asanakoy/kaggle-lyft-motion-prediction-av

The 3rd place solution for competition "Lyft Motion Prediction for Autonomous Vehicles" at Kaggle

The 3rd place solution for competition "Lyft Motion Prediction for Autonomous Vehicles" at Kaggle

Team behind this solution:

1. Artsiom Sanakoyeu [Homepage] [Twitter] [Telegram Channel] [LinkedIn]
2. Dmytro Poplavskiy [Kaggle] [LinkedIn]
3. Artsem Zhyvalkouski [Kaggle] [Twitter] [GitHub] [LinkedIn]

Explanation of the solution:

▶️ Video: link
📜 Blogpost: link
📝 Brief solution writeup: link

How to reproduce results

0. [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

1. 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).

2. Download and prepare data.

bash prepare_data_train.sh

3. Train 1st level models.

bash train.sh

4. 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

4. 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 2nd_level directory.

6. 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.

Extra

• To skip training the 1st level models, you can download the pretrained weights by running bash download_1st_level_weights.sh.
• 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.