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pip install -r requirements.txt 1└── dataset
2 └── benchmark
3 ├── Set5
4 ├── HR
5 | ├── baby.png
6 | ├── ...
7 └── LR_bicubic
8 └──X2
9 ├──babyx2.png
10 ├── ...
11 ├── Set14
12 ├── ... one_image_inference.py on how to use1 parser = argparse.ArgumentParser(description='EDSR and MDSR')
2 parser.add_argument('--onnx_path', type=str, default='SESR_int8.onnx',
3 help='onnx path')
4 parser.add_argument('--image_path', default='test_data/test.png',
5 help='path of your image')
6 parser.add_argument('--output_path', default='test_data/sr.png',
7 help='path of your image')
8 parser.add_argument('--ipu', action='store_true',
9 help='use ipu')
10 parser.add_argument('--provider_config', type=str, default=None,
11 help='provider config path')
12 args = parser.parse_args()
13 if args.ipu:
14 providers = ["VitisAIExecutionProvider"]
15 provider_options = [{"config_file": args.provider_config}]
16 else:
17 providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
18 provider_options = None
19
20 onnx_file_name = args.onnx_path
21 image_path = args.image_path
22 output_path = args.output_path
23
24 ort_session = onnxruntime.InferenceSession(onnx_file_name, providers=providers, provider_options=provider_options)
25 lr = cv2.imread(image_path)[np.newaxis,:,:,:].transpose((0,3,1,2)).astype(np.float32)
26 sr = tiling_inference(ort_session, lr, 8, (56, 56))
27 sr = np.clip(sr, 0, 255)
28 sr = sr.squeeze().transpose((1,2,0)).astype(np.uint8)
29 sr = cv2.imwrite(output_path, sr)python one_image_inference.py --onnx_path SESR_int8.onnx --image_path /Path/To/Your/Image --ipu --provider_config Path/To/vaip_config.jsonpython test.py --onnx_path SESR_int8.onnx --data_test Set5 --ipu --provider_config Path/To/vaip_config.json | Method | Scale | Flops | Set5 |
|---|---|---|---|
| SESR-S (float) | X2 | 10.22G | 37.21 |
| SESR-S (INT8) | X2 | 10.22G | 36.81 |
1@misc{bhardwaj2022collapsible,
2 title={Collapsible Linear Blocks for Super-Efficient Super Resolution},
3 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},
4 year={2022},
5 eprint={2103.09404},
6 archivePrefix={arXiv},
7 primaryClass={eess.IV}
8}