编译之后，会生成 install 文件夹，里面有一个运行 demo 和部署所需的库
|
# 成功编译之后，会生成 install 文件夹，里面有一个运行 demo 和部署所需的库
|
||||||
@@ -41,7 +41,7 @@ make install
|
|||||||
5. 基于 adb 工具部署 ResNet50_vd 分类模型到 Rockchip RV1126，可使用如下命令：
|
5. 基于 adb 工具部署 ResNet50_vd 分类模型到 Rockchip RV1126，可使用如下命令：
|
||||||
```bash
|
```bash
|
||||||
# 进入 install 目录
|
# 进入 install 目录
|
||||||
cd FastDeploy/examples/vision/classification/paddleclas/rk1126/cpp/build/install/
|
cd FastDeploy/examples/vision/classification/paddleclas/rv1126/cpp/build/install/
|
||||||
# 如下命令表示：bash run_with_adb.sh 需要运行的demo 模型路径 图片路径 设备的DEVICE_ID
|
# 如下命令表示：bash run_with_adb.sh 需要运行的demo 模型路径 图片路径 设备的DEVICE_ID
|
||||||
bash run_with_adb.sh infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg $DEVICE_ID
|
bash run_with_adb.sh infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg $DEVICE_ID
|
||||||
```
|
```
|
||||||
@@ -50,4 +50,4 @@ bash run_with_adb.sh infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg $
|
|||||||
|
|
||||||
<img width="640" src="https://user-images.githubusercontent.com/30516196/200767389-26519e50-9e4f-4fe1-8d52-260718f73476.png">
|
<img width="640" src="https://user-images.githubusercontent.com/30516196/200767389-26519e50-9e4f-4fe1-8d52-260718f73476.png">
|
||||||
|
|
||||||
需要特别注意的是，在 RK1126 上部署的模型需要是量化后的模型，模型的量化请参考：[模型量化](../../../../../../docs/cn/quantize.md)
|
需要特别注意的是，在 RV1126 上部署的模型需要是量化后的模型，模型的量化请参考：[模型量化](../../../../../../docs/cn/quantize.md)
|
||||||
2
examples/vision/detection/paddledetection/quantize/README.md
Normal file → Executable file
2
examples/vision/detection/paddledetection/quantize/README.md
Normal file → Executable file
@@ -4,7 +4,7 @@ FastDeploy已支持部署量化模型,并提供一键模型自动化压缩的工
|
|||||||
|
|
||||||
## FastDeploy一键模型自动化压缩工具
|
## FastDeploy一键模型自动化压缩工具
|
||||||
FastDeploy 提供了一键模型自动化压缩工具, 能够简单地通过输入一个配置文件, 对模型进行量化.
|
FastDeploy 提供了一键模型自动化压缩工具, 能够简单地通过输入一个配置文件, 对模型进行量化.
|
||||||
详细教程请见: [一键模型自动化压缩工具](../../../../../tools/auto_compression/)
|
详细教程请见: [一键模型自动化压缩工具](../../../../../tools/common_tools/auto_compression/)
|
||||||
|
|
||||||
## 下载量化完成的PP-YOLOE-l模型
|
## 下载量化完成的PP-YOLOE-l模型
|
||||||
用户也可以直接下载下表中的量化模型进行部署.(点击模型名字即可下载)
|
用户也可以直接下载下表中的量化模型进行部署.(点击模型名字即可下载)
|
||||||
|
|||||||
2
examples/vision/detection/paddledetection/quantize/cpp/README.md
Normal file → Executable file
2
examples/vision/detection/paddledetection/quantize/cpp/README.md
Normal file → Executable file
@@ -9,7 +9,7 @@
|
|||||||
|
|
||||||
### 量化模型准备
|
### 量化模型准备
|
||||||
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
||||||
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
|
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的检测模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
|
||||||
|
|
||||||
## 以量化后的PP-YOLOE-l模型为例, 进行部署。支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
## 以量化后的PP-YOLOE-l模型为例, 进行部署。支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
||||||
在本目录执行如下命令即可完成编译,以及量化模型部署.
|
在本目录执行如下命令即可完成编译,以及量化模型部署.
|
||||||
|
|||||||
2
examples/vision/detection/paddledetection/quantize/python/README.md
Normal file → Executable file
2
examples/vision/detection/paddledetection/quantize/python/README.md
Normal file → Executable file
@@ -8,7 +8,7 @@
|
|||||||
|
|
||||||
### 量化模型准备
|
### 量化模型准备
|
||||||
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
||||||
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
|
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
|
||||||
|
|
||||||
|
|
||||||
## 以量化后的PP-YOLOE-l模型为例, 进行部署
|
## 以量化后的PP-YOLOE-l模型为例, 进行部署
|
||||||
|
|||||||
@@ -1,25 +0,0 @@
|
|||||||
#!/bin/bash
|
|
||||||
|
|
||||||
set -e
|
|
||||||
|
|
||||||
DETECTION_MODEL_DIR="$(pwd)/picodet_detection/models"
|
|
||||||
LIBS_DIR="$(pwd)"
|
|
||||||
|
|
||||||
DETECTION_MODEL_URL="https://paddlelite-demo.bj.bcebos.com/Paddle-Lite-Demo/models/picodetv2_relu6_coco_no_fuse.tar.gz"
|
|
||||||
LIBS_URL="https://paddlelite-demo.bj.bcebos.com/Paddle-Lite-Demo/Paddle-Lite-libs.tar.gz"
|
|
||||||
|
|
||||||
download_and_uncompress() {
|
|
||||||
local url="$1"
|
|
||||||
local dir="$2"
|
|
||||||
|
|
||||||
echo "Start downloading ${url}"
|
|
||||||
curl -L ${url} > ${dir}/download.tar.gz
|
|
||||||
cd ${dir}
|
|
||||||
tar -zxvf download.tar.gz
|
|
||||||
rm -f download.tar.gz
|
|
||||||
}
|
|
||||||
|
|
||||||
download_and_uncompress "${DETECTION_MODEL_URL}" "${DETECTION_MODEL_DIR}"
|
|
||||||
download_and_uncompress "${LIBS_URL}" "${LIBS_DIR}"
|
|
||||||
|
|
||||||
echo "Download successful!"
|
|
||||||
@@ -1,62 +0,0 @@
|
|||||||
cmake_minimum_required(VERSION 3.10)
|
|
||||||
set(CMAKE_SYSTEM_NAME Linux)
|
|
||||||
if(TARGET_ARCH_ABI STREQUAL "armv8")
|
|
||||||
set(CMAKE_SYSTEM_PROCESSOR aarch64)
|
|
||||||
set(CMAKE_C_COMPILER "aarch64-linux-gnu-gcc")
|
|
||||||
set(CMAKE_CXX_COMPILER "aarch64-linux-gnu-g++")
|
|
||||||
elseif(TARGET_ARCH_ABI STREQUAL "armv7hf")
|
|
||||||
set(CMAKE_SYSTEM_PROCESSOR arm)
|
|
||||||
set(CMAKE_C_COMPILER "arm-linux-gnueabihf-gcc")
|
|
||||||
set(CMAKE_CXX_COMPILER "arm-linux-gnueabihf-g++")
|
|
||||||
else()
|
|
||||||
message(FATAL_ERROR "Unknown arch abi ${TARGET_ARCH_ABI}, only support armv8 and armv7hf.")
|
|
||||||
return()
|
|
||||||
endif()
|
|
||||||
|
|
||||||
project(object_detection_demo)
|
|
||||||
|
|
||||||
message(STATUS "TARGET ARCH ABI: ${TARGET_ARCH_ABI}")
|
|
||||||
message(STATUS "PADDLE LITE DIR: ${PADDLE_LITE_DIR}")
|
|
||||||
include_directories(${PADDLE_LITE_DIR}/include)
|
|
||||||
link_directories(${PADDLE_LITE_DIR}/libs/${TARGET_ARCH_ABI})
|
|
||||||
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
|
|
||||||
if(TARGET_ARCH_ABI STREQUAL "armv8")
|
|
||||||
set(CMAKE_CXX_FLAGS "-march=armv8-a ${CMAKE_CXX_FLAGS}")
|
|
||||||
set(CMAKE_C_FLAGS "-march=armv8-a ${CMAKE_C_FLAGS}")
|
|
||||||
elseif(TARGET_ARCH_ABI STREQUAL "armv7hf")
|
|
||||||
set(CMAKE_CXX_FLAGS "-march=armv7-a -mfloat-abi=hard -mfpu=neon-vfpv4 ${CMAKE_CXX_FLAGS}")
|
|
||||||
set(CMAKE_C_FLAGS "-march=armv7-a -mfloat-abi=hard -mfpu=neon-vfpv4 ${CMAKE_C_FLAGS}" )
|
|
||||||
endif()
|
|
||||||
|
|
||||||
include_directories(${PADDLE_LITE_DIR}/libs/${TARGET_ARCH_ABI}/third_party/yaml-cpp/include)
|
|
||||||
link_directories(${PADDLE_LITE_DIR}/libs/${TARGET_ARCH_ABI}/third_party/yaml-cpp)
|
|
||||||
|
|
||||||
find_package(OpenMP REQUIRED)
|
|
||||||
if(OpenMP_FOUND OR OpenMP_CXX_FOUND)
|
|
||||||
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
|
|
||||||
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
|
|
||||||
message(STATUS "Found OpenMP ${OpenMP_VERSION} ${OpenMP_CXX_VERSION}")
|
|
||||||
message(STATUS "OpenMP C flags: ${OpenMP_C_FLAGS}")
|
|
||||||
message(STATUS "OpenMP CXX flags: ${OpenMP_CXX_FLAGS}")
|
|
||||||
message(STATUS "OpenMP OpenMP_CXX_LIB_NAMES: ${OpenMP_CXX_LIB_NAMES}")
|
|
||||||
message(STATUS "OpenMP OpenMP_CXX_LIBRARIES: ${OpenMP_CXX_LIBRARIES}")
|
|
||||||
else()
|
|
||||||
message(FATAL_ERROR "Could not found OpenMP!")
|
|
||||||
return()
|
|
||||||
endif()
|
|
||||||
find_package(OpenCV REQUIRED)
|
|
||||||
if(OpenCV_FOUND OR OpenCV_CXX_FOUND)
|
|
||||||
include_directories(${OpenCV_INCLUDE_DIRS})
|
|
||||||
message(STATUS "OpenCV library status:")
|
|
||||||
message(STATUS " version: ${OpenCV_VERSION}")
|
|
||||||
message(STATUS " libraries: ${OpenCV_LIBS}")
|
|
||||||
message(STATUS " include path: ${OpenCV_INCLUDE_DIRS}")
|
|
||||||
else()
|
|
||||||
message(FATAL_ERROR "Could not found OpenCV!")
|
|
||||||
return()
|
|
||||||
endif()
|
|
||||||
|
|
||||||
|
|
||||||
add_executable(object_detection_demo object_detection_demo.cc)
|
|
||||||
|
|
||||||
target_link_libraries(object_detection_demo paddle_full_api_shared dl ${OpenCV_LIBS} yaml-cpp)
|
|
||||||
@@ -1,343 +0,0 @@
|
|||||||
# 目标检测 C++ API Demo 使用指南
|
|
||||||
|
|
||||||
在 ARMLinux 上实现实时的目标检测功能，此 Demo 有较好的的易用性和扩展性，如在 Demo 中跑自己训练好的模型等。
|
|
||||||
- 如果该开发板使用搭载了芯原 NPU （瑞芯微、晶晨、JQL、恩智浦）的 Soc，将有更好的加速效果。
|
|
||||||
|
|
||||||
## 如何运行目标检测 Demo
|
|
||||||
|
|
||||||
### 环境准备
|
|
||||||
|
|
||||||
* 准备 ARMLiunx 开发版，将系统刷为 Ubuntu，用于 Demo 编译和运行。请注意，本 Demo 是使用板上编译，而非交叉编译，因此需要图形界面的开发板操作系统。
|
|
||||||
* 如果需要使用 芯原 NPU 的计算加速，对 NPU 驱动版本有严格要求，请务必注意事先参考 [芯原 TIM-VX 部署示例](https://paddle-lite.readthedocs.io/zh/develop/demo_guides/verisilicon_timvx.html#id6)，将 NPU 驱动改为要求的版本。
|
|
||||||
* Paddle Lite 当前已验证的开发板为 Khadas VIM3（芯片为 Amlogic A311d）、荣品 RV1126、荣品RV1109，其它平台用户可自行尝试；
|
|
||||||
- Khadas VIM3：由于 VIM3 出厂自带 Android 系统，请先刷成 Ubuntu 系统，在此提供刷机教程：[VIM3/3L Linux 文档](https://docs.khadas.com/linux/zh-cn/vim3)，其中有详细描述刷机方法。以及系统镜像：VIM3 Linux：VIM3_Ubuntu-gnome-focal_Linux-4.9_arm64_EMMC_V1.0.7-210625：[官方链接](http://dl.khadas.com/firmware/VIM3/Ubuntu/EMMC/VIM3_Ubuntu-gnome-focal_Linux-4.9_arm64_EMMC_V1.0.7-210625.img.xz)；[百度云备用链接](https://paddlelite-demo.bj.bcebos.com/devices/verisilicon/firmware/khadas/vim3/VIM3_Ubuntu-gnome-focal_Linux-4.9_arm64_EMMC_V1.0.7-210625.img.xz)
|
|
||||||
- 荣品 RV1126、1109：由于出场自带 buildroot 系统，如果使用 GUI 界面的 demo，请先刷成 Ubuntu 系统，在此提供刷机教程：[RV1126/1109 教程](https://paddlelite-demo.bj.bcebos.com/Paddle-Lite-Demo/os_img/rockchip/RV1126-RV1109%E4%BD%BF%E7%94%A8%E6%8C%87%E5%AF%BC%E6%96%87%E6%A1%A3-V3.0.pdf)，[刷机工具](https://paddlelite-demo.bj.bcebos.com/Paddle-Lite-Demo/os_img/rockchip/RKDevTool_Release.zip)，以及镜像：[1126镜像](https://paddlelite-demo.bj.bcebos.com/Paddle-Lite-Demo/os_img/update-pro-rv1126-ubuntu20.04-5-720-1280-v2-20220505.img)，[1109镜像](https://paddlelite-demo.bj.bcebos.com/Paddle-Lite-Demo/os_img/update-pro-rv1109-ubuntu20.04-5.5-720-1280-v2-20220429.img)。完整的文档和各种镜像请参考[百度网盘链接](https://pan.baidu.com/s/1Id0LMC0oO2PwR2YcYUAaiQ#list/path=%2F&parentPath=%2Fsharelink2521613171-184070898837664)，密码：2345。
|
|
||||||
* 准备 usb camera，注意使用 openCV capture 图像时，请注意 usb camera 的 video序列号作为入参。
|
|
||||||
* 请注意，瑞芯微芯片不带有 HDMI 接口，图像显示是依赖 MIPI DSI，所以请准备好 MIPI 显示屏（我们提供的镜像是 720*1280 分辨率，网盘中有更多分辨率选择，注意：请选择 camera-gc2093x2 的镜像）。
|
|
||||||
* 配置开发板的网络。如果是办公网络红区，可以将开发板和PC用以太网链接，然后PC共享网络给开发板。
|
|
||||||
* gcc g++ opencv cmake 的安装（以下所有命令均在设备上操作）
|
|
||||||
|
|
||||||
```bash
|
|
||||||
$ sudo apt-get update
|
|
||||||
$ sudo apt-get install gcc g++ make wget unzip libopencv-dev pkg-config
|
|
||||||
$ wget https://www.cmake.org/files/v3.10/cmake-3.10.3.tar.gz
|
|
||||||
$ tar -zxvf cmake-3.10.3.tar.gz
|
|
||||||
$ cd cmake-3.10.3
|
|
||||||
$ ./configure
|
|
||||||
$ make
|
|
||||||
$ sudo make install
|
|
||||||
```
|
|
||||||
|
|
||||||
### 部署步骤
|
|
||||||
|
|
||||||
1. 将本 repo 上传至 VIM3 开发板，或者直接开发板上下载或者 git clone 本 repo
|
|
||||||
2. 目标检测 Demo 位于 `Paddle-Lite-Demo/object_detection/linux/picodet_detection` 目录
|
|
||||||
3. 进入 `Paddle-Lite-Demo/object_detection/linux` 目录, 终端中执行 `download_models_and_libs.sh` 脚本自动下载模型和 Paddle Lite 预测库
|
|
||||||
|
|
||||||
```shell
|
|
||||||
cd Paddle-Lite-Demo/object_detection/linux # 1. 终端中进入 Paddle-Lite-Demo/object_detection/linux
|
|
||||||
sh download_models_and_libs.sh # 2. 执行脚本下载依赖项 （需要联网）
|
|
||||||
```
|
|
||||||
|
|
||||||
下载完成后会出现提示： `Download successful!`
|
|
||||||
4. 执行用例(保证 ARMLinux 环境准备完成)
|
|
||||||
|
|
||||||
```shell
|
|
||||||
cd picodet_detection # 1. 终端中进入
|
|
||||||
sh build.sh armv8 # 2. 编译 Demo 可执行程序，默认编译 armv8，如果是 32bit 环境，则改成 sh build.sh armv7hf。
|
|
||||||
sh run.sh armv8 # 3. 执行物体检测（picodet 模型） demo，会直接开启摄像头，启动图形界面并呈现检测结果。如果是 32bit 环境，则改成 sh run.sh armv7hf
|
|
||||||
```
|
|
||||||
|
|
||||||
### Demo 结果如下:（注意，示例的 picodet 仅使用 coco 数据集，在实际场景中效果一般，请使用实际业务场景重新训练）
|
|
||||||
|
|
||||||
<img src="https://paddlelite-demo.bj.bcebos.com/Paddle-Lite-Demo/demo_view.jpg" alt="demo_view" style="zoom: 10%;" />
|
|
||||||
|
|
||||||
## 更新预测库
|
|
||||||
|
|
||||||
* Paddle Lite 项目：https://github.com/PaddlePaddle/Paddle-Lite
|
|
||||||
* 参考 [芯原 TIM-VX 部署示例](https://paddle-lite.readthedocs.io/zh/develop/demo_guides/verisilicon_timvx.html#tim-vx)，编译预测库
|
|
||||||
* 编译最终产物位于 `build.lite.xxx.xxx.xxx` 下的 `inference_lite_lib.xxx.xxx`
|
|
||||||
* 替换 c++ 库
|
|
||||||
* 头文件
|
|
||||||
将生成的 `build.lite.linux.armv8.gcc/inference_lite_lib.armlinux.armv8.nnadapter/cxx/include` 文件夹替换 Demo 中的 `Paddle-Lite-Demo/object_detection/linux/Paddle-Lite/include`
|
|
||||||
* armv8
|
|
||||||
将生成的 `build.lite.linux.armv8.gcc/inference_lite_lib.armlinux.armv8.nnadapter/cxx/libs/libpaddle_full_api_shared.so、libnnadapter.so、libtim-vx.so、libverisilicon_timvx.so` 库替换 Demo 中的 `Paddle-Lite-Demo/object_detection/linux/Paddle-Lite/libs/armv8/` 目录下同名 so
|
|
||||||
* armv7hf
|
|
||||||
将生成的 `build.lite.linux.armv7hf.gcc/inference_lite_lib.armlinux.armv7hf.nnadapter/cxx/libs/libpaddle_full_api_shared.so、libnnadapter.so、libtim-vx.so、libverisilicon_timvx.so` 库替换 Demo 中的 `Paddle-Lite-Demo/object_detection/linux/Paddle-Lite/libs/armv7hf/` 目录下同名 so
|
|
||||||
|
|
||||||
## Demo 内容介绍
|
|
||||||
|
|
||||||
先整体介绍下目标检测 Demo 的代码结构，然后再简要地介绍 Demo 每部分功能.
