-
Notifications
You must be signed in to change notification settings - Fork 74
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
wrapping pytorch operations - grid_sample #47
Comments
Hi @ahyunSeo What do you need Wrapping it in an EquivariantModule (as done in r2upsampling) is indeed not a problem from an implementation point of view. Are you maybe assuming some specific grid which guarantees equivariance? Best, |
Hi @Gabri95 You made a fair point. Regards, |
Hi @ahyunSeo , If the operation you are trying to implement is equivariant you can just wrap it in an EquivariantModule as I did for r2upsampling. You can precompute the grid in the init of the module such that you can ensure the grid allows equivariance. Let me know if this helps Gabriele |
Thanks a lot! |
Hello,
Thank you for your nice work!
I'm about to use the e2cnn based network for my project.
I need to use the method grid_sample in PyTorch and its underlying implementation is just some interpolation.
https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html?highlight=grid_sample#torch.nn.functional.grid_sample
I noticed the upsample in this repo simply computes the value and reassigns the output type.
https://github.com/QUVA-Lab/e2cnn/blob/master/e2cnn/nn/modules/r2upsampling.py#L86
I think I can do exactly like this with grid_sample.
Please let me know if there will be any issues with this.
Ahyun
The text was updated successfully, but these errors were encountered: