AMMF: Attention-based Multi-phase Multi-task Fusion Network for Robust 3D Detection in Autonomous Driving
This repository contains the public release of the Python implementation of our AMMF network for 3D object detection.
[AMMF: Attention-based Multi-phase Multi-task Fusion Network for Robust 3D Detection in Autonomous Driving]
Bangquan Xie, [Zongming Yang], [Liang Yang], [Ruifa Luo], [Jun Lu], [Ailin Wei], [Xiaoxiong Weng] and [Bing Li]
Implemented and tested on Ubuntu 20.04 with Python 3.6 and Tensorflow-gpu 1.15.0 with CUDA 10.1, pytorch 1.7.1.
- Clone this repo
git clone https://github.com/b-xie/AMMF.git --recurse-submodules
If you forget to clone the wavedata submodule:
git submodule update --init --recursive
- Create environment
conda create -n ammf python=3.6 pip
conda activate ammf
pip install --upgrade pip
- Install Python dependencies
cd AMMF/ammf
pip install -r requirements.txt
pip install tensorflow-gpu==1.15.0
pip install torch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2
- Compile integral image library in wavedata
cd utils
sh scripts/install/build_integral_image_lib.bash
- ammf uses Protobufs to configure model and training parameters. Before the framework can be used, the protos must be compiled (from top level ammf folder):
cd ../..
sh ammf/protos/run_protoc.sh
Alternatively, you can run the protoc
command directly:
protoc ammf/protos/*.proto --python_out=.
To train on the Kitti Object Detection Dataset:
- Download the data (left color images, Velodyne point clouds and other if you need) and place it in your home folder at
~/Kitti/object
- Go here and download the
train.txt
,val.txt
andtrainval.txt
splits into~/Kitti/object
. Also download theplanes
folder into~/Kitti/object/training
The folder should look something like the following:
Kitti
object
testing
training
calib
image_2
label_2
planes
velodyne
train.txt
trainval.txt
val.txt
https://drive.google.com/drive/folders/1A-_wfcO_BthlOlGONPXTSVSSX0DcqQPN?usp=sharing
Public soon...
Copyright (c) 2021 Bangquan Xie, [Zongming Yang], [Liang Yang], [Ruifa Luo], [Jun Lu], [Ailin Wei], [Xiaoxiong Weng] and [Bing Li]
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Our implementation leverages on the source code from the following repositories: