English | [简体中文](README_CN.md) # 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 - 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md) - 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md) 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 ```bash # 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
```bash 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 ```python 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 > ```python > 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](../../../../../docs/api/vision_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. ## Other Documents - [AdaFace Model Description](..) - [AdaFace C++ Deployment](../cpp) - [Model Prediction Results](../../../../../docs/api/vision_results/) - [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)