English | [简体中文](README_CN.md) # 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](../../../../../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 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 ```bash # 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
```bash 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 ```python 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 > ```python > 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](../../../../../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 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](../cpp) - [Model Prediction Results](../../../../../docs/api/vision_results/) - [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)