# retinaface

## 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:**
```bash
# Build for arm64-v8a
cd examples/retinaface/cpp
AMLNN_HOME=/path/to/amlnn-toolkit ./build-android.sh -a arm64-v8a
```

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

#### 2. Run

```bash
# Push executable to device
adb push build/android/retinaface_demo /data/local/tmp/
adb push model/RetinaFace_int8_A311D2.adla /data/local/tmp/
adb push imgs /data/local/tmp/

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

# Usage: ./retinaface_demo <model_path> <image_dir>
./retinaface_demo RetinaFace_int8_A311D2.adla ./imgs
```

**Note:** Replace `RetinaFace_int8_A311D2.adla` with your actual model file path.

### Python

**Prerequisites:**
- Python 3.10
- Required packages: `numpy`, `opencv-python`, `amlnnlite`

**Install dependencies:**
```bash
pip install numpy opencv-python amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
```

**Run on device:**
```bash
python RetinaFace.py \
    --model-path ./RetinaFace_int8_A311D2.adla \
    --image-dir ./imgs \
    --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 |
| --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.

**Detection Output**

The program will print the detection count. The output images, featuring bounding boxes and five facial landmarks (eyes, nose, and mouth corners), will be saved to the `{model_name}_result` folder.


You can pull the result folder back to view it:
```bash
adb pull /data/local/tmp/RetinaFace_int8_A311D2_result
```
![alt text](result.jpg)


**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:
```bash
adb pull /data/local/tmp/RetinaFace_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.

![alt text](Visualization.png)