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HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps

This is the Pytorch reimplementation of HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps.

For detailed experiments please go to the following project page

Visualization

Model generation steps

Dependencies

Python 3, PyTorch(1.2.0)

Other dependencies can be installed via

pip install -r requirements.txt

Important Config Variables

• max_num_nodes - This has to be set manually to highest number of nodes that can occur in a graph in training data. If not set correctly it leads to error in dataloader.
• has_stop_node - This has only be tested for graph with subnode prediction. It should still work for graphs without subnode prediction, but it might also fail.

Other config variables have self-explanatory names.

To understand the code flow properly please use the file: config/gran_grid_small.yaml and set num_subgraph_batch to 1.

Run Demo/Train/Test

Demo

• The pretrained weights are inside the folder: exp/GRAN. There description is as follows

  • base_5 : Pretrained weights for network trained to generate only adjacency for 5x5 grid.
  • embed_5: Pretrained weights for network trained to generate adjacency and node coordinates.
  • subnode_5_noise: Pretrained weights for network with node and subnode and adjacency.
  • subnode_5_no_noise: Same as previous, but trained without noise based augmentation. Gives worse results.
  • GRANMixtureBernoulli_nuplan_2024-Mar-02-16-11-53_2285727: Weights for subset of nuplan dataset trained for 425 epochs.

• For running the demo, select the correct config then change the config variables test_model_dir and test_model_name.

• To run the test of experiments X

  python run_exp.py -c config/X.yaml -t

Train

• To run the training of experiment X where X is one of {gran_subnode, gran_nuplan, gran_embed, gran_grid_small}:

  python run_exp.py -c config/X.yaml

Test

• After training, you can specify the test_model field of the configuration yaml file with the path of your best model snapshot, e.g.,

  test_model: exp/gran_grid/xxx/model_snapshot_best.pth

• To run the test of experiments X:

  python run_exp.py -c config/X.yaml -t

Cite

To cite this reimplementation please use:

@misc{agshar96HDMapGenReimplementation,
	author = {},
	title = {{H}{D}{M}ap{G}en: {R}eimplementation and {E}xperiments --- agshar96.github.io},
	howpublished = {\url{https://agshar96.github.io/HDMapGen_Reimplemented/}},
	year = {2024},
}

Acknowledgements

Our implementation extends the model described in the paper GRAN. The code for the paper can be found here