Lyft Motion Prediction for Autonomous Vehicles

Code for the 4th place solution of Lyft Motion Prediction for Autonomous Vehicles on Kaggle.

Directory structure

input               --- Please locate data here
|-ensemble          --- For 4. Ensemble scripts
|-lib               --- Library codes
|-modeling          --- For 1. training, 2. prediction and 3. evaluation scripts
  |-results         --- Training, prediction and evaluation results will be stored here           --- This instruction file
requirements.txt    --- For python library versions

Hardware (The following specs were used to create the original solution)

  • Ubuntu 18.04 LTS
  • 32 CPUs
  • 128GB RAM
  • 8 x NVIDIA Tesla V100 GPUs

Software (python packages are detailed separately in requirements.txt):

Python 3.8.5
CUDA 10.1.243
cuddn 7.6.5
nvidia drivers v.55.23.0
-- Equivalent Dockerfile for the GPU installs: Use nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04 as base image

Also, we installed OpenMPI==4.0.4 for running pytorch distributed training.

Python Library

Deep learning framework, base library

  • torch==1.6.0+cu101
  • torchvision==0.7.0
  • l5kit==1.1.0
  • cupy-cuda101==7.0.0
  • pytorch-ignite==0.4.1
  • pytorch-pfn-extras==0.3.1

CNN models

Data processing/augmentation

  • albumentations==0.4.3
  • scikit-learn==0.22.2.post1

We also installed apex

Please refer requirements.txt for more details.

Environment Variable

We recommend to set following environment variables for better performance.


Data setup

Please download competition data:

For the lyft-motion-prediction-autonomous-vehicles dataset,
extract them under input/lyft-motion-prediction-autonomous-vehicles directory.

For the lyft-full-training-set data which only contains train_full.zarr,
please place it under input/lyft-motion-prediction-autonomous-vehicles/scenes as follows:

    |-train_full.zarr (Place here!)
    |-... (other data)
  |-... (other data)


Our submission pipeline consists of 1. Training, 2. Prediction, 3. Ensemble.

Training with training/validation dataset

The training script is located under src/modeling. is the training script and
the training configuration is specified by flags yaml file.

[Note] If you want to run training from scratch, please remove results folder once.
The training script tries to resume from results folder when resume_if_possible=True is set.

[Note] For the first time of training, it creates cache for training to run efficiently.
This cache creation should be done in single process,
so please try with the single GPU training until training loop starts.
The cache is directly created under input directory.

Once the cache is created, we can run multi-GPU training using same script,
with mpiexec command.

$ cd src/modeling

# Single GPU training (Please run this for first time, for input data cache creation)
$ python --yaml_filepath ./flags/20201104_cosine_aug.yaml

# Multi GPU training (-n 8 for 8 GPU training)
$ mpiexec -x MASTER_ADDR=localhost -x MASTER_PORT=8899 -n 8 \
  python --yaml_filepath ./flags/20201104_cosine_aug.yaml

We have trained 9 different models for final submission.
Each training configuration can be found in src/modeling/flags,
and the training results are located in src/modeling/results.

Prediction for test dataset under src/modeling executes the prediction for test data.

Specify out as trained directory, the script uses trained model of this directory to inference.
Please set --convert_world_from_agent true after l5kit==1.1.0.

$ cd src/modeling
$ python --out results/20201104_cosine_aug --use_ema true --convert_world_from_agent true

Predicted results are stored under out directory.
For example, results/20201104_cosine_aug/prediction_ema/submission.csv is created with above setting.

We executed this prediction for all 9 trained models.
We can submit this submission.csv file as the single model prediction.

(Optional) Evaluation with validation dataset under src/modeling executes the evaluation for validation data (chopped data).

python --out results/20201104_cosine_aug --use_ema true

The script shows validation error, which is useful for local evaluation of model performance.


Finally all trained models' predictions are ensembled using GMM fitting.

The ensemble script is located under src/ensemble.

# Please execute from root of this repository.
$ python src/ensemble/ --yaml_filepath src/ensemble/flags/20201126_ensemble.yaml

The location of final ensembled submission.csv is specified in the yaml file.
You can submit this submission.csv by uploading it as dataset, and submit via Kaggle kernel.
Please follow Save your time, submit without kernel inference
for the submission procedure.