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GraphTextRetrieval

Source code for cross-modality retrieval for Natural Language-informed Understanding of Molecule Graphs. Please go to MoMu to see the whole codebase.

Workspace Prepare

If you want to explore our job, you can following the instructions in this section

  • Step 1: Download the zip or clone the repository to your workspace.
  • Step 2: Download the littlegin=graphclinit_bert=kvplm_epoch=299-step=18300.ckpt and littlegin=graphclinit_bert=scibert_epoch=299-step=18300.ckpt from BaiduNetdisk(the Password is 1234). Create a new directory by mkdir all_checkpoints and then put the downloaded model under the directory. Rename littlegin=graphclinit_bert=kvplm_epoch=299-step=18300.ckpt to MoMu-K.ckpt and littlegin=graphclinit_bert=scibert_epoch=299-step=18300.ckpt to MoMu-S.ckpt
  • Step 3: Download files from Sci-Bert. Create a new directory by mkdir bert_pretrained and then put these files under the directory.
  • Step 4: Install python environment. Some important requirements are listed as follows(In fact, the environment is the almost same as GraphTextPretrain, so you do not need to install again if you have follow its instructions): 
    Ubuntu 16.04.7
python 3.8.13
cuda 10.1

# pytorch
pip install torch==1.8.1+cu101 torchvision==0.9.1+cu101 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html

# torch_geometric 
# you can download the following *.whl files in https://data.pyg.org/whl/
wget https://data.pyg.org/whl/torch-1.8.0%2Bcu101/torch_cluster-1.5.9-cp38-cp38-linux_x86_64.whl
wget https://data.pyg.org/whl/torch-1.8.0%2Bcu101/torch_scatter-2.0.8-cp38-cp38-linux_x86_64.whl
wget https://data.pyg.org/whl/torch-1.8.0%2Bcu101/torch_sparse-0.6.12-cp38-cp38-linux_x86_64.whl
pip install torch_cluster-1.5.9-cp38-cp38-linux_x86_64.whl
pip install torch_scatter-2.0.8-cp38-cp38-linux_x86_64.whl
pip install torch_sparse-0.6.12-cp38-cp38-linux_x86_64.whl
pip install torch-geometric

# transformers (4.18.0)
pip install transformers 

# rdkit
pip install rdkit-pypi

# ogb
pip install ogb

# pytorch_lightning (1.6.2)
pip install pytorch_lightning

File Usage

The users may be going to use or edit the files below:

  • main.py: Fine-tuning and testing code for cross-modality retrival.
  • data/
    • kv_data/: Pairs of (Graph, Text) data from KV-PLM a.k.a PCdes
    • phy_data/: Pairs of (Graph, Text) data collected by us
  • all_checkpoints/
    • MoMu-S.ckpt: Pretrained model of MoMu-S
    • MoMu-K.ckpt: Pretrained model of MoMu-K
  • data_provider/
    • match_dataset.py: Dataloader file
  • model/
    • bert.py: Text encoder
    • gin_model.py: Graph encoder
    • constrastiv_gin.py Constrastive model with text encoder and graph encoder

Zeroshot Testing

Zeroshot testing means cross-modality retrieval with origin MoMu. You can conduct zeroshot testing with differen settings as follows:

1. zeroshot testing on phy_data with paragraph-level:

python main.py --init_checkpoint all_checkpoints/MoMu-S.ckpt --data_type 0 --if_test 2 --if_zeroshot 1 --pth_test data/phy_data

2. zeroshot testing on phy_data with sentence-level:

python main.py --init_checkpoint all_checkpoints/MoMu-S.ckpt --data_type 1 --if_test 2 --if_zeroshot 1 --pth_test data/phy_data

3. zeroshot testing on kv_data with paragraph-level:

python main.py --init_checkpoint all_checkpoints/MoMu-S.ckpt --data_type 0 --if_test 2 --if_zeroshot 1 --pth_test data/kv_data/test

4. zeroshot testing on kv_data with sentence-level:

python main.py --init_checkpoint all_checkpoints/MoMu-S.ckpt --data_type 1 --if_test 2 --if_zeroshot 1 --pth_test data/kv_data/test

Finetuning and Testing

To make MoMu satisfy the cross-modality retrieval task better, you can finetune MoMu and then test. Befor fintuning, you should create a new directory to save finetuned model by mkdir finetune_save.

1. finetuning on kv_data with paragraph-level and testing:

# finetune MoMu and save as 'finetune_save/finetune_para.pt '
python main.py --init_checkpoint all_checkpoints/MoMu-S.ckpt --output finetune_save/finetune_para.pt --data_type 0 --if_test 0 --if_zeroshot 0 

# test with fintuned model
python main.py --init_checkpoint all_checkpoints/MoMu-S.ckpt --output finetune_save/finetune_para.pt --data_type 0 --if_test 2 --if_zeroshot 0

2. finetuning on kv_data with sentence-level and testing:

# finetune MoMu and save as 'finetune_save/finetune_sent.pt '
python main.py --init_checkpoint all_checkpoints/MoMu-S.ckpt --output finetune_save/finetune_sent.pt --data_type 1 --if_test 0 --if_zeroshot 0

# test with fintuned model
python main.py --init_checkpoint all_checkpoints/MoMu-S.ckpt --output finetune_save/finetune_sent.pt --data_type 1 --if_test 2 --if_zeroshot 0

Sample Result

Taking zeroshot testing on phy_data with paragraph-level as an example, we show the excuting result here. It takes almost 10s to calculate the accuracy of retrieval, while calculating the Rec@20 takes about 2mins.

python main.py --init_checkpoint all_checkpoints/MoMu-S.ckpt --data_type 0 --if_test 2 --if_zeroshot 1 --pth_test data/phy_data
Namespace(batch_size=64, data_type=0, epoch=30, graph_aug='dnodes', if_test=2, if_zeroshot=1, init_checkpoint='all_checkpoints/MoMu-S.ckpt', lr=5e-05, margin=0.2, output='finetune_save/sent_MoMu-S_73.pt', pth_dev='data/kv_data/dev', pth_test='data/phy_data', pth_train='data/kv_data/train', seed=73, text_max_len=128, total_steps=5000, warmup=0.2, weight_decay=0)
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 87/87 [00:16<00:00,  5.31it/s]
Test Acc1: 0.4565587918015103
Test Acc2: 0.4317727436174038
Rec@20 1: 0.4579036317871269
Rec@20 2: 0.4348471772743617

Acknowledgment

This repository uses some code from KV-PLM. Thanks to the original authors for their work!

Citation

Please cite the following paper if you use the codes:

@article{su2022molecular,
  title={Natural Language-informed Understanding of Molecule Graphs},
  author={Bing Su, Dazhao Du, Zhao Yang, Yujie Zhou, Jiangmeng Li, Anyi Rao, Hao Sun, Zhiwu Lu, Ji-Rong Wen},
  year={2022}
}