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AdaFace Python Deployment Example

This directory provides examples that infer_xxx.py fast finishes the deployment of AdaFace on CPU/GPU and GPU accelerated by TensorRT.

Before deployment, two steps require confirmation

Taking AdaFace as an example, we demonstrate how infer.py fast finishes the deployment of AdaFace 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/adaface/python/

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

# Run the following code if the model is in Paddle format
wget https://bj.bcebos.com/paddlehub/fastdeploy/mobilefacenet_adaface.tgz
tar zxvf mobilefacenet_adaface.tgz -C ./

# CPU inference
python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
                --params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
                --face face_0.jpg \
                --face_positive face_1.jpg \
                --face_negative face_2.jpg \
                --device cpu
# GPU inference
python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
                --params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
                --face face_0.jpg \
                --face_positive face_1.jpg \
                --face_negative face_2.jpg \
                --device gpu
# TensorRT inference on GPU
python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
                --params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
                --face face_0.jpg \
                --face_positive face_1.jpg \
                --face_negative face_2.jpg \
                --device gpu \
                --use_trt True

# KunlunXin XPU inference
python infer.py --model mobilefacenet_adaface/mobilefacenet_adaface.pdmodel \
                --params_file mobilefacenet_adaface/mobilefacenet_adaface.pdiparams \
                --face test_lite_focal_arcface_0.JPG \
                --face_positive test_lite_focal_arcface_1.JPG \
                --face_negative test_lite_focal_arcface_2.JPG \
                 --device kunlunxin

The visualized result after running is as follows

FaceRecognitionResult: [Dim(512), Min(-0.133213), Max(0.148838), Mean(0.000293)]
FaceRecognitionResult: [Dim(512), Min(-0.102777), Max(0.120130), Mean(0.000615)]
FaceRecognitionResult: [Dim(512), Min(-0.116685), Max(0.142919), Mean(0.001595)]
Cosine 01:  0.7483505506964364
Cosine 02:  -0.09605773855893639

AdaFace Python Interface

fastdeploy.vision.faceid.AdaFace(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.PADDLE)

AdaFace model loading and initialization, among which model_file is the exported ONNX model format or PADDLE static graph 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. Paddle format by default

predict function

AdaFace.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

The 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 alpha, and calculated as x'=x*alpha+beta. beta defaults to [-1.f, -1.f, -1.f]
  • swap_rb(bool): Whether to convert BGR to RGB in pre-processing. Default true

Member variables of AdaFacePostprocessor

The member variables of AdaFacePostprocessor are as follows

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

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