Introduction

This is the official source code for Dual Semantic Relations Attention Network(DSRAN) proposed in our journal paper Learning Dual Semantic Relations with Graph Attention for Image-Text Matching (TCSVT 2020). It is built on top of the VSE++ in PyTorch.

The framework of DSRAN:

The results on MSCOCO and Flickr30K dataset:(With BERT or GRU)

Requirements and Installation

We recommended the following dependencies.

• Python 3.6
• PyTorch 1.1.0
• NumPy (>1.12.1)
• torchtext
• pycocotools
• nltk

Download data

Download the raw images, pre-computed image features, pre-trained BERT models, pre-trained ResNet152 model and pre-trained DSRAN models. As for the raw images, they can be downloaded from VSE++.

wget http://www.cs.toronto.edu/~faghri/vsepp/data.tar
wget http://www.cs.toronto.edu/~faghri/vsepp/vocab.tar

We refer to the path of extracted files for data.tar as $DATA_PATH while only raw images are used which are coco and f30k.

For pre-computed image features, they can be obtained from VLP. These zip files should be extracted into the fold data/joint-pretrain. We refer to the path of extracted region_bbox_file(.h5) as $REGION_BBOX_FILE and regional feature paths feat_cls_1000/ for COCO and trainval/ for FLICKR30K as $FEATURE_PATH.

Pre-trained ResNet152 model can be downloaded from torchvision and put in the root directory.

wget https://download.pytorch.org/models/resnet152-b121ed2d.pth

For our trained DSRAN models, you can download runs.zip on Google Drive or GRU.zip together with BERT.zip on BaiduNetDisk(extract code:1119). There are totally 8 models (4 for each dataset).

Pre-trained BERT models are obtained form an old version of transformers. It is noticed that there's a simpler way of using BERT as seen in transformers. We'll update the code in the future. The pre-trained models we use can be downloaded from the same Google Drive and BaiduNetDisk(extract code:1119) links. We refer to the path of extracted files for uncased_L-12_H-768_A-12.zip as $BERT_PATH.

Data Structure

├── data/
|   ├── coco/           /* MSCOCO raw images
|   |   ├── images/
|   |   |   ├── train2014/
|   |   |   ├── val2014/
|   |   ├── annotations/
|   ├── f30k/           /* Flickr30K raw images
|   |   ├── images/
|   |   ├── dataset_flickr30k.json
|   ├── joint-pretrain/           /* pre-computed image features
|   |   ├── COCO/
|   |   |   ├── region_feat_gvd_wo_bgd/
|   |   |   |   ├── feat_cls_1000/           /* $FEATURE_PATH
|   |   |   |   ├── coco_detection_vg_thresh0.2_feat_gvd_checkpoint_trainvaltest.h5  /* $REGION_BBOX_FILE
|   |   |   ├── annotations/
|   |   ├── flickr30k/
|   |   |   ├── region_feat_gvd_wo_bgd/
|   |   |   |   ├── trainval/                /* $FEATURE_PATH
|   |   |   |   ├── flickr30k_detection_vg_thresh0.2_feat_gvd_checkpoint_trainvaltest.h5  /* $REGION_BBOX_FILE
|   |   |   ├── annotations/

Evaluate trained models

Test on single model:

• Test on MSCOCO dataset (1K and 5K simultaneously):

  • Test on BERT-based models:

  python evaluation_bert.py --model BERT/cc_model1 --fold --data_path "$DATA_PATH" --region_bbox_file "$REGION_BBOX_FILE" --feature_path "$FEATURE_PATH"
  

  • Test on GRU-based models:

  python evaluation.py --model GRU/cc_model1 --fold --data_path "$DATA_PATH" --region_bbox_file "$REGION_BBOX_FILE" --feature_path "$FEATURE_PATH"
  

• Test on Flickr30K dataset:

  • Test on BERT-based models:

  python evaluation_bert.py --model BERT/f_model1 --data_path "$DATA_PATH" --region_bbox_file "$REGION_BBOX_FILE" --feature_path "$FEATURE_PATH"
  

  • Test on GRU-based models:

  python evaluation.py --model GRU/f_model1 --data_path "$DATA_PATH" --region_bbox_file "$REGION_BBOX_FILE" --feature_path "$FEATURE_PATH"
  

Test on two-models ensemble and re-rank:

/* Remember to modify the "$DATA_PATH", "$REGION_BBOX_FILE" and "$FEATURE_PATH" in the .sh files.

• Test on MSCOCO dataset (1K and 5K simultaneously):

  • Test on BERT-based models:

  sh test_bert_cc.sh
  

  • Test on GRU-based models:

  sh test_gru_cc.sh
  

• Test on Flickr30K dataset:

  • Test on BERT-based models:

  sh test_bert_f.sh
  

  • Test on GRU-based models:

  sh test_gru_f.sh
  

Train new models

Train a model with BERT on MSCOCO:

python train_bert.py --data_path "$DATA_PATH" --data_name coco --num_epochs 18 --batch_size 320 --lr_update 9 --logger_name runs/cc_bert --bert_path "$BERT_PATH" --ft_bert --warmup 0.1 --K 4 --feature_path "$FEATURE_PATH" --region_bbox_file "$REGION_BBOX_FILE"

Train a model with BERT on Flickr30K:

python train_bert.py --data_path "$DATA_PATH" --data_name f30k --num_epochs 12 --batch_size 128 --lr_update 6 --logger_name runs/f_bert --bert_path "$BERT_PATH" --ft_bert --warmup 0.1 --K 2 --feature_path "$FEATURE_PATH" --region_bbox_file "$REGION_BBOX_FILE"

Train a model with GRU on MSCOCO:

python train.py --data_path "$DATA_PATH" --data_name coco --num_epochs 18 --batch_size 300 --lr_update 9 --logger_name runs/cc_gru --use_restval --K 2 --feature_path "$FEATURE_PATH" --region_bbox_file "$REGION_BBOX_FILE"

Train a model with GRU on Flickr30K:

python train.py --data_path "$DATA_PATH" --data_name f30k --num_epochs 16 --batch_size 128 --lr_update 8 --logger_name runs/f_gru --use_restval --K 2 --feature_path "$FEATURE_PATH" --region_bbox_file "$REGION_BBOX_FILE"

Acknowledgement

We thank Linyang Li for the help with the code and provision of some computing resources.

Reference

If DSRAN is useful for your research, please cite our paper:

@ARTICLE{9222079,
  author={Wen, Keyu and Gu, Xiaodong and Cheng, Qingrong},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={Learning Dual Semantic Relations With Graph Attention for Image-Text Matching}, 
  year={2021},
  volume={31},
  number={7},
  pages={2866-2879},
  doi={10.1109/TCSVT.2020.3030656}}

License

Apache License 2.0