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https://github.com/maudzung/CenterNet3D-PyTorch
Unofficial PyTorch implementation of the paper: "CenterNet3D: An Anchor free Object Detector for Autonomous Driving"
https://github.com/maudzung/CenterNet3D-PyTorch
3d-object-detection centernet centernet3d lidar object-detection sparse-convolution spconv voxel voxelnet
Last synced: 7 days ago
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Unofficial PyTorch implementation of the paper: "CenterNet3D: An Anchor free Object Detector for Autonomous Driving"
- Host: GitHub
- URL: https://github.com/maudzung/CenterNet3D-PyTorch
- Owner: maudzung
- License: gpl-3.0
- Created: 2020-08-17T08:19:47.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2020-08-20T09:15:41.000Z (about 4 years ago)
- Last Synced: 2024-08-01T03:43:52.892Z (3 months ago)
- Topics: 3d-object-detection, centernet, centernet3d, lidar, object-detection, sparse-convolution, spconv, voxel, voxelnet
- Language: Python
- Homepage: https://arxiv.org/pdf/2007.07214.pdf
- Size: 1.11 MB
- Stars: 71
- Watchers: 5
- Forks: 13
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CenterNet3D-PyTorch
[![python-image]][python-url]
[![pytorch-image]][pytorch-url]
The PyTorch Implementation of the paper:
[CenterNet3D: An Anchor free Object Detector for Autonomous Driving](https://arxiv.org/pdf/2007.07214.pdf)
---
## Features
- [x] LiDAR-based realtime 3D object detection
- [x] Support [distributed data parallel training](https://github.com/pytorch/examples/tree/master/distributed/ddp)
- [ ] Release pre-trained models
## 2. Getting Started
### 2.1. Requirement
```shell script
pip install -U -r requirements.txt
```
- For [`mayavi`](https://docs.enthought.com/mayavi/mayavi/installation.html) library, please refer to the installation instructions from its official website.
- To build the `CenterNet3D` model, I have used the [spconv](https://github.com/traveller59/spconv) library. Please follow the
instruction from the repo to install the library. I also wrote notes for the installation [here](./INSTALL_spconv.md)
### 2.2. Data Preparation
Download the 3D KITTI detection dataset from [here](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d).
The downloaded data includes:
- Velodyne point clouds _**(29 GB)**_
- Training labels of object data set _**(5 MB)**_
- Camera calibration matrices of object data set _**(16 MB)**_
- **Left color images** of object data set _**(12 GB)**_
Please make sure that you construct the source code & dataset directories structure as below.
### 2.3. CenterNet3D architecture
### 2.4. How to run
#### 2.4.1. Visualize the dataset
```shell script
cd src/data_process
```
- To visualize 3D point clouds with 3D boxes, let's execute:
```shell script
python kitti_dataset.py
```
*An example of the KITTI dataset:*
#### 2.4.2. Inference
```
python test.py --gpu_idx 0 --peak_thresh 0.2
```
#### 2.4.4. Training
##### 2.4.4.1. Single machine, single gpu
```shell script
python train.py --gpu_idx 0 --batch_size --num_workers ...
```
##### 2.4.4.2. Multi-processing Distributed Data Parallel Training
We should always use the `nccl` backend for multi-processing distributed training since it currently provides the best
distributed training performance.
- **Single machine (node), multiple GPUs**
```shell script
python train.py --dist-url 'tcp://127.0.0.1:29500' --dist-backend 'nccl' --multiprocessing-distributed --world-size 1 --rank 0
```
- **Two machines (two nodes), multiple GPUs**
_**First machine**_
```shell script
python train.py --dist-url 'tcp://IP_OF_NODE1:FREEPORT' --dist-backend 'nccl' --multiprocessing-distributed --world-size 2 --rank 0
```
_**Second machine**_
```shell script
python train.py --dist-url 'tcp://IP_OF_NODE2:FREEPORT' --dist-backend 'nccl' --multiprocessing-distributed --world-size 2 --rank 1
```
To reproduce the results, you can run the bash shell script
```bash
./train.sh
```
#### Tensorboard
- To track the training progress, go to the `logs/` folder and
```shell script
cd logs//tensorboard/
tensorboard --logdir=./
```
- Then go to [http://localhost:6006/](http://localhost:6006/):
## Contact
If you think this work is useful, please give me a star!
If you find any errors or have any suggestions, please contact me (**Email:** `[email protected]`).
Thank you!
## Citation
```bash
@article{CenterNet3D,
author = {Guojun Wang, Bin Tian, Yunfeng Ai, Tong Xu, Long Chen, Dongpu Cao},
title = {CenterNet3D: An Anchor free Object Detector for Autonomous Driving},
year = {2020},
journal = {arXiv},
}
@misc{CenterNet3D-PyTorch,
author = {Nguyen Mau Dung},
title = {{CenterNet3D-PyTorch: PyTorch Implementation of the CenterNet3D paper}},
howpublished = {\url{https://github.com/maudzung/CenterNet3D-PyTorch}},
year = {2020}
}
```
## References
[1] CenterNet: [Objects as Points paper](https://arxiv.org/abs/1904.07850), [PyTorch Implementation](https://github.com/xingyizhou/CenterNet)
[2] VoxelNet: [PyTorch Implementation](https://github.com/skyhehe123/VoxelNet-pytorch)
## Folder structure
```
${ROOT}
└── checkpoints/
├── centernet3d.pth
└── dataset/
└── kitti/
├──ImageSets/
│ ├── test.txt
│ ├── train.txt
│ └── val.txt
├── training/
│ ├── image_2/ (left color camera)
│ ├── calib/
│ ├── label_2/
│ └── velodyne/
└── testing/
│ ├── image_2/ (left color camera)
│ ├── calib/
│ └── velodyne/
└── classes_names.txt
└── src/
├── config/
│ ├── train_config.py
│ └── kitti_config.py
├── data_process/
│ ├── kitti_dataloader.py
│ ├── kitti_dataset.py
│ └── kitti_data_utils.py
├── models/
│ ├── centernet3d.py
│ ├── deform_conv_v2.py
│ └── model_utils.py
└── utils/
│ ├── evaluation_utils.py
│ ├── logger.py
│ ├── misc.py
│ ├── torch_utils.py
│ └── train_utils.py
├── evaluate.py
├── test.py
├── train.py
└── train.sh
├── README.md
└── requirements.txt
```
[python-image]: https://img.shields.io/badge/Python-3.6-ff69b4.svg
[python-url]: https://www.python.org/
[pytorch-image]: https://img.shields.io/badge/PyTorch-1.5-2BAF2B.svg
[pytorch-url]: https://pytorch.org/