OFPNet

OFPNet (Occupancy and flow predictive network) is developed for end-to-end prediction of occupancy map and flow using reccurent blocks, additional convolutional heads, etc.

OFPNet is a baseline solution for Waymo Occupancy and Flow Prediction

Main Metrics

Metrics	Observed Occupancy	Occluded Occupancy	Flow	Flow-Grounded Occupancy

Model	AUC	Soft IoU	AUC	Soft IoU	EPE	AUC	Soft IoU
UNet_LSTM	0.6559	0.4007	0.1227	0.0261	20.5876	0.5768	0.4280
UNet_LSTM_Head	0.6517	0.3859	0.1199	0.0225	20.1838	0.5840	0.4119
unext	0.6485	0.3580	0.0376	0.0084	21.6873	0.5598	0.4098
unext_head	0.7119	0.4257	0.1451	0.0309	21.6873	0.5691	0.4243

Basic Installation

Docker container:

Using nvidia-docker with cuda-11.3, Pytorch

cd path/to/workspace
git clone https://github.com/YoushaaMurhij/Occ_Flow_Pred.git
cd Occ_Flow_Pred/docker
./build.sh
cd ..
./docker/start.sh
./docker/into.sh

Conda environment (not recommended):

conda create --name occ_flow 
conda activate occ_flow
conda install pytorch torchvision cudatoolkit=11.3 -c pytorch
git clone https://github.com/YoushaaMurhij/Occ_Flow_Pred.git
cd Occ_Flow_Pred
pip install -r requirements.txt

# add Occ_Flow_Pred to PYTHONPATH by adding the following line to ~/.bashrc (change the path accordingly)
export PYTHONPATH="${PYTHONPATH}:/path/to/Occ_Flow_Pred/"

TODOs:

• change data input
• add more aux losses

Contribution:

Questions, suggestions and pull-requests are welcome!
Feel free to open an issue or a pull-request ☺️

Contacts:

Youshaa Murhij 📬 yosha[dot]morheg[at]phystech[dot]edu
Dmitry Yudin 📬 yudin[dot]da[at]mipt[dot]ru