MobilenetV2
This tutorial guides you through running the MobilenetV2 model on RevyOS using either the CPU or NPU. MobilenetV2 is an efficient deep neural network model, widely used for optimization on mobile and embedded systems.
Before proceeding, please ensure you have completed the environment setup section.
Obtaining Example Code
The example code for this tutorial is available on Github. Clone it locally using the following command:
$ git clone https://github.com/zhangwm-pt/lpi4a-example.git
The relevant code for this tutorial is located in the classification/mobilenetv2
directory.
Obtaining the Model
The model used in this tutorial is from the ONNX Model Zoo. Download the MobilenetV2 model with the following command:
$ curl -OL https://raw.githubusercontent.com/onnx/models/raw/main/validated/vision/classification/mobilenet/model/mobilenetv2-10.onnx
If you encounter network issues accessing GitHub from mainland China, consider using a network proxy tool to accelerate access.
Model Information
input name | output name | input shape | layout | channel order | scale value | mean values |
---|---|---|---|---|---|---|
input | output | 1, 3, 224, 224 | NCHW | RGB | 0.017 | 124, 117, 104 |
Model Conversion and Compilation
On an x86 machine, use the HHB tool to convert the ONNX model into a computation graph and glue code suitable for RevyOS. Before proceeding, ensure you have started the HHB container and cloned the example repository as described in the environment setup section.
Model Conversion with HHB
In this step, the onnx
model is converted into a format compatible with the HHB platform.
Navigate to the classification/mobilenetv2
directory and execute the following commands:
- NPU
- CPU
$ hhb -D --model-file mobilenetv2-12.onnx \
--data-scale 0.017 --data-mean "124 117 104" \
--board c920 --postprocess save_and_top5 \
-in "input" -on "output" -is "1 3 224 224" \
--quantization-scheme float16
$ hhb -D --model-file mobilenetv2-12.onnx \
--data-scale 0.017 --data-mean "124 117 104" \
--board th1520 --postprocess save_and_top5 \
--input-name "input" --output-name "output" \
--input-shape "1 3 224 224" --calibrate-dataset persian_cat.jpg \
--quantization-scheme "int8_asym"
-D
: Specifies the HHB process to stop at the executable generation stage--model-file
: Specifies the input model file--data-mean
: Specifies the mean values--data-scale
: Specifies the scale value--board
: Target platform, C920 (CPU) or TH1520 (NPU)--input-name
: Model input tensor name--output-name
: Model output tensor name--input-shape
: Model input tensor shape--postprocess
: Specifies the post-processing behavior for the generated glue code.save_and_top5
saves the output and prints the top 5 results--quantization-scheme
: Specifies the quantization type (float16 for CPU, int8_asym for NPU)
You can run hhb --help
to view all available parameters and options.
After execution, an hhb_out
subdirectory will be generated in the current directory, containing files such as hhb_runtime
, model.c
, and others:
hhb.bm
: HHB model file, including quantized weights and related datahhb_runtime
: Executable for the development board, compiled from the C files in the directorymain.c
: Reference entry for the generated example programmodel.c
: Model structure representation filemodel.params
: Model weights fileio.c
: Example program with file I/O helper functionsio.h
: Declarations for I/O helper functionsprocess.c
: Example program with image preprocessing functionsprocess.h
: Declarations for preprocessing functions
For more details on HHB options, refer to the HHB User Manual.
Compiling the Application
The glue code generated by HHB only tests the model's functionality. For complete image preprocessing and postprocessing, an application using OpenCV is provided to load the model and perform inference.
In the classification/mobilenetv2
directory, compile the application with:
$ export OPENCV_DIR=../../modules/opencv/ # Set the path to OpenCV
$ riscv64-unknown-linux-gnu-g++ main.cpp -I${OPENCV_DIR}/include/opencv4 -L${OPENCV_DIR}/lib \
-lopencv_imgproc -lopencv_imgcodecs -L${OPENCV_DIR}/lib/opencv4/3rdparty/ \
-llibjpeg-turbo -llibwebp -llibpng -llibtiff -llibopenjp2 \
-lopencv_core -ldl -lpthread -lrt -lzlib -lcsi_cv -latomic -static \
-o mobilenetv2_example # Compile the program
The example code uses OpenCV for model input preprocessing. Please ensure OpenCV is installed as described in the environment setup section.
