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[Model] 新增scrfd rknpu2代码 (#504)
* * 新增scrfd rknpu2代码 * * 新增scrfd python代码 * 修正文档 * 修正文档以及部分错误 * 修改文档 * 修复部分错误 * 修复部分错误 * 修复部分错误 * scrfd更新代码 * scrfd更新代码
This commit is contained in:
Zheng_Bicheng
73
examples/vision/facedet/scrfd/rknpu2/README.md
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examples/vision/facedet/scrfd/rknpu2/README.md
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# SCRFD RKNPU2部署模型
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- [SCRFD](https://github.com/deepinsight/insightface/tree/17cdeab12a35efcebc2660453a8cbeae96e20950)
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- （1）[官方库](https://github.com/deepinsight/insightface/)中提供的*.pt通过[导出ONNX模型](#导出ONNX模型)操作后，可进行部署；
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- （2）开发者基于自己数据训练的SCRFD模型，可按照[导出ONNX模型](#导出ONNX模型)后，完成部署。
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## 下载预训练ONNX模型
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为了方便开发者的测试，下面提供了SCRFD导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
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| 模型 | 大小 | 精度 |
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|:---------------------------------------------------------------- |:----- |:----- |
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| [SCRFD-500M-kps-160](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_bnkps_shape160x160.onnx) | 2.5MB | - |
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| [SCRFD-500M-160](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_shape160x160.onnx) | 2.2MB | - |
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| [SCRFD-500M-kps-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_bnkps_shape320x320.onnx) | 2.5MB | - |
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| [SCRFD-500M-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_shape320x320.onnx) | 2.2MB | - |
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| [SCRFD-500M-kps-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_bnkps_shape640x640.onnx) | 2.5MB | 90.97% |
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| [SCRFD-500M-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_shape640x640.onnx) | 2.2MB | 90.57% |
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| [SCRFD-1G-160](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_1g_shape160x160.onnx ) | 2.5MB | - |
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| [SCRFD-1G-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_1g_shape320x320.onnx) | 2.5MB | - |
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| [SCRFD-1G-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_1g_shape640x640.onnx) | 2.5MB | 92.38% |
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| [SCRFD-2.5G-kps-160](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_2.5g_bnkps_shape160x160.onnx) | 3.2MB | - |
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| [SCRFD-2.5G-160](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_2.5g_shape160x160.onnx) | 2.6MB | - |
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| [SCRFD-2.5G-kps-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_2.5g_bnkps_shape320x320.onnx) | 3.2MB | - |
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| [SCRFD-2.5G-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_2.5g_shape320x320.onnx) | 2.6MB | - |
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| [SCRFD-2.5G-kps-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_2.5g_bnkps_shape640x640.onnx) | 3.2MB | 93.8% |
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| [SCRFD-2.5G-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_2.5g_shape640x640.onnx) | 2.6MB | 93.78% |
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| [SCRFD-10G-kps-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_10g_bnkps_shape320x320.onnx) | 17MB | - |
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| [SCRFD-10G-320](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_10g_shape320x320.onnx) | 15MB | - |
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| [SCRFD-10G-kps-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_10g_bnkps_shape640x640.onnx) | 17MB | 95.4% |
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| [SCRFD-10G-640](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_10g_shape640x640.onnx) | 15MB | 95.16% |
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| [SCRFD-10G-kps-1280](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_10g_bnkps_shape1280x1280.onnx) | 17MB | - |
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| [SCRFD-10G-1280](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_10g_shape1280x1280.onnx) | 15MB | - |
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## 导出ONNX模型
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```bash
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#下载scrfd模型文件
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e.g. download from https://onedrive.live.com/?authkey=%21ABbFJx2JMhNjhNA&id=4A83B6B633B029CC%215542&cid=4A83B6B633B029CC
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# 安装官方库配置环境，此版本导出环境为：
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- 手动配置环境
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torch==1.8.0
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mmcv==1.3.5
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mmdet==2.7.0
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- 通过docker配置
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docker pull qyjdefdocker/onnx-scrfd-converter:v0.3
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# 导出onnx格式文件
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- 手动生成
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python tools/scrfd2onnx.py configs/scrfd/scrfd_500m.py weights/scrfd_500m.pth --shape 640 --input-img face-xxx.jpg
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- docker
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docker的onnx目录中已有生成好的onnx文件
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```
