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View Code? Open in Web Editor NEW[CVPR 2023] Official repository for FJMP: Factorized Joint Multi-Agent Motion Prediction
License: MIT License
[CVPR 2023] Official repository for FJMP: Factorized Joint Multi-Agent Motion Prediction
License: MIT License
Thank you for this excellent work, but when will you please public the code?
Hi,Luke:
Sorry for disturbing again!When i want to evaluate FJMP result on INTERACTION leaderboard,i got no reply despite i follow all format requirements mentioned.Could you please provide your test result csv files so that i can see what format mistake i comitted when submitting result file.
Moreover,INTERACTION dataset asks to submit psirad predictions.But I haven't find prediction about psirad in your model.
Looking forward for a reply!Thanks!
Thank you for this excellent work, but I have a question.Here, there is no command provided for training on the Argoverse 2 dataset using a single GPU. However, there is such a command available for the INTERACTION dataset.Is it because the model's memory requirements exceed the memory limit of a single GPU?Do I have enough with only 12 GB of memory to run this model?
Hello,Luke:
I think your excellent work is deserved to study.But when i'm running your code with horovod commands horovodrun -np 4 -H localhost,it says:
[mpiexec@esc8000-g4] match_arg (utils/args/args.c:163): unrecognized argument allow-run-as-root
[mpiexec@esc8000-g4] HYDU_parse_array (utils/args/args.c:178): argument matching returned error
[mpiexec@esc8000-g4] parse_args (ui/mpich/utils.c:1642): error parsing input array
[mpiexec@esc8000-g4] HYD_uii_mpx_get_parameters (ui/mpich/utils.c:1694): unable to parse user arguments
[mpiexec@esc8000-g4] main (ui/mpich/mpiexec.c:148): error parsing parameters
Seem it has something wrong with my mpi4py installing when following your instructions in README file pip install mpi4py == 3.1.4.But i can't fix it.I'd appreciate it if anyone can solve my problem!
Dear authors,
Congratulations on this nice work being accepted!
We notice that the Leaderboard of Argoverse 2 multi-world forecasting has re-opened. We would appreciate if you could provide the result on the test set for our comparison with your excellent work.
Best
Dear author,
Hello, you gave to ‘’FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction "code links to the code of other articles, Please check whether the link is incorrect
Hi Luke,
Cheers for your wonderful work! Out of curiosity, when will the trained checkpoints be released? I would love to have a try without having to train and tune the model from scratch lol. Thanks!
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