A question about a priori computation in X-Net.py

[LanJ0402]

A question about a priori computation in X-Net.py

[llu025]

Hello,

Although the paper was published in 2021, the code used to produce the initial results was much older than that and was written in TF version 1.14. In that legacy code (found here), the prior was computed using zoomed in, normal, and zoomed out images.

In an effort to translate to TF version 2.4, the prior calculation was rewritten (see here). In this new version the prior are calculated as stated in the paper.

HOWEVER, I just now realise that the function in the new version of the code does not seem to work the way I intended it. In fact, I realised that the logic inside the for loop (and especially the logic behind the if i < 0 statement) seems to be flawed. Unfortunately, I do not have the time to fix it at the moment, so I will have to leave the issue open for now.

In any case, if the legacy method works for you, please use that one. I do not expect results to differ much between zooming in and out the images or doubling and halving the value of k (except for larger computational load).

[LanJ0402]

OK, I get it, thank you for your reply!