Awesome

ScePT

Environment Setup

First, we'll create a conda environment to hold the dependencies.

conda create --name ScePT python=3.8 -y
source activate ScePT
pip install -r requirements.txt

Data Setup

Pedestrian Datasets

We've already included preprocessed data splits for the ETH and UCY Pedestrian datasets in this repository, you can see them in experiments/pedestrians/raw. In order to process them into a data format that our model can work with, execute the follwing.

cd experiments/pedestrians
python process_data.py # 

nuScenes Dataset

Download the nuScenes dataset (this requires signing up on their website). You can start with the mini dataset as it is smaller. Extract the downloaded zip file's contents and place them in the experiments/nuScenes directory. Then, download the map expansion pack (v1.1) and copy the contents of the extracted maps folder into the experiments/nuScenes/v1.0-mini/maps folder. Finally, process them into a data format that our model can work with.

cd experiments/nuScenes

# For the mini nuScenes dataset, use the following
python process_data.py --data=./v1.0-mini --version="v1.0-mini" --output_path=../processed --num_worker=X

# For the full nuScenes dataset, use the following
python process_data.py --data=./v1.0 --version="v1.0-trainval" --output_path=../processed --num_worker=X

In case you also want a validation set generated (by default this will just produce the training and test sets), replace line 406 in process_data.py with:

    val_scene_names = val_scenes

Model Training

Pedestrian Dataset

To train a model on the ETH and UCY Pedestrian datasets, you can execute a version of the following command from within the ScePT/ directory.

python -m torch.distributed.launch --nproc_per_node=X train.py --train_data_dict <dataset>_train.pkl --eval_data_dict <dataset>_val.pkl --offline_scene_graph yes --preprocess_workers X --log_dir ../experiments/pedestrians/models  --train_epochs X --augment --conf ../config/clique_ped_config.json --indexing_workers=X --batch_size=X --vis_every=X --eval_every=X

nuScenes Dataset

To train a model on the nuScenes dataset, you can execute a version of the following command from within the ScePT/ directory.

python -m torch.distributed.launch --nproc_per_node=1 train_clique.py --train_data_dict nuScenes_train.pkl --eval_data_dict nuScenes_val.pkl --offline_scene_graph yes --preprocess_workers X --log_dir ../experiments/nuScenes/models  --train_epochs X --augment --conf ../config/clique_nusc_config.json --indexing_workers=X --batch_size=X --vis_every=X --map_encoding --incl_robot_node --eval_every=X

Model Evaluation

To evaluate a model, you can try the following command

python evaluate.py --eval_data_dict=nuScenes_val.pkl --iter_num=10 --log_dir=../experiments/nuscenes/models/ --trained_model_dir=XXX(e.g. 01_Dec_2022_00_00_00) --eval_task=eval_statistics