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
src
|-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
README.md --- 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
github.com/nvidia/apex
Please refer requirements.txt
for more details.
Environment Variable
We recommend to set following environment variables for better performance.
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=1
export NUMEXPR_NUM_THREADS=1
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:
input
|-lyft-motion-prediction-autonomous-vehicles
|-scenes
|-train_full.zarr (Place here!)
|-train.zarr
|-validate.zarr
|-test.zarr
|-... (other data)
|-... (other data)
Pipeline
Our submission pipeline consists of 1. Training, 2. Prediction, 3. Ensemble.
Training with training/validation dataset
The training script is located under src/modeling
.
train_lyft.py
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 train_lyft.py
script,
with mpiexec
command.
$ cd src/modeling
# Single GPU training (Please run this for first time, for input data cache creation)
$ python train_lyft.py --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 train_lyft.py --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
predict_lyft.py
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 predict_lyft.py --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
eval_lyft.py
under src/modeling
executes the evaluation for validation data (chopped data).
python eval_lyft.py --out results/20201104_cosine_aug --use_ema true
The script shows validation error, which is useful for local evaluation of model performance.
Ensemble
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/ensemble_test.py --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.
GitHub
github.com/pfnet-research/kaggle-lyft-motion-prediction-4th-place-solution
Read more here: Source link