https://github.com/PointsCoder/Pyramid-RCNN

Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection
https://github.com/PointsCoder/Pyramid-RCNN

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Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection

• Host: GitHub
• URL: https://github.com/PointsCoder/Pyramid-RCNN
• Owner: PointsCoder
• Created: 2021-09-06T13:27:12.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-07-08T13:14:56.000Z (about 2 years ago)
• Last Synced: 2024-07-21T21:43:34.010Z (about 2 months ago)
• Language: Python
• Homepage:
• Size: 165 KB
• Stars: 65
• Watchers: 2
• Forks: 12
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Pyramid R-CNN

This is a reproduced repo of Pyramid R-CNN for 3D object detection. 

The code is mainly based on [OpenPCDet](https://github.com/open-mmlab/OpenPCDet).

## Introduction

We provide code and training configurations of Pyramid-V/PV on the KITTI and Waymo Open dataset. Checkpoints will not be released. The dataset organization is same with PCDet. 

## Requirements

The codes are tested in the following environment:

* Ubuntu 18.04

* Python 3.6

* PyTorch 1.5

* CUDA 10.1

* OpenPCDet v0.3.0

* spconv v1.2.1

## Installation

a. Clone this repository.

```shell

git clone https://github.com/PointsCoder/Pyramid_R-CNN.git

```

b. Install the dependent libraries as follows:

* Install the dependent python libraries: 

```

pip install -r requirements.txt 

```

* Install the SparseConv library, we use the implementation from [`[spconv]`](https://github.com/traveller59/spconv). 

    * If you use PyTorch 1.1, then make sure you install the `spconv v1.0` with ([commit 8da6f96](https://github.com/traveller59/spconv/tree/8da6f967fb9a054d8870c3515b1b44eca2103634)) instead of the latest one.

    * If you use PyTorch 1.3+, then you need to install the `spconv v1.2`. As mentioned by the author of [`spconv`](https://github.com/traveller59/spconv), you need to use their docker if you use PyTorch 1.4+. 

c. Compile CUDA operators by running the following command:

```shell

python setup.py develop

```

## Training

We train all the models with 8 Tesla V100 GPU (32Gb), and all the configs (epochs/learning rate/batch size) are for 8-GPU Distributed Data Parallel (DDP) training.

Users may change those training parameters if they want to run with different GPU numbers and memories. 

* models

```shell script

# pyramid_rcnn_pv.yaml: pyramid roi head on the point-voxel backbone

# pyramid_rcnn_v.yaml: pyramid roi head on the spconv u-net backbone

```

* DDP training

```shell script

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 sh scripts/dist_train.sh 8 --cfg_file cfgs/waymo_models/pyramid_rcnn_pv.yaml

```

* DDP testing

```shell script

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 sh scripts/dist_test.sh 8 --cfg_file cfgs/waymo_models/pyramid_rcnn_pv.yaml --eval_all

```

## Citation 

If you find this project useful in your research, please consider cite:

```

@article{mao2021pyramid,

  title={Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection},

  author={Mao, Jiageng and Niu, Minzhe and Bai, Haoyue and Liang, Xiaodan and Xu, Hang and Xu, Chunjing},

  journal={ICCV},

  year={2021}

}

```