amlnn-model-playground/examples/resnet

resnet

1.Overview

2.Model Download

• Open Source model

  • Open Source projects:

  • Export Model Step:

    • Install ultralytics

      pip install torch==2.4.1

      pip install torchvision==0.19.1

      pip install ultralytics==8.3.0

    • Download weights

    • Export Model

• Exported Model

  ​ link to amlogic server( onnx model or quantized tflite)

3. Model Conversion

cd model
Usage:   ./adla_convert.sh model_path adla_toolkit_path target_platform

example

Parameter	Description
model_path	onnx model path
adla_toolkit_path	path to adla_toolkit
target_platform	Specify target platform. for A311D2 : PRODUCT_PID0XA003. for S905X5: PRODUCT_PID0XA005

4. Demo Run

CPP

1. Compile

Prerequisites:

• Android NDK (r25e recommended)
• ANDROID_NDK_PATH environment variable set

Build:

# Build for arm64-v8a
cd examples/resnet/cpp
AMLNN_HOME=/path/to/amlnn-toolkit ./build-android.sh -a arm64-v8a

The executable will be generated at build/android/resnet_demo (Note: executable name may vary, verify in build folder).

2. Run

# Push executable to device
adb push build/android/resnet_demo /data/local/tmp/
adb push model/res2net50_int8_A311D2.adla /data/local/tmp/
adb push imgs /data/local/tmp/
adb push labels.txt /data/local/tmp/

# Run on device
adb shell
cd /data/local/tmp
chmod +x resnet_demo
export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)

# Usage: ./resnet_demo <model_path> <image_dir> <labels.txt>
./resnet_demo res2net50_int8_A311D2.adla imgs/ labels.txt

Note: Replace res2net50_int8_A311D2.adla with your actual model file path.

Python

Prerequisites:

• Python 3.10
• Required packages: numpy, opencv-python, amlnnlite

Install dependencies:

pip install numpy opencv-python amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl

Run on device:

python resnet.py \
    --model-path ./res2net50_int8_A311D2.adla \
    --image-dir ./imgs \
    --labels labels.txt \
    --run-cycles 1 \
    --loglevel INFO

Argument Descriptions:

Argument	Description
--board-work-path	Work path on board, default is /data/local/tmp
--model-path	path to .adla model
--image-dir	Directory containing test images
--labels	Path to synset_words.txt or labels.txt
--run-cycles	Number of inference cycles, default is 1
--loglevel	Logging level: DEBUG / INFO / WARNING / ERROR, default is WARNING

The script will automatically process all image files (.jpg, .jpeg, .png, .bmp) in the current directory and save results to a {model_name}_result folder.

5.Results

Performance Feedback

By setting the loglevel to INFO, the program provides real-time performance metrics upon completion. The console log will display essential hardware and execution details, including:

• Hardware Information: System and ADLA library versions.
• Model Overview: Basic input/output configurations.
• NPU Metrics: Total inference time (latency) and total DRAM bandwidth consumption.

Classification Output

For each image, the program prints the Top-5 classification results with their respective scores:

============================================================
Processing image 1/1: dog.jpg
============================================================                                                                                        Top-5 Results:                                                                                                          
1: Pekinese                     score=9.851644                                                                                 
2: West Highland white terrier  score=5.055449                                                                          
3: Maltese dog                  score=4.796195                                                                                 
4: basenji                      score=3.111045                                                                                 
5: Scotch terrier               score=2.786978                                                                                 ============================================================ 

Profiling Visualization

After a successful run of the Python demo, a folder named after the model (e.g., {model_name}) will be generated in the script directory. This folder contains 5 HTML files that provide a visual and detailed breakdown of per-layer performance:

• hard_op_chart.html & soft_op_chart.html: Hardware/Software op execution details.
• dram_rd_chart.html & dram_wr_chart.html: Bandwidth read/write distribution.
• pie_charts_distribution.html: Overall resource allocation.

You can pull the result folder back to view it:

adb pull /data/local/tmp/res2net50_int8_A311D2

Taking hard_op_chart.html as an example (shown below), each layer's ADLA operator name includes parentheses containing the index of the corresponding quantized .tflite layer(s); by default, these indices are suppressed, and operators are labeled generically as "hardware" or "software" without numerical suffixes.