|
|
||||||
|
|
||||||
1. `object_detection_demo.cc`： C++ 预测代码
|
|
||||||
|
|
||||||
```shell
|
|
||||||
# 位置：
|
|
||||||
Paddle-Lite-Demo/object_detection/linux/picodet_detection/object_detection_demo.cc
|
|
||||||
```
|
|
||||||
|
|
||||||
2. `models` : 模型文件夹 (执行 download_models_and_libs.sh 后会下载 picodet Paddle 模型), label 使用 Paddle-Lite-Demo/object_detection/assets/labels 目录下 coco_label_list.txt
|
|
||||||
|
|
||||||
```shell
|
|
||||||
# 位置：
|
|
||||||
Paddle-Lite-Demo/object_detection/linux/picodet_detection/models/picodetv2_relu6_coco_no_fuse
|
|
||||||
Paddle-Lite-Demo/object_detection/assets/labels/coco_label_list.txt
|
|
||||||
```
|
|
||||||
|
|
||||||
3. `Paddle-Lite`：内含 Paddle-Lite 头文件和 动态库，默认带有 timvx 加速库，以及第三方库 yaml-cpp 用于解析 yml 配置文件（执行 download_models_and_libs.sh 后会下载）
|
|
||||||
|
|
||||||
```shell
|
|
||||||
# 位置
|
|
||||||
# 如果要替换动态库 so，则将新的动态库 so 更新到此目录下
|
|
||||||
Paddle-Lite-Demo/object_detection/linux/Paddle-Lite/libs/armv8
|
|
||||||
Paddle-Lite-Demo/object_detection/linux/Paddle-Lite/include
|
|
||||||
```
|
|
||||||
|
|
||||||
4. `CMakeLists.txt` : C++ 预测代码的编译脚本，用于生成可执行文件
|
|
||||||
|
|
||||||
```shell
|
|
||||||
# 位置
|
|
||||||
Paddle-Lite-Demo/object_detection/linux/picodet_detection/CMakeLists.txt
|
|
||||||
# 如果有cmake 编译选项更新，可以在 CMakeLists.txt 进行修改即可，默认编译 armv8 可执行文件；
|
|
||||||
```
|
|
||||||
|
|
||||||
5. `build.sh` : 编译脚本
|
|
||||||
|
|
||||||
```shell
|
|
||||||
# 位置
|
|
||||||
Paddle-Lite-Demo/object_detection/linux/picodet_detection/build.sh
|
|
||||||
```
|
|
||||||
|
|
||||||
6. `run.sh` : 运行脚本，请注意设置 arm-aarch，armv8 或者 armv7hf。默认为armv8
|
|
||||||
|
|
||||||
```shell
|
|
||||||
# 位置
|
|
||||||
Paddle-Lite-Demo/object_detection/linux/picodet_detection/run.sh
|
|
||||||
```
|
|
||||||
- 请注意，运行需要5个元素：测试程序、模型、label 文件、异构配置、yaml 文件。
|
|
||||||
|
|
||||||
## 代码讲解 （使用 Paddle Lite `C++ API` 执行预测）
|
|
||||||
|
|
||||||
ARMLinux 示例基于 C++ API 开发，调用 Paddle Lite `C++s API` 包括以下五步。更详细的 `API` 描述参考：[Paddle Lite C++ API ](https://paddle-lite.readthedocs.io/zh/latest/api_reference/cxx_api_doc.html)。
|
|
||||||
|
|
||||||
```c++
|
|
||||||
#include <iostream>
|
|
||||||
// 引入 C++ API
|
|
||||||
#include "include/paddle_api.h"
|
|
||||||
#include "include/paddle_use_ops.h"
|
|
||||||
#include "include/paddle_use_kernels.h"
|
|
||||||
|
|
||||||
// 使用在线编译模型的方式（等价于使用 opt 工具）
|
|
||||||
|
|
||||||
// 1. 设置 CxxConfig
|
|
||||||
paddle::lite_api::CxxConfig cxx_config;
|
|
||||||
std::vector<paddle::lite_api::Place> valid_places;
|
|
||||||
valid_places.push_back(
|
|
||||||
paddle::lite_api::Place{TARGET(kARM), PRECISION(kInt8)});
|
|
||||||
valid_places.push_back(
|
|
||||||
paddle::lite_api::Place{TARGET(kARM), PRECISION(kFloat)});
|
|
||||||
// 如果只需要 cpu 计算，那到此结束即可，下面是设置 NPU 的代码段
|
|
||||||
valid_places.push_back(
|
|
||||||
paddle::lite_api::Place{TARGET(kNNAdapter), PRECISION(kInt8)});
|
|
||||||
cxx_config.set_valid_places(valid_places);
|
|
||||||
std::string device = "verisilicon_timvx";
|
|
||||||
cxx_config.set_nnadapter_device_names({device});
|
|
||||||
// 设置定制化的异构策略 （如需要）
|
|
||||||
cxx_config.set_nnadapter_subgraph_partition_config_buffer(
|
|
||||||
nnadapter_subgraph_partition_config_string);
|
|
||||||
|
|
||||||
// 2. 生成 nb 模型 （等价于 opt 工具的产出）
|
|
||||||
std::shared_ptr<paddle::lite_api::PaddlePredictor> predictor = nullptr;
|
|
||||||
predictor = paddle::lite_api::CreatePaddlePredictor(cxx_config);
|
|
||||||
predictor->SaveOptimizedModel(
|
|
||||||
model_path, paddle::lite_api::LiteModelType::kNaiveBuffer);
|
|
||||||
|
|
||||||
// 3. 设置 MobileConfig
|
|
||||||
MobileConfig config;
|
|
||||||
config.set_model_from_file(modelPath); // 设置 NaiveBuffer 格式模型路径
|
|
||||||
config.set_power_mode(LITE_POWER_NO_BIND); // 设置 CPU 运行模式
|
|
||||||
config.set_threads(4); // 设置工作线程数
|
|
||||||
|
|
||||||
// 4. 创建 PaddlePredictor
|
|
||||||
predictor = CreatePaddlePredictor<MobileConfig>(config);
|
|
||||||
|
|
||||||
// 5. 设置输入数据，注意，如果是带后处理的 picodet ，则是有两个输入
|
|
||||||
std::unique_ptr<Tensor> input_tensor(std::move(predictor->GetInput(0)));
|
|
||||||
input_tensor->Resize({1, 3, 416, 416});
|
|
||||||
auto* data = input_tensor->mutable_data<float>();
|
|
||||||
// scale_factor tensor
|
|
||||||
auto scale_factor_tensor = predictor->GetInput(1);
|
|
||||||
scale_factor_tensor->Resize({1, 2});
|
|
||||||
auto scale_factor_data = scale_factor_tensor->mutable_data<float>();
|
|
||||||
scale_factor_data[0] = 1.0f;
|
|
||||||
scale_factor_data[1] = 1.0f;
|
|
||||||
|
|
||||||
// 6. 执行预测
|
|
||||||
predictor->run();
|
|
||||||
|
|
||||||
// 7. 获取输出数据
|
|
||||||
std::unique_ptr<const Tensor> output_tensor(std::move(predictor->GetOutput(0)));
|
|
||||||
|
|
||||||
```
|
|
||||||
|
|
||||||
## 如何更新模型和输入/输出预处理
|
|
||||||
|
|
||||||
### 更新模型
|
|
||||||
1. 请参考 PaddleDetection 中 [picodet 重训和全量化文档](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/picodet/FULL_QUANTIZATION.md)，基于用户自己数据集重训并且重新全量化
|
|
||||||
2. 将模型存放到目录 `object_detection_demo/models/` 下；
|
|
||||||
3. 模型名字跟工程中模型名字一模一样，即均是使用 `model`、`params`；
|
|
||||||
|
|
||||||
```shell
|
|
||||||
# shell 脚本 `object_detection_demo/run.sh`
|
|
||||||
TARGET_ABI=armv8 # for 64bit, such as Amlogic A311D
|
|
||||||
#TARGET_ABI=armv7hf # for 32bit, such as Rockchip 1109/1126
|
|
||||||
if [ -n "$1" ]; then
|
|
||||||
TARGET_ABI=$1
|
|
||||||
fi
|
|
||||||
export LD_LIBRARY_PATH=../Paddle-Lite/libs/$TARGET_ABI/
|
|
||||||
export GLOG_v=0 # Paddle-Lite 日志等级
|
|
||||||
export VSI_NN_LOG_LEVEL=0 # TIM-VX 日志等级
|
|
||||||
export VIV_VX_ENABLE_GRAPH_TRANSFORM=-pcq:1 # NPU 开启 perchannel 量化模型
|
|
||||||
export VIV_VX_SET_PER_CHANNEL_ENTROPY=100 # 同上
|
|
||||||
build/object_detection_demo models/picodetv2_relu6_coco_no_fuse ../../assets/labels/coco_label_list.txt models/picodetv2_relu6_coco_no_fuse/subgraph.txt models/picodetv2_relu6_coco_no_fuse/picodet.yml # 执行 Demo 程序，4个 arg 分别为：模型、 label 文件、 自定义异构配置、 yaml
|
|
||||||
```
|
|
||||||
|
|
||||||
- 如果需要更新 `label_list` 或者 `yaml` 文件，则修改 `object_detection_demo/run.sh` 中执行命令的第二个和第四个 arg 指定为新的 label 文件和 yaml 配置文件；
|
|
||||||
|
|
||||||
```shell
|
|
||||||
# 代码文件 `object_detection_demo/rush.sh`
|
|
||||||
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${PADDLE_LITE_DIR}/libs/${TARGET_ARCH_ABI}
|
|
||||||
build/object_detection_demo {模型} {label} {自定义异构配置文件} {yaml}
|
|
||||||
```
|
|
||||||
|
|
||||||
### 更新输入/输出预处理
|
|
||||||
|
|
||||||
1. 更新输入预处理
|
|
||||||
预处理完全根据 yaml 文件来，如果完全按照 PaddleDetection 中 picodet 重训，只需要替换 yaml 文件即可
|
|
||||||
|
|
||||||
2. 更新输出预处理
|
|
||||||
此处需要更新 `object_detection_demo/object_detection_demo.cc` 中的 `postprocess` 方法
|
|
||||||
|
|
||||||
```c++
|
|
||||||
std::vector<RESULT> postprocess(const float *output_data, int64_t output_size,
|
|
||||||
const std::vector<std::string> &word_labels,
|
|
||||||
const float score_threshold,
|
|
||||||
cv::Mat &output_image, double time) {
|
|
||||||
std::vector<RESULT> results;
|
|
||||||
std::vector<cv::Scalar> colors = {
|
|
||||||
cv::Scalar(237, 189, 101), cv::Scalar(0, 0, 255),
|
|
||||||
cv::Scalar(102, 153, 153), cv::Scalar(255, 0, 0),
|
|
||||||
cv::Scalar(9, 255, 0), cv::Scalar(0, 0, 0),
|
|
||||||
cv::Scalar(51, 153, 51)};
|
|
||||||
for (int64_t i = 0; i < output_size; i += 6) {
|
|
||||||
if (output_data[i + 1] < score_threshold) {
|
|
||||||
continue;
|
|
||||||
}
|
|
||||||
int class_id = static_cast<int>(output_data[i]);
|
|
||||||
float score = output_data[i + 1];
|
|
||||||
RESULT result;
|
|
||||||
std::string class_name = "Unknown";
|
|
||||||
if (word_labels.size() > 0 && class_id >= 0 &&
|
|
||||||
class_id < word_labels.size()) {
|
|
||||||
class_name = word_labels[class_id];
|
|
||||||
}
|
|
||||||
result.class_name = class_name;
|
|
||||||
result.score = score;
|
|
||||||
result.left = output_data[i + 2] / 416; // 此处416根据输入的 HW 得来
|
|
||||||
result.top = output_data[i + 3] / 416;
|
|
||||||
result.right = output_data[i + 4] / 416;
|
|
||||||
result.bottom = output_data[i + 5] / 416;
|
|
||||||
int lx = static_cast<int>(result.left * output_image.cols);
|
|
||||||
int ly = static_cast<int>(result.top * output_image.rows);
|
|
||||||
int w = static_cast<int>(result.right * output_image.cols) - lx;
|
|
||||||
int h = static_cast<int>(result.bottom * output_image.rows) - ly;
|
|
||||||
cv::Rect bounding_box =
|
|
||||||
cv::Rect(lx, ly, w, h) &
|
|
||||||
cv::Rect(0, 0, output_image.cols, output_image.rows);
|
|
||||||
if (w > 0 && h > 0 && score <= 1) {
|
|
||||||
cv::Scalar color = colors[results.size() % colors.size()];
|
|
||||||
