When the model is split into multiple
subgraphs, it cannot be automatically run with API_0, API_1, and API_2. You have to
deploy the model subgraph by subgraph. Graph runner is new API for deploying models in
this release. It converts the model into a single graph and makes deployment easier for
multiple subgraph models.
- Git clone the corresponding Vitis AI Library from https://github.com/Xilinx/Vitis-AI.
- Install the cross-compilation system on the host side, refer to Installation.
- Check the model to see if it has multiple subgraphs. If yes, you can check
whether the ops not supported by DPU are in our supported scope. You can find
the operations supported by the Vitis AI Library in Vitis-AI/tools/Vitis-AI-Library/cpu_task/ops.Note: If the operation is not in the supported list in cpu_task, then you cannot use the graph_runner directly. You may encounter an error when you compile the model. You must first solve it, then add the operation under cpu_task.
- Create the model_test.cpp source file.
The main flow is shown in the following figure. See Vitis-AI/demo/Vitis-AI-Library/samples/graph_runner/platenum_graph_runner/platenum_graph_runner.cpp
for a complete code example.Figure 1. Flow for Developing with Vitis AI API_3
- Create a build.sh file as
shown below, or copy one from the Vitis AI
library demo and modify
it.
result=0 && pkg-config --list-all | grep opencv4 && result=1 if [ $result -eq 1 ]; then OPENCV_FLAGS=$(pkg-config --cflags --libs-only-L opencv4) else OPENCV_FLAGS=$(pkg-config --cflags --libs-only-L opencv) fi CXX=${CXX:-g++} $CXX -std=c++17 -O2 -I. \ -o platenum_graph_runner \ platenum_graph_runner.cpp \ -lglog \ -lxir \ -lvart-runner \ -lvitis_ai_library-graph_runner \ ${OPENCV_FLAGS} \ -lopencv_core \ -lopencv_imgcodecs \ -lopencv_imgproc - Cross compile the
program.
sh -x build.sh - Copy the executable program to the target board using scp.
scp test_model root@IP_OF_BOARD:~/ - Install the latest VART. For more information, refer to Step 3: Installing AI Library Package.
- Execute the program on the target board. Before running the
program, make sure the target board has the Vitis AI library installed, and prepare the images you want to
test.
./model_test <model> <image>