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## ONNX模型转换RKNN模型
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```bash
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wget https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_bnkps_shape640x640.onnx
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python tools/rknpu2/export.py --config_path tools/rknpu2/config/RK3588/scrfd.yaml
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```
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## 详细部署文档
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- [Python部署](python/README.md)
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- [C++部署](cpp/README.md)
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## 版本说明
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- 本版本文档和代码基于[SCRFD CommitID:17cdeab](https://github.com/deepinsight/insightface/tree/17cdeab12a35efcebc2660453a8cbeae96e20950) 编写
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36
examples/vision/facedet/scrfd/rknpu2/cpp/CMakeLists.txt
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36
examples/vision/facedet/scrfd/rknpu2/cpp/CMakeLists.txt
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CMAKE_MINIMUM_REQUIRED(VERSION 3.10)
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project(rknpu_test)
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set(CMAKE_CXX_STANDARD 14)
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# 指定下载解压后的fastdeploy库路径
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set(FASTDEPLOY_INSTALL_DIR "thirdpartys/fastdeploy-0.7.0")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeployConfig.cmake)
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include_directories(${FastDeploy_INCLUDE_DIRS})
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add_executable(rknpu_test infer.cc)
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target_link_libraries(rknpu_test
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${FastDeploy_LIBS}
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)
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set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/build/install)
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install(TARGETS rknpu_test DESTINATION ./)
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install(DIRECTORY model DESTINATION ./)
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install(DIRECTORY images DESTINATION ./)
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file(GLOB FASTDEPLOY_LIBS ${FASTDEPLOY_INSTALL_DIR}/lib/*)
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message("${FASTDEPLOY_LIBS}")
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install(PROGRAMS ${FASTDEPLOY_LIBS} DESTINATION lib)
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file(GLOB ONNXRUNTIME_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/onnxruntime/lib/*)
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install(PROGRAMS ${ONNXRUNTIME_LIBS} DESTINATION lib)
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install(DIRECTORY ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/opencv/lib DESTINATION ./)
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file(GLOB PADDLETOONNX_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/paddle2onnx/lib/*)
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install(PROGRAMS ${PADDLETOONNX_LIBS} DESTINATION lib)
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file(GLOB RKNPU2_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/rknpu2_runtime/RK3588/lib/*)
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install(PROGRAMS ${RKNPU2_LIBS} DESTINATION lib)
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72
examples/vision/facedet/scrfd/rknpu2/cpp/README.md
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72
examples/vision/facedet/scrfd/rknpu2/cpp/README.md
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# SCRFD C++部署示例
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本目录下提供`infer.cc`快速完成SCRFD在NPU加速部署的示例。
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在部署前，需确认以下两个步骤:
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1. 软硬件环境满足要求
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2. 根据开发环境，下载预编译部署库或者从头编译FastDeploy仓库
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以上步骤请参考[RK2代NPU部署库编译](../../../../../../docs/cn/build_and_install/rknpu2.md)实现
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## 生成基本目录文件
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该例程由以下几个部分组成
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```text
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.
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├── CMakeLists.txt
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├── build # 编译文件夹
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├── image # 存放图片的文件夹
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├── infer_cpu_npu.cc
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├── infer_cpu_npu.h
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├── main.cc
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├── model # 存放模型文件的文件夹
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└── thirdpartys # 存放sdk的文件夹
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```
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首先需要先生成目录结构
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```bash
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mkdir build
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mkdir images
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mkdir model
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mkdir thirdpartys
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```
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## 编译
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### 编译并拷贝SDK到thirdpartys文件夹
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请参考[RK2代NPU部署库编译](../../../../../../docs/cn/build_and_install/rknpu2.md)仓库编译SDK，编译完成后，将在build目录下生成
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fastdeploy-0.7.0目录，请移动它至thirdpartys目录下.