cv::rectangle(output_image, bounding_box, color);
|
|
||||||
cv::rectangle(output_image, cv::Point2d(lx, ly),
|
|
||||||
cv::Point2d(lx + w, ly - 10), color, -1);
|
|
||||||
cv::putText(output_image, std::to_string(results.size()) + "." +
|
|
||||||
class_name + ":" + std::to_string(score),
|
|
||||||
cv::Point2d(lx, ly), cv::FONT_HERSHEY_PLAIN, 1,
|
|
||||||
cv::Scalar(255, 255, 255));
|
|
||||||
results.push_back(result);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
return results;
|
|
||||||
}
|
|
||||||
```
|
|
||||||
|
|
||||||
## 更新模型后，自定义 NPU-CPU 异构配置（如需使用 NPU 加速）
|
|
||||||
由于使用芯原 NPU 在 8bit 量化的情况下有最优的性能，因此部署时，我们往往会考虑量化
|
|
||||||
- 由于量化可能会引入一定程度的精度问题，所以我们可以通过自定义的异构定制，来将部分有精度问题的 layer 异构至cpu，从而达到最优的精度
|
|
||||||
|
|
||||||
### 第一步，确定模型量化后在 arm cpu 上的精度
|
|
||||||
如果在 arm cpu 上，精度都无法满足，那量化本身就是失败的，此时可以考虑修改训练集或者预处理。
|
|
||||||
- 修改 Demo 程序，仅用 arm cpu 计算
|
|
||||||
```c++
|
|
||||||
paddle::lite_api::CxxConfig cxx_config;
|
|
||||||
std::vector<paddle::lite_api::Place> valid_places;
|
|
||||||
valid_places.push_back(
|
|
||||||
paddle::lite_api::Place{TARGET(kARM), PRECISION(kInt8)});
|
|
||||||
valid_places.push_back(
|
|
||||||
paddle::lite_api::Place{TARGET(kARM), PRECISION(kFloat)});
|
|
||||||
// 仅用 arm cpu 计算， 注释如下代码即可
|
|
||||||
/*
|
|
||||||
valid_places.push_back(
|
|
||||||
paddle::lite_api::Place{TARGET(kNNAdapter), PRECISION(kInt8)});
|
|
||||||
valid_places.push_back(
|
|
||||||
paddle::lite_api::Place{TARGET(kNNAdapter), PRECISION(kFloat)});
|
|
||||||
*/
|
|
||||||
```
|
|
||||||
如果 arm cpu 计算结果精度达标，则继续
|
|
||||||
|
|
||||||
### 第二步，获取整网拓扑信息
|
|
||||||
- 回退第一步的修改，不再注释，使用 NPU 加速
|
|
||||||
- 运行 Demo，如果此时精度良好，则无需参考后面步骤，模型部署和替换非常顺利，enjoy it。
|
|
||||||
- 如果精度不行，请参考后续步骤。
|
|
||||||
|
|
||||||
### 第三步，获取整网拓扑信息
|
|
||||||
- 回退第一步的修改，使用
|
|
||||||
- 修改 run.sh ，将其中 export GLOG_v=0 改为 export GLOG_v=5
|
|
||||||
- 运行 Demo，等摄像头启动，即可 ctrl+c 关闭 Demo
|
|
||||||
- 收集日志，搜索关键字 "subgraph operators" 随后那一段，便是整个模型的拓扑信息，其格式如下：
|
|
||||||
- 每行记录由『算子类型:输入张量名列表:输出张量名列表』组成（即以分号分隔算子类型、输入和输出张量名列表），以逗号分隔输入、输出张量名列表中的每个张量名；
|
|
||||||
- 示例说明：
|
|
||||||
```
|
|
||||||
op_type0:var_name0,var_name1:var_name2 表示将算子类型为 op_type0、输入张量为var_name0 和 var_name1、输出张量为 var_name2 的节点强制运行在 ARM CPU 上
|
|
||||||
```
|
|
||||||
|
|
||||||
### 第四步，修改异构配置文件
|
|
||||||
- 首先看到示例 Demo 中 Paddle-Lite-Demo/object_detection/linux/picodet_detection/models/picodetv2_relu6_coco_no_fuse 目录下的 subgraph.txt 文件。(feed 和 fetch 分别代表整个模型的输入和输入)
|
|
||||||
```
|
|
||||||
feed:feed:scale_factor
|
|
||||||
feed:feed:image
|
|
||||||
|
|
||||||
sqrt:tmp_3:sqrt_0.tmp_0
|
|
||||||
reshape2:sqrt_0.tmp_0:reshape2_0.tmp_0,reshape2_0.tmp_1
|
|
||||||
|
|
||||||
matmul_v2:softmax_0.tmp_0,auto_113_:linear_0.tmp_0
|
|
||||||
reshape2:linear_0.tmp_0:reshape2_2.tmp_0,reshape2_2.tmp_1
|
|
||||||
|
|
||||||
sqrt:tmp_6:sqrt_1.tmp_0
|
|
||||||
reshape2:sqrt_1.tmp_0:reshape2_3.tmp_0,reshape2_3.tmp_1
|
|
||||||
|
|
||||||
matmul_v2:softmax_1.tmp_0,auto_113_:linear_1.tmp_0
|
|
||||||
reshape2:linear_1.tmp_0:reshape2_5.tmp_0,reshape2_5.tmp_1
|
|
||||||
|
|
||||||
sqrt:tmp_9:sqrt_2.tmp_0
|
|
||||||
reshape2:sqrt_2.tmp_0:reshape2_6.tmp_0,reshape2_6.tmp_1
|
|
||||||
|
|
||||||
matmul_v2:softmax_2.tmp_0,auto_113_:linear_2.tmp_0
|
|
||||||
...
|
|
||||||
```
|
|
||||||
- 在 txt 中的都是需要异构至 cpu 计算的 layer，在示例 Demo 中，我们把 picodet 后处理的部分异构至 arm cpu 做计算，不必担心，Paddle-Lite 的 arm kernel 性能也是非常卓越。
|
|
||||||
- 如果新训练的模型没有额外修改 layer，则直接复制使用示例 Demo 中的 subgraph.txt 即可
|
|
||||||
- 此时 ./run.sh 看看精度是否符合预期，如果精度符合预期，恭喜，可以跳过本章节，enjoy it。
|
|
||||||
- 如果精度不符合预期，则将上文『第二步，获取整网拓扑信息』中获取的拓扑信息，从 "feed" 之后第一行，直到 "sqrt" 之前，都复制进 sugraph.txt。这一步代表了将大量的 backbone 部分算子放到 arm cpu 计算。
|
|
||||||
- 此时 ./run.sh 看看精度是否符合预期，如果精度达标，那说明在 backbone 中确实存在引入 NPU 精度异常的层（再次重申，在 subgraph.txt 的代表强制在 arm cpu 计算）。
|
|
||||||
- 逐行删除、成片删除、二分法，发挥开发人员的耐心，找到引入 NPU 精度异常的 layer，将其留在 subgraph.txt 中，按照经验，如果有 NPU 精度问题，可能会有 1~5 层conv layer 需要异构。
|
|
||||||
- 剩余没有精度问题的 layer 在 subgraph.txt 中删除即可
|
|
||||||
@@ -1,17 +0,0 @@
|
|||||||
#!/bin/bash
|
|
||||||
USE_FULL_API=TRUE
|
|
||||||
# configure
|
|
||||||
TARGET_ARCH_ABI=armv8 # for RK3399, set to default arch abi
|
|
||||||
#TARGET_ARCH_ABI=armv7hf # for Raspberry Pi 3B
|
|
||||||
PADDLE_LITE_DIR=../Paddle-Lite
|
|
||||||
THIRD_PARTY_DIR=./third_party
|
|
||||||
if [ "x$1" != "x" ]; then
|
|
||||||
TARGET_ARCH_ABI=$1
|
|
||||||
fi
|
|
||||||
|
|
||||||
# build
|
|
||||||
rm -rf build
|
|
||||||
mkdir build
|
|
||||||
cd build
|
|
||||||
cmake -DPADDLE_LITE_DIR=${PADDLE_LITE_DIR} -DTARGET_ARCH_ABI=${TARGET_ARCH_ABI} -DTHIRD_PARTY_DIR=${THIRD_PARTY_DIR} ..
|
|
||||||
make
|
|
||||||
@@ -1,411 +0,0 @@
|
|||||||
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
|
||||||
//
|
|
||||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
||||||
// you may not use this file except in compliance with the License.
|
|
||||||
// You may obtain a copy of the License at
|
|
||||||
//
|
|
||||||
// http://www.apache.org/licenses/LICENSE-2.0
|
|
||||||
//
|
|
||||||
// Unless required by applicable law or agreed to in writing, software
|
|
||||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
||||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
||||||
// See the License for the specific language governing permissions and
|
|
||||||
// limitations under the License.
|
|
||||||
|
|
||||||
#include "paddle_api.h"
|
|
||||||
#include "yaml-cpp/yaml.h"
|
|
||||||
#include <arm_neon.h>
|
|
||||||
#include <fstream>
|
|
||||||
#include <limits>
|
|
||||||
#include <opencv2/core.hpp>
|
|
||||||
#include <opencv2/highgui.hpp>
|
|
||||||
#include <opencv2/opencv.hpp>
|
|
||||||
#include <stdio.h>
|
|
||||||
#include <sys/time.h>
|
|
||||||
#include <unistd.h>
|
|
||||||
#include <vector>
|
|
||||||
|
|
||||||
int WARMUP_COUNT = 0;
|
|
||||||
int REPEAT_COUNT = 1;
|
|
||||||
const int CPU_THREAD_NUM = 2;
|
|
||||||
const paddle::lite_api::PowerMode CPU_POWER_MODE =
|
|
||||||
paddle::lite_api::PowerMode::LITE_POWER_HIGH;
|
|
||||||
const std::vector<int64_t> INPUT_SHAPE = {1, 3, 416, 416};
|
|
||||||
std::vector<float> INPUT_MEAN = {0.f, 0.f, 0.f};
|
|
||||||
std::vector<float> INPUT_STD = {1.f, 1.f, 1.f};
|
|
||||||
float INPUT_SCALE = 1 / 255.f;
|
|
||||||
const float SCORE_THRESHOLD = 0.35f;
|
|
||||||
|
|
||||||
struct RESULT {
|
|
||||||
std::string class_name;
|
|
||||||
float score;
|
|
||||||
float left;
|
|
||||||
float top;
|
|
||||||
float right;
|
|
||||||
float bottom;
|
|
||||||
};
|
|
||||||
|
|
||||||
inline int64_t get_current_us() {
|
|
||||||
struct timeval time;
|
|
||||||
gettimeofday(&time, NULL);
|
|
||||||
return 1000000LL * (int64_t)time.tv_sec + (int64_t)time.tv_usec;
|
|
||||||
}
|
|
||||||
|
|
||||||
bool read_file(const std::string &filename, std::vector<char> *contents,
|
|
||||||
bool binary = true) {
|
|
||||||
FILE *fp = fopen(filename.c_str(), binary ? "rb" : "r");
|
|
||||||
if (!fp)
|
|
||||||
return false;
|
|
||||||
fseek(fp, 0, SEEK_END);
|
|
||||||
size_t size = ftell(fp);
|
|
||||||
fseek(fp, 0, SEEK_SET);
|
|
||||||
contents->clear();
|
|
||||||
contents->resize(size);
|
|
||||||
size_t offset = 0;
|
|
||||||
char *ptr = reinterpret_cast<char *>(&(contents->at(0)));
|
|
||||||
while (offset < size) {
|
|
||||||
size_t already_read = fread(ptr, 1, size - offset, fp);
|
|
||||||
offset += already_read;
|
|
||||||
ptr += already_read;
|
|
||||||
}
|
|
||||||
fclose(fp);
|
|
||||||
return true;
|
|
||||||
}
|
|
||||||
|
|
||||||
std::vector<std::string> load_labels(const std::string &path) {
|
|
||||||
std::ifstream file;
|
|
||||||
std::vector<std::string> labels;
|
|
||||||
file.open(path);
|
|
||||||
while (file) {
|
|
||||||
std::string line;
|
|
||||||
std::getline(file, line);
|
|
||||||
labels.push_back(line);
|
|
||||||
}
|
|
||||||
file.clear();
|
|
||||||
file.close();
|
|
||||||
return labels;
|
|
||||||
}
|
|
||||||
|
|
||||||
bool load_yaml_config(std::string yaml_path) {
|
|
||||||
YAML::Node cfg;
|
|
||||||
try {
|
|
||||||
std::cout << "before loadFile" << std::endl;
|
|
||||||
cfg = YAML::LoadFile(yaml_path);
|
|
||||||
} catch (YAML::BadFile &e) {
|
|
||||||
std::cout << "Failed to load yaml file " << yaml_path
|
|
||||||
<< ", maybe you should check this file." << std::endl;
|
|
||||||
return false;
|
|
||||||
}
|
|
||||||
auto preprocess_cfg = cfg["TestReader"]["sample_transforms"];
|
|
||||||
for (const auto &op : preprocess_cfg) {
|
|
||||||
if (!op.IsMap()) {
|
|
||||||
std::cout << "Require the transform information in yaml be Map type."
