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

Python 3, PyTorch(1.2.0)

Other dependencies can be installed via

pip install -r requirements.txt

• 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.

• 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

• 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

• 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

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},
}

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