README.md

---
license: apache-2.0
datasets:
- Set5
- Div2K
language:
- en
tags:
- RyzenAI
- PAN
- Pytorch
- Super Resolution
- Vision
pipeline_tag: image-to-image
---

## Model description

PAN is an lightwight image super-resolution method with pixel pttention. It was introduced in the paper [Efficient Image Super-Resolution Using Pixel Attention](https://arxiv.org/abs/2010.01073) by Hengyuan Zhao et al. and first released in [this repository](https://github.com/zhaohengyuan1/PAN).

We changed the negative slope of the leaky ReLU of the original model and replaced the sigmoid activation with hard sigmoid to make the model compatible with [AMD Ryzen AI](https://onnxruntime.ai/docs/execution-providers/Vitis-AI-ExecutionProvider.html). We loaded the published model parameters and fine-tuned them on the DIV2K dataset.


## Intended uses & limitations

You can use the raw model for super resolution. See the [model hub](https://huggingface.co/models?search=amd/pan) to look for all available PAN models.


## How to use

### Installation

   Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI.
   Run the following script to install pre-requisites for this model.
   ```bash
   pip install -r requirements.txt 
   ```


### Data Preparation (optional: for accuracy evaluation)

1. Download the benchmark(https://cv.snu.ac.kr/research/EDSR/benchmark.tar) dataset.
3. Unzip the dataset and put it under the project folder. Organize the dataset directory as follows:
```Plain
PAN
└── dataset
     └── benchmark
          ├── Set5
               ├── HR
               |   ├── baby.png
               |   ├── ...
               └── LR_bicubic
                   └──X2
                      ├──babyx2.png
                      ├── ...
          ├── Set14
          ├── ...    
```

### Test & Evaluation

- Code snippet from [`infer_onnx.py`](infer_onnx.py) on how to use
```python
    parser = argparse.ArgumentParser(description='PAN SR')
    parser.add_argument('--onnx_path',
                        type=str, 
                        default='PAN_int8.onnx',
                        help='Onnx path')
    parser.add_argument('--image_path', 
                        type=str,
                        default='test_data/test.png',
                        help='Path to your input image.')
    parser.add_argument('--output_path', 
                        type=str,
                        default='test_data/sr.png',
                        help='Path to your output image.')
    parser.add_argument('--provider_config',
                        type=str,
                        default="vaip_config.json",
                        help="Path of the config file for seting provider_options.")
    parser.add_argument('--ipu', action='store_true', help='Use Ipu for interence.')

    args = parser.parse_args()
   
    onnx_file_name = args.onnx_path
    image_path = args.image_path
    output_path = args.output_path

    if args.ipu:
        providers = ["VitisAIExecutionProvider"]
        provider_options = [{"config_file": args.provider_config}]
    else:
        providers = ['CPUExecutionProvider']
        provider_options = None
    ort_session = onnxruntime.InferenceSession(onnx_file_name,  providers=providers, provider_options=provider_options) 

    lr = cv2.imread(image_path)[np.newaxis,:,:,:].transpose((0,3,1,2)).astype(np.float32)
    sr = tiling_inference(ort_session, lr, 8, (56, 56))
    sr = np.clip(sr, 0, 255)
    sr = sr.squeeze().transpose((1,2,0)).astype(np.uint8)
    sr = cv2.imwrite(output_path, sr)
```

 - Run inference for a single image
  ```python
  python infer_onnx.py --onnx_path PAN_int8.onnx --image_path /Path/To/Your/Image --ipu --provider_config Path\To\vaip_config.json
  ```

 - Test accuracy of the quantized model
  ```python
  python eval_onnx.py --onnx_path PAN_int8.onnx --data_test Set5 --ipu --provider_config Path\To\vaip_config.json
  ```

Note: **vaip_config.json**  is located at the setup package of Ryzen AI (refer to [Installation](https://huggingface.co/amd/yolox-s#installation))

### Performance

| Method     | Scale | Flops | Set5         |
|------------|-------|-------|--------------|
|PAN (float) |X2 |141G |38.00 / 0.961|
|PAN_amd (float) |X2  |141G |37.859 / 0.960|
|PAN_amd (int8) |X2  |141G |37.18 / 0.952|
- Note: the Flops is calculated with the output resolution is 360x640

```bibtex
@inproceedings{zhao2020efficient,
  title={Efficient image super-resolution using pixel attention},
  author={Zhao, Hengyuan and Kong, Xiangtao and He, Jingwen and Qiao, Yu and Dong, Chao},
  booktitle={European Conference on Computer Vision},
  pages={56--72},
  year={2020},
  organization={Springer}
}
```