Alan

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Hi, your configuration looks good. nuscens has 3 splits: train, train_val, and val. We train MTR on train and validate on val. There are 32k samples in train and 9k...

Can you try to comment the 'try, catch' in base_dataset.py. In this way you can see the errors in data loading.

Hi, you need to convert the test data into ScenarioNet format. Please let me know if this doesn't work.

Hi, the setting you mentioned is called 'scene-centric' prediction. You can find more information in this paper https://proceedings.neurips.cc/paper_files/paper/2023/file/b37c2e26b75ee02fcabd65a2a0367136-Paper-Conference.pdf

Yes. We are working on implementing SMART with UniTraj.

Hi, Sorry for the late response. The processed dataset is much larger than the original, making it impossible for cluster-transferring. However I'm glad to answer your questions in data processing

remove 'verbose in 'scheduler = MultiStepLR(optimizer, milestones=self.config['learning_rate_sched'], gamma=0.5,'

try pip install waymo-open-dataset-tf-2-12-0==1.6.7