|
|
||||||
<< std::endl;
|
|
||||||
std::abort();
|
|
||||||
}
|
|
||||||
auto op_name = op.begin()->first.as<std::string>();
|
|
||||||
if (op_name == "NormalizeImage") {
|
|
||||||
INPUT_MEAN = op.begin()->second["mean"].as<std::vector<float>>();
|
|
||||||
INPUT_STD = op.begin()->second["std"].as<std::vector<float>>();
|
|
||||||
INPUT_SCALE = op.begin()->second["scale"].as<float>();
|
|
||||||
}
|
|
||||||
}
|
|
||||||
return true;
|
|
||||||
}
|
|
||||||
|
|
||||||
void preprocess(cv::Mat &input_image, std::vector<float> &input_mean,
|
|
||||||
std::vector<float> &input_std, float input_scale,
|
|
||||||
int input_width, int input_height, float *input_data) {
|
|
||||||
cv::Mat resize_image;
|
|
||||||
cv::resize(input_image, resize_image, cv::Size(input_width, input_height), 0,
|
|
||||||
0);
|
|
||||||
if (resize_image.channels() == 4) {
|
|
||||||
cv::cvtColor(resize_image, resize_image, cv::COLOR_BGRA2RGB);
|
|
||||||
}
|
|
||||||
cv::Mat norm_image;
|
|
||||||
resize_image.convertTo(norm_image, CV_32FC3, input_scale);
|
|
||||||
// NHWC->NCHW
|
|
||||||
int image_size = input_height * input_width;
|
|
||||||
const float *image_data = reinterpret_cast<const float *>(norm_image.data);
|
|
||||||
float32x4_t vmean0 = vdupq_n_f32(input_mean[0]);
|
|
||||||
float32x4_t vmean1 = vdupq_n_f32(input_mean[1]);
|
|
||||||
float32x4_t vmean2 = vdupq_n_f32(input_mean[2]);
|
|
||||||
float32x4_t vscale0 = vdupq_n_f32(1.0f / input_std[0]);
|
|
||||||
float32x4_t vscale1 = vdupq_n_f32(1.0f / input_std[1]);
|
|
||||||
float32x4_t vscale2 = vdupq_n_f32(1.0f / input_std[2]);
|
|
||||||
float *input_data_c0 = input_data;
|
|
||||||
float *input_data_c1 = input_data + image_size;
|
|
||||||
float *input_data_c2 = input_data + image_size * 2;
|
|
||||||
int i = 0;
|
|
||||||
for (; i < image_size - 3; i += 4) {
|
|
||||||
float32x4x3_t vin3 = vld3q_f32(image_data);
|
|
||||||
float32x4_t vsub0 = vsubq_f32(vin3.val[0], vmean0);
|
|
||||||
float32x4_t vsub1 = vsubq_f32(vin3.val[1], vmean1);
|
|
||||||
float32x4_t vsub2 = vsubq_f32(vin3.val[2], vmean2);
|
|
||||||
float32x4_t vs0 = vmulq_f32(vsub0, vscale0);
|
|
||||||
float32x4_t vs1 = vmulq_f32(vsub1, vscale1);
|
|
||||||
float32x4_t vs2 = vmulq_f32(vsub2, vscale2);
|
|
||||||
vst1q_f32(input_data_c0, vs0);
|
|
||||||
vst1q_f32(input_data_c1, vs1);
|
|
||||||
vst1q_f32(input_data_c2, vs2);
|
|
||||||
image_data += 12;
|
|
||||||
input_data_c0 += 4;
|
|
||||||
input_data_c1 += 4;
|
|
||||||
input_data_c2 += 4;
|
|
||||||
}
|
|
||||||
for (; i < image_size; i++) {
|
|
||||||
*(input_data_c0++) = (*(image_data++) - input_mean[0]) / input_std[0];
|
|
||||||
*(input_data_c1++) = (*(image_data++) - input_mean[1]) / input_std[1];
|
|
||||||
*(input_data_c2++) = (*(image_data++) - input_mean[2]) / input_std[2];
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
std::vector<RESULT> postprocess(const float *output_data, int64_t output_size,
|
|
||||||
const std::vector<std::string> &word_labels,
|
|
||||||
const float score_threshold,
|
|
||||||
cv::Mat &output_image, double time) {
|
|
||||||
std::vector<RESULT> results;
|
|
||||||
std::vector<cv::Scalar> colors = {
|
|
||||||
cv::Scalar(237, 189, 101), cv::Scalar(0, 0, 255),
|
|
||||||
cv::Scalar(102, 153, 153), cv::Scalar(255, 0, 0),
|
|
||||||
cv::Scalar(9, 255, 0), cv::Scalar(0, 0, 0),
|
|
||||||
cv::Scalar(51, 153, 51)};
|
|
||||||
for (int64_t i = 0; i < output_size; i += 6) {
|
|
||||||
if (output_data[i + 1] < score_threshold) {
|
|
||||||
continue;
|
|
||||||
}
|
|
||||||
int class_id = static_cast<int>(output_data[i]);
|
|
||||||
float score = output_data[i + 1];
|
|
||||||
RESULT result;
|
|
||||||
std::string class_name = "Unknown";
|
|
||||||
if (word_labels.size() > 0 && class_id >= 0 &&
|
|
||||||
class_id < word_labels.size()) {
|
|
||||||
class_name = word_labels[class_id];
|
|
||||||
}
|
|
||||||
result.class_name = class_name;
|
|
||||||
result.score = score;
|
|
||||||
result.left = output_data[i + 2] / 416;
|
|
||||||
result.top = output_data[i + 3] / 416;
|
|
||||||
result.right = output_data[i + 4] / 416;
|
|
||||||
result.bottom = output_data[i + 5] / 416;
|
|
||||||
int lx = static_cast<int>(result.left * output_image.cols);
|
|
||||||
int ly = static_cast<int>(result.top * output_image.rows);
|
|
||||||
int w = static_cast<int>(result.right * output_image.cols) - lx;
|
|
||||||
int h = static_cast<int>(result.bottom * output_image.rows) - ly;
|
|
||||||
cv::Rect bounding_box =
|
|
||||||
cv::Rect(lx, ly, w, h) &
|
|
||||||
cv::Rect(0, 0, output_image.cols, output_image.rows);
|
|
||||||
if (w > 0 && h > 0 && score <= 1) {
|
|
||||||
cv::Scalar color = colors[results.size() % colors.size()];
|
|
||||||
cv::rectangle(output_image, bounding_box, color);
|
|
||||||
cv::rectangle(output_image, cv::Point2d(lx, ly),
|
|
||||||
cv::Point2d(lx + w, ly - 10), color, -1);
|
|
||||||
cv::putText(output_image, std::to_string(results.size()) + "." +
|
|
||||||
class_name + ":" + std::to_string(score),
|
|
||||||
cv::Point2d(lx, ly), cv::FONT_HERSHEY_PLAIN, 1,
|
|
||||||
cv::Scalar(255, 255, 255));
|
|
||||||
results.push_back(result);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
return results;
|
|
||||||
}
|
|
||||||
|
|
||||||
cv::Mat process(cv::Mat &input_image, std::vector<std::string> &word_labels,
|
|
||||||
std::shared_ptr<paddle::lite_api::PaddlePredictor> &predictor) {
|
|
||||||
// Preprocess image and fill the data of input tensor
|
|
||||||
std::unique_ptr<paddle::lite_api::Tensor> input_tensor(
|
|
||||||
std::move(predictor->GetInput(0)));
|
|
||||||
input_tensor->Resize(INPUT_SHAPE);
|
|
||||||
int input_width = INPUT_SHAPE[3];
|
|
||||||
int input_height = INPUT_SHAPE[2];
|
|
||||||
auto *input_data = input_tensor->mutable_data<float>();
|
|
||||||
#if 1
|
|
||||||
// scale_factor tensor
|
|
||||||
auto scale_factor_tensor = predictor->GetInput(1);
|
|
||||||
scale_factor_tensor->Resize({1, 2});
|
|
||||||
auto scale_factor_data = scale_factor_tensor->mutable_data<float>();
|
|
||||||
scale_factor_data[0] = 1.0f;
|
|
||||||
scale_factor_data[1] = 1.0f;
|
|
||||||
#endif
|
|
||||||
|
|
||||||
double preprocess_start_time = get_current_us();
|
|
||||||
preprocess(input_image, INPUT_MEAN, INPUT_STD, INPUT_SCALE, input_width,
|
|
||||||
input_height, input_data);
|
|
||||||
double preprocess_end_time = get_current_us();
|
|
||||||
double preprocess_time =
|
|
||||||
(preprocess_end_time - preprocess_start_time) / 1000.0f;
|
|
||||||
|
|
||||||
double prediction_time;
|
|
||||||
// Run predictor
|
|
||||||
// warm up to skip the first inference and get more stable time, remove it in
|
|
||||||
// actual products
|
|
||||||
for (int i = 0; i < WARMUP_COUNT; i++) {
|
|
||||||
predictor->Run();
|
|
||||||
}
|
|
||||||
// repeat to obtain the average time, set REPEAT_COUNT=1 in actual products
|
|
||||||
double max_time_cost = 0.0f;
|
|
||||||
double min_time_cost = std::numeric_limits<float>::max();
|
|
||||||
double total_time_cost = 0.0f;
|
|
||||||
for (int i = 0; i < REPEAT_COUNT; i++) {
|
|
||||||
auto start = get_current_us();
|
|
||||||
predictor->Run();
|
|
||||||
auto end = get_current_us();
|
|
||||||
double cur_time_cost = (end - start) / 1000.0f;
|
|
||||||
if (cur_time_cost > max_time_cost) {
|
|
||||||
max_time_cost = cur_time_cost;
|
|
||||||
}
|
|
||||||
if (cur_time_cost < min_time_cost) {
|
|
||||||
min_time_cost = cur_time_cost;
|
|
||||||
}
|
|
||||||
total_time_cost += cur_time_cost;
|
|
||||||
prediction_time = total_time_cost / REPEAT_COUNT;
|
|
||||||
printf("iter %d cost: %f ms\n", i, cur_time_cost);
|
|
||||||
}
|
|
||||||
printf("warmup: %d repeat: %d, average: %f ms, max: %f ms, min: %f ms\n",
|
|
||||||
WARMUP_COUNT, REPEAT_COUNT, prediction_time, max_time_cost,
|
|
||||||
min_time_cost);
|
|
||||||
|
|
||||||
// Get the data of output tensor and postprocess to output detected objects
|
|
||||||
std::unique_ptr<const paddle::lite_api::Tensor> output_tensor(
|
|
||||||
std::move(predictor->GetOutput(0)));
|
|
||||||
const float *output_data = output_tensor->mutable_data<float>();
|
|
||||||
int64_t output_size = 1;
|
|
||||||
for (auto dim : output_tensor->shape()) {
|
|
||||||
output_size *= dim;
|
|
||||||
}
|
|
||||||
cv::Mat output_image = input_image.clone();
|
|
||||||
double postprocess_start_time = get_current_us();
|
|
||||||
std::vector<RESULT> results =
|
|
||||||
postprocess(output_data, output_size, word_labels, SCORE_THRESHOLD,
|
|
||||||
output_image, prediction_time);
|
|
||||||
double postprocess_end_time = get_current_us();
|
|
||||||
double postprocess_time =
|
|
||||||
(postprocess_end_time - postprocess_start_time) / 1000.0f;
|
|
||||||
|
|
||||||
printf("results: %d\n", results.size());
|
|
||||||
for (int i = 0; i < results.size(); i++) {
|
|
||||||
printf("[%d] %s - %f %f,%f,%f,%f\n", i, results[i].class_name.c_str(),
|
|
||||||
results[i].score, results[i].left, results[i].top, results[i].right,
|
|
||||||
results[i].bottom);
|
|
||||||
}
|
|
||||||
printf("Preprocess time: %f ms\n", preprocess_time);
|
|
||||||
printf("Prediction time: %f ms\n", prediction_time);
|
|
||||||
printf("Postprocess time: %f ms\n\n", postprocess_time);
|
|
||||||
|
|
||||||
return output_image;
|
|
||||||
}
|
|
||||||
|
|
||||||
int main(int argc, char **argv) {
|
|
||||||
if (argc < 5 || argc == 6) {
|
|
||||||
printf("Usage: \n"
|
|
||||||
"./object_detection_demo model_dir label_path [input_image_path] "
|
|
||||||
"[output_image_path]"
|
|
||||||
"use images from camera if input_image_path and input_image_path "
|
|
||||||
"isn't provided.");
|
|
||||||
return -1;
|
|
||||||
}
|
|
||||||
|
|
||||||
std::string model_path = argv[1];
|
|
||||||
std::string label_path = argv[2];
|
|
||||||
std::vector<std::string> word_labels = load_labels(label_path);
|
|
||||||
std::string nnadapter_subgraph_partition_config_path = argv[3];
|
|
||||||
|
|
||||||
std::string yaml_path = argv[4];
|
|
||||||
if (yaml_path != "null") {
|
|
||||||
load_yaml_config(yaml_path);
|
|
||||||
}
|
|
||||||
|
|
||||||
// Run inference by using full api with CxxConfig
|
|
||||||
paddle::lite_api::CxxConfig cxx_config;
|
|
||||||
if (1) { // combined model
|
|
||||||
cxx_config.set_model_file(model_path + "/model");
|
|
||||||
cxx_config.set_param_file(model_path + "/params");
|
|
||||||
} else {
|
|
||||||
cxx_config.set_model_dir(model_path);
|
|
||||||
}
|
|
||||||
cxx_config.set_threads(CPU_THREAD_NUM);
|
|
||||||
cxx_config.set_power_mode(CPU_POWER_MODE);
|
|
||||||
|
|
||||||
std::shared_ptr<paddle::lite_api::PaddlePredictor> predictor = nullptr;
|
|
||||||
std::vector<paddle::lite_api::Place> valid_places;
|
|
||||||
valid_places.push_back(
|
|
||||||
paddle::lite_api::Place{TARGET(kARM), PRECISION(kInt8)});
|
|
||||||
valid_places.push_back(
|
|
||||||
paddle::lite_api::Place{TARGET(kARM), PRECISION(kFloat)});
|
|
||||||
valid_places.push_back(
|
|
||||||
paddle::lite_api::Place{TARGET(kNNAdapter), PRECISION(kInt8)});
|
|
||||||
valid_places.push_back(
|
|
||||||
paddle::lite_api::Place{TARGET(kNNAdapter), PRECISION(kFloat)});
|
|
||||||
cxx_config.set_valid_places(valid_places);
|
|
||||||
std::string device = "verisilicon_timvx";
|
|
||||||
cxx_config.set_nnadapter_device_names({device});
|
|
||||||
// cxx_config.set_nnadapter_context_properties(nnadapter_context_properties);
|
|
||||||
|
|
||||||
// cxx_config.set_nnadapter_model_cache_dir(nnadapter_model_cache_dir);
|
|
||||||
// Set the subgraph custom partition configuration file
|
|
||||||
|
|
||||||
if (!nnadapter_subgraph_partition_config_path.empty()) {
|
|
||||||
std::vector<char> nnadapter_subgraph_partition_config_buffer;
|
|
||||||
if (read_file(nnadapter_subgraph_partition_config_path,
|
|
||||||
&nnadapter_subgraph_partition_config_buffer, false)) {
|
|
||||||
if (!nnadapter_subgraph_partition_config_buffer.empty()) {
|
|
||||||
std::string nnadapter_subgraph_partition_config_string(
|
|
||||||
nnadapter_subgraph_partition_config_buffer.data(),
|
|
||||||
nnadapter_subgraph_partition_config_buffer.size());
|
|
||||||
cxx_config.set_nnadapter_subgraph_partition_config_buffer(
