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- Version: Ryzen AI Software v1.0
- Support: AMD Ryzen 7040U, 7040HS series mobile processors with Windows 11 OS.
- Last update: 4 Dec. 2023
Ryzen™ AI is a dedicated AI accelerator integrated on-chip with the CPU cores. The AMD Ryzen™ AI SDK enables developers to take machine learning models trained in PyTorch or TensorFlow and run them on laptops powered by Ryzen AI which can intelligently optimizes tasks and workloads, freeing-up CPU and GPU resources, and ensuring optimal performance at lower power.
In this Deep Learning(DL) tutorial, you will see how to deploy the Yolov8 detection model with ONNX framework on Ryzen AI laptop.
- Linux server (GPU is preferred)
- AMD Ryzen AI Laptop with Windows 11 OS
- Visual Studio 2019 (with Desktop dev c++ & MSVC v142-vs2019 x64/x86 Spectre-mitigated libs)
- Anaconda or Miniconda
- Git
- openCV (version = 4.6.0)
- glog
- gflags
- cmake (version >= 3.26)
- python (version >= 3.9) (Recommended for python 3.9.13 64bit)
- IPU driver & IPU xclbin = 1.0 release
- voe package = 1.0 release
Please refer to the installation instructions to properlly install the Ryzen AI software.
There are some more libraries you need to install for the Yolov8 inference.
# pip install cmake
Output:
Collecting cmake
Obtaining dependency information for cmake from https://files.pythonhosted.org/packages/e0/67/3cc8ccb0cebac463033e1f8588328de32f8f85cfd9d3150c05b57b827893/cmake-3.27.4.1-py2.py3-none-win_amd64.whl.metadata
Downloading cmake-3.27.4.1-py2.py3-none-win_amd64.whl.metadata (6.8 kB)
Downloading cmake-3.27.4.1-py2.py3-none-win_amd64.whl (34.6 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 34.6/34.6 MB 147.5 kB/s eta 0:00:00
Installing collected packages: cmake
Successfully installed cmake-3.27.4.1
It is recommended to build OpenCV form source code and use static build. Git is required to clone the repository.
Start a Git Bash. In the Git Bash, clone the repository
# git clone https://github.com/opencv/opencv.git -b 4.6.0
Switch back to the Conda Prompt, and compile the OpenCV source code with cmake.
# cd opencv
# mkdir mybuild
# cd mybuild
# cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON -DBUILD_SHARED_LIBS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON -DCMAKE_CONFIGURATION_TYPES=Release -A x64 -T host=x64 -G "Visual Studio 16 2019" '-DCMAKE_INSTALL_PREFIX=C:\Program Files\opencv' '-DCMAKE_PREFIX_PATH=.\opencv' -DCMAKE_BUILD_TYPE=Release -DBUILD_opencv_python2=OFF -DBUILD_opencv_python3=OFF -DBUILD_WITH_STATIC_CRT=OFF -B build -S ../
# cmake --build build --config Release
# cmake --install build --config Release
# cd ../..
All the dependencies on the Ryzen AI laptop are installed completely. User could run a end to end Yolov8 deplomyment progress with the following Section 4, which will start from the FP32 Yolov8 model. The whole progress will last for several hours or one day depending on the hardware computing ability.
Alternatively, user who wants a quick benchmark could skip Section 4 and start from Section 5 with pre-quantized model.
In this section, we will leverage the Ryzen AI docker container on Linux GPU server for a quantized awared training(QAT).
Please follow the instrucion here to build your docker container or pull prebuild docker from docker hub.
This tutorial will take GPU docker as a reference.
$ cd <ryzen-ai-gpudockerfiles>
$ ./docker_build.sh -t gpu -f pytorchDownload the COCO dataset from https://cocodataset.org/#download following the instruction and make sure the dataset structure is restored as below. Please also update variable "DATA_PATH" in "coco.yaml" to point to the correct location.
+ datasets/
+ coco/
+ labels/
+ annotations/
+ images/
+ test-dev2017.txt
+ train2017.txt
+ val2017.txtClone the RyzenAI-SW repository.
