FastDeploy/examples/vision/faceid/insightface/python/README.md

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InsightFace Python Deployment Example

This directory provides examples that infer_xxx.py fast finishes the deployment of InsighFace, including ArcFace\CosFace\VPL\Partial_FC 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. Install FastDeploy Python whl package. Refer to FastDeploy Python Installation

Taking ArcFace as an example, we demonstrate how infer_arcface.py fast finishes the deployment of ArcFace on CPU/GPU and GPU accelerated by TensorRT. The script is as follows

# Download the example code for deployment
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/faceid/insightface/python/

# Download ArcFace model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r100.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/face_demo.zip
unzip face_demo.zip

# CPU inference
python infer_arcface.py --model ms1mv3_arcface_r100.onnx \
                        --face face_0.jpg \
                        --face_positive face_1.jpg \
                        --face_negative face_2.jpg \
                        --device cpu
# GPU inference
python infer_arcface.py --model ms1mv3_arcface_r100.onnx \
                        --face face_0.jpg \
                        --face_positive face_1.jpg \
                        --face_negative face_2.jpg \
                        --device gpu
# TensorRT inference on GPU 
python infer_arcface.py --model ms1mv3_arcface_r100.onnx \
                        --face face_0.jpg \
                        --face_positive face_1.jpg \
                        --face_negative face_2.jpg \
                        --device gpu \
                        --use_trt True

The visualized result after running is as follows

Prediction Done!
--- [Face 0]:FaceRecognitionResult: [Dim(512), Min(-2.309220), Max(2.372197), Mean(0.016987)]
--- [Face 1]:FaceRecognitionResult: [Dim(512), Min(-2.288258), Max(1.995104), Mean(-0.003400)]
--- [Face 2]:FaceRecognitionResult: [Dim(512), Min(-3.243411), Max(3.875866), Mean(-0.030682)]
Detect Done! Cosine 01: 0.814385, Cosine 02:-0.059388

InsightFace Python Interface

fastdeploy.vision.faceid.ArcFace(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
fastdeploy.vision.faceid.CosFace(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
fastdeploy.vision.faceid.PartialFC(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
fastdeploy.vision.faceid.VPL(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)

ArcFace 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

ArcFace.predict(image_data)

Model prediction interface. Input images and output detection results.

Parameter

• image_data(np.ndarray): Input data in HWC or BGR format

Return

Return fastdeploy.vision.FaceRecognitionResult structure. Refer to Vision Model Prediction Results for its description.

Class Member Property

Pre-processing Parameter

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

Member variables of AdaFacePreprocessor

Member variables of AdaFacePreprocessor are as follows

• size(list[int]): This parameter changes the size of the resize during preprocessing, containing two integer elements for [width, height] with default value [112, 112]
• alpha(list[float]): Preprocess normalized alpha, and calculated as x'=x*alpha+beta. alpha defaults to [1. / 127.5, 1.f / 127.5, 1. / 127.5]
• beta(list[float]): Preprocess normalized beta, and calculated as x'=x*alpha+beta，beta defaults to [-1.f, -1.f, -1.f]

Member variables of AdaFacePostprocessor

Member variables of AdaFacePostprocessor are as follows

• l2_normalize(bool): Whether to perform l2 normalization before outputting the face vector. Default False.

Other Documents

• InsightFace Model Description
• InsightFace C++ Deployment
• Model Prediction Results
• How to switch the model inference backend engine