amd/ryzenai-sesr

SESR for 2x Single Image Super Resolution

We provide 2x super-resolution models at resolution 256x256.

It was introduced in the paper Collapsible Linear Blocks for Super-Efficient Super Resolution by Bhardwaj. The official code for this work is available at sesr.

We have developed a modified version optimized for AMD Ryzen AI.

Model description

SESR is based on linear overparameterization of CNNs and creates an efficient model architecture for SISR.

Intended uses & limitations

You can use this model for single image super resolution tasks. See the model hub for all available models.

How to use

Installation

# inference only
pip install -r requirements-infer.txt
# inference & evaluation
pip install -r requirements-eval.txt

Data Preparation (optional: for evaluation)

Run python download_edsr_benchmark.py to automatically download and extract the EDSR benchmark dataset into the datasets directory. After it completes, your datasets folder should have the following structure:

datasets/edsr_benchmark
    └── B100
          └── HR
            ├── 3096.png
            ├── ...
          └── LR_bicubic/X2
            ├── 3096x4.png
            ├── ...
    └── Set5
          └── HR
            ├── baby.png
            ├── ...
          └── LR_bicubic/X2
            ├── babyx4.png
            ├── ...

Test & Evaluation

• Run inference on images

python onnx_inference.py --onnx sesr_nchw_fp32.onnx --input /Path/To/Image --out-dir outputs
python onnx_inference.py --onnx sesr_nchw_int8.onnx --input /Path/To/Image --out-dir outputs

Arguments:

--input: Accepts either a single image file path or a directory path. If it's a file, the script will process that image only. If it's a directory, the script will recursively scan for .png, .jpg, and .jpeg files and process all of them.

--out-dir: Output directory where the restored images will be saved.

• Evaluate the quantized model

Arguments:

--onnx: Path to the ONNX model file.

--hq-dir: Directory containing high-quality (ground truth) images.

--lq-dir: Directory containing low-quality (input) images.

--out-dir: Output directory where evaluation results will be saved.

--max-samples: (Optional) Limit the number of samples to evaluate. Useful for debugging. If not specified, all samples will be evaluated.

-clean: (Optional) If specified, the generated super-resolution images will be deleted after evaluation to save disk space.

# ===================== eval int8 =====================
python onnx_eval.py \
  --onnx sesr_nchw_int8.onnx \
  --hq-dir datasets/edsr_benchmark/Set5/HR \
  --lq-dir datasets/edsr_benchmark/Set5/LR_bicubic/X2 \
  --out-dir outputs/Set5 -clean

python onnx_eval.py \
  --onnx sesr_nchw_int8.onnx \
  --hq-dir datasets/edsr_benchmark/Set14/HR \
  --lq-dir datasets/edsr_benchmark/Set14/LR_bicubic/X2 \
  --out-dir outputs/Set14 -clean

python onnx_eval.py \
  --onnx sesr_nchw_int8.onnx \
  --hq-dir datasets/edsr_benchmark/B100/HR \
  --lq-dir datasets/edsr_benchmark/B100/LR_bicubic/X2 \
  --out-dir outputs/B100 -clean

python onnx_eval.py \
  --onnx sesr_nchw_int8.onnx \
  --hq-dir datasets/edsr_benchmark/Urban100/HR \
  --lq-dir datasets/edsr_benchmark/Urban100/LR_bicubic/X2 \
  --out-dir outputs/Urban100 -clean


# ===================== eval fp32 =====================
python onnx_eval.py \
  --onnx sesr_nchw_fp32.onnx \
  --hq-dir datasets/edsr_benchmark/Set5/HR \
  --lq-dir datasets/edsr_benchmark/Set5/LR_bicubic/X2 \
  --out-dir outputs/Set5 -clean

python onnx_eval.py \
  --onnx sesr_nchw_fp32.onnx \
  --hq-dir datasets/edsr_benchmark/Set14/HR \
  --lq-dir datasets/edsr_benchmark/Set14/LR_bicubic/X2 \
  --out-dir outputs/Set14 -clean

python onnx_eval.py \
  --onnx sesr_nchw_fp32.onnx \
  --hq-dir datasets/edsr_benchmark/B100/HR \
  --lq-dir datasets/edsr_benchmark/B100/LR_bicubic/X2 \
  --out-dir outputs/B100 -clean

python onnx_eval.py \
  --onnx sesr_nchw_fp32.onnx \
  --hq-dir datasets/edsr_benchmark/Urban100/HR \
  --lq-dir datasets/edsr_benchmark/Urban100/LR_bicubic/X2 \
  --out-dir outputs/Urban100 -clean

Performance

Model		Set5			Set14			B100			Urban100
	PSNR(↑)	MS_SSIM(↑)	FID(↓)	PSNR(↑)	MS_SSIM(↑)	FID(↓)	PSNR(↑)	MS_SSIM (↑)	FID(↓)	PSNR(↑)	MS_SSIM(↑)	FID(↓)
sesr(fp32)	35.65	0.9971	26.46	30.98	0.9935	17.69	30.23	0.9921	17.00	28.84	0.9929	0.25
sesr(int8)	34.65	0.9952	28.37	30.46	0.9916	20.70	29.80	0.9900	19.38	28.25	0.9906	1.47

---

@article{bhardwaj2021collapsible,
  title={Collapsible Linear Blocks for Super-Efficient Super Resolution},
  author={Bhardwaj, Kartikeya and Milosavljevic, Milos and O'Neil, Liam and Gope, Dibakar and Matas, Ramon and Chalfin, Alex and Suda, Naveen and Meng, Lingchuan and Loh, Danny},
  journal={arXiv preprint arXiv:2103.09404},
  year={2021}
}