The 3rd place solution for competition “Lyft Motion Prediction for Autonomous Vehicles” at Kaggle
Team behind this solution:
- Artsiom Sanakoyeu [Homepage] [Twitter] [Telegram Channel] [LinkedIn]
- Dmytro Poplavskiy [Kaggle] [LinkedIn]
- Artsem Zhyvalkouski [Kaggle] [Twitter] [GitHub] [LinkedIn]
Explanation of the solution:
How to reproduce results
- [Optional] Set the paths in the configs. But the default paths should work as well.
- Set path where to store prerendered dataset in src/1st_level/config.py
- Set path where the predicts of the 1st level models are saved in src/2nd_level/config.py
- Install dependencies.
pip install -r requirements.txt
- Apply patch to l5kit with
./apply_l5kit_patch.sh(it disables processing of rasterized images to allow rasterizer to return multiple results).
- Download and prepare data.
- Train 1st level models.
- Run inference of 1st level models on the test set.
You may need to change which chekpoints to load when predicting (in predict_test_l1.sh), as the best epoch may change after you retrain the models.
bash prepare_data_test.sh bash predict_test_l1.sh
- Train 2nd level model on the predicts of the 1st level models on the test set.
cd src/2nd_level && python train.py
Make sure you’ve set all paths right in
2nd_level/config.py w.r.t. the
- Predict on the test set using the 2nd level model.
cd src/2nd_level && python infer.py
The file witn final predictions will be saved to `src/2nd_level/submission.csv’.
Directory structure example (i.e., how it should look like after everything is trained and predicted) is in directory_structure.txt.
- To skip training the 1st level models, you can download the pretrained weights by running
- To skip training and inference of the 1st level models, you can download all predicts. More details on this are in src/1st_level/submissions.
- More details on how to use 2nd level model are in src/2nd_level.
- Our final 2nd level model with 9.404 Private LB score is already committed in this repository (src/2nd_level/transformer.bin). To run inference using this model you can directly execute
cd src/2nd_level && python infer.py.