mirror of https://github.com/PaddlePaddle/FastDeploy.git synced 2025-11-01 04:12:58 +08:00

Files

DefTruth adddd3c452 Add RetinaFace Model support (#48 )

* 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

2022-07-28 10:35:27 +08:00

2.8 KiB

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编译RetinaFace示例

当前支持模型版本为：RetinaFace CommitID:b984b4b

下载和解压预测库

wget https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-linux-x64-0.0.3.tgz
tar xvf fastdeploy-linux-x64-0.0.3.tgz

编译示例代码

mkdir build & cd build
cmake ..
make -j

下载模型和图片

wget https://github.com/DefTruth/Pytorch_Retinaface/releases/download/v0.1/Pytorch_RetinaFace_mobile0.25-640-640.onnx
wget https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/raw/master/imgs/3.jpg

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

./retinaface_demo

执行完后可视化的结果保存在本地vis_result.jpg，同时会将检测框输出在终端，如下所示

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)

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