pose-hg-train
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A PyTorch version
Thanks for sharing your code!
I wrote a pytorch version of hourglass network. Hope this could be helpful for who are not familiar with Torch. Many codes for data processing are brought from your code (src/pypose
). Thank the author again!
However, the code cannot reproduce the results perfectly (83.58 [email protected] score for the simplified 4-stack hourglass). Some details might be missed, especially the post processing part (e.g., coordinates mapping, etc). Anyone interested in this project is welcomed to contribute to this project!
Do u have comparison experiments on using different number of hourglass?
I noticed in both lua and pytorch implementation, the residual modules structure is bn->relu->conv
. But the resnet modules are conv->bn->relu
. Is there any particular reason why you modify the sequence this way?
Okay, so actually, this repo's residual module is not the same as the demo residual module. I feel the final demo version makes more sense, since it is the same with resnet implementations.
The change in the resnet module is based on this paper: https://arxiv.org/pdf/1603.05027.pdf which discusses the effects of switching between pre- and post-activation. Can't remember now if there was much difference when I changed the code.
Thanks for the note! I was wondering why resent in pytorch model zoo didn't adopt the bn->relu->conv
which everybody else seems to be using.