- -I../prebuilt_opencv/include/opencv4: Header file search path, pointing to the OpenCV headers
- -L../prebuilt_opencv/lib: Library search path, pointing to the precompiled OpenCV binaries
- -lopencv_imgproc -lopencv_imgcodecs -lopencv_core: OpenCV libraries
- -llibjpeg-turbo -llibwebp -llibpng -llibtiff -llibopenjp2 -lcsi_cv: OpenCV dependencies
- -static: Static linking
- -o mobilenetv2_example: Output executable name
After successful compilation, the mobilenetv2_example
file will be generated in the example directory.
Uploading and Running the Application
Upload to the Development Board
Package all files in this directory and upload them to the development board. For example, use the scp
command to upload to /home/debian/npu
:
$ scp -r ../mobilenetv2/ debian@<board_ip>:/home/debian/mobilenetv2/
Alternatively, you may use other methods such as USB storage devices or network sharing.
Running the Program
On the development board, navigate to /home/debian/mobilenetv2
. Ensure the SHL library is installed and LD_LIBRARY_PATH
is configured. Then run:
$ ./mobilenetv2_example
If you encounter the following error:
hhb_out/hhb_runtime: error while loading shared libraries: libshl_th1520.so.2: cannot open shared object file: No such file or directory
Ensure LD_LIBRARY_PATH
is correctly set. If the issue persists, run pip show shl-python
to check the version.
If the version is 3.x.x
, it is too high. The program requires shl-python
version 2.x. Downgrade with:
$ pip install shl-python==2.6.17
If you encounter the following error:
FATAL: could not open driver '/dev/vha0': Permission denied
Check if the current user has read/write permissions for /dev/vha0
. Set permissions with:
$ sudo chmod 0666 /dev/vha0
It is recommended to configure udev
rules for automatic permission setting. Consult AI or documentation for udev
configuration.
In theory, the program should run quickly. However, the first run may take over 5 minutes due to JIT compilation when loading the model on the NPU. Due to HHB runtime design, JIT compilation occurs on every run, resulting in long execution times.
For more details, refer to Common Issues and Solutions.
Sample output:
In this tutorial, the input is a picture of a Persian cat. The expected result for ResNet50 is that the largest value is at index 283, corresponding to Persian cat
.
- NPU
- CPU
./mobilenetv2_example
********** preprocess image **********
********** run mobilenetv2 **********
INFO: NNA clock:406105 [kHz]
INFO: Heap :anonymous (0x2)
INFO: Heap :dmabuf (0x2)
INFO: Heap :unified (0x5)
Run graph execution time: 15.12841ms, FPS=66.10
=== tensor info ===
shape: 1 3 224 224
data pointer: 0x2ce2af40
=== tensor info ===
shape: 1 1000
data pointer: 0x3fa3217000
The max_value of output: 16.053827
The min_value of output: -8.026914
The mean_value of output: -0.001889
The std_value of output: 9.203342
============ top5: ===========
283: 16.053827
281: 14.165141
287: 11.709850
285: 11.615416
282: 11.332113
********** postprocess result **********
********** probability top5: **********
n02123394 Persian cat
n02123045 tabby, tabby cat
n02127052 lynx, catamount
n02124075 Egyptian cat
n02123159 tiger cat
$ ./mobilenetv2_example
********** preprocess image **********
********** run mobilenetv2 **********
Run graph execution time: 82.31609ms, FPS=12.15
=== tensor info ===
shape: 1 3 224 224
data pointer: 0x30762380
=== tensor info ===
shape: 1 1000
data pointer: 0x306ce340
The max_value of output: 16.843750
The min_value of output: -7.414062
The mean_value of output: 0.001131
The std_value of output: 9.056762
============ top5: ===========
283: 16.843750
281: 13.789062
287: 12.257812
282: 10.898438
285: 10.765625
********** postprocess result **********
********** probability top5: **********
n02123394 Persian cat
n02123045 tabby, tabby cat
n02127052 lynx, catamount
n02123159 tiger cat
n02124075 Egyptian cat