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PIPNet Python Deployment Example
Before deployment, two steps require confirmation
-
- Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
-
- Install FastDeploy Python whl package. Refer to FastDeploy Python Installation
This directory provides examples that infer.py fast finishes the deployment of PIPNet on CPU/GPU and GPU accelerated by TensorRT. FastDeploy version 0.7.0 or above is required to support this model. The script is as follows
# Download the example code for deployment
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/facealign/pipnet/python
# Download PIPNet model files, test images and videos
## Original ONNX Model
wget https://bj.bcebos.com/paddlehub/fastdeploy/pipnet_resnet18_10x19x32x256_aflw.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png
# CPU inference
python infer.py --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device cpu
# GPU inference
python infer.py --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device gpu
# TRT inference
python infer.py --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device gpu --backend trt
The visualized result after running is as follows
PIPNet Python Interface
fd.vision.facealign.PIPNet(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
PIPNet model loading and initialization, among which model_file is the exported ONNX model format.
Parameter
- model_file(str): Model file path
- params_file(str): Parameter file path. No need to set when the model is in ONNX format
- runtime_option(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
- model_format(ModelFormat): Model format. ONNX format by default
predict function
PIPNet.predict(input_image)Model prediction interface. Input images and output landmarks results.
Parameter
- input_image(np.ndarray): Input data in HWC or BGR format
Return
Return
fastdeploy.vision.FaceAlignmentResultstructure. Refer to Vision Model Prediction Results for the description of the structure.