update simmim using conv decoder which replace simple header #216
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oh hey! yea I believe it is equivalent at least, I could do an extra rearrange on the predicted pixels to get back the reconstructed image |
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Thank you for your reply. Yes, it is. I thought the original restoration method should be followed (using Conv+PixelShuffle), if you don't think it's important then you could close the PR . |
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@wnma3mz i think being able to get back the reconstructed image is a good idea, let me get the function out when i find some time. feel free to leave this open! |
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It's hard not to support this and it will help many more people, including me. If there is anything I can do to help, please contact me directly. |
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When I read the code of the authors of the paper, I found that the authors used Conv and PixelShuffle layers. To ensure the effectiveness of the MIM method, maybe we should use this decoder method.
P.S. Unfortunately, I have not modified the loss function for the time being because of the difference in the way the mask is generated.
https://github.com/microsoft/SimMIM/blob/bec329f54c2f84db16974035c3f010a88f1b3eb0/models/simmim.py#L104-L121