Training speed about classsr HOT 4 CLOSED

Thanks for your attention.
1.
If adapting ClassSR to the denoise task, you may need to take care of the training patch size. SR models process images in a low resolution and upsample them at the end of a network, but denoise models process images in the same resolution as GT. It brings much computational consumption.
2.
Firstly, you need well-trained branches, which all can process the corresponding input well. I guess this phenomenon is caused by the branches is not good enough for the corresponding inputs (check the branches performance) or the missing match of branches and inputs (100k+ iters training of classifier should be enough).

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Thank you very much for your detailed reply！

1. If I reduce the patch size, will it affect the result performance？
2. In fact, I have pre trained the branch model, and their PSNR values are 39.3/39.7/39.9 respectively. However, the PSNR of the 30000 iter testing is only 38.3. I don't understand this phenomenon. Should I check my code？

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1. It will bring a little performance drop.
2. It is normal. You test the branches in sub-images and evaluate the whole ClassSR model on another complete image, this operation causes the difference. You can compare (testing them on same test images) the ClassSR model and the most complex branches (trained with all data) to observe the performance.

from classsr.

I will do as you suggest! Thank you very much for your reply！

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