Comments (3)
hualie
commented on May 18, 2024
1
The code only supports single GPU training.
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Camuvingian
commented on May 18, 2024
@hualie is there any fundamental reason why the training model cannot be made multi-GPU? anonymoustbd, have you made any progress with a multi-GPU training model?
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HalleyJiang
commented on May 18, 2024
Hi, all. Please refer to my updated repository (https://github.com/HalleyJiang/UniFuse-Unidirectional-Fusion) for multi-GPU training. --batch_size should be set as the size for each GPU. My implementation refers to https://github.com/dougsouza/pytorch-sync-batchnorm-example.
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Related Issues (16)
- The quantitative results in the paper cannot be reproduced HOT 7
- A question about metrics.py HOT 2
- The problem about testing results using the 3D60 dataset HOT 1
- How to deal with Matterport3D images HOT 2
- How to deal with Stanford2d3d? HOT 1
- Data Split HOT 2
- How to obtain GPU memeory of network?
- About Datasets
- train.py HOT 2
- Is there anything about Matterport Preprocessing HOT 6
- How to get the 3d60 train data set HOT 3
- PanoSUNCG HOT 2
- Pretrained Model
- About 'cube_inputs' for unifuse.py. HOT 2
- evaluate.py HOT 2
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