|
|
||||||
nnadapter_subgraph_partition_config_string);
|
|
||||||
}
|
|
||||||
} else {
|
|
||||||
printf("Failed to load the subgraph custom partition configuration file "
|
|
||||||
"%s\n",
|
|
||||||
nnadapter_subgraph_partition_config_path.c_str());
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
try {
|
|
||||||
predictor = paddle::lite_api::CreatePaddlePredictor(cxx_config);
|
|
||||||
predictor->SaveOptimizedModel(
|
|
||||||
model_path, paddle::lite_api::LiteModelType::kNaiveBuffer);
|
|
||||||
} catch (std::exception e) {
|
|
||||||
printf("An internal error occurred in PaddleLite(cxx config).\n");
|
|
||||||
}
|
|
||||||
|
|
||||||
paddle::lite_api::MobileConfig config;
|
|
||||||
config.set_model_from_file(model_path + ".nb");
|
|
||||||
config.set_threads(CPU_THREAD_NUM);
|
|
||||||
config.set_power_mode(CPU_POWER_MODE);
|
|
||||||
config.set_nnadapter_device_names({device});
|
|
||||||
predictor =
|
|
||||||
paddle::lite_api::CreatePaddlePredictor<paddle::lite_api::MobileConfig>(
|
|
||||||
config);
|
|
||||||
if (argc > 5) {
|
|
||||||
WARMUP_COUNT = 1;
|
|
||||||
REPEAT_COUNT = 5;
|
|
||||||
std::string input_image_path = argv[5];
|
|
||||||
std::string output_image_path = argv[6];
|
|
||||||
cv::Mat input_image = cv::imread(input_image_path);
|
|
||||||
cv::Mat output_image = process(input_image, word_labels, predictor);
|
|
||||||
cv::imwrite(output_image_path, output_image);
|
|
||||||
cv::imshow("Object Detection Demo", output_image);
|
|
||||||
cv::waitKey(0);
|
|
||||||
} else {
|
|
||||||
cv::VideoCapture cap(1);
|
|
||||||
cap.set(cv::CAP_PROP_FRAME_WIDTH, 640);
|
|
||||||
cap.set(cv::CAP_PROP_FRAME_HEIGHT, 480);
|
|
||||||
if (!cap.isOpened()) {
|
|
||||||
return -1;
|
|
||||||
}
|
|
||||||
while (1) {
|
|
||||||
cv::Mat input_image;
|
|
||||||
cap >> input_image;
|
|
||||||
cv::Mat output_image = process(input_image, word_labels, predictor);
|
|
||||||
cv::imshow("Object Detection Demo", output_image);
|
|
||||||
if (cv::waitKey(1) == char('q')) {
|
|
||||||
break;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
cap.release();
|
|
||||||
cv::destroyAllWindows();
|
|
||||||
}
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
@@ -1,15 +0,0 @@
|
|||||||
#!/bin/bash
|
|
||||||
#run
|
|
||||||
|
|
||||||
TARGET_ABI=armv8 # for 64bit
|
|
||||||
#TARGET_ABI=armv7hf # for 32bit
|
|
||||||
if [ -n "$1" ]; then
|
|
||||||
TARGET_ABI=$1
|
|
||||||
fi
|
|
||||||
export LD_LIBRARY_PATH=../Paddle-Lite/libs/$TARGET_ABI/
|
|
||||||
export GLOG_v=0
|
|
||||||
export VSI_NN_LOG_LEVEL=0
|
|
||||||
export VIV_VX_ENABLE_GRAPH_TRANSFORM=-pcq:1
|
|
||||||
export VIV_VX_SET_PER_CHANNEL_ENTROPY=100
|
|
||||||
export TIMVX_BATCHNORM_FUSION_MAX_ALLOWED_QUANT_SCALE_DEVIATION=30000
|
|
||||||
build/object_detection_demo models/picodetv2_relu6_coco_no_fuse ../../assets/labels/coco_label_list.txt models/picodetv2_relu6_coco_no_fuse/subgraph.txt models/picodetv2_relu6_coco_no_fuse/picodet.yml
|
|
||||||
11
examples/vision/detection/paddledetection/rv1126/README.md
Executable file
11
examples/vision/detection/paddledetection/rv1126/README.md
Executable file
@@ -0,0 +1,11 @@
|
|||||||
|
# PP-YOLOE 量化模型在 RV1126 上的部署
|
||||||
|
目前 FastDeploy 已经支持基于 PaddleLite 部署 PP-YOLOE 量化模型到 RV1126 上。
|
||||||
|
|
||||||
|
模型的量化和量化模型的下载请参考：[模型量化](../quantize/README.md)
|
||||||
|
|
||||||
|
|
||||||
|
## 详细部署文档
|
||||||
|
|
||||||
|
在 RV1126 上只支持 C++ 的部署。
|
||||||
|
|
||||||
|
- [C++部署](cpp)
|
||||||
38
examples/vision/detection/paddledetection/rv1126/cpp/CMakeLists.txt
Executable file
38
examples/vision/detection/paddledetection/rv1126/cpp/CMakeLists.txt
Executable file
@@ -0,0 +1,38 @@
|
|||||||
|
PROJECT(infer_demo C CXX)
|
||||||
|
CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
|
||||||
|
|
||||||
|
# 指定下载解压后的fastdeploy库路径
|
||||||
|
option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
|
||||||
|
|
||||||
|
include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
|
||||||
|
|
||||||
|
# 添加FastDeploy依赖头文件
|
||||||
|
include_directories(${FASTDEPLOY_INCS})
|
||||||
|
include_directories(${FastDeploy_INCLUDE_DIRS})
|
||||||
|
|
||||||
|
add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer_ppyoloe.cc)
|
||||||
|
# 添加FastDeploy库依赖
|
||||||
|
target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
|
||||||
|
|
||||||
|
set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/build/install)
|
||||||
|
|
||||||
|
install(TARGETS infer_demo DESTINATION ./)
|
||||||
|
|
||||||
|
install(DIRECTORY models DESTINATION ./)
|
||||||
|
install(DIRECTORY images DESTINATION ./)
|
||||||
|
# install(DIRECTORY run_with_adb.sh DESTINATION ./)
|
||||||
|
|
||||||
|
file(GLOB FASTDEPLOY_LIBS ${FASTDEPLOY_INSTALL_DIR}/lib/*)
|
||||||
|
install(PROGRAMS ${FASTDEPLOY_LIBS} DESTINATION lib)
|
||||||
|
|
||||||
|
file(GLOB OPENCV_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/opencv/lib/lib*)
|
||||||
|
install(PROGRAMS ${OPENCV_LIBS} DESTINATION lib)
|
||||||
|
|
||||||
|
file(GLOB PADDLELITE_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/paddlelite/lib/lib*)
|
||||||
|
install(PROGRAMS ${PADDLELITE_LIBS} DESTINATION lib)
|
||||||
|
|
||||||
|
file(GLOB TIMVX_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/paddlelite/lib/verisilicon_timvx/*)
|
||||||
|
install(PROGRAMS ${TIMVX_LIBS} DESTINATION lib)
|
||||||
|
|
||||||
|
file(GLOB ADB_TOOLS run_with_adb.sh)
|
||||||
|
install(PROGRAMS ${ADB_TOOLS} DESTINATION ./)
|
||||||
55
examples/vision/detection/paddledetection/rv1126/cpp/README.md
Executable file
55
examples/vision/detection/paddledetection/rv1126/cpp/README.md
Executable file
@@ -0,0 +1,55 @@
|
|||||||
|
# PP-YOLOE 量化模型 C++ 部署示例
|
||||||
|
|
||||||
|
本目录下提供的 `infer.cc`，可以帮助用户快速完成 PP-YOLOE 量化模型在 RV1126 上的部署推理加速。
|
||||||
|
|
||||||
|
## 部署准备
|
||||||
|
### FastDeploy 交叉编译环境准备
|
||||||
|
- 1. 软硬件环境满足要求，以及交叉编译环境的准备，请参考：[FastDeploy 交叉编译环境准备](../../../../../../docs/cn/build_and_install/rv1126.md#交叉编译环境搭建)
|
||||||
|
|
||||||
|
### 模型准备
|
||||||
|
- 1. 用户可以直接使用由 FastDeploy 提供的量化模型进行部署。
|
||||||
|
- 2. 用户可以先使用 PaddleDetection 自行导出 Float32 模型，注意导出模型模型时设置参数：use_shared_conv=False，更多细节请参考：[PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe)
|
||||||
|
- 3. 用户可以使用 FastDeploy 提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/)，自行进行模型量化, 并使用产出的量化模型进行部署。（注意: 推理量化后的检测模型仍然需要FP32模型文件夹下的 infer_cfg.yml 文件，自行量化的模型文件夹内不包含此 yaml 文件，用户从 FP32 模型文件夹下复制此yaml文件到量化后的模型文件夹内即可。）
|
||||||
|
- 更多量化相关相关信息可查阅[模型量化](../../quantize/README.md)
|
||||||
|
|
||||||
|
## 在 RV1126 上部署量化后的 PP-YOLOE 检测模型
|
||||||
|
请按照以下步骤完成在 RV1126 上部署 PP-YOLOE 量化模型：
|
||||||
|
1. 交叉编译编译 FastDeploy 库，具体请参考：[交叉编译 FastDeploy](../../../../../../docs/cn/build_and_install/rv1126.md#基于-paddlelite-的-fastdeploy-交叉编译库编译)
|
||||||
|
|
||||||
|
2. 将编译后的库拷贝到当前目录，可使用如下命令：
|
||||||
|
```bash
|
||||||
|
cp -r FastDeploy/build/fastdeploy-tmivx/ FastDeploy/examples/vision/detection/yolov5/rv1126/cpp
|
||||||
|
```
|
||||||
|
|
||||||
|
3. 在当前路径下载部署所需的模型和示例图片：
|
||||||
|
```bash
|
||||||
|
mkdir models && mkdir images
|
||||||
|
wget https://bj.bcebos.com/fastdeploy/models/ppyoloe_noshare_qat.tar.gz
|
||||||
|
tar -xvf ppyoloe_noshare_qat.tar.gz
|
||||||
|
cp -r ppyoloe_noshare_qat models
|
||||||
|
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||||
|
cp -r 000000014439.jpg images
|
||||||
|
```
|
||||||
|
|
||||||
|
4. 编译部署示例，可使入如下命令：
|
||||||
|
```bash
|
||||||
|
mkdir build && cd build
|
||||||
|
cmake -DCMAKE_TOOLCHAIN_FILE=${PWD}/../fastdeploy-tmivx/timvx.cmake -DFASTDEPLOY_INSTALL_DIR=${PWD}/../fastdeploy-tmivx ..
|
||||||
|
make -j8
|
||||||
|
make install
|
||||||
|
# 成功编译之后，会生成 install 文件夹，里面有一个运行 demo 和部署所需的库
|
||||||
|
```
|
||||||
|
|
||||||
|
5. 基于 adb 工具部署 PP-YOLOE 检测模型到 Rockchip RV1126，可使用如下命令：
|
||||||
|
```bash
|
||||||
|
# 进入 install 目录
|
||||||
|
cd FastDeploy/examples/vision/detection/paddledetection/rv1126/cpp/build/install/
|
||||||
|
# 如下命令表示：bash run_with_adb.sh 需要运行的demo 模型路径 图片路径 设备的DEVICE_ID
|
||||||
|
bash run_with_adb.sh infer_demo ppyoloe_noshare_qat 000000014439.jpg $DEVICE_ID
|
||||||
|
```
|
||||||
|
|
||||||
|
部署成功后运行结果如下：
|
||||||
|
|
||||||
|
<img width="640" src="https://user-images.githubusercontent.com/30516196/203708564-43c49485-9b48-4eb2-8fe7-0fa517979fff.png">
|
||||||
|
|
||||||
|
需要特别注意的是，在 RV1126 上部署的模型需要是量化后的模型，模型的量化请参考：[模型量化](../../../../../../docs/cn/quantize.md)
|
||||||
66
examples/vision/detection/paddledetection/rv1126/cpp/infer_ppyoloe.cc
Executable file
66
examples/vision/detection/paddledetection/rv1126/cpp/infer_ppyoloe.cc
Executable file
@@ -0,0 +1,66 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
|
||||||
|
#include "fastdeploy/vision.h"
|
||||||
|
#ifdef WIN32
|
||||||
|
const char sep = '\\';
|
||||||
|
#else
|
||||||
|
const char sep = '/';
|
||||||
|
#endif
|
||||||
|
|
||||||
|
void InitAndInfer(const std::string& model_dir, const std::string& image_file) {
|
||||||
|
auto model_file = model_dir + sep + "model.pdmodel";
|
||||||
|
auto params_file = model_dir + sep + "model.pdiparams";
|
||||||
|
auto config_file = model_dir + sep + "infer_cfg.yml";
|
||||||
|
auto subgraph_file = model_dir + sep + "subgraph.txt";
|
||||||
|
|
||||||
|
fastdeploy::RuntimeOption option;
|
||||||
|
option.UseTimVX();
|
||||||
|
option.SetLiteSubgraphPartitionPath(subgraph_file);
|
||||||
|
|
||||||
|
auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file,
|
||||||
|
config_file, option);
|
||||||
|
assert(model.Initialized());
|
||||||
|
|
||||||
|
auto im = cv::imread(image_file);
|
||||||
|
|
||||||
|
fastdeploy::vision::DetectionResult res;
|
||||||
|
if (!model.Predict(im, &res)) {
|
||||||
|
std::cerr << "Failed to predict." << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::cout << res.Str() << std::endl;
|
||||||
|
|
||||||
|
auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
|
||||||
|
cv::imwrite("vis_result.jpg", vis_im);
|
||||||
|
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
int main(int argc, char* argv[]) {
|
||||||
|
if (argc < 3) {
|
||||||
|
std::cout << "Usage: infer_demo path/to/quant_model "
|
||||||
|
"path/to/image "
|
||||||
|
"run_option, "
|
||||||
|
"e.g ./infer_demo ./PPYOLOE_L_quant ./test.jpeg"
|
||||||
|
<< std::endl;
|
||||||
|
return -1;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::string model_dir = argv[1];
|
||||||
|
std::string test_image = argv[2];
|
||||||
|
InitAndInfer(model_dir, test_image);
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
47
examples/vision/detection/paddledetection/rv1126/cpp/run_with_adb.sh
Executable file
47
examples/vision/detection/paddledetection/rv1126/cpp/run_with_adb.sh
Executable file
@@ -0,0 +1,47 @@
|
|||||||
|
#!/bin/bash
|
||||||
|
HOST_SPACE=${PWD}
|
||||||
|
echo ${HOST_SPACE}
|
||||||
|
WORK_SPACE=/data/local/tmp/test
|
||||||
|
|
||||||
|
# The first parameter represents the demo name
|
||||||
|
DEMO_NAME=image_classification_demo
|
||||||
|
if [ -n "$1" ]; then
|
||||||
|
DEMO_NAME=$1
|
||||||
|
fi
|
||||||
|
|
||||||
|
# The second parameter represents the model name
|
||||||
|
MODEL_NAME=mobilenet_v1_fp32_224
|
||||||
|
if [ -n "$2" ]; then
|
||||||
|
MODEL_NAME=$2
|
||||||
|
fi
|
||||||
|
|
||||||
|
# The third parameter indicates the name of the image to be tested
|
||||||
|
IMAGE_NAME=0001.jpg
|
||||||
|
if [ -n "$3" ]; then
|
||||||
|
IMAGE_NAME=$3
|
||||||
|
fi
|
||||||
|
|
||||||
|
# The fourth parameter represents the ID of the device
|
||||||
|
ADB_DEVICE_NAME=
|
||||||
|
if [ -n "$4" ]; then
|
||||||
|
ADB_DEVICE_NAME="-s $4"
|
||||||
|
fi
|
||||||
|
|
||||||
|
# Set the environment variables required during the running process
|
||||||
|
EXPORT_ENVIRONMENT_VARIABLES="export GLOG_v=5; export SUBGRAPH_ONLINE_MODE=true; export RKNPU_LOGLEVEL=5; export RKNN_LOG_LEVEL=5; ulimit -c unlimited; export VIV_VX_ENABLE_GRAPH_TRANSFORM=-pcq:1; export VIV_VX_SET_PER_CHANNEL_ENTROPY=100; export TIMVX_BATCHNORM_FUSION_MAX_ALLOWED_QUANT_SCALE_DEVIATION=300000; export VSI_NN_LOG_LEVEL=5;"
|
||||||
|
|
||||||
|
EXPORT_ENVIRONMENT_VARIABLES="${EXPORT_ENVIRONMENT_VARIABLES}export LD_LIBRARY_PATH=${WORK_SPACE}/lib:\$LD_LIBRARY_PATH;"
