RelTR: Relation Transformer for Scene Graph Generation

PyTorch Implementation of the Paper RelTR: Relation Transformer for Scene Graph Generation

Different from most existing advanced approaches that infer the dense relationships between all entity proposals, our one-stage method can directly generate a sparse scene graph by decoding the visual appearance.

0. Checklist

• Inference Code 🎉
• Training Code for Visual Genome 🎉
• Evaluation Code for Visual Genome 🎉
• Training Code for OpenImages V6 🕘
• Evaluation Code for OpenImages V6 🕘

1. Installation

Download RelTR Repo with:

git clone https://github.com/yrcong/RelTR.git
cd RelTR

For Inference

😄 It is super easy to configure the RelTR environment.

If you want to infer an image, only python=3.6, PyTorch=1.6 and matplotlib are required! You can configure the environment as follows:

# create a conda environment 
conda create -n reltr python=3.6
conda activate reltr

# install packages
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch
conda install matplotlib

Training/Evaluation on Visual Genome

If you want to train/evaluate RelTR on Visual Genome, you need a little more preparation:

a) Scipy (we used 1.5.2) and pycocotools are required.

conda install scipy
pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

b) Download the annotations of Visual Genome (in COCO-format) and unzip it in the data/ forder.

c) Download the the images of VG Part1 and Part2. Unzip and place all images in a folder data/vg/images/

d) Some widely-used evaluation code (IoU) need to be compiled... We will replace it with Pytorch code.

# compile the code computing box intersection
cd lib/fpn
sh make.sh

The directory structure looks like:

RelTR
| 
│
└───data
│   └───vg
│       │   rel.json
│       │   test.json
│       |   train.json
|       |   val.json
|       |   images
└───datasets    
... 

2. Usage

Inference

a) Download our RelTR model pretrained on the Visual Genome dataset and put it under

ckpt/checkpoint0149.pth

b) Infer the relationships in an image with the command:

python inference.py --img_path $IMAGE_PATH --resume $MODEL_PATH

We attached 5 images from VG dataset and 1 image from internet. You can also test with your customized image. The result should look like:

Training

a) Train RelTR on Visual Genome on a single node with 8 GPUs (2 images per GPU):

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --dataset vg --img_folder data/vg/images/ --batch_size 2 --output_dir ckpt

Evaluation

b) Evaluate the pretrained RelTR on Visual Genome with a single GPU (1 image per GPU):

python main.py --dataset vg --img_folder data/vg/images/ --eval --batch_size 1 --resume ckpt/checkpoint0149.pth