FastDeploy/examples/vision/facedet/retinaface/python

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RetinaFace Python Deployment Example

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

This directory provides examples that infer.py fast finishes the deployment of RetinaFace 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//retinaface/python/

# Download retinaface model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/Pytorch_RetinaFace_mobile0.25-640-640.onnx
wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg

# CPU inference
python infer.py --model Pytorch_RetinaFace_mobile0.25-640-640.onnx --image test_lite_face_detector_3.jpg --device cpu
# GPU inference
python infer.py --model Pytorch_RetinaFace_mobile0.25-640-640.onnx --image test_lite_face_detector_3.jpg --device gpu
# TensorRT inference on GPU 
python infer.py --model Pytorch_RetinaFace_mobile0.25-640-640.onnx --image test_lite_face_detector_3.jpg --device gpu --use_trt True

The visualized result after running is as follows

RetinaFace Python Interface

fastdeploy.vision.facedet.RetinaFace(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)

RetinaFace 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

RetinaFace.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)

Model prediction interface. Input images and output detection results.

Parameter

• image_data(np.ndarray): Input data in HWC or BGR format
• conf_threshold(float): Filtering threshold of detection box confidence
• nms_iou_threshold(float): iou threshold during NMS processing

Return

Return fastdeploy.vision.FaceDetectionResult 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

• size(list[int]): This parameter changes the size of the resize used during preprocessing, containing two integer elements for [width, height] with default value [640, 640]
• variance(list[float]): Specify the variance value in retinaface. Default [0.1,0.2]. Normally without modification.
• min_sizes(list[list[int]]): Set width and height of anchor in retinaface. Default {{16, 32}, {64, 128}, {256, 512}}, corresponding to the step size 8, 16 and 32
• downsample_strides(list[int]): This parameter is used to change the down-sampling multiple of the feature map that generates anchor, containing three integer elements that represent the default down-sampling multiple for generating anchor. Default value [8, 16, 32]
• landmarks_per_face(int): Specify the number of keypoints in the face detected. Default 5.

Other Documents

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