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### 拷贝模型文件，以及配置文件至model文件夹
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在Paddle动态图模型 -> Paddle静态图模型 -> ONNX模型的过程中，将生成ONNX文件以及对应的yaml配置文件，请将配置文件存放到model文件夹内。
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转换为RKNN后的模型文件也需要拷贝至model。
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### 准备测试图片至image文件夹
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```bash
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wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
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cp test_lite_face_detector_3.jpg ./images
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```
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### 编译example
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```bash
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cd build
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cmake ..
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make -j8
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make install
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```
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## 运行例程
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```bash
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cd ./build/install
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./rknpu_test
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```
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运行完成可视化结果如下图所示
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<img width="640" src="https://user-images.githubusercontent.com/67993288/184301789-1981d065-208f-4a6b-857c-9a0f9a63e0b1.jpg">
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- [模型介绍](../../README.md)
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- [Python部署](../python/README.md)
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- [视觉模型预测结果](../../../../../../docs/api/vision_results/README.md)
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79
examples/vision/facedet/scrfd/rknpu2/cpp/infer.cc
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79
examples/vision/facedet/scrfd/rknpu2/cpp/infer.cc
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#include <iostream>
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#include <string>
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#include "fastdeploy/vision.h"
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void InferScrfd(const std::string& device = "cpu");
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int main() {
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InferScrfd("npu");
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return 0;
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}
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fastdeploy::RuntimeOption GetOption(const std::string& device) {
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auto option = fastdeploy::RuntimeOption();
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if (device == "npu") {
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option.UseRKNPU2();
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} else {
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option.UseCpu();
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}
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return option;
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}
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fastdeploy::ModelFormat GetFormat(const std::string& device) {
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auto format = fastdeploy::ModelFormat::ONNX;
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if (device == "npu") {
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format = fastdeploy::ModelFormat::RKNN;
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} else {
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format = fastdeploy::ModelFormat::ONNX;
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}
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return format;
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}
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std::string GetModelPath(std::string& model_path, const std::string& device) {
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if (device == "npu") {
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model_path += "rknn";
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} else {
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model_path += "onnx";
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}
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return model_path;
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}
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void InferScrfd(const std::string& device) {
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std::string model_file =
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"./model/scrfd_500m_bnkps_shape640x640_rk3588.";
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std::string params_file;
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fastdeploy::RuntimeOption option = GetOption(device);
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fastdeploy::ModelFormat format = GetFormat(device);
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model_file = GetModelPath(model_file, device);
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auto model = fastdeploy::vision::facedet::SCRFD(
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model_file, params_file, option, format);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto image_file =
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"./images/test_lite_face_detector_3.jpg";
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auto im = cv::imread(image_file);
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if (device == "npu") {
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model.DisableNormalizeAndPermute();
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}
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fastdeploy::vision::FaceDetectionResult res;
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clock_t start = clock();
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if (!model.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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clock_t end = clock();
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auto dur = static_cast<double>(end - start);
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printf("InferScrfd use time:%f\n",
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(dur / CLOCKS_PER_SEC));
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std::cout << res.Str() << std::endl;
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auto vis_im = fastdeploy::vision::Visualize::VisFaceDetection(im, res);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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32
examples/vision/facedet/scrfd/rknpu2/python/README.md
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32
examples/vision/facedet/scrfd/rknpu2/python/README.md
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# SCRFD Python部署示例
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在部署前，需确认以下两个步骤
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|
||||||
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/rknpu2.md)
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本目录下提供`infer.py`快速完成SCRFD在RKNPU上部署的示例。执行如下脚本即可完成
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```bash
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/facedet/scrfd/rknpu2/python
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# 下载图片
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wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
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# 推理
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python3 infer.py --model_file ./scrfd_500m_bnkps_shape640x640_rk3588.rknn \
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--image test_lite_face_detector_3.jpg
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```
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|
|
||||||
|
|
||||||
|
## 注意事项
|
||||||
|
RKNPU上对模型的输入要求是使用NHWC格式，且图片归一化操作会在转RKNN模型时，内嵌到模型中，因此我们在使用FastDeploy部署时，
|
||||||
|
需要先调用DisableNormalizePermute(C++)或`disable_normalize_permute(Python)，在预处理阶段禁用归一化以及数据格式的转换。
|
||||||
|
## 其它文档
|
||||||
|
|
||||||
|
- [SCRFD 模型介绍](../README.md)
|
||||||
|
- [SCRFD C++部署](../cpp/README.md)
|
||||||
|
- [模型预测结果说明](../../../../../../docs/api/vision_results/README.md)
|
||||||
|
- [转换SCRFD RKNN模型文档](../README.md)
