Not running for "all55" version on "interpolate_posterior.sh"

[dqj5182]

I wish to do interpolation with "all55" model but the "interpolate_posterior.sh" does not work with the following error.
Could you give insight on how to solve this issue?

[noahcao]

@ZENGXH I am facing the same problem here. what is the definition of the missing file of datasets.neuralspline_datasets? Also, does this mean that unconditional/all55 checkpoint is trained by a different dataset convention from others?

[ZENGXH]

@dqj5182 you could set data.type to be datasets.pointflow_datasets; if you want to do interpolation of all55 class, you need to load the all55 dataset, but the training dataset is not released. one thing you could do is to modify the dataloader a bit to load the released validation point cloud for the all55 model.

Note the coordinates system of the all55 data are a bit different from the released pointflow datasets, so if you want use pointflow point cloud for poterior interpolation you need to write a small function to convert the coordinates system, you could use the validation point cloud for the all55 model as reference to check if that's correct or not

[ZENGXH]

@noahcao the two dataset are all from shapenet, the difference is all55 has different coordinates system, and all shape are using box normalization.
the all55 shapes are converted from the surface so there is not points indicating the internal structure

[ZENGXH]

example scripts to convert the coordinates: https://github.com/nv-tlabs/LION/blob/main/utils/render_mitsuba_pc.py#L49-L50
I use this when convert the all55 dataset (we called it Shapenet-vol in the paper) points to be in the same coordinates as the point flow (we called it Shapenet dataset in the paper). (for the use case here, we want to convert pointflow pointcloud back to the corrdinate system of all55 dataset)

it's always better to visualize them to make sure the data is align with the validation point cloud of all55 model to make sure you get correct input for the all55 model.