EfficientNet
This tutorial provides guidance on running the EfficientNet b0 model on RevyOS using either the CPU or NPU. EfficientNet is a highly efficient deep neural network model, widely adopted 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/efficientnet directory.
Obtaining the Model
The model used in this tutorial is from the efficientnet-pytorch model repository. Download the EfficientNet model with the following commands:
$ git clone https://github.com/rwightman/gen-efficientnet-pytorch.git
$ cd gen-efficientnet-pytorch
Due to version issues, you need to modify line 97 in the onnx_export.py file, changing torch_out = torch.onnx._export to torch_out = torch.onnx.export, and then export the model.
$ nano onnx_export.py # Change line 97 from torch_out = torch.onnx._export to torch_out = torch.onnx.export
$ python3 onnx_export.py --model efficientnet_b0 efficientnet_b0.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 |
|---|---|---|---|---|---|---|
| input0 | output0 | 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/efficientnet directory and execute the following commands:
$ hhb -D --model-file ./efficientnet_b0.onnx \
--data-scale 0.017 --data-mean "124 117 104" \
--board c920 --postprocess save_and_top5 \
--input-name "input0" --output-name "output0" \
--input-shape "1 3 224 224" --quantization-scheme float16
-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_top5saves the output and prints the top 5 results--quantization-scheme: Specifies the quantization type as float16
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 efficientnet_example
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 efficientnet_example: Output executable name
After successful compilation, the efficientnet_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 ../efficientnet/ debian@<board_ip>:/home/debian/efficientnet/
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/efficientnet. Ensure the SHL library is installed and LD_LIBRARY_PATH is configured. Then run:
$ ./efficientnet_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.
$ ./efficientnet_example
********** preprocess image **********
********** run model **********
Run graph execution time: 191.89818ms, FPS=5.21
=== tensor info ===
shape: 1 3 224 224
data pointer: 0x28c5daf0
=== tensor info ===
shape: 1 1000
data pointer: 0x28caaa50
The max_value of output: 9.648438
The min_value of output: -2.396484
The mean_value of output: -0.043881
The std_value of output: 0.778041
============ top5: ===========
283: 9.648438
281: 6.589844
287: 5.355469
282: 4.910156
285: 4.437500
********** postprocess result **********
********** probability top5: **********
n02123394 Persian cat
n02123045 tabby, tabby cat
n02127052 lynx, catamount
n02123159 tiger cat
n02124075 Egyptian cat