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English | 简体中文

PIPNet 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PIPNet在CPU/GPU，以及GPU上通过TensorRT加速部署的示例，保证 FastDeploy 版本 >= 0.7.0 支持PIPNet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/pipnet/python

# 下载PIPNet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/pipnet_resnet18_10x19x32x256_aflw.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png

# CPU推理
python infer.py --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device cpu
# GPU推理
python infer.py --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device gpu
# TRT推理
python infer.py --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device gpu --backend trt

运行完成可视化结果如下图所示

PIPNet Python接口

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

PIPNet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

PIPNet.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

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