|
||||||
|
|
||||||
|
# Please install adb, and DON'T run this in the docker.
|
||||||
|
set -e
|
||||||
|
adb $ADB_DEVICE_NAME shell "rm -rf $WORK_SPACE"
|
||||||
|
adb $ADB_DEVICE_NAME shell "mkdir -p $WORK_SPACE"
|
||||||
|
|
||||||
|
# Upload the demo, librarys, model and test images to the device
|
||||||
|
adb $ADB_DEVICE_NAME push ${HOST_SPACE}/lib $WORK_SPACE
|
||||||
|
adb $ADB_DEVICE_NAME push ${HOST_SPACE}/${DEMO_NAME} $WORK_SPACE
|
||||||
|
adb $ADB_DEVICE_NAME push models $WORK_SPACE
|
||||||
|
adb $ADB_DEVICE_NAME push images $WORK_SPACE
|
||||||
|
|
||||||
|
# Execute the deployment demo
|
||||||
|
adb $ADB_DEVICE_NAME shell "cd $WORK_SPACE; ${EXPORT_ENVIRONMENT_VARIABLES} chmod +x ./${DEMO_NAME}; ./${DEMO_NAME} ./models/${MODEL_NAME} ./images/$IMAGE_NAME"
|
||||||
2
examples/vision/detection/yolov5/quantize/README.md
Normal file → Executable file
2
examples/vision/detection/yolov5/quantize/README.md
Normal file → Executable file
@@ -4,7 +4,7 @@ FastDeploy已支持部署量化模型,并提供一键模型自动化压缩的工
|
|||||||
|
|
||||||
## FastDeploy一键模型自动化压缩工具
|
## FastDeploy一键模型自动化压缩工具
|
||||||
FastDeploy 提供了一键模型自动化压缩工具, 能够简单地通过输入一个配置文件, 对模型进行量化.
|
FastDeploy 提供了一键模型自动化压缩工具, 能够简单地通过输入一个配置文件, 对模型进行量化.
|
||||||
详细教程请见: [一键模型自动化压缩工具](../../../../../tools/auto_compression/)
|
详细教程请见: [一键模型自动化压缩工具](../../../../../tools/common_tools/auto_compression/)
|
||||||
|
|
||||||
## 下载量化完成的YOLOv5s模型
|
## 下载量化完成的YOLOv5s模型
|
||||||
用户也可以直接下载下表中的量化模型进行部署.(点击模型名字即可下载)
|
用户也可以直接下载下表中的量化模型进行部署.(点击模型名字即可下载)
|
||||||
|
|||||||
2
examples/vision/detection/yolov5/quantize/README_EN.md
Normal file → Executable file
2
examples/vision/detection/yolov5/quantize/README_EN.md
Normal file → Executable file
@@ -6,7 +6,7 @@ Users can use the one-click model quantization tool to quantize and deploy the m
|
|||||||
## FastDeploy One-Click Model Quantization Tool
|
## FastDeploy One-Click Model Quantization Tool
|
||||||
|
|
||||||
FastDeploy provides a one-click quantization tool that allows users to quantize a model simply with a configuration file.
|
FastDeploy provides a one-click quantization tool that allows users to quantize a model simply with a configuration file.
|
||||||
For a detailed tutorial, please refer to: [One-Click Model Quantization Tool](../../../../../tools/auto_compression/)
|
For a detailed tutorial, please refer to: [One-Click Model Quantization Tool](../../../../../tools/common_tools/auto_compression/)
|
||||||
|
|
||||||
## Download Quantized YOLOv5s Model
|
## Download Quantized YOLOv5s Model
|
||||||
|
|
||||||
|
|||||||
2
examples/vision/detection/yolov5/quantize/cpp/README.md
Normal file → Executable file
2
examples/vision/detection/yolov5/quantize/cpp/README.md
Normal file → Executable file
@@ -9,7 +9,7 @@
|
|||||||
|
|
||||||
### 量化模型准备
|
### 量化模型准备
|
||||||
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
||||||
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
|
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
|
||||||
|
|
||||||
## 以量化后的YOLOv5s模型为例, 进行部署
|
## 以量化后的YOLOv5s模型为例, 进行部署
|
||||||
在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
||||||
|
|||||||
2
examples/vision/detection/yolov5/quantize/python/README.md
Normal file → Executable file
2
examples/vision/detection/yolov5/quantize/python/README.md
Normal file → Executable file
@@ -8,7 +8,7 @@
|
|||||||
|
|
||||||
### 量化模型准备
|
### 量化模型准备
|
||||||
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
||||||
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
|
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
|
||||||
|
|
||||||
|
|
||||||
## 以量化后的YOLOv5s模型为例, 进行部署
|
## 以量化后的YOLOv5s模型为例, 进行部署
|
||||||
|
|||||||
11
examples/vision/detection/yolov5/rv1126/README.md
Executable file
11
examples/vision/detection/yolov5/rv1126/README.md
Executable file
@@ -0,0 +1,11 @@
|
|||||||
|
# YOLOv5 量化模型在 RV1126 上的部署
|
||||||
|
目前 FastDeploy 已经支持基于 PaddleLite 部署 YOLOv5 量化模型到 RV1126 上。
|
||||||
|
|
||||||
|
模型的量化和量化模型的下载请参考：[模型量化](../quantize/README.md)
|
||||||
|
|
||||||
|
|
||||||
|
## 详细部署文档
|
||||||
|
|
||||||
|
在 RV1126 上只支持 C++ 的部署。
|
||||||
|
|
||||||
|
- [C++部署](cpp)
|
||||||
37
examples/vision/detection/yolov5/rv1126/cpp/CMakeLists.txt
Executable file
37
examples/vision/detection/yolov5/rv1126/cpp/CMakeLists.txt
Executable file
@@ -0,0 +1,37 @@
|
|||||||
|
PROJECT(infer_demo C CXX)
|
||||||
|
CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
|
||||||
|
|
||||||
|
# 指定下载解压后的fastdeploy库路径
|
||||||
|
option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
|
||||||
|
|
||||||
|
include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
|
||||||
|
|
||||||
|
# 添加FastDeploy依赖头文件
|
||||||
|
include_directories(${FASTDEPLOY_INCS})
|
||||||
|
include_directories(${FastDeploy_INCLUDE_DIRS})
|
||||||
|
|
||||||
|
add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
|
||||||
|
# 添加FastDeploy库依赖
|
||||||
|
target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
|
||||||
|
|
||||||
|
set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/build/install)
|
||||||
|
|
||||||
|
install(TARGETS infer_demo DESTINATION ./)
|
||||||
|
|
||||||
|
install(DIRECTORY models DESTINATION ./)
|
||||||
|
install(DIRECTORY images DESTINATION ./)
|
||||||
|
|
||||||
|
file(GLOB FASTDEPLOY_LIBS ${FASTDEPLOY_INSTALL_DIR}/lib/*)
|
||||||
|
install(PROGRAMS ${FASTDEPLOY_LIBS} DESTINATION lib)
|
||||||
|
|
||||||
|
file(GLOB OPENCV_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/opencv/lib/lib*)
|
||||||
|
install(PROGRAMS ${OPENCV_LIBS} DESTINATION lib)
|
||||||
|
|
||||||
|
file(GLOB PADDLELITE_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/paddlelite/lib/lib*)
|
||||||
|
install(PROGRAMS ${PADDLELITE_LIBS} DESTINATION lib)
|
||||||
|
|
||||||
|
file(GLOB TIMVX_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/paddlelite/lib/verisilicon_timvx/*)
|
||||||
|
install(PROGRAMS ${TIMVX_LIBS} DESTINATION lib)
|
||||||
|
|
||||||
|
file(GLOB ADB_TOOLS run_with_adb.sh)
|
||||||
|
install(PROGRAMS ${ADB_TOOLS} DESTINATION ./)
|
||||||
54
examples/vision/detection/yolov5/rv1126/cpp/README.md
Executable file
54
examples/vision/detection/yolov5/rv1126/cpp/README.md
Executable file
@@ -0,0 +1,54 @@
|
|||||||
|
# YOLOv5 量化模型 C++ 部署示例
|
||||||
|
|
||||||
|
本目录下提供的 `infer.cc`，可以帮助用户快速完成 YOLOv5 量化模型在 RV1126 上的部署推理加速。
|
||||||
|
|
||||||
|
## 部署准备
|
||||||
|
### FastDeploy 交叉编译环境准备
|
||||||
|
- 1. 软硬件环境满足要求，以及交叉编译环境的准备，请参考：[FastDeploy 交叉编译环境准备](../../../../../../docs/cn/build_and_install/rv1126.md#交叉编译环境搭建)
|
||||||
|
|
||||||
|
### 量化模型准备
|
||||||
|
- 1. 用户可以直接使用由 FastDeploy 提供的量化模型进行部署。
|
||||||
|
- 2. 用户可以使用 FastDeploy 提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署。
|
||||||
|
- 更多量化相关相关信息可查阅[模型量化](../../quantize/README.md)
|
||||||
|
|
||||||
|
## 在 RV1126 上部署量化后的 YOLOv5 检测模型
|
||||||
|
请按照以下步骤完成在 RV1126 上部署 YOLOv5 量化模型：
|
||||||
|
1. 交叉编译编译 FastDeploy 库，具体请参考：[交叉编译 FastDeploy](../../../../../../docs/cn/build_and_install/rv1126.md#基于-paddlelite-的-fastdeploy-交叉编译库编译)
|
||||||
|
|
||||||
|
2. 将编译后的库拷贝到当前目录，可使用如下命令：
|
||||||
|
```bash
|
||||||
|
cp -r FastDeploy/build/fastdeploy-tmivx/ FastDeploy/examples/vision/detection/yolov5/rv1126/cpp
|
||||||
|
```
|
||||||
|
|
||||||
|
3. 在当前路径下载部署所需的模型和示例图片：
|
||||||
|
```bash
|
||||||
|
mkdir models && mkdir images
|
||||||
|
wget https://bj.bcebos.com/fastdeploy/models/yolov5s_ptq_model.tar.gz
|
||||||
|
tar -xvf yolov5s_ptq_model.tar.gz
|
||||||
|
cp -r yolov5s_ptq_model models
|
||||||
|
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||||
|
cp -r 000000014439.jpg images
|
||||||
|
```
|
||||||
|
|
||||||
|
4. 编译部署示例，可使入如下命令：
|
||||||
|
```bash
|
||||||
|
mkdir build && cd build
|
||||||
|
cmake -DCMAKE_TOOLCHAIN_FILE=${PWD}/../fastdeploy-tmivx/timvx.cmake -DFASTDEPLOY_INSTALL_DIR=${PWD}/../fastdeploy-tmivx ..
|
||||||
|
make -j8
|
||||||
|
make install
|
||||||
|
# 成功编译之后，会生成 install 文件夹，里面有一个运行 demo 和部署所需的库
|
||||||
|
```
|
||||||
|
|
||||||
|
5. 基于 adb 工具部署 YOLOv5 检测模型到 Rockchip RV1126，可使用如下命令：
|
||||||
|
```bash
|
||||||
|
# 进入 install 目录
|
||||||
|
cd FastDeploy/examples/vision/detection/yolov5/rv1126/cpp/build/install/
|
||||||
|
# 如下命令表示：bash run_with_adb.sh 需要运行的demo 模型路径 图片路径 设备的DEVICE_ID
|
||||||
|
bash run_with_adb.sh infer_demo yolov5s_ptq_model 000000014439.jpg $DEVICE_ID
|
||||||
|
```
|
||||||
|
|
||||||
|
部署成功后，vis_result.jpg 保存的结果如下：
|
||||||
|
|
||||||
|
<img width="640" src="https://user-images.githubusercontent.com/30516196/203706969-dd58493c-6635-4ee7-9421-41c2e0c9524b.png">
|
||||||
|
|
||||||
|
需要特别注意的是，在 RV1126 上部署的模型需要是量化后的模型，模型的量化请参考：[模型量化](../../../../../../docs/cn/quantize.md)
|
||||||
64
examples/vision/detection/yolov5/rv1126/cpp/infer.cc
Executable file
64
examples/vision/detection/yolov5/rv1126/cpp/infer.cc
Executable file
@@ -0,0 +1,64 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
|
||||||
|
#include "fastdeploy/vision.h"
|
||||||
|
#ifdef WIN32
|
||||||
|
const char sep = '\\';
|
||||||
|
#else
|
||||||
|
const char sep = '/';
|
||||||
|
#endif
|
||||||
|
|
||||||
|
void InitAndInfer(const std::string& model_dir, const std::string& image_file) {
|
||||||
|
auto model_file = model_dir + sep + "model.pdmodel";
|
||||||
|
auto params_file = model_dir + sep + "model.pdiparams";
|
||||||
|
auto subgraph_file = model_dir + sep + "subgraph.txt";
|
||||||
|
|
||||||
|
fastdeploy::RuntimeOption option;
|
||||||
|
option.UseTimVX();
|
||||||
|
option.SetLiteSubgraphPartitionPath(subgraph_file);
|
||||||
|
|
||||||
|
auto model = fastdeploy::vision::detection::YOLOv5(
|
||||||
|
model_file, params_file, option, fastdeploy::ModelFormat::PADDLE);
|
||||||
|
assert(model.Initialized());
|
||||||
|
|
||||||
|
auto im = cv::imread(image_file);
|
||||||
|
|
||||||
|
fastdeploy::vision::DetectionResult res;
|
||||||
|
if (!model.Predict(im, &res)) {
|
||||||
|
std::cerr << "Failed to predict." << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::cout << res.Str() << std::endl;
|
||||||
|
|
||||||
|
auto vis_im = fastdeploy::vision::VisDetection(im, res);
|
||||||
|
cv::imwrite("vis_result.jpg", vis_im);
|
||||||
|
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||||
|
}
|
||||||
|
|
||||||
|
int main(int argc, char* argv[]) {
|
||||||
|
if (argc < 3) {
|
||||||
|
std::cout << "Usage: infer_demo path/to/quant_model "
|
||||||
|
"path/to/image "
|
||||||
|
"run_option, "
|
||||||
|
"e.g ./infer_demo ./yolov5s_quant ./000000014439.jpg"
|
||||||
|
<< std::endl;
|
||||||
|
return -1;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::string model_dir = argv[1];
|
||||||
|
std::string test_image = argv[2];
|
||||||
|
InitAndInfer(model_dir, test_image);
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
47
examples/vision/detection/yolov5/rv1126/cpp/run_with_adb.sh
Executable file
47
examples/vision/detection/yolov5/rv1126/cpp/run_with_adb.sh
Executable file
@@ -0,0 +1,47 @@
|
|||||||
|
#!/bin/bash
|
||||||
|
HOST_SPACE=${PWD}
|
||||||
|
echo ${HOST_SPACE}
|
||||||
|
WORK_SPACE=/data/local/tmp/test
|
||||||
|
|
||||||
|
# The first parameter represents the demo name
|
||||||
|
DEMO_NAME=image_classification_demo
|
||||||
|
if [ -n "$1" ]; then
|
||||||
|
DEMO_NAME=$1
|
||||||
|
fi
|
||||||
|
|
||||||
|
# The second parameter represents the model name
|
||||||
|
MODEL_NAME=mobilenet_v1_fp32_224
|
||||||
|
if [ -n "$2" ]; then
|
||||||
|
MODEL_NAME=$2
|
||||||
|
fi
|
||||||
|
|
||||||
|
# The third parameter indicates the name of the image to be tested
|
||||||
|
IMAGE_NAME=0001.jpg
|
||||||
|
if [ -n "$3" ]; then
|
||||||
|
IMAGE_NAME=$3
|
||||||
|
fi
|
||||||
|
|
||||||
|
# The fourth parameter represents the ID of the device
|
||||||
|
ADB_DEVICE_NAME=
|
||||||
|
if [ -n "$4" ]; then
|
||||||
|
ADB_DEVICE_NAME="-s $4"
|
||||||
|
fi
|
||||||
|
|
||||||
|
# Set the environment variables required during the running process
|
||||||
|
EXPORT_ENVIRONMENT_VARIABLES="export GLOG_v=5; export VIV_VX_ENABLE_GRAPH_TRANSFORM=-pcq:1; export VIV_VX_SET_PER_CHANNEL_ENTROPY=100; export TIMVX_BATCHNORM_FUSION_MAX_ALLOWED_QUANT_SCALE_DEVIATION=300000; export VSI_NN_LOG_LEVEL=5;"
|
||||||
|
|
||||||
|
EXPORT_ENVIRONMENT_VARIABLES="${EXPORT_ENVIRONMENT_VARIABLES}export LD_LIBRARY_PATH=${WORK_SPACE}/lib:\$LD_LIBRARY_PATH;"
