How To Run The Project:

Step 1: Open Docker container

NOTE: If you have a compatible Nvidia graphics card with CUDA support, you may install the GPU docker. Remember to run the GPU docker with replacement of vitis-ai-gpu:latest.

$ cd {VITIS_AI_PATH}
$ sudo chmod 666 /var/run/docker.sock
$ ./docker_run.sh --device /dev/video0 xilinx/vitis-ai-cpu:latest

Step 2: Quantize model with Vitis-AI:

1. Quantization

• Using a subset (70 images) of validation data for calibration.

$ python model_quant.py --quant_mode calib --subset_len 70

2. Export xmodel

$ python model_quant.py --quant_mode test --subset_len 1 --batch_size 1  --deploy

Step 3: Setup environments:

1. Setup VCK5000

$ cd setup/vck5000
$ source ./setup.sh

2. Conda Pytorch enviroments

$ conda activate vitis-ai-pytorch
$ source ./setup.sh

3. Check DPU

$ sudo chmod o=rw /dev/dri/render*
$ xdputil query

Step 4: Vitis-AI compilation:

$ cd /workspace/
$ vai_c_xir -x HarDMSEG_int.xmodel -a arch.json -o ./ -n dpu_HarDMSEG

Step 5: Install necesarry package:

These packages are for showing the windows on local screens.

$ export DISPLAY=":0"
$ sudo apt update
$ sudo apt-get install libcanberra-gtk-module libcanberra-gtk3-module

Step 6: Demo:

Note: At most 8 videos are supported due to the limitation of DPU.

$ cd {FOLDER_PATH}
$ bash -x build.sh
$ ./{FOLDER_NAME} dpu_HarDMSEG.xmodel {VIDEO_PATH1} {VIDEO_PATH2} {VIDEO_PATH3} {VIDEO_PATH4}

Result

• Use only one CPU : Intel® Core™ i7-3770