An Efficient, Flexible, and General deep learning framework that retains minimal. Users can use EFG to explore any research topics following project templates.

What's New

• 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).

0. Benchmarking

1. Installation

1.1 Prerequisites

• 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

1.2 Build from source

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

2. Data

2.1 Data Preparation - Waymo

# 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

2.2 Data Preparation - nuScenes

# 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)

3. Get Started

3.1 Training & Evaluation

# 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

4. Model ZOO

All models are trained and evaluated on 8 x NVIDIA A100 GPUs.

Waymo Open Dataset - 3D Object Detection (val - mAPH/L2)

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

nuScenes - 3D Object Detection (val)

Methods	Schedule	mAP	NDS	Logs
CenterPoint	20	59.0	66.7

5. Call for contributions

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.

6. Citation

@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}
}