|
||||||
|
|
||||||
|
# Please install adb, and DON'T run this in the docker.
|
||||||
|
set -e
|
||||||
|
adb $ADB_DEVICE_NAME shell "rm -rf $WORK_SPACE"
|
||||||
|
adb $ADB_DEVICE_NAME shell "mkdir -p $WORK_SPACE"
|
||||||
|
|
||||||
|
# Upload the demo, librarys, model and test images to the device
|
||||||
|
adb $ADB_DEVICE_NAME push ${HOST_SPACE}/lib $WORK_SPACE
|
||||||
|
adb $ADB_DEVICE_NAME push ${HOST_SPACE}/${DEMO_NAME} $WORK_SPACE
|
||||||
|
adb $ADB_DEVICE_NAME push models $WORK_SPACE
|
||||||
|
adb $ADB_DEVICE_NAME push images $WORK_SPACE
|
||||||
|
|
||||||
|
# Execute the deployment demo
|
||||||
|
adb $ADB_DEVICE_NAME shell "cd $WORK_SPACE; ${EXPORT_ENVIRONMENT_VARIABLES} chmod +x ./${DEMO_NAME}; ./${DEMO_NAME} ./models/${MODEL_NAME} ./images/$IMAGE_NAME"
|
||||||
2
examples/vision/detection/yolov6/quantize/README.md
Normal file → Executable file
2
examples/vision/detection/yolov6/quantize/README.md
Normal file → Executable file
@@ -4,7 +4,7 @@ FastDeploy已支持部署量化模型,并提供一键模型自动化压缩的工
|
|||||||
|
|
||||||
## FastDeploy一键模型自动化压缩工具
|
## FastDeploy一键模型自动化压缩工具
|
||||||
FastDeploy 提供了一键模型自动化压缩工具, 能够简单地通过输入一个配置文件, 对模型进行量化.
|
FastDeploy 提供了一键模型自动化压缩工具, 能够简单地通过输入一个配置文件, 对模型进行量化.
|
||||||
详细教程请见: [一键模型自动化压缩工具](../../../../../tools/auto_compression/)
|
详细教程请见: [一键模型自动化压缩工具](../../../../../tools/common_tools/auto_compression/)
|
||||||
## 下载量化完成的YOLOv6s模型
|
## 下载量化完成的YOLOv6s模型
|
||||||
用户也可以直接下载下表中的量化模型进行部署.(点击模型名字即可下载)
|
用户也可以直接下载下表中的量化模型进行部署.(点击模型名字即可下载)
|
||||||
|
|
||||||
|
|||||||
2
examples/vision/detection/yolov6/quantize/README_EN.md
Normal file → Executable file
2
examples/vision/detection/yolov6/quantize/README_EN.md
Normal file → Executable file
@@ -6,7 +6,7 @@ Users can use the one-click model quantization tool to quantize and deploy the m
|
|||||||
## FastDeploy One-Click Model Quantization Tool
|
## FastDeploy One-Click Model Quantization Tool
|
||||||
|
|
||||||
FastDeploy provides a one-click quantization tool that allows users to quantize a model simply with a configuration file.
|
FastDeploy provides a one-click quantization tool that allows users to quantize a model simply with a configuration file.
|
||||||
For detailed tutorial, please refer to : [One-Click Model Quantization Tool](../../../../../tools/auto_compression/)
|
For detailed tutorial, please refer to : [One-Click Model Quantization Tool](../../../../../tools/common_tools/auto_compression/)
|
||||||
|
|
||||||
## Download Quantized YOLOv6s Model
|
## Download Quantized YOLOv6s Model
|
||||||
|
|
||||||
|
|||||||
2
examples/vision/detection/yolov6/quantize/cpp/README.md
Normal file → Executable file
2
examples/vision/detection/yolov6/quantize/cpp/README.md
Normal file → Executable file
@@ -9,7 +9,7 @@
|
|||||||
|
|
||||||
### 量化模型准备
|
### 量化模型准备
|
||||||
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
||||||
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
|
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
|
||||||
|
|
||||||
## 以量化后的YOLOv6s模型为例, 进行部署
|
## 以量化后的YOLOv6s模型为例, 进行部署
|
||||||
在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
||||||
|
|||||||
2
examples/vision/detection/yolov6/quantize/python/README.md
Normal file → Executable file
2
examples/vision/detection/yolov6/quantize/python/README.md
Normal file → Executable file
@@ -8,7 +8,7 @@
|
|||||||
|
|
||||||
### 量化模型准备
|
### 量化模型准备
|
||||||
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
||||||
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
|
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
|
||||||
|
|
||||||
## 以量化后的YOLOv6s模型为例, 进行部署
|
## 以量化后的YOLOv6s模型为例, 进行部署
|
||||||
```bash
|
```bash
|
||||||
|
|||||||
2
examples/vision/detection/yolov7/quantize/README.md
Normal file → Executable file
2
examples/vision/detection/yolov7/quantize/README.md
Normal file → Executable file
@@ -4,7 +4,7 @@ FastDeploy已支持部署量化模型,并提供一键模型自动化压缩的工
|
|||||||
|
|
||||||
## FastDeploy一键模型自动化压缩工具
|
## FastDeploy一键模型自动化压缩工具
|
||||||
FastDeploy 提供了一键模型自动化压缩工具, 能够简单地通过输入一个配置文件, 对模型进行量化.
|
FastDeploy 提供了一键模型自动化压缩工具, 能够简单地通过输入一个配置文件, 对模型进行量化.
|
||||||
详细教程请见: [一键模型自动化压缩工具](../../../../../tools/auto_compression/)
|
详细教程请见: [一键模型自动化压缩工具](../../../../../tools/common_tools/auto_compression/)
|
||||||
|
|
||||||
## 下载量化完成的YOLOv7模型
|
## 下载量化完成的YOLOv7模型
|
||||||
用户也可以直接下载下表中的量化模型进行部署.(点击模型名字即可下载)
|
用户也可以直接下载下表中的量化模型进行部署.(点击模型名字即可下载)
|
||||||
|
|||||||
2
examples/vision/detection/yolov7/quantize/README_EN.md
Normal file → Executable file
2
examples/vision/detection/yolov7/quantize/README_EN.md
Normal file → Executable file
@@ -6,7 +6,7 @@ Users can use the one-click model quantization tool to quantize and deploy the m
|
|||||||
## FastDeploy One-Click Model Quantization Tool
|
## FastDeploy One-Click Model Quantization Tool
|
||||||
|
|
||||||
FastDeploy provides a one-click quantization tool that allows users to quantize a model simply with a configuration file.
|
FastDeploy provides a one-click quantization tool that allows users to quantize a model simply with a configuration file.
|
||||||
For detailed tutorial, please refer to : [One-Click Model Quantization Tool](../../../../../tools/auto_compression/)
|
For detailed tutorial, please refer to : [One-Click Model Quantization Tool](../../../../../tools/common_tools/auto_compression/)
|
||||||
|
|
||||||
## Download Quantized YOLOv7 Model
|
## Download Quantized YOLOv7 Model
|
||||||
|
|
||||||
|
|||||||
2
examples/vision/detection/yolov7/quantize/cpp/README.md
Normal file → Executable file
2
examples/vision/detection/yolov7/quantize/cpp/README.md
Normal file → Executable file
@@ -9,7 +9,7 @@
|
|||||||
|
|
||||||
### 量化模型准备
|
### 量化模型准备
|
||||||
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
||||||
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
|
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
|
||||||
|
|
||||||
## 以量化后的YOLOv7模型为例, 进行部署
|
## 以量化后的YOLOv7模型为例, 进行部署
|
||||||
在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
||||||
|
|||||||
2
examples/vision/detection/yolov7/quantize/python/README.md
Normal file → Executable file
2
examples/vision/detection/yolov7/quantize/python/README.md
Normal file → Executable file
@@ -8,7 +8,7 @@
|
|||||||
|
|
||||||
### 量化模型准备
|
### 量化模型准备
|
||||||
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
||||||
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
|
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
|
||||||
|
|
||||||
## 以量化后的YOLOv7模型为例, 进行部署
|
## 以量化后的YOLOv7模型为例, 进行部署
|
||||||
```bash
|
```bash
|
||||||
|
|||||||
2
examples/vision/segmentation/paddleseg/quantize/README.md
Normal file → Executable file
2
examples/vision/segmentation/paddleseg/quantize/README.md
Normal file → Executable file
@@ -4,7 +4,7 @@ FastDeploy已支持部署量化模型,并提供一键模型自动化压缩的工
|
|||||||
|
|
||||||
## FastDeploy一键模型自动化压缩工具
|
## FastDeploy一键模型自动化压缩工具
|
||||||
FastDeploy 提供了一键模型自动化压缩工具, 能够简单地通过输入一个配置文件, 对模型进行量化.
|
FastDeploy 提供了一键模型自动化压缩工具, 能够简单地通过输入一个配置文件, 对模型进行量化.
