FastDeploy/examples/vision/keypointdetection/tiny_pose/python

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PP-TinyPose Python Deployment Example

Before deployment, two steps require confirmation

• 1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
• 2. Download the precompiled deployment library and samples code according to your development environment. Refer to FastDeploy Precompiled Library

This directory provides the Multi-person keypoint detection in a single image example that pptinypose_infer.py fast finishes the deployment of PP-TinyPose on CPU/GPU and GPU accelerated by TensorRT. The script is as follows

Attention: single model currently only supports single-person keypoint detection in a single image. Therefore, the input image should contain one person only or should be cropped. For multi-person keypoint detection, refer to PP-TinyPose Pipeline

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

# Download PP-TinyPose model files and test images 
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_TinyPose_256x192_infer.tgz
tar -xvf PP_TinyPose_256x192_infer.tgz
wget https://bj.bcebos.com/paddlehub/fastdeploy/hrnet_demo.jpg

# CPU inference
python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device cpu
# GPU inference
python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device gpu
# TensorRT inference on GPU（Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device gpu --use_trt True
# KunlunXin XPU inference
python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device kunlunxin

The visualized result after running is as follows

PP-TinyPose Python Interface

fd.vision.keypointdetection.PPTinyPose(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)

PP-TinyPose model loading and initialization, among which model_file, params_file, and config_file are the Paddle inference files exported from the training model. Refer to Model Export for more information

Parameter

• model_file(str): Model file path
• params_file(str): Parameter file path
• config_file(str): Inference deployment configuration file
• 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

PPTinyPose.predict(input_image)

Model prediction interface. Input images and output detection results.

Parameter

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

Return

Return fastdeploy.vision.KeyPointDetectionResult structure. Refer to Vision Model Prediction Results for the description of the structure.

Class Member Property

Post-processing Parameter

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

• use_dark(bool): • Whether to use DARK for post-processing. Refer to Reference Paper

Other Documents

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