Open mohammed-amr opened 2 years ago
Sorry for not having that part in the repository. Both should work and give similar results since training is done with square shaped images and gradient flow disabled. As far as I can remember, non-differentiable function was having issues with the GPU memory space on GTX 1080Ti for some reason during training (which is already quite maxed out with the current batch and sub-sequence sizes). Therefore,
fusionnet/run-testing.py
..detach()
the prediction tensor while assigning it to the previous_depth
variable to disable the gradient flow.Config.training_image_width
and Config.training_image_height
I can not test this right now, so please let me know how it goes or if you need more info.
Thanks!
I've gotten it working given the changes you've suggested and the available code snippets. I've put my version of the file here.
It's training now. I'll let you decide if you want to close this issue, or keep it to signpost for others.
Thanks again for your quick reply and help.
Hello,
I'm looking at your description for how to train the fusion model in the supplemental:
The current training script at
fusionnet/run-training.py
doesn't have a flag for this. I can see that the GT depth is used for warping the current state at line 249.What should I use as a depth estimator for this step? Should I borrow from this line at
fusionnet/run-testing.py
? Or (more likely) this differentiable estimator at line 157 inutils.py
?Thanks.