VART

Vitis AI v1.3

Introduction

This directory contains instructions for running DPUCZDX8G on Zynq Ultrascale+ MPSoC platforms. DPUCZDX8G is a configurable computation engine dedicated for convolutional neural networks. It includes a set of highly optimized instructions, and supports most convolutional neural networks, such as VGG, ResNet, GoogleNet, YOLO, SSD, MobileNet, FPN, and others. With Vitis-AI, Xilinx has integrated all the edge and cloud solutions under a unified API and toolset.

Step1: Setup cross-compiler

1. Download the sdk-2020.2.0.0.sh

2. Install the cross-compilation system environment, follow the prompts to install.

Please install it on your local host linux system, not in the docker system.

./sdk-2020.2.0.0.sh

Note that the ~/petalinux_sdk path is recommended for the installation. Regardless of the path you choose for the installation, make sure the path has read-write permissions. Here we install it under ~/petalinux_sdk.

3. When the installation is complete, follow the prompts and execute the following command.

source ~/petalinux_sdk/environment-setup-aarch64-xilinx-linux

Note that if you close the current terminal, you need to re-execute the above instructions in the new terminal interface.

4. Download the vitis_ai_2020.2-r1.3.2.tar.gz and install it to the petalinux system.

tar -xzvf vitis_ai_2020.2-r1.3.2.tar.gz -C ~/petalinux_sdk/sysroots/aarch64-xilinx-linux

5. Cross compile the sample, take resnet50 as an example.

cd ~/Vitis-AI/demo/VART/resnet50
bash -x build.sh

If the compilation process does not report any error and the executable file resnet50 is generated, the host environment is installed correctly.

Step2: Setup the Target

To improve the user experience, the Vitis AI Runtime packages, VART samples, Vitis-AI-Library samples and models have been built into the board image. Therefore, user does not need to install Vitis AI Runtime packages and model package on the board separately. However, users can still install the model or Vitis AI Runtime on their own image or on the official image by following these steps.

1. Installing a Board Image.

   • Download the SD card system image files from the following links:

     ZCU102

     ZCU104

     Note: The version of the board image should be 2020.2 or above.

   • Use Etcher software to burn the image file onto the SD card.

   • Insert the SD card with the image into the destination board.

   • Plug in the power and boot the board using the serial port to operate on the system.

   • Set up the IP information of the board using the serial port.

   For the details, please refer to Setting Up the Evaluation Board

2. (Optional) Running zynqmp_dpu_optimize.sh to optimize the board setting.

   The script runs automatically after the board boots up with the official image. But you can also download the dpu_sw_optimize.tar.gz from here.

   cd ~/dpu_sw_optimize/zynqmp/
   ./zynqmp_dpu_optimize.sh
   

3. (Optional) How to update Vitis AI Runtime and install them separately.

   If you want to update the Vitis AI Runtime or install them to your custom board image, follow these steps.

   • Download the Vitis AI Runtime 1.3.2.
   • Untar the runtime packet and copy the following folder to the board using scp.

   tar -xzvf vitis-ai-runtime-1.3.2.tar.gz
   scp -r vitis-ai-runtime-1.3.2/aarch64/centos root@IP_OF_BOARD:~/
   

   • Log in to the board using ssh. You can also use the serial port to login.
   • Install the Vitis AI Runtime. Execute the following command.

   cd ~/centos
   bash setup.sh
   

4. (Optional) Download the model. For each model, there will be a yaml file which is used for describe all the details about the model. In the yaml, you will find the model's download links for different platforms. Please choose the corresponding model and download it. Click Xilinx AI Model Zoo to view all the models.

   • Take resnet50 of ZCU102 as an example.

     cd /workspace
     wget https://www.xilinx.com/bin/public/openDownload?filename=resnet50-zcu102_zcu104-r1.3.1.tar.gz -O resnet50-zcu102_zcu104-r1.3.1.tar.gz
   

   • Copy the downloaded file to the board using scp with the following command.

     scp resnet50-zcu102_zcu104-r1.3.1.tar.gz root@IP_OF_BOARD:~/
   

   • Log in to the board (using ssh or serial port) and install the model package.

     tar -xzvf resnet50-zcu102_zcu104-r1.3.1.tar.gz
     cp resnet50 /usr/share/vitis_ai_library/models -r
   

Step3: Run the Vitis AI Examples

1. Download the vitis_ai_runtime_r1.3.x_image_video.tar.gz from host to the target using scp with the following command.

   [Host]$scp vitis_ai_runtime_r1.3.x_image_video.tar.gz root@[IP_OF_BOARD]:~/
   

2. Unzip the vitis_ai_runtime_r1.3.x_image_video.tar.gz package on the target.

   cd ~
   tar -xzvf vitis_ai_runtime_r*1.3*_image_video.tar.gz -C Vitis-AI/demo/VART
   

3. Enter the directory of samples in the target board. Take resnet50 as an example.

   cd ~/Vitis-AI/demo/VART/resnet50
   

4. Run the example.

   ./resnet50 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel
   

   For examples with video input, only webm and raw format are supported by default with the official system image. If you want to support video data in other formats, you need to install the relevant packages on the system.

Launching Commands for VART Samples on edge

No.	Example Name	Command
1	resnet50	./resnet50 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel
2	resnet50_mt_py	python3 resnet50.py 1 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel
3	inception_v1_mt_py	python3 inception_v1.py 1 /usr/share/vitis_ai_library/models/inception_v1_tf/inception_v1_tf.xmodel
4	pose_detection	./pose_detection video/pose.webm /usr/share/vitis_ai_library/models/sp_net/sp_net.xmodel /usr/share/vitis_ai_library/models/ssd_pedestrian_pruned_0_97/ssd_pedestrian_pruned_0_97.xmodel
5	video_analysis	./video_analysis video/structure.webm /usr/share/vitis_ai_library/models/ssd_traffic_pruned_0_9/ssd_traffic_pruned_0_9.xmodel
6	adas_detection	./adas_detection video/adas.webm /usr/share/vitis_ai_library/models/yolov3_adas_pruned_0_9/yolov3_adas_pruned_0_9.xmodel
7	segmentation	./segmentation video/traffic.webm /usr/share/vitis_ai_library/models/fpn/fpn.xmodel
8	squeezenet_pytorch	./squeezenet_pytorch /usr/share/vitis_ai_library/models/squeezenet_pt/squeezenet_pt.xmodel

References

• Vitis AI User Guide