akashadhikari / kaggle-lyft-motion-prediction-av

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.