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mit-han-lab / spvnas

[ECCV 2020] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
http://spvnas.mit.edu/
MIT License
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Why sync seed? #82

Closed martinrebane closed 2 years ago

martinrebane commented 2 years ago

Hi! I guess seed is fixed in train.py to be able to repeat the results. But just to be sure - the seed is sync'ed for the same reason, syncing is not required to train a new model in SPVNAS or torchsparse? 

seed = configs.train.seed + dist.rank(
    ) * configs.workers_per_gpu * configs.num_epochs
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)

If I remove fixed seed from config, I can also remove syncing (unless I need to reproduce the results later)? Thanks!

CCInc commented 2 years ago

You can set any seed you like, it will only vary the results. You're right that it's just fixed for reproducibility purposes.

On Thu, Dec 16, 2021, 2:27 PM Martin Rebane @.***> wrote:

Hi! I guess seed is fixed in train.py to be able to repeat the results. But just to be sure - the seed is sync'ed for the same reason, syncing is not required to train a new model in SPVNAS or torchsparse?

seed = configs.train.seed + dist.rank( ) configs.workers_per_gpu configs.num_epochs random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed)

If I remove fixed seed from config, I can also remove syncing (unless I need to reproduce the results later)? Thanks!

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martinrebane commented 2 years ago

Thanks for confirming!