train the net about gcnet HOT 6 CLOSED

Hi, thanks for your interest.

1. Different epochs are set for different datasets. The configuration can be seen in configs/*.yaml .
   For 3DMatch/3DLoMatch, max_epoch is set 40, which can be seen in https://github.com/zhulf0804/NgeNet/blob/d4917f22e55195132ec6fc602554102d321ce4b5/configs/threedmatch.yaml#L46
2. The low training speed is due to the correspondences construction operation (shown below) on CPU. For example, we trained 3DMatch on one RTX 3090 card using about 40 hours.
   https://github.com/zhulf0804/NgeNet/blob/d4917f22e55195132ec6fc602554102d321ce4b5/utils/o3d.py#L75-L88

Best regards.

from gcnet.

Thanks for such a quick answer！

from gcnet.

Hello, when I evaluate and visualize, the process is killed when it reaches 14%. It may be a memory overflow problem. Is there any good solution?my graphics card is titan

from gcnet.

Hello,

What's your GPU memory size ? I didn't record the runtime memory size before, but GTX 1080Ti is enough for me.

One way your can try is to use torch.cuda.empty_cache() after each iteration.

Best.

from gcnet.

from gcnet.

Hi,

1. python eval_3dmatch.py --benchmark 3DMatch --data_root your_path/indoor --checkpoint your_path/3dmatch.pth --saved_path work_dirs/3dmatch menas using cuda for evaluation.
2. python eval_3dmatch.py --benchmark 3DMatch --data_root your_path/indoor --checkpoint your_path/3dmatch.pth --saved_path work_dirs/3dmatch --vis menas using cuda for visualization.
3. python eval_3dmatch.py --benchmark 3DMatch --data_root your_path/indoor --checkpoint your_path/3dmatch.pth --saved_path work_dirs/3dmatch --vis --no_cuda menas visualizing on cpu.

Besides, you may add torch.cuda.empty_cache() in line 199 as follows:
https://github.com/zhulf0804/NgeNet/blob/d4917f22e55195132ec6fc602554102d321ce4b5/eval_3dmatch.py#L194-L198

Best.

from gcnet.