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--- |
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license: apache-2.0 |
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tags: |
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- RyzenAI |
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- Int8 quantization |
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- Face Restoration |
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- PSFRGAN |
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- ONNX |
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- Computer Vision |
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metrics: |
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- PSNR |
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- MS_SSIM |
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- FID |
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--- |
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# PSFRGAN for face restoration |
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The model operates at 512x512 resolution and is particularly effective at restoring faces with various degradations including blur, noise, and low resolution. |
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It was introduced in the paper _Progressive Semantic-Aware Style Transformation for Blind Face Restoration_ by Chaofeng Chen et al. at CVPR 2021. |
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We have developed a modified version optimized for [AMD Ryzen AI](https://onnxruntime.ai/docs/execution-providers/Vitis-AI-ExecutionProvider.html). |
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## Model description |
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PSFRGAN (Progressive Semantic-aware Face Restoration Generative Adversarial Network) is a deep learning model designed for blind face restoration, capable of recovering high-quality face images from severely degraded inputs. |
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## Intended uses & limitations |
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You can use this model for face restoration tasks. See the [model hub](https://huggingface.co/models?search=amd/ryzenai-psfrgan) for all available psfrgan models. |
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## How to use |
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### Installation |
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```bash |
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# inference only |
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pip install -r requirements-infer.txt |
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# inference & evaluation |
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pip install -r requirements-eval.txt |
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``` |
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### Data Preparation (optional: for evaluation) |
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1. Download `CelebA-Test (LQ)` and `CelebA-Test (HQ)` from [GFP-GAN homepage](https://xinntao.github.io/projects/gfpgan) |
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2. Organize the dataset directory as follows: |
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```Plain |
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└── datasets |
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└── celeba_512_validation |
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├── 00000000.png |
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├── ... |
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├── celeba_512_validation_lq |
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├── 00000000.png |
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├── ... |
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``` |
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### Test & Evaluation |
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- **Run inference on images** |
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```bash |
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python onnx_inference.py --onnx psfrgan_nchw_fp32.onnx --latent latent.npy --input /Path/To/Image --out-dir outputs |
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python onnx_inference.py --onnx psfrgan_nhwc_int8.onnx --latent latent.npy --input /Path/To/Image --out-dir outputs |
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``` |
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_Arguments:_ |
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`--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. |
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`--latent`: (Optional) Path to the latent code file (.npy). If not provided, random latent values will be generated with a fixed seed for reproducibility. |
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`--out-dir`: Output directory where the restored images will be saved. |
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- **Evaluate the quantized model** |
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```bash |
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# eval fp32 |
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python onnx_eval.py \ |
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--onnx psfrgan_nchw_fp32.onnx \ |
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--latent latent.npy \ |
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--hq-dir datasets/celeba_512_validation \ |
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--lq-dir datasets/celeba_512_validation_lq \ |
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--out-dir outputs/fp32 -clean |
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# eval int8 |
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python onnx_eval.py \ |
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--onnx psfrgan_nhwc_int8.onnx \ |
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--latent latent.npy \ |
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--hq-dir datasets/celeba_512_validation \ |
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--lq-dir datasets/celeba_512_validation_lq \ |
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--out-dir outputs/int8 -clean |
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``` |
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### Performance |
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| Model | PSNR(↑) | MS_SSIM(↑) | FID(↓) | |
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| -------------- | ------- | ---------- | ------ | |
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| PSFRGAN (fp32) | 25.27 | 0.8500 | 21.99 | |
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| PSFRGAN (int8) | 25.27 | 0.8487 | 24.34 | |
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--- |
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```bibtex |
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@inproceedings{ChenPSFRGAN, |
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author = {Chen, Chaofeng and Li, Xiaoming and Lingbo, Yang and Lin, Xianhui and Zhang, Lei and Wong, Kwan-Yee~K.}, |
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title = {Progressive Semantic-Aware Style Transformation for Blind Face Restoration}, |
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Journal = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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year = {2021} |
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} |
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``` |
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