Using the AI Library's Post-Processing Library - 1.4.1 English

Post-processing is an important step in the whole process. Each neural network has different post-processing methods. The xnnpp post-processing library is provided in Vitis AI Library to facilitate user calls. It is a closed source library. It supports the following neural network post-processing.

• Classification
• Face detection
• Face landmark detection
• SSD detection
• Pose detection
• Semantic segmentation
• Road line detection
• YOLOv3 detection
• YOLOv2 detection
• Openpose detection
• RefineDet detection
• ReID detection
• Multi-task
• Face recognition
• Plate detection
• Plate recognition
• Medical segmentation
• Medical detection
• Face quality
• Hourglass
• Retinaface
• Centerpoint
• Multitaskv3
• Pointpillars_nuscenes
• Rcan

There are two ways to call xnnpp:

• One is an automatic call, through vitis::ai::<model>::create, to create the task, such as vitis::ai::YOLOv3::create("yolov3_bdd", true). After <model>->run finished, xnnpp is automatically processed. You can modify the parameters through the model configuration file.
• One is a manual call, through vitis::ai::DpuTask::create, to create the task. Then, create the object of the post-process and run the post-process. Take SSD post-processing as an example, the specific steps are as follows:
  1. Create a configuration and set the correlating data to control post-process.

     using DPU_conf = vitis::ai::proto::DpuModelParam;
     DPU_conf config;

  2. If it is a caffemodel, set the "is_tf" as false.

     config.set_is_tf(false);

  3. Fill the other parameters.

     fillconfig(config);

  4. Create an object of SSDPostProcess.

     auto input_tensor = task->getInputTensor();
     auto output_tensor = task->getOutputTensor();
     auto ssd = vitis::ai::SSDPostProcess::create(input_tensor, output_tensor,config);

  5. Run the post-process.

     auto results = ssd->ssd_post_process();