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How to adjust SuperPoint number to avoid CUDA memory full? #96
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I believe the error should comes from the KPConvFPN backbone. I can modify the backbone to more stages. But it requires to re-train the network. But it may be less accurate than the original setting. |
Hi
I'm running GeoTransformer on fused indoor point cloud (from ScanNet). I run it using
demo.py
. My GPU is Nvidia3090 with 24GB memory. But the program frequently fails due to "not enough CUDA memory". I downsample the point cloud with2.5cm
voxel size if it is>30000
.I understand the key part is reduce the superpoint number. So I follow #16 to adjust the parameters
But it stopped at
RPEMultiHeadAttention
and it shows,Is any suggestion on how to adjust the parameter properly?
Thanks
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