About Training Parallelism

[xheon]

About Training Parallelism

[xheon]

The model was trained on a RTX 2080Ti with 11GB memory.

Few things you can check:

• Increase the number of iterations of lower resolutions are trained (LEVEL_ITERATIONS_64, LEVEL_ITERATIONS_128) before you train the entire model. If your lower resolution predictions are not good enough a lot of voxels could be created on the final resolution, which will require a lot of memory.
• Increase the masking threshold (SPARSE_THRESHOLD_128, SPARSE_THRESHOLD_256). With that you can control the level of "confidence" an occupied voxel needs to be considered for the next resolution.
• Generally, the 2D features (80 channels) did not contribute too much to the final performance, but actually require some memory. You can remove that part of the 3D model.

[macromogic]

Thanks for the reply! However, I still could not proceed with the level-256 training. I am using the latest version of BlenderProc, so I suspect it was because the format of my generated data is different from yours (which may influence the performance of level-64 and 128). I am still figuring out why.

I inspected the 3D-FRONT dataset and read the code of your forked BlenderProc. There is something I am still wondering about:

1. In the SegMapRenderer class, you seemed to map the instance to another integer ID (while my data is in float64 type). Does it affect the model performance?
2. I noticed the dataset contains both raw_model and normalized_model files. How is the normalization performed? Does it have anything to do with geometry data generation?