|
||||||
58
examples/vision/facedet/scrfd/rknpu2/python/infer.py
Normal file
58
examples/vision/facedet/scrfd/rknpu2/python/infer.py
Normal file
@@ -0,0 +1,58 @@
|
|||||||
|
# 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.
|
||||||
|
import fastdeploy as fd
|
||||||
|
import cv2
|
||||||
|
import os
|
||||||
|
|
||||||
|
|
||||||
|
def parse_arguments():
|
||||||
|
import argparse
|
||||||
|
import ast
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument(
|
||||||
|
"--model_file", required=True, help="Path of FaceDet model.")
|
||||||
|
parser.add_argument(
|
||||||
|
"--image", type=str, required=True, help="Path of test image file.")
|
||||||
|
return parser.parse_args()
|
||||||
|
|
||||||
|
|
||||||
|
def build_option(args):
|
||||||
|
option = fd.RuntimeOption()
|
||||||
|
option.use_rknpu2()
|
||||||
|
return option
|
||||||
|
|
||||||
|
|
||||||
|
args = parse_arguments()
|
||||||
|
|
||||||
|
# 配置runtime，加载模型
|
||||||
|
runtime_option = build_option(args)
|
||||||
|
model_file = args.model_file
|
||||||
|
params_file = ""
|
||||||
|
model = fd.vision.facedet.SCRFD(
|
||||||
|
model_file,
|
||||||
|
params_file,
|
||||||
|
runtime_option=runtime_option,
|
||||||
|
model_format=fd.ModelFormat.RKNN)
|
||||||
|
|
||||||
|
model.disable_normalize_and_permute()
|
||||||
|
|
||||||
|
# 预测图片分割结果
|
||||||
|
im = cv2.imread(args.image)
|
||||||
|
result = model.predict(im.copy())
|
||||||
|
print(result)
|
||||||
|
|
||||||
|
# 可视化结果
|
||||||
|
vis_im = fd.vision.vis_face_detection(im, result)
|
||||||
|
cv2.imwrite("visualized_result.jpg", vis_im)
|
||||||
|
print("Visualized result save in ./visualized_result.jpg")
|
||||||
@@ -68,6 +68,7 @@ SCRFD::SCRFD(const std::string& model_file, const std::string& params_file,
|
|||||||
} else {
|
} else {
|
||||||
valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
|
valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
|
||||||
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
||||||
|
valid_rknpu_backends = {Backend::RKNPU2};
|
||||||
}
|
}
|
||||||
runtime_option = custom_option;
|
runtime_option = custom_option;
|
||||||
runtime_option.model_format = model_format;
|
runtime_option.model_format = model_format;
|
||||||
@@ -135,19 +136,22 @@ bool SCRFD::Preprocess(Mat* mat, FDTensor* output,
|
|||||||
is_scale_up, stride);
|
is_scale_up, stride);
|
||||||
|
|
||||||
BGR2RGB::Run(mat);
|
BGR2RGB::Run(mat);
|
||||||
// Normalize::Run(mat, std::vector<float>(mat->Channels(), 0.0),