|
||||||
详细教程请见: [一键模型自动化压缩工具](../../../../../tools/auto_compression/)
|
详细教程请见: [一键模型自动化压缩工具](../../../../../tools/common_tools/auto_compression/)
|
||||||
注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可。
|
注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可。
|
||||||
|
|
||||||
## 下载量化完成的PaddleSeg模型
|
## 下载量化完成的PaddleSeg模型
|
||||||
|
|||||||
2
examples/vision/segmentation/paddleseg/quantize/cpp/README.md
Normal file → Executable file
2
examples/vision/segmentation/paddleseg/quantize/cpp/README.md
Normal file → Executable file
@@ -8,7 +8,7 @@
|
|||||||
|
|
||||||
### 量化模型准备
|
### 量化模型准备
|
||||||
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
||||||
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
|
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
|
||||||
|
|
||||||
## 以量化后的PP_LiteSeg_T_STDC1_cityscapes模型为例, 进行部署
|
## 以量化后的PP_LiteSeg_T_STDC1_cityscapes模型为例, 进行部署
|
||||||
在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
||||||
|
|||||||
2
examples/vision/segmentation/paddleseg/quantize/python/README.md
Normal file → Executable file
2
examples/vision/segmentation/paddleseg/quantize/python/README.md
Normal file → Executable file
@@ -8,7 +8,7 @@
|
|||||||
|
|
||||||
### 量化模型准备
|
### 量化模型准备
|
||||||
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
|
||||||
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
|
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
|
||||||
|
|
||||||
|
|
||||||
## 以量化后的PP_LiteSeg_T_STDC1_cityscapes模型为例, 进行部署
|
## 以量化后的PP_LiteSeg_T_STDC1_cityscapes模型为例, 进行部署
|
||||||
|
|||||||
11
examples/vision/segmentation/paddleseg/rv1126/README.md
Executable file
11
examples/vision/segmentation/paddleseg/rv1126/README.md
Executable file
@@ -0,0 +1,11 @@
|
|||||||
|
# PP-LiteSeg 量化模型在 RV1126 上的部署
|
||||||
|
目前 FastDeploy 已经支持基于 PaddleLite 部署 PP-LiteSeg 量化模型到 RV1126 上。
|
||||||
|
|
||||||
|
模型的量化和量化模型的下载请参考：[模型量化](../quantize/README.md)
|
||||||
|
|
||||||
|
|
||||||
|
## 详细部署文档
|
||||||
|
|
||||||
|
在 RV1126 上只支持 C++ 的部署。
|
||||||
|
|
||||||
|
- [C++部署](cpp)
|
||||||
38
examples/vision/segmentation/paddleseg/rv1126/cpp/CMakeLists.txt
Executable file
38
examples/vision/segmentation/paddleseg/rv1126/cpp/CMakeLists.txt
Executable file
@@ -0,0 +1,38 @@
|
|||||||
|
PROJECT(infer_demo C CXX)
|
||||||
|
CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
|
||||||
|
|
||||||
|
# 指定下载解压后的fastdeploy库路径
|
||||||
|
option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
|
||||||
|
|
||||||
|
include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
|
||||||
|
|
||||||
|
# 添加FastDeploy依赖头文件
|
||||||
|
include_directories(${FASTDEPLOY_INCS})
|
||||||
|
include_directories(${FastDeploy_INCLUDE_DIRS})
|
||||||
|
|
||||||
|
add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
|
||||||
|
# 添加FastDeploy库依赖
|
||||||
|
target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
|
||||||
|
|
||||||
|
set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/build/install)
|
||||||
|
|
||||||
|
install(TARGETS infer_demo DESTINATION ./)
|
||||||
|
|
||||||
|
install(DIRECTORY models DESTINATION ./)
|
||||||
|
install(DIRECTORY images DESTINATION ./)
|
||||||
|
# install(DIRECTORY run_with_adb.sh DESTINATION ./)
|
||||||
|
|
||||||
|
file(GLOB FASTDEPLOY_LIBS ${FASTDEPLOY_INSTALL_DIR}/lib/*)
|
||||||
|
install(PROGRAMS ${FASTDEPLOY_LIBS} DESTINATION lib)
|
||||||
|
|
||||||
|
file(GLOB OPENCV_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/opencv/lib/lib*)
|
||||||
|
install(PROGRAMS ${OPENCV_LIBS} DESTINATION lib)
|
||||||
|
|
||||||
|
file(GLOB PADDLELITE_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/paddlelite/lib/lib*)
|
||||||
|
install(PROGRAMS ${PADDLELITE_LIBS} DESTINATION lib)
|
||||||
|
|
||||||
|
file(GLOB TIMVX_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/paddlelite/lib/verisilicon_timvx/*)
|
||||||
|
install(PROGRAMS ${TIMVX_LIBS} DESTINATION lib)
|
||||||
|
|
||||||
|
file(GLOB ADB_TOOLS run_with_adb.sh)
|
||||||
|
install(PROGRAMS ${ADB_TOOLS} DESTINATION ./)
|
||||||
54
examples/vision/segmentation/paddleseg/rv1126/cpp/README.md
Executable file
54
examples/vision/segmentation/paddleseg/rv1126/cpp/README.md
Executable file
@@ -0,0 +1,54 @@
|
|||||||
|
# PP-LiteSeg 量化模型 C++ 部署示例
|
||||||
|
|
||||||
|
本目录下提供的 `infer.cc`，可以帮助用户快速完成 PP-LiteSeg 量化模型在 RV1126 上的部署推理加速。
|
||||||
|
|
||||||
|
## 部署准备
|
||||||
|
### FastDeploy 交叉编译环境准备
|
||||||
|
- 1. 软硬件环境满足要求，以及交叉编译环境的准备，请参考：[FastDeploy 交叉编译环境准备](../../../../../../docs/cn/build_and_install/rv1126.md#交叉编译环境搭建)
|
||||||
|
|
||||||
|
### 模型准备
|
||||||
|
- 1. 用户可以直接使用由 FastDeploy 提供的量化模型进行部署。
|
||||||
|
- 2. 用户可以使用 FastDeploy 提供的一键模型自动化压缩工具,自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的 deploy.yaml 文件, 自行量化的模型文件夹内不包含此 yaml 文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
|
||||||
|
- 更多量化相关相关信息可查阅[模型量化](../../quantize/README.md)
|
||||||
|
|
||||||
|
## 在 RV1126 上部署量化后的 PP-LiteSeg 分割模型
|
||||||
|
请按照以下步骤完成在 RV1126 上部署 PP-LiteSeg 量化模型：
|
||||||
|
1. 交叉编译编译 FastDeploy 库，具体请参考：[交叉编译 FastDeploy](../../../../../../docs/cn/build_and_install/rv1126.md#基于-paddlelite-的-fastdeploy-交叉编译库编译)
|
||||||
|
|
||||||
|
2. 将编译后的库拷贝到当前目录，可使用如下命令：
|
||||||
|
```bash
|
||||||
|
cp -r FastDeploy/build/fastdeploy-tmivx/ FastDeploy/examples/vision/segmentation/paddleseg/rv1126/cpp
|
||||||
|
```
|
||||||
|
|
||||||
|
3. 在当前路径下载部署所需的模型和示例图片：
|
||||||
|
```bash
|
||||||
|
mkdir models && mkdir images
|
||||||
|
wget https://bj.bcebos.com/fastdeploy/models/rk1/ppliteseg.tar.gz
|
||||||
|
tar -xvf ppliteseg.tar.gz
|
||||||
|
cp -r ppliteseg models
|
||||||
|
wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
|
||||||
|
cp -r cityscapes_demo.png images
|
||||||
|
```
|
||||||
|
|
||||||
|
4. 编译部署示例，可使入如下命令：
|
||||||
|
```bash
|
||||||
|
mkdir build && cd build
|
||||||
|
cmake -DCMAKE_TOOLCHAIN_FILE=${PWD}/../fastdeploy-tmivx/timvx.cmake -DFASTDEPLOY_INSTALL_DIR=${PWD}/../fastdeploy-tmivx ..
|
||||||
|
make -j8
|
||||||
|
make install
|
||||||
|
# 成功编译之后，会生成 install 文件夹，里面有一个运行 demo 和部署所需的库
|
||||||
|
```
|
||||||
|
|
||||||
|
5. 基于 adb 工具部署 PP-LiteSeg 分割模型到 Rockchip RV1126，可使用如下命令：
|
||||||
|
```bash
|
||||||
|
# 进入 install 目录
|
||||||
|
cd FastDeploy/examples/vision/segmentation/paddleseg/rv1126/cpp/build/install/
|
||||||
|
# 如下命令表示：bash run_with_adb.sh 需要运行的demo 模型路径 图片路径 设备的DEVICE_ID
|
||||||
|
bash run_with_adb.sh infer_demo ppliteseg cityscapes_demo.png $DEVICE_ID
|
||||||
|
```
|
||||||
|
|
||||||
|
部署成功后运行结果如下：
|
||||||
|
|
||||||
|
<img width="640" src="https://user-images.githubusercontent.com/30516196/205544166-9b2719ff-ed82-4908-b90a-095de47392e1.png">
|
||||||
|
|
||||||
|
需要特别注意的是，在 RV1126 上部署的模型需要是量化后的模型，模型的量化请参考：[模型量化](../../../../../../docs/cn/quantize.md)
|
||||||
66
examples/vision/segmentation/paddleseg/rv1126/cpp/infer.cc
Executable file
66
examples/vision/segmentation/paddleseg/rv1126/cpp/infer.cc
Executable file
@@ -0,0 +1,66 @@
|
|||||||
|
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
//
|
||||||
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
// you may not use this file except in compliance with the License.
|
||||||
|
// You may obtain a copy of the License at
|
||||||
|
//
|
||||||
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
//
|
||||||
|
// Unless required by applicable law or agreed to in writing, software
|
||||||
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
// See the License for the specific language governing permissions and
|
||||||
|
// limitations under the License.
|
||||||
|
|
||||||
|
#include "fastdeploy/vision.h"
|
||||||
|
#ifdef WIN32
|
||||||
|
const char sep = '\\';
|
||||||
|
#else
|
||||||
|
const char sep = '/';
|
||||||
|
#endif
|
||||||
|
|
||||||
|
void InitAndInfer(const std::string& model_dir, const std::string& image_file) {
|
||||||
|
auto model_file = model_dir + sep + "model.pdmodel";
|
||||||
|
auto params_file = model_dir + sep + "model.pdiparams";
|
||||||
|
auto config_file = model_dir + sep + "deploy.yaml";
|
||||||
|
auto subgraph_file = model_dir + sep + "subgraph.txt";
|
||||||
|
|
||||||
|
fastdeploy::RuntimeOption option;
|
||||||
|
option.UseTimVX();
|
||||||
|
option.SetLiteSubgraphPartitionPath(subgraph_file);
|
||||||
|
|
||||||
|
auto model = fastdeploy::vision::segmentation::PaddleSegModel(
|
||||||
|
model_file, params_file, config_file,option);
|
||||||
|
|
||||||
|
assert(model.Initialized());
|
||||||
|
|
||||||
|
auto im = cv::imread(image_file);
|
||||||
|
|
||||||
|
fastdeploy::vision::SegmentationResult res;
|
||||||
|
if (!model.Predict(im, &res)) {
|
||||||
|
std::cerr << "Failed to predict." << std::endl;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::cout << res.Str() << std::endl;
|
||||||
|
|
||||||
|
auto vis_im = fastdeploy::vision::VisSegmentation(im, res, 0.5);
|
||||||
|
cv::imwrite("vis_result.jpg", vis_im);
|
||||||
|
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||||
|
}
|
||||||
|
|
||||||
|
int main(int argc, char* argv[]) {
|
||||||
|
if (argc < 3) {
|
||||||
|
std::cout << "Usage: infer_demo path/to/quant_model "
|
||||||
|
"path/to/image "
|
||||||
|
"run_option, "
|
||||||
|
"e.g ./infer_demo ./ResNet50_vd_quant ./test.jpeg"
|
||||||
|
<< std::endl;
|
||||||
|
return -1;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::string model_dir = argv[1];
|
||||||
|
std::string test_image = argv[2];
|
||||||
|
InitAndInfer(model_dir, test_image);
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
47
examples/vision/segmentation/paddleseg/rv1126/cpp/run_with_adb.sh
Executable file
47
examples/vision/segmentation/paddleseg/rv1126/cpp/run_with_adb.sh
Executable file
@@ -0,0 +1,47 @@
|
|||||||
|
#!/bin/bash
|
||||||
|
HOST_SPACE=${PWD}
|
||||||
|
echo ${HOST_SPACE}
|
||||||
|
WORK_SPACE=/data/local/tmp/test
|
||||||
|
|
||||||
|
# The first parameter represents the demo name
|
||||||
|
DEMO_NAME=image_classification_demo
|
||||||
|
if [ -n "$1" ]; then
|
||||||
|
DEMO_NAME=$1
|
||||||
|
fi
|
||||||
|
|
||||||
|
# The second parameter represents the model name
|
||||||
|
MODEL_NAME=mobilenet_v1_fp32_224
|
||||||
|
if [ -n "$2" ]; then
|
||||||
|
MODEL_NAME=$2
|
||||||
|
fi
|
||||||
|
|
||||||
|
# The third parameter indicates the name of the image to be tested
|
||||||
|
IMAGE_NAME=0001.jpg
|
||||||
|
if [ -n "$3" ]; then
|
||||||
|
IMAGE_NAME=$3
|
||||||
|
fi
|
||||||
|
|
||||||
|
# The fourth parameter represents the ID of the device
|
||||||
|
ADB_DEVICE_NAME=
|
||||||
|
if [ -n "$4" ]; then
|
||||||
|
ADB_DEVICE_NAME="-s $4"
|
||||||
|
fi
|
||||||
|
|
||||||
|
# Set the environment variables required during the running process
|
||||||
|
EXPORT_ENVIRONMENT_VARIABLES="export GLOG_v=5; export VIV_VX_ENABLE_GRAPH_TRANSFORM=-pcq:1; export VIV_VX_SET_PER_CHANNEL_ENTROPY=100; export TIMVX_BATCHNORM_FUSION_MAX_ALLOWED_QUANT_SCALE_DEVIATION=300000; export VSI_NN_LOG_LEVEL=5;"
|
||||||
|
|
||||||
|
EXPORT_ENVIRONMENT_VARIABLES="${EXPORT_ENVIRONMENT_VARIABLES}export LD_LIBRARY_PATH=${WORK_SPACE}/lib:\$LD_LIBRARY_PATH;"
|
||||||
|
|
||||||
|
# Please install adb, and DON'T run this in the docker.
|
||||||
|
set -e
|
||||||
|
adb $ADB_DEVICE_NAME shell "rm -rf $WORK_SPACE"
|
||||||
|
adb $ADB_DEVICE_NAME shell "mkdir -p $WORK_SPACE"
|
||||||
|
|
||||||
|
# Upload the demo, librarys, model and test images to the device
|
||||||
|
adb $ADB_DEVICE_NAME push ${HOST_SPACE}/lib $WORK_SPACE
|
||||||
|
adb $ADB_DEVICE_NAME push ${HOST_SPACE}/${DEMO_NAME} $WORK_SPACE
|
||||||
|
adb $ADB_DEVICE_NAME push models $WORK_SPACE
|
||||||
|
adb $ADB_DEVICE_NAME push images $WORK_SPACE
|
||||||
|
|
||||||
|
# Execute the deployment demo
|
||||||
|
adb $ADB_DEVICE_NAME shell "cd $WORK_SPACE; ${EXPORT_ENVIRONMENT_VARIABLES} chmod +x ./${DEMO_NAME}; ./${DEMO_NAME} ./models/${MODEL_NAME} ./images/$IMAGE_NAME"
|
||||||
@@ -72,7 +72,7 @@ void LiteBackend::BuildOption(const LiteBackendOption& option) {
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
if(option_.enable_timvx){
|
if(option_.enable_timvx) {
|
||||||
config_.set_nnadapter_device_names({"verisilicon_timvx"});
|
config_.set_nnadapter_device_names({"verisilicon_timvx"});
|
||||||
valid_places.push_back(
|
valid_places.push_back(
|
||||||
paddle::lite_api::Place{TARGET(kNNAdapter), PRECISION(kInt8)});
|
paddle::lite_api::Place{TARGET(kNNAdapter), PRECISION(kInt8)});
|
||||||
|
|||||||
@@ -27,6 +27,7 @@ YOLOv5::YOLOv5(const std::string& model_file, const std::string& params_file,
|
|||||||
} else {
|
} else {
|
||||||
valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
|
valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
|
||||||
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
||||||
|
valid_timvx_backends = {Backend::LITE};
|
||||||
}
|
}
|
||||||
runtime_option = custom_option;
|
runtime_option = custom_option;
|
||||||
runtime_option.model_format = model_format;
|
runtime_option.model_format = model_format;
|
||||||
|
|||||||
1
fastdeploy/vision/detection/ppdet/model.h
Normal file → Executable file
1
fastdeploy/vision/detection/ppdet/model.h
Normal file → Executable file
@@ -64,6 +64,7 @@ class FASTDEPLOY_DECL PPYOLOE : public PPDetBase {
|
|||||||
valid_cpu_backends = {Backend::OPENVINO, Backend::ORT,
|
valid_cpu_backends = {Backend::OPENVINO, Backend::ORT,
|
||||||
Backend::PDINFER, Backend::LITE};
|
Backend::PDINFER, Backend::LITE};
|
||||||
valid_gpu_backends = {Backend::ORT, Backend::PDINFER, Backend::TRT};
|
valid_gpu_backends = {Backend::ORT, Backend::PDINFER, Backend::TRT};
|
||||||
|
valid_timvx_backends = {Backend::LITE};
|
||||||
initialized = Initialize();
|
initialized = Initialize();
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|||||||
1
fastdeploy/vision/segmentation/ppseg/model.cc
Normal file → Executable file
1
fastdeploy/vision/segmentation/ppseg/model.cc
Normal file → Executable file
@@ -27,6 +27,7 @@ PaddleSegModel::PaddleSegModel(const std::string& model_file,
|
|||||||
valid_cpu_backends = {Backend::OPENVINO, Backend::PDINFER, Backend::ORT, Backend::LITE};
|
valid_cpu_backends = {Backend::OPENVINO, Backend::PDINFER, Backend::ORT, Backend::LITE};
|
||||||
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
||||||
valid_rknpu_backends = {Backend::RKNPU2};
|
valid_rknpu_backends = {Backend::RKNPU2};
|
||||||
|
valid_timvx_backends = {Backend::LITE};
|
||||||
runtime_option = custom_option;
|
runtime_option = custom_option;
|
||||||
runtime_option.model_format = model_format;
|
runtime_option.model_format = model_format;
|
||||||
runtime_option.model_file = model_file;
|
runtime_option.model_file = model_file;
|
||||||
|
|||||||
Reference in New Issue
Block a user