Before running the samples on the cloud, make sure that the Alveo card, such as U50, U50LV, or U280, is installed on the server and the docker system is loaded and running.
If you have downloaded Vitis-AI, entered Vitis-AI directory, and then started Docker.
Thus, VART examples is located in the path of /workspace/demo/VART/ in the docker system.
- Download the vitis_ai_runtime_r1.3.0_image_video.tar.gz package and unzip
it.
tar -xzvf vitis_ai_runtime_r1.3.0_image_video.tar.gz -C /workspace/demo/VART
- Compile the sample, take resnet50 as
an
example.
cd /workspace/demo/VART/resnet50 bash –x build.sh
When the compilation is complete, the executable resnet50 is generated in the current directory.
- Download the model. The download link of the model is described in yaml file
of the model. You can find the yaml file in
Vitis-AI/models/AI-Model-Zoo
and download the model of the corresponding model. Takeresnet50
as an example:wget https://www.xilinx.com/bin/public/openDownload?filename=resnet50-u50-r1.3.0.tar.gz -O resnet50-u50-r1.3.0.tar.gz
- Untar the model on the target and install it.If theThen install the model package.
/usr/share/vitis_ai_library/models
folder does not exist, create it first.sudo mkdir -p /usr/share/vitis_ai_library/models
tar -xzvf resnet50-u50-r1.3.0.tar.gz sudo cp resnet50 /usr/share/vitis_ai_library/models -r
- Run the
sample.
./resnet50 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel
The following table shows the run commands for all the Vitis AI samples in the cloud.
ID | Example Name | Command |
---|---|---|
1 | resnet50 | ./resnet50 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel |
2 | resnet50_mt_py | /usr/bin/python3 resnet50.py 1 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel |
3 | inception_v1_mt_py | /usr/bin/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.mp4 /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.mp4 /usr/share/vitis_ai_library/models/ssd_traffic_pruned_0_9/ssd_traffic_pruned_0_9.xmodel |
6 | adas_detection | ./adas_detection video/adas.avi /usr/share/vitis_ai_library/models/yolov3_adas_pruned_0_9/yolov3_adas_pruned_0_9.xmodel |
7 | segmentation | ./segmentation video/traffic.mp4 /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 |