Xunjiang (Alfred) Gu
Xunjiang (Alfred) Gu
An ugly workaround is to set every torch inverse to follows: `torch.inverse(lidar2ego_rots.to("cpu")).to("cuda:0")`
The performance seems far off from what is being published. The Shapely version is 1.8.5.post1.
As you can see, the result is quite poor with the uploaded checkpoint on the nuscenes mini. I am wondering why this is the case?
@colahe On my local machine
@colahe Sorry for the late reply. It's been a while since I explored this repo. I don't quite remember the specific arguments but I think it is a similar command...
I do not quite remember, did you try on the entire val set? You can try visualizing it to see whether it is really poor or not
I also tried training end-to-end on the mini dataset, the problem still occurs. So I am guessing that the small dataset is not enough to train at least some minimal...
@yuyuyuyuyuty I think the above explanation is the reason.
Hi, can you maybe double check how you obtain hivt trj? It should be in 3 seconds, but yours is in 2 seconds, seems like the history to me. Also,...
To debug, maybe look at what this [line](https://github.com/alfredgu001324/MapUncertaintyPrediction/blob/8ab64116982303d373eb85fea2501e139a09e781/HiVT_modified/models/hivt.py#L138) outputs?