Unfortunately, the implementation of the 5 Monte Carlo forward passes - mentioned in the paper - confuses me.
In montecarlo_sampling.py, badge_sampling.py and crb_sampling.py there is no indication that a sample is forwarded to the model 5 (or multiple) times. After checking montecarlo_sampling.py in detail it also does not seem to be present in the dataloader or other locations.
Could you elaborate where the multiple forward passes take place?
Additionally, I am wondering why Badge is using Monte Carlo dropout at all, I double checked my knowledge about Badge and it is not mentioning it in the paper or using Monto Carlo dropout in their implementation. I could not find it in your paper. Is there a reason for including it, contrasting to the original paper.
Dear Authors,
thanks for providing this nice framework.
Unfortunately, the implementation of the 5 Monte Carlo forward passes - mentioned in the paper - confuses me.
In montecarlo_sampling.py, badge_sampling.py and crb_sampling.py there is no indication that a sample is forwarded to the model 5 (or multiple) times. After checking montecarlo_sampling.py in detail it also does not seem to be present in the dataloader or other locations.
https://github.com/Luoyadan/CRB-active-3Ddet/blob/9af7e973892930e7244700b337c9ad0179f27824/pcdet/query_strategies/montecarlo_sampling.py#L45
Could you elaborate where the multiple forward passes take place?
Additionally, I am wondering why Badge is using Monte Carlo dropout at all, I double checked my knowledge about Badge and it is not mentioning it in the paper or using Monto Carlo dropout in their implementation. I could not find it in your paper. Is there a reason for including it, contrasting to the original paper.
https://openreview.net/pdf?id=ryghZJBKPS https://github.com/JordanAsh/badge/blob/master/query_strategies/badge_sampling.py
Thanks in advance.