Thanks for reply.
Not an adoption request. Just that using SWA I am getting 2dB inferior results, so wanted to go through PyTorch code of someone who has gotten good results with SWA. So far I see SWA used in image classification and NLP tasks only. Just wondering it works well for CV tasks like denoising, segmentation, super resolution, etc.
One last thing, sadly PyTorch does not allow SWA to be used with inbuilt Weight Normalization found in nn.utils. But i am sure this wont be the reason for 2dB lower PSNR.