|
if(!this->disable_normalize_and_permute_){
|
||||||
// std::vector<float>(mat->Channels(), 1.0));
|
// Normalize::Run(mat, std::vector<float>(mat->Channels(), 0.0),
|
||||||
// Compute `result = mat * alpha + beta` directly by channel
|
// std::vector<float>(mat->Channels(), 1.0));
|
||||||
// Original Repo/tools/scrfd.py: cv2.dnn.blobFromImage(img, 1.0/128,
|
// Compute `result = mat * alpha + beta` directly by channel
|
||||||
// input_size, (127.5, 127.5, 127.5), swapRB=True)
|
// Original Repo/tools/scrfd.py: cv2.dnn.blobFromImage(img, 1.0/128,
|
||||||
std::vector<float> alpha = {1.f / 128.f, 1.f / 128.f, 1.f / 128.f};
|
// input_size, (127.5, 127.5, 127.5), swapRB=True)
|
||||||
std::vector<float> beta = {-127.5f / 128.f, -127.5f / 128.f, -127.5f / 128.f};
|
std::vector<float> alpha = {1.f / 128.f, 1.f / 128.f, 1.f / 128.f};
|
||||||
Convert::Run(mat, alpha, beta);
|
std::vector<float> beta = {-127.5f / 128.f, -127.5f / 128.f, -127.5f / 128.f};
|
||||||
|
Convert::Run(mat, alpha, beta);
|
||||||
|
HWC2CHW::Run(mat);
|
||||||
|
Cast::Run(mat, "float");
|
||||||
|
}
|
||||||
|
|
||||||
// Record output shape of preprocessed image
|
// Record output shape of preprocessed image
|
||||||
(*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
|
(*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
|
||||||
static_cast<float>(mat->Width())};
|
static_cast<float>(mat->Width())};
|
||||||
HWC2CHW::Run(mat);
|
|
||||||
Cast::Run(mat, "float");
|
|
||||||
mat->ShareWithTensor(output);
|
mat->ShareWithTensor(output);
|
||||||
output->shape.insert(output->shape.begin(), 1); // reshape to n, h, w, c
|
output->shape.insert(output->shape.begin(), 1); // reshape to n, h, w, c
|
||||||
return true;
|
return true;
|
||||||
@@ -347,7 +351,9 @@ bool SCRFD::Predict(cv::Mat* im, FaceDetectionResult* result,
|
|||||||
}
|
}
|
||||||
return true;
|
return true;
|
||||||
}
|
}
|
||||||
|
void SCRFD::DisableNormalizeAndPermute(){
|
||||||
|
this->disable_normalize_and_permute_ = true;
|
||||||
|
}
|
||||||
} // namespace facedet
|
} // namespace facedet
|
||||||
} // namespace vision
|
} // namespace vision
|
||||||
} // namespace fastdeploy
|
} // namespace fastdeploy
|
||||||
@@ -89,6 +89,9 @@ class FASTDEPLOY_DECL SCRFD : public FastDeployModel {
|
|||||||
*/
|
*/
|
||||||
unsigned int num_anchors;
|
unsigned int num_anchors;
|
||||||
|
|
||||||
|
/// This function will disable normalize and hwc2chw in preprocessing step.
