Thank you very much for your contribution to point cloud instance segmentation, but I encountered an oom problem when reproducing the experiment.
My experimental environment is a cloud server with 40G video memory. I even adjusted the batch_size to 2 and still oom.
First of all, when I executed test.py on the stplsed data set, I used more than 30 G of video memory. Although there are only 25 point cloud files under the val_250m file. Secondly, when I train on the stpls3d data set, it will oom whenever val is used.
I don't know why this is happening. I noticed that you can complete the experiment with 32G of video memory on v100. Looking forward to your reply.
Thank you very much for your contribution to point cloud instance segmentation, but I encountered an oom problem when reproducing the experiment.
My experimental environment is a cloud server with 40G video memory. I even adjusted the batch_size to 2 and still oom.
First of all, when I executed test.py on the stplsed data set, I used more than 30 G of video memory. Although there are only 25 point cloud files under the val_250m file. Secondly, when I train on the stpls3d data set, it will oom whenever val is used.
I don't know why this is happening. I noticed that you can complete the experiment with 32G of video memory on v100. Looking forward to your reply.