FastDeploy/examples/vision/facealign/pipnet/cpp

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PIPNet C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of PIPNet on CPU/GPU and GPU accelerated by TensorRT.

Before deployment, two steps require confirmation

• 1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
• 2. Download the precompiled deployment library and samples code according to your development environment. Refer to FastDeploy Precompiled Library

Taking the CPU inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.

mkdir build
cd build
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above 
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j

# Download the official converted PIPNet model files and test images 
wget https://bj.bcebos.com/paddlehub/fastdeploy/pipnet_resnet18_10x19x32x256_aflw.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png

# CPU inference
./infer_demo --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device cpu
# GPU inference
./infer_demo --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device gpu
# TensorRT inference on GPU
./infer_demo --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device gpu --backend trt

The visualized result after running is as follows

The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:

• How to use FastDeploy C++ SDK in Windows

PIPNet C++ Interface

PIPNet Class

fastdeploy::vision::facealign::PIPNet(
        const string& model_file,
        const string& params_file = "",
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& 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. Only passing an empty string 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(cv::Mat* im, FaceAlignmentResult* result)

Model prediction interface. Input images and output landmarks results.

Parameter

• im: Input images in HWC or BGR format
• result: landmarks result. Refer to Vision Model Prediction Results for FaceAlignmentResult

Class Member Variable

Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results

• size(vector<int>): This parameter changes the size of the resize used during preprocessing, containing two integer elements for [width, height] with default value [256, 256]

• Model Description
• Python Deployment
• Vision Model Prediction Results
• How to switch the model inference backend engine