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--- |
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license: apache-2.0 |
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datasets: |
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- eugenesiow/Div2k |
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- eugenesiow/Set5 |
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language: |
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- en |
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tags: |
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- RyzenAI |
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- super resolution |
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- SISR |
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- pytorch |
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--- |
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## Model description |
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SESR is based on linear overparameterization of CNNs and creates an efficient model architecture for SISR. It was introduced in the paper [Collapsible Linear Blocks for Super-Efficient Super Resolution](https://arxiv.org/abs/2103.09404). |
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The official code for this work is available at this |
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https://github.com/ARM-software/sesr |
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We develop a modified version that could be supported by [AMD Ryzen AI](https://onnxruntime.ai/docs/execution-providers/Vitis-AI-ExecutionProvider.html). |
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## Intended uses & limitations |
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You can use the raw model for super resolution. See the [model hub](https://huggingface.co./models?search=amd/sesr) to look for all available models. |
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## How to use |
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### Installation |
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Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI. |
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Run the following script to install pre-requisites for this model. |
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```bash |
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pip install -r requirements.txt |
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``` |
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### Data Preparation (optional: for accuracy evaluation) |
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1. Download the benchmark(https://cv.snu.ac.kr/research/EDSR/benchmark.tar) dataset. |
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2. Organize the dataset directory as follows: |
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```Plain |
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└── dataset |
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└── benchmark |
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├── Set5 |
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├── HR |
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| ├── baby.png |
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| ├── ... |
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└── LR_bicubic |
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└──X2 |
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├──babyx2.png |
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├── ... |
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├── Set14 |
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├── ... |
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``` |
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### Test & Evaluation |
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- Code snippet from [`one_image_inference.py`](one_image_inference.py) on how to use |
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```python |
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parser = argparse.ArgumentParser(description='EDSR and MDSR') |
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parser.add_argument('--onnx_path', type=str, default='SESR_int8.onnx', |
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help='onnx path') |
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parser.add_argument('--image_path', default='test_data/test.png', |
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help='path of your image') |
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parser.add_argument('--output_path', default='test_data/sr.png', |
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help='path of your image') |
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parser.add_argument('--ipu', action='store_true', |
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help='use ipu') |
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parser.add_argument('--provider_config', type=str, default=None, |
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help='provider config path') |
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args = parser.parse_args() |
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if args.ipu: |
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providers = ["VitisAIExecutionProvider"] |
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provider_options = [{"config_file": args.provider_config}] |
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else: |
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providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] |
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provider_options = None |
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onnx_file_name = args.onnx_path |
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image_path = args.image_path |
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output_path = args.output_path |
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ort_session = onnxruntime.InferenceSession(onnx_file_name, providers=providers, provider_options=provider_options) |
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lr = cv2.imread(image_path)[np.newaxis,:,:,:].transpose((0,3,1,2)).astype(np.float32) |
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sr = tiling_inference(ort_session, lr, 8, (56, 56)) |
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sr = np.clip(sr, 0, 255) |
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sr = sr.squeeze().transpose((1,2,0)).astype(np.uint8) |
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sr = cv2.imwrite(output_path, sr) |
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``` |
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- Run inference for a single image |
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```python |
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python one_image_inference.py --onnx_path SESR_int8.onnx --image_path /Path/To/Your/Image --ipu --provider_config Path/To/vaip_config.json |
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``` |
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Note: **vaip_config.json** is located at the setup package of Ryzen AI (refer to [Installation](https://huggingface.co./amd/yolox-s#installation)) |
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- Test accuracy of the quantized model |
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```python |
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python test.py --onnx_path SESR_int8.onnx --data_test Set5 --ipu --provider_config Path/To/vaip_config.json |
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``` |
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### Performance |
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| Method | Scale | Flops | Set5 | |
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|------------|-------|-------|--------------| |
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|SESR-S (float) |X2 |10.22G |37.21| |
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|SESR-S (INT8) |X2 |10.22G |36.81| |
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- Note: the Flops is calculated with the input resolution is 256x256 |
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```bibtex |
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@misc{bhardwaj2022collapsible, |
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title={Collapsible Linear Blocks for Super-Efficient Super Resolution}, |
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author={Kartikeya Bhardwaj and Milos Milosavljevic and Liam O'Neil and Dibakar Gope and Ramon Matas and Alex Chalfin and Naveen Suda and Lingchuan Meng and Danny Loh}, |
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year={2022}, |
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eprint={2103.09404}, |
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archivePrefix={arXiv}, |
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primaryClass={eess.IV} |
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} |
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``` |
