4.1.3. PTQ Model Conversion Samples Guide

Tip

1. Before using this manual, we recommend you to read the Toolchain Overview and PTQ Principle And Steps to get a full understanding of the PTQ model conversion scheme (hereafter referred to as the PTQ scheme).

2. If you want to know how to use the PTQ floating point and fixed point model conversion scheme tool when you perform a custom model conversion. Please read: PTQ Tools Guide.

3. fter using this guide to complete the model conversion of the PTQ sample package, please continue to use AI_Benchmark Doc and BPU SDK API Doc for embedded development work and deploy the model on the board.

The PTQ sample package helps developers to quickly get started with the PTQ scheme. Using the PTQ sample package, developers can not only experience converting a 32-bit floating-point (FLOAT32) precision sample model to an 8-bit fixed-point integer (INT8) precision bin model that can run on the D-Robotics X3 platform, but also learn how to use the tools provided by the PTQ solution and design custom model conversion business logic scripts based on the sample scripts.

This guide is intended to show developers how to use the D-Robotics X3 algorithm toolchain’s PTQ model transformation sample package (hereafter referred to as the PTQ sample package). You can obtain the PTQ sample package in horizon_xj3_open_explorer at ddk/samples/ai_toolchain/horizon_model_convert_sample.

The next chapter will introduce you the contents of the PTQ sample package.

• 4.1.3.1. Release Introduction
  • 4.1.3.1.1. Model Quantification Example Package: horizon_model_convert_sample
  • 4.1.3.1.2. Model Release: model_zoo
• 4.1.3.2. Common Algorithm Model Samples
  • 4.1.3.2.1. Where to Find
  • 4.1.3.2.2. Prepare Dataset
    • 4.1.3.2.2.1. Dataset Download Address
    • 4.1.3.2.2.2. Dataset Reference Structure
      • 4.1.3.2.2.2.1. ImageNet
      • 4.1.3.2.2.2.2. COCO
      • 4.1.3.2.2.2.3. VOC
      • 4.1.3.2.2.2.4. Cityscapes
      • 4.1.3.2.2.2.5. CIFAR-10
  • 4.1.3.2.3. Prepare Models
    • 4.1.3.2.3.1. Fcos_efficientnetb0
    • 4.1.3.2.3.2. Fcos_efficientnetb1
    • 4.1.3.2.3.3. Fcos_efficientnetb2
    • 4.1.3.2.3.4. MobileNetv1/v2
    • 4.1.3.2.3.5. GoogleNet
    • 4.1.3.2.3.6. ResNet18
    • 4.1.3.2.3.7. Mobilenet_onnx
    • 4.1.3.2.3.8. EfficientNet_Lite0/1/2/3/4
    • 4.1.3.2.3.9. YOLOv2_Darknet19
    • 4.1.3.2.3.10. YOLOv3_Darknet53
    • 4.1.3.2.3.11. YOLOv5s
    • 4.1.3.2.3.12. SSD_MobileNetv1
    • 4.1.3.2.3.13. EfficientDetd0
    • 4.1.3.2.3.14. CenterNet_Resnet50
    • 4.1.3.2.3.15. Fcos_efficientnetb0
    • 4.1.3.2.3.16. UNet_mobilenet
    • 4.1.3.2.3.17. DeeplabV3plus_efficientnetb0
    • 4.1.3.2.3.18. Fastscnn_efficientnetb0
  • 4.1.3.2.4. A Demonstration of the Algorithm Model Samples
    • 4.1.3.2.4.1. Docker Container Prep
    • 4.1.3.2.4.2. Obtain the Original Model and Calibration Dataset
    • 4.1.3.2.4.3. Check If the Model is Executable
    • 4.1.3.2.4.4. Prepare Calibration Dataset
    • 4.1.3.2.4.5. Building Heterogeneous Models
    • 4.1.3.2.4.6. Single Image Inference
    • 4.1.3.2.4.7. Accuracy Evaluation
  • 4.1.3.2.5. FAQ
    • 4.1.3.2.5.1. How to match the inference results of the ONNX original floating-point model and the hb_mapper makertbin tool generated *_original_float_model.onnx model?
    • 4.1.3.2.5.2. Why is the reproduced accuracy slightly different from the indicators in the documentation?
    • 4.1.3.2.5.3. Why is the fixed-point model accuracy not aligned with the on-board accuracy of the bin file in the ai_benchmark example?
• 4.1.3.3. Other Algorithm Model Samples
  • 4.1.3.3.1. How to Use
  • 4.1.3.3.2. Miscellaneous Samples
    • 4.1.3.3.2.1. lenet_gray
      • 4.1.3.3.2.1.1. Directory
      • 4.1.3.3.2.1.2. Description
    • 4.1.3.3.2.2. resnet50_feature
      • 4.1.3.3.2.2.1. Directory
      • 4.1.3.3.2.2.2. Description
    • 4.1.3.3.2.3. vector-diff
      • 4.1.3.3.2.3.1. Directory
      • 4.1.3.3.2.3.2. Output List
    • 4.1.3.3.2.4. multi_input_example
      • 4.1.3.3.2.4.1. Directory
      • 4.1.3.3.2.4.2. Description
    • 4.1.3.3.2.5. model_verifier
      • 4.1.3.3.2.5.1. Directory
      • 4.1.3.3.2.5.2. Description
    • 4.1.3.3.2.6. model_info
      • 4.1.3.3.2.6.1. Directory
      • 4.1.3.3.2.6.2. Description
    • 4.1.3.3.2.7. mobilenet_bgr
      • 4.1.3.3.2.7.1. Directory
      • 4.1.3.3.2.7.2. Description
    • 4.1.3.3.2.8. mobilenet_yuv444
      • 4.1.3.3.2.8.1. Directory
      • 4.1.3.3.2.8.2. Description
  • 4.1.3.3.3. User Custom OP Sample
    • 4.1.3.3.3.1. Directory
    • 4.1.3.3.3.2. Description