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RelTransformer

Our Architecture

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This is a Pytorch implementation for RelTransformer

Requirements

conda env create -f reltransformer_env.yml

Compilation

Compile the CUDA code in the Detectron submodule and in the repo:

cd $ROOT/lib
sh make.sh

Annotations

create a data folder at the top-level directory of the repository

# ROOT = path/to/cloned/repository
cd $ROOT
mkdir data

GQA

Download it here. Unzip it under the data folder. You should see a gvqa folder unzipped there. It contains seed folder called seed0 that contains .json annotations that suit the dataloader used in this repo.

Visual Genome

Download it here. Unzip it under the data folder. You should see a vg8k folder unzipped there. It contains seed folder called seed3 that contains .json annotations that suit the dataloader used in this repo.

Word2Vec Vocabulary

Create a folder named word2vec_model under data. Download the Google word2vec vocabulary from here. Unzip it under the word2vec_model folder and you should see GoogleNews-vectors-negative300.bin there.

Images

GQA

Create a folder for all images:

# ROOT=path/to/cloned/repository
cd $ROOT/data/gvqa
mkdir images

Download GQA images from the here

Visual Genome

Create a folder for all images:

# ROOT=path/to/cloned/repository
cd $ROOT/data/vg8k
mkdir VG_100K

Download Visual Genome images from the official page. Unzip all images (part 1 and part 2) into VG_100K/. There should be a total of 108249 files.

Pre-trained Object Detection Models

Download pre-trained object detection models here. Unzip it under the root directory and you should see a detection_models folder there.

<!-- ## Our pre-trained Relationship Detection models --> <!-- Download our trained models [here](). Unzip it under the root folder and you should see a `trained_models` folder there. -->

Evaluating Pre-trained Relationship Detection models

DO NOT CHANGE anything in the provided config files(configs/xx/xxxx.yaml) even if you want to test with less or more than 8 GPUs. Use the environment variable CUDA_VISIBLE_DEVICES to control how many and which GPUs to use. Remove the --multi-gpu-test for single-gpu inference.

Training Relationship Detection Models

It requires 8 GPUS for trianing.

GVQA

Train our relationship network using a VGG16 backbone, run

python -u tools/train_net_reltransformer.py --dataset gvqa --cfg configs/gvqa/e2e_relcnn_VGG16_8_epochs_gvqa_reltransformer.yaml --nw 8 --use_tfboard --seed 1

Train our relationship network using a VGG16 backbone with WCE loss, run

python -u tools/train_net_reltransformer_WCE.py --dataset gvqa --cfg configs/gvqa/e2e_relcnn_VGG16_8_epochs_gvqa_reltransformer_WCE.yaml --nw 8 --use_tfboard --seed 1

To test the trained networks, run

python tools/test_net_reltransformer.py --dataset gvqa --cfg configs/gvqa/e2e_relcnn_VGG16_8_epochs_gvqa_reltransformer.yaml --load_ckpt  model-path  --use_gt_boxes --use_gt_labels --do_val

To test the trained networks, run

python tools/test_net_reltransformer_WCE.py --dataset gvqa --cfg configs/gvqa/e2e_relcnn_VGG16_8_epochs_gvqa_reltransformer_WCE.yaml --load_ckpt  model-path  --use_gt_boxes --use_gt_labels --do_val

VG8K

Train our relationship network using a VGG16 backbone, run

python -u tools/train_net_reltransformer.py --dataset vg8k --cfg configs/vg8k/e2e_relcnn_VGG16_8_epochs_vg8k_reltransformer.yaml  --nw 8 --use_tfboard --seed 3

Train our relationship network using a VGG16 backbone with WCE loss, run

python -u tools/train_net_reltransformer_wce.py --dataset vg8k --cfg configs/vg8k/e2e_relcnn_VGG16_8_epochs_vg8k_reltransformer_wce.yaml --nw 8 --use_tfboard --seed3

To test the trained networks, run

python tools/test_net_reltransformer.py --dataset vg8k --cfg configs/vg8k/e2e_relcnn_VGG16_8_epochs_vg8k_reltransformer.yaml --load_ckpt  model-path  --use_gt_boxes --use_gt_labels --do_val

To test the trained model with WCE loss function, run

python tools/test_net_reltransformer_wce.py --dataset vg8k --cfg configs/vg8k/e2e_relcnn_VGG16_8_epochs_vg8k_reltransformer_wce.yaml --load_ckpt  model-path  --use_gt_boxes --use_gt_labels --do_val

Acknowledgements

This repository uses code based on the LTVRD source code by sherif, as well as code from the Detectron.pytorch repository by Roy Tseng.

Citing

If you use this code in your research, please use the following BibTeX entry.

@InProceedings{Chen_2022_CVPR,
    author    = {Chen, Jun and Agarwal, Aniket and Abdelkarim, Sherif and Zhu, Deyao and Elhoseiny, Mohamed},
    title     = {RelTransformer: A Transformer-Based Long-Tail Visual Relationship Recognition},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {19507-19517}
}

@article{chen2021reltransformer,
  title={RelTransformer: Balancing the Visual Relationship Detection from Local Context, Scene and Memory},
  author={Chen, Jun and Agarwal, Aniket and Abdelkarim, Sherif and Zhu, Deyao and Elhoseiny, Mohamed},
  journal={arXiv preprint arXiv:2104.11934},
  year={2021}
}