|
||||||
|
void DisableNormalizeAndPermute();
|
||||||
|
|
||||||
private:
|
private:
|
||||||
bool Initialize();
|
bool Initialize();
|
||||||
|
|
||||||
@@ -117,6 +120,9 @@ class FASTDEPLOY_DECL SCRFD : public FastDeployModel {
|
|||||||
} SCRFDPoint;
|
} SCRFDPoint;
|
||||||
|
|
||||||
std::unordered_map<int, std::vector<SCRFDPoint>> center_points_;
|
std::unordered_map<int, std::vector<SCRFDPoint>> center_points_;
|
||||||
|
|
||||||
|
// for recording the switch of normalize and hwc2chw
|
||||||
|
bool disable_normalize_and_permute_ = false;
|
||||||
};
|
};
|
||||||
} // namespace facedet
|
} // namespace facedet
|
||||||
} // namespace vision
|
} // namespace vision
|
||||||
|
|||||||
@@ -28,6 +28,7 @@ void BindSCRFD(pybind11::module& m) {
|
|||||||
self.Predict(&mat, &res, conf_threshold, nms_iou_threshold);
|
self.Predict(&mat, &res, conf_threshold, nms_iou_threshold);
|
||||||
return res;
|
return res;
|
||||||
})
|
})
|
||||||
|
.def("disable_normalize_and_permute",&vision::facedet::SCRFD::DisableNormalizeAndPermute)
|
||||||
.def_readwrite("size", &vision::facedet::SCRFD::size)
|
.def_readwrite("size", &vision::facedet::SCRFD::size)
|
||||||
.def_readwrite("padding_value", &vision::facedet::SCRFD::padding_value)
|
.def_readwrite("padding_value", &vision::facedet::SCRFD::padding_value)
|
||||||
.def_readwrite("is_mini_pad", &vision::facedet::SCRFD::is_mini_pad)
|
.def_readwrite("is_mini_pad", &vision::facedet::SCRFD::is_mini_pad)
|
||||||
@@ -41,6 +42,7 @@ void BindSCRFD(pybind11::module& m) {
|
|||||||
.def_readwrite("num_anchors", &vision::facedet::SCRFD::num_anchors)
|
.def_readwrite("num_anchors", &vision::facedet::SCRFD::num_anchors)
|
||||||
.def_readwrite("landmarks_per_face",
|
.def_readwrite("landmarks_per_face",
|
||||||
&vision::facedet::SCRFD::landmarks_per_face);
|
&vision::facedet::SCRFD::landmarks_per_face);
|
||||||
|
|
||||||
}
|
}
|
||||||
|
|
||||||
} // namespace fastdeploy
|
} // namespace fastdeploy
|
||||||
|
|||||||
@@ -51,6 +51,12 @@ class SCRFD(FastDeployModel):
|
|||||||
return self._model.predict(input_image, conf_threshold,
|
return self._model.predict(input_image, conf_threshold,
|
||||||
nms_iou_threshold)
|
nms_iou_threshold)
|
||||||
|
|
||||||
|
def disable_normalize_and_permute(self):
|
||||||
|
"""
|
||||||
|
This function will disable normalize and hwc2chw in preprocessing step.
|
||||||
|
"""
|
||||||
|
self._model.disable_normalize_and_permute()
|
||||||
|
|
||||||
# 一些跟SCRFD模型有关的属性封装
|
# 一些跟SCRFD模型有关的属性封装
|
||||||
# 多数是预处理相关，可通过修改如model.size = [640, 640]改变预处理时resize的大小（前提是模型支持）
|
# 多数是预处理相关，可通过修改如model.size = [640, 640]改变预处理时resize的大小（前提是模型支持）
|
||||||
@property
|
@property
|
||||||
|
|||||||
7
tools/rknpu2/config/RK3568/scrfd.yaml
Normal file
7
tools/rknpu2/config/RK3568/scrfd.yaml
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
model_path: ./scrfd_500m_bnkps_shape640x640.onnx
|
||||||
|
output_folder: ./
|
||||||
|
target_platform: RK3568
|
||||||
|
normalize:
|
||||||
|
mean: [[0.5,0.5,0.5]]
|
||||||
|
std: [[0.5,0.5,0.5]]
|
||||||
|
outputs: None
|
||||||
7
tools/rknpu2/config/RK3588/scrfd.yaml
Normal file
7
tools/rknpu2/config/RK3588/scrfd.yaml
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
model_path: ./scrfd_500m_bnkps_shape640x640.onnx
|
||||||
|
output_folder: ./
|
||||||
|
target_platform: RK3588
|
||||||
|
normalize:
|
||||||
|
mean: [[0.5,0.5,0.5]]
|
||||||
|
std: [[0.5,0.5,0.5]]
|
||||||
|
outputs: None
|
||||||
Reference in New Issue
Block a user