mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 03:46:40 +08:00
adddd3c452
* update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * Add RetinaFace Model support * fixed retinaface/api.md typos
RetinaFace部署示例
当前支持模型版本为：RetinaFace CommitID:b984b4b
本文档说明如何进行RetinaFace的快速部署推理。本目录结构如下
.
├── cpp # C++ 代码目录
│ ├── CMakeLists.txt # C++ 代码编译CMakeLists文件
│ ├── README.md # C++ 代码编译部署文档
│ └── retinaface.cc # C++ 示例代码
├── api.md # API 说明文档
├── README.md # RetinaFace 部署文档
└── retinaface.py # Python示例代码
安装FastDeploy
使用如下命令安装FastDeploy，注意到此处安装的是vision-cpu，也可根据需求安装vision-gpu
# 安装fastdeploy-python工具
pip install fastdeploy-python
# 安装vision-cpu模块
fastdeploy install vision-cpu
Python部署
执行如下代码即会自动下载RetinaFace模型和测试图片
python retinaface.py
手动获取ONNX模型文件
自动下载的模型文件是我们事先转换好的，如果您需要从RetinaFace官方repo导出ONNX，请参考以下步骤。
- 下载官方仓库并
git clone https://github.com/biubug6/Pytorch_Retinaface.git
- 下载预训练权重并放在weights文件夹
./weights/
mobilenet0.25_Final.pth
mobilenetV1X0.25_pretrain.tar
Resnet50_Final.pth
- 运行convert_to_onnx.py导出ONNX模型文件
PYTHONPATH=. python convert_to_onnx.py --trained_model ./weights/mobilenet0.25_Final.pth --network mobile0.25 --long_side 640 --cpu
PYTHONPATH=. python convert_to_onnx.py --trained_model ./weights/Resnet50_Final.pth --network resnet50 --long_side 640 --cpu
注意：需要先对convert_to_onnx.py脚本中的--long_side参数增加类型约束，type=int.
- 使用onnxsim对模型进行简化
onnxsim FaceDetector.onnx Pytorch_RetinaFace_mobile0.25-640-640.onnx # mobilenet
onnxsim FaceDetector.onnx Pytorch_RetinaFace_resnet50-640-640.onnx # resnet50
执行完成后会将可视化结果保存在本地vis_result.jpg，同时输出检测结果如下
FaceDetectionResult: [xmin, ymin, xmax, ymax, score, (x, y) x 5]
403.339783,254.192413, 490.002747, 351.931213, 0.999427, (425.657257,293.820740), (467.249451,293.667267), (446.830078,315.016388), (428.903381,326.129425), (465.764648,325.837341)
296.834564,181.992035, 384.516876, 277.461243, 0.999194, (313.605164,224.800110), (352.888977,219.088043), (333.530182,239.872787), (325.395203,255.463852), (358.417175,250.529892)
742.206238,263.547424, 840.871765, 366.171387, 0.999068, (762.715759,308.939880), (809.019653,304.544830), (786.174194,329.286163), (771.952271,341.376038), (812.717529,337.528839)
545.351685,228.015930, 635.423584, 335.458649, 0.998681, (559.295654,269.971619), (598.439758,273.823608), (567.496643,292.894348), (558.160034,306.637238), (592.175781,309.493591)
180.078125,241.787888, 257.213135, 320.321777, 0.998342, (203.702591,272.032715), (237.497726,271.356445), (222.380402,288.225708), (208.015259,301.360352), (233.943451,300.801636)