![](https://raw.githubusercontent.com/Cedarch/EFG/master/.github/efg_logo.jpeg)
An Efficient, Flexible, and General deep learning framework that retains minimal. Users can use EFG to explore any research topics following project templates.
- 2023.08.22 Code release of ICCV2023 paper: TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses.
- 2023.04.13 Support COCO Panoptic Segmentation with Mask2Former.
- 2023.03.30 Support Pytorch 2.0.
- 2023.03.21 Code release of CVPR2023 Highlight paper: ConQueR: Query Contrast Voxel-DETR for 3D Object Detection.
- 2023.03.21 Code release of EFG codebase, with support for 2D object detection (MS COCO dataset) and 3D object detection (Waymo and nuScenes dataset).
![](https://raw.githubusercontent.com/Cedarch/EFG/master/.github/efg_benchmark.png)
- gcc 5 - 7
- python >= 3.6
- cuda >= 10.2
- pytorch >= 1.6
# spconv
spconv_cu11{X} (set X according to your cuda version)
# waymo_open_dataset
## python 3.6
waymo-open-dataset-tf-2-1-0==1.2.0
## python 3.7, 3.8
waymo-open-dataset-tf-2-4-0==1.3.1
git clone https://github.com/poodarchu/EFG.git
cd EFG
pip install -v -e .
# set logging path to save model checkpoints, training logs, etc.
echo "export EFG_CACHE_DIR=/path/to/your/logs/dir" > ~/.bashrc
# download waymo dataset v1.2.0 (or v1.3.2, etc)
gsutil -m cp -r \
"gs://waymo_open_dataset_v_1_2_0_individual_files/testing" \
"gs://waymo_open_dataset_v_1_2_0_individual_files/training" \
"gs://waymo_open_dataset_v_1_2_0_individual_files/validation" \
.
# extract frames from tfrecord to pkl
CUDA_VISIBLE_DEVICES=-1 python cli/data_preparation/waymo/waymo_converter.py --record_path "/path/to/waymo/training/*.tfrecord" --root_path "/path/to/waymo/train/"
CUDA_VISIBLE_DEVICES=-1 python cli/data_preparation/waymo/waymo_converter.py --record_path "/path/to/waymo/validation/*.tfrecord" --root_path "/path/to/waymo/val/"
# create softlink to datasets
cd /path/to/EFG/datasets; ln -s /path/to/waymo/dataset/root waymo; cd ..
# create data summary and gt database from extracted frames
python cli/data_preparation/waymo/create_data.py --root-path datasets/waymo --split train --nsweeps 1
python cli/data_preparation/waymo/create_data.py --root-path datasets/waymo --split val --nsweeps 1
# create softlink to datasets
cd /path/to/EFG/datasets; ln -s /path/to/nuscenes/dataset/root nuscenes; cd ..
python cli/data_preparation/nuscenes/create_data.py --root-path datasets/nuscenes --version v1.0-trainval --nsweeps 11 # 1 sample frame + 10 sweeps frame (0.5s)
# cd playground/path/to/experiment/directory
efg_run --num-gpus x # default 1
efg_run --num-gpus x task [train | val | test]
efg_run --num-gpus x --resume
efg_run --num-gpus x dataloader.num_workers 0 # dynamically change options in config.yaml
Models will be evaluated automatically at the end of training. Or,
efg_run --num-gpus x task val
All models are trained and evaluated on 8 x NVIDIA A100 GPUs.
Methods | Frames | Schedule | VEHICLE | PEDESTRIAN | CYCLIST |
---|---|---|---|---|---|
CenterPoint | 1 | 36 | 66.9/66.4 | 68.2/62.9 | 69.0/67.9 |
CenterPoint | 4 | 36 | 70.0/69.5 | 72.8/69.7 | 72.6/71.8 |
Voxel-DETR | 1 | 6 | 67.6/67.1 | 69.5/63.0 | 69.0/67.8 |
ConQueR | 1 | 6 | 68.7/68.2 | 70.9/64.7 | 71.4/70.1 |
Methods | Schedule | mAP | NDS | Logs |
---|---|---|---|---|
CenterPoint | 20 | 59.0 | 66.7 |
EFG is currently in a relatively preliminary stage, and we still have a lot of work to do, if you are interested in contributing, you can email me at poodarchu@gmail.com.
@misc{zhu2023efg,
title={EFG: An Efficient, Flexible, and General deep learning framework that retains minimal},
author={EFG Contributors},
howpublished = {\url{https://github.com/poodarchu/efg}},
year={2023}
}