Skip to content
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

The effect on training speed using softras #46

Closed
FishWoWater opened this issue Mar 26, 2020 · 2 comments
Closed

The effect on training speed using softras #46

FishWoWater opened this issue Mar 26, 2020 · 2 comments

Comments

@FishWoWater
Copy link

Hi! Thanks for sharing your nice work!
I am doing human mesh reconstruction task(with SMPL model). Before I use the mask to supervise our network(i.e. use soft rasterizer), 1 epoch finishes in about 10 mins, but after using softras... It becomes very slow(about 3h a epoch). I don't know whether this is a normal phenomenon. Does the rasterization generally affect the speed a lot?(In particular, I use 6890 vertices and 13776 faces). Thanks!

@ShichenLiu
Copy link
Owner

I think this is due to too many triangles and also large image size. My solution would be use less triangles / train in a hierarchical way to reduce computation.

@ssjsusie
Copy link

Hi! Thanks for sharing your nice work!
I am doing human mesh reconstruction task(with SMPL model). Before I use the mask to supervise our network(i.e. use soft rasterizer), 1 epoch finishes in about 10 mins, but after using softras... It becomes very slow(about 3h a epoch). I don't know whether this is a normal phenomenon. Does the rasterization generally affect the speed a lot?(In particular, I use 6890 vertices and 13776 faces). Thanks!

Hello, I am also trying the same job. How do you get the non-square rendering result? I see that the SoftRasterizeFunction part makes image_size=256. thank you very much!

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

3 participants