Developing with User Model and AI Library API_2

Developing with User Model and AI Library API_2 - 1.4 English

Vitis AI Library User Guide (UG1354)

Document ID

UG1354

Release Date

2021-07-22

Version

1.4 English

When you use your own models, it is important to note that your model framework should be within the scope supported by the Vitis AI library. The following is an introduction of how to deploy a retrained YOLOv3 Caffe model to ZCU102 platform based on Vitis AI library step by step.

Download the corresponding docker image from https://github.com/Xilinx/Vitis-AI.
Load and run the docker.
Create a folder and place the float model under it on the host side, then use AI Quantizer tool to do the quantization. For more details, see Vitis AI User Guide in the Vitis AI User Documentation (UG1431).
Use AI Compiler tool to do the model compiling to get the xmodel file, such as yolov3_custom.xmodel. For more information, see Vitis AI User Guide in the Vitis AI User Documentation (UG1431).

Create the yolov3_custom.prototxt, as shown in the following.

model {
  name: "yolov3_custom"
  kernel {
     name: "yolov3_custom"
     mean: 0.0
     mean: 0.0
     mean: 0.0
     scale: 0.00390625
     scale: 0.00390625
     scale: 0.00390625
  }
  model_type : YOLOv3
  yolo_v3_param {
    num_classes: 20
    anchorCnt: 3
    layer_name: "59"
    layer_name: "67"
    layer_name: "75"
    conf_threshold: 0.3
    nms_threshold: 0.45
    biases: 10
    biases: 13
    biases: 16
    biases: 30
    biases: 33
    biases: 23
    biases: 30
    biases: 61
    biases: 62
    biases: 45
    biases: 59
    biases: 119
    biases: 116
    biases: 90
    biases: 156
    biases: 198
    biases: 373
    biases: 326
    test_mAP: false
  }
}

Note: The <model_name>.prototxt is effective only when you use AI Library API_1.

When you use AI Library API_2, the parameter of the model needs to be loaded and read manually by the program. See the Vitis-AI/demo/Vitis-AI-Library/samples/dpu_task/yolov3/demo_yolov3.cpp for details.

Create the demo_yolov3.cpp file. See Vitis-AI/demo/Vitis-AI-Library/samples/dpu_task/yolov3/demo_yolov3.cpp for reference.

Create a build.sh file as shown below, or copy one from the Vitis AI library demo and modify it.

#/bin/sh
CXX=${CXX:-g++}
$CXX -std=c++17 -O3 -I. -o demo_yolov3 demo_yolov3.cpp -lopencv_core -lopencv_video -lopencv_videoio -lopencv_imgproc -lopencv_imgcodecs -lopencv_highgui -lglog -lxnnpp-xnnpp -lvitis_ai_library-model_config -lprotobuf -lvitis_ai_library-dpu_task

Exit the docker tool system and start the docker runtime system.
Cross compile the program and generate executable file demo_yolov3.
```
sh -x build.sh
```
Create model folder under /usr/share/vitis_ai_library/models on the target side.
```
mkdir yolov3_custom /usr/share/vitis_ai_library/models
```
Note: /usr/share/vitis_ai_library/models is the default location for the program to read the model. You can also place the model folder in the same directory as the executable program.
Copy the yolov3_custom.xmodel and yolov3_custom.prototxt to the target and put them under /usr/share/vitis_ai_library/models/yolov3_custom.
```
scp yolov3_custom.xmodel  yolov3_custom.prototxt root@IP_OF_BOARD:/usr/share/vitis_ai_library/models/yolov3_custom
```
Copy the executable program to the target board using scp.
```
scp demo_yolov3 root@IP_OF_BOARD:~/
```
Execute the program on the target board and get the following results. Before running the program, make sure the target board has the Vitis AI library installed, and prepare the images you want to test.
```
./demo_yolov3 yolov3_custom sample.jpg 
```