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.gitignore

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README.md

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+# Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering
+Source code for ACL 2022 paper: "Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering", Yu-Jung Heo, Eun-Sol Kim, Woo Suk Choi, and Byoung-Tak Zhang
+* [[Paper]](https://aclanthology.org/2022.acl-long.29.pdf) [[Slides]](https://www.dropbox.com/s/kkbmmm1sy7f1ldr/acl22_HGT_slides.pdf)
+
+> To answer complex questions requiring multi-hop reasoning under weak supervision is considered as a challenging problem since i) no supervision is given to the reasoning process and ii) high-order semantics of multi-hop knowledge facts need to be captured. In this paper, we introduce a concept of hypergraph to encode high-level semantics of a question and a knowledge base, and to learn high-order associations between them. The proposed model, Hypergraph Transformer, constructs a question hypergraph and a query-aware knowledge hypergraph, and infers an answer by encoding inter-associations between two hypergraphs and intra-associations in both hypergraph itself. 
+
+![HGT](assets/acl22.png)
+
+## Requirements
+This code runs on Python 3.7 and PyTorch 1.5.1. We recommend using Anaconda to install all dependencies.
+```
+git clone https://github.com/YuJungHeo/kbvqa-public.git
+conda env create --file environment.yml --name kbvqa
+```
+
+## Setup
+Download preprocessed KVQA, PQ-{2H, 3H, M}, PQL-{2H, 3H, M} datasets.
+```
+bash download.sh
+```
+
+## Training
+
+Train on KVQA dataset
+* `--cfg` specifies a configuration yaml file.
+* `--q_opt` specifies a question type among original (ORG) and paraphrased (PRP).
+* `--n_hop` specifies a number of graph walk (e.g., 1-hop, 2-hop, and 3-hop).
+
+```
+# ORG, 3-hop on oracle setting
+python main.py --model_name ht --cfg ht_kvqa --n_hop 3 --q_opt org --lr 0.00001 --exp_name ht_kvqa_org_3hop
+```
+