$ git clone https://github.com/amd/RyzenAI-SW.gitStart a Docker container using the image.
$ docker run -it --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host --shm-size 8G xilinx/vitis-ai-pytorch-gpu:<Your-Image-Tag>You can also pass the -v argument to mount any data directories from the host onto the container.
Then, setup the environment with following commands.
$ cd RyzenAI-SW/tutorial/yolov8_e2e
$ sudo bash env_setup.sh
$ cd code
$ python3 setup.py developUser could use the run_test.sh script to validate the float point model first before the quantization.
$ bash run_test.shThen Quantize the model with following script.
$ bash run_ptq.shThen quantize the model with QAT technique.
$ bash run_qat.shCopy the quantized model to Ryzen AI laptop for the following deployment.
If the section 4 is skiped, please start a Git Bash. In the Git Bash, clone the repository
# git clone https://github.com/amd/RyzenAI-SW.git
Switch back to the Conda Prompt, and compile the Yolov8 source code.
# cd RyzenAI-SW/tutorial/yolov8_e2e/implement
# build.bat
The output will be generated as below.
......
-- Installing: C:/Users/ibane/Desktop/voe-win_amd64-with_xcompiler_on-c07e419-latest/bin/camera_yolov8.exe
-- Installing: C:/Users/ibane/Desktop/voe-win_amd64-with_xcompiler_on-c07e419-latest/bin/camera_yolov8_nx1x4.exe
-- Installing: C:/Users/ibane/Desktop/voe-win_amd64-with_xcompiler_on-c07e419-latest/bin/test_jpeg_yolov8.exe
To validate your setup, the following command will do the inference with single image.
Please modify the conda env path in the batch file before execution.
# run_jpeg.bat DetectionModel_int.onnx sample_yolov8.jpg
The output will be generated as below.
result: 0 person 490.38498 85.79535 640.00488 475.18262 0.932453
result: 0 person 65.96048 97.76373 320.66068 473.83783 0.924142
result: 0 person 182.15485 306.91266 445.14795 475.26132 0.893309
result: 27 tie 584.48022 221.15732 632.27008 244.21243 0.851953
result: 27 tie 175.62622 224.15210 235.84900 248.83557 0.651355
To run with live camera, user needs to change the display and camera settings manually as below.
Please modify the conda env name in the batch file before execution.
- Go to
Display settings, change Scale to 100% in theScale & layoutsection. - Go to
Bluetooth & devices->Cameras->USB2.0 FHD UVC WebCam, turn off the Background effects in theWindows Studio Effectssection.
camera.bat
Possible options to run the yolov8 demo.
# camera.bat -h
Options:
-c [parallel runs]: Specifies the (max) number of runs to invoke simultaneously. Default:1.
-s [input_stream] set input stream, E.g. set 0 to use default camera.
-x [intra_op_num_threads]: Sets the number of threads used to parallelize the execution within nodes, A value of 0 means ORT will pick a default. Must >=0.
-y [inter_op_num_threads]: Sets the number of threads used to parallelize the execution of the graph (across nodes), A value of 0 means ORT will pick a default. Must >=0.
-D [Disable thread spinning]: disable spinning entirely for thread owned by onnxruntime intra-op thread pool.
-Z [Force thread to stop spinning between runs]: disallow thread from spinning during runs to reduce cpu usage.
-T [Set intra op thread affinities]: Specify intra op thread affinity string.
[Example]: -T 1,2;3,4;5,6 or -T 1-2;3-4;5-6
Use semicolon to separate configuration between threads.
E.g. 1,2;3,4;5,6 specifies affinities for three threads, the first thread will be attached to the first and second logical processor.
-R [Set camera resolution]: Specify the camera resolution by string.
[Example]: -R 1280x720
Default:1920x1080.
-r [Set Display resolution]: Specify the display resolution by string.
[Example]: -r 1280x720
Default:1920x1080.
-L Print detection log when turning on.
-h: help
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