High memory consumption

[MickShen7558]

Hi,

Thank you for the work and the code!

When I tried to run the DiLiGenT-MV benchmark objects, it seems that the final saving requires very large memory:

So is there a way to sacrifice the performance by a little bit and get the result?

[xucao-42]

Have you installed pyembree library? This step is to find the ray-mesh intersection for geometry evaluation.

Or you can bypass this step by setting val.eval_metric_freq as a larger number than train.end_iter in the configure file so that the evaluation will not be performed. To still extract the mesh, you can set val.val_mesh_freq as 5000, for example, so that a mesh will be extracted for every 5000 step.