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WJJ1995 7150e6405c [Model] Add FSANet model (#448) 
* add yolov5cls

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* fixed bugs

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* Add YOLOv5Cls Note

* add yolov5cls test

* add rvm support

* support rvm model

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Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
2022-11-04 11:00:35 +08:00
..
infer.py
[Model] Add FSANet model (#448)
2022-11-04 11:00:35 +08:00
README.md
[Model] Add FSANet model (#448)
2022-11-04 11:00:35 +08:00

README.md

PFLD Python部署示例

在部署前，需确认以下两个步骤

本目录下提供infer.py快速完成PFLD在CPU/GPU，以及GPU上通过TensorRT加速部署的示例，保证 FastDeploy 版本 >= 0.6.0 支持PFLD模型。执行如下脚本即可完成

#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/facealign/pfld/python

# 下载PFLD模型文件和测试图片以及视频
## 原版ONNX模型
wget https://bj.bcebos.com/paddlehub/fastdeploy/pfld-106-lite.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png
# CPU推理
python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device cpu
# GPU推理
python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device gpu
# TRT推理
python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device gpu --backend trt

运行完成可视化结果如下图所示

PFLD Python接口

fd.vision.facealign.PFLD(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)

PFLD模型加载和初始化，其中model_file为导出的ONNX模型格式

参数

  • model_file(str): 模型文件路径
  • params_file(str): 参数文件路径，当模型格式为ONNX格式时，此参数无需设定
  • runtime_option(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
  • model_format(ModelFormat): 模型格式，默认为ONNX

predict函数

PFLD.predict(input_image)

模型预测结口，输入图像直接输出landmarks坐标结果。

参数

  • input_image(np.ndarray): 输入数据，注意需为HWC，BGR格式

返回

返回fastdeploy.vision.FaceAlignmentResult结构体，结构体说明参考文档视觉模型预测结果

其它文档