Comments (3)
Hi @xvdp,
Thanks for pointing out these issues. To clarify, were you not able to run train.py when you just specified one GPU with CUDA_VISIBLE_DEVICES?
If you'd like to make a PR to fix the dataset_path for cats and carla that'd be great! To guarantee reproducibility of results let's not merge in the linting changes. For non-trivial changes we'd need to retrain models and confirm we can get the same FID scores.
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hi @marcoamonteiro
Im working on a setup that uses cuda just fine, but does not have CUDA_VISIBLE_DEVICES
. Maybe I set up my environment incorrectly but so far in a couple of years and dozens of projects ran on pytorch, tensorflow and jax, this is the only one where I found I need to set it up. I suppose I should set up a docker with your configurations and test it but that deviates from tasks at hand.
FYI: There are a few other issues in there I noticed - in most cases loading and saving needs to be done by state_dict(), not directly saving and loading the module. I'm not sure how you are avoiding that.
Also you don't create os.path.join(opt.output_dir, 'evaluation/generated'))
folder anywhere in the train code. You have a ton of double class definitions. You request a device which you never pass in def create_world2cam_matrix(forward_vector, origin)
Im of the opinion that the more general setup is the one you want out there, and yet I get it that you dont want to change things that worked for your paper, so never mind .
Having worked with various nerfs, siren and stylegans, I read thru the code and I like what you are doing, it is logical. Having tried modifying and combining those papers myself however I am curious at the thought process - the failure points. You arrived at a great point, but where did you find dead ends. Forgive my stream of thought, I know this is no forum. In any case, good work.
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And yes, you are right, the fix to multigpu is much cleaner by putting, insinde of __main__
os.makedirs(opt.output_dir, exist_ok=True)
os.makedirs(osp.join(opt.output_dir, 'evaluation/generated'), exist_ok=True)
if "CUDA_VISIBLE_DEVICES" not in os.environ:
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="0"
num_gpus = len(os.environ['CUDA_VISIBLE_DEVICES'].split(','))
mp.spawn(train, args=(num_gpus, opt), nprocs=num_gpus, join=True)
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Related Issues (20)
- generator.losses can not visual using tensorboard
- Can't pickle weakref objects HOT 7
- Reproducing the shape results on the project page
- Inverse rendering script assumes strictly oriented input image
- Wrong dataset_path for CARLA and CATS
- hello,how to get the 3d presentation as you mentioned in fig.9 by projecting the depth map? thanks
- Query about Training Time HOT 19
- generated images colours HOT 1
- Intuition behind scaling frequency output of mapping network?
- How many pictures did you use to calculate the FID value of different datasets in the paper? HOT 1
- the link to the datasets HOT 1
- Error when Rendering Videos HOT 4
- 3d mesh reconstruction quality
- Inverse code can't render fine detail as in demo
- Use the same z for training G and D in each iteration.
- Out of memory by training with several 1080ti
- Why I cannot load the pretrained model meeting 'no module named torch_ema'? HOT 1
- Does the pi-GAN discriminator receive the camera pose during training?
- About the lr for G and D
- Dataset cannot be found
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