This guide provides instructions on how to run an autoencoder in SLEDGE. The tutorial below shows the key functionalities of the raster-to-vector autoencoder (RVAE).
Similar to the nuplan-devkit, pre-processing of the training data is recommended. The cache for the RVAE can be created by running:
cd $SLEDGE_DEVKIT_ROOT/scripts/autoencoder/rvae/
bash feature_caching_rvae.shThis script pre-processes the vector features of several maps sequentially. The cached features only store the local map and agents in a general vector format. The features are further processed and rasterized on the fly during training. This two-step processing enables fast access to training data and allows data augmentation (e.g. random rotation and translation) for RVAE training. The feature cache is compatible with other autoencoders.
After creating or downloading the autoencoder cache, you can start the training. We provide an example script in the same folder.
bash training_rvae.shYou can find the experiment folder of training in $SLEDGE_EXP_ROOT/exp and monitor the run with tensorboard.
You must first cache the latent variables to run a latent diffusion model with the trained autoencoder. In SLEDGE, we cache the latent variables into the autoencoder cache directory (i.e. $SLEDGE_EXP_ROOT/caches/autoencoder_cache). The bash script is provided in the RVAE folder.
bash latent_caching_rvae.shImportantly, data augmentation is disabled for latent caching. We also only cache the samples from the training split.
Coming soon!