PLANNER.md

Planner Instructions

Training planner

To train a model on real data run:

python scripts/train.py +experiment=planning.yaml ++data.loaders.train.dataset.percentage_to_use=0.1

To train a model on LidarDM generated data run:

python scripts/train.py +experiment=planning_generated.yaml ++data.loaders.train.dataset.percentage_to_use=0.10

Runs will be named based on a timestamp and stored in the runs/ folder. During training, checkpoints are stored in the checkpoints/ subfolder.

To finetune a model on real data, modify the following command with the PATH_TO_PRETRAINED with the path to a checkpoint file (ends with .ckpt) for a planner trained on generated data:

python scripts/train.py +experiment=planning_resume.yaml ++data.loaders.train.dataset.percentage_to_use=0.10 ++model.pretrained=PATH_TO_PRETRAINED

Evaluating Metrics

After training a planner model, L2 distance and collision percentage can be evaluated for a checkpoint with:

python scripts/metric/planning_metrics.py +experiment=planning.yaml +pmetrics.trajbank_dir=../../pretrained_models/waymo_trajbank.npy  +model.pretrained=PATH_TO_PRETRAINED