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
| 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 |
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 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/"- change data input
- add more aux losses
Questions, suggestions and pull-requests are welcome!
Feel free to open an issue or a pull-request
Youshaa Murhij 📬 yosha[dot]morheg[at]phystech[dot]edu
Dmitry Yudin 📬 yudin[dot]da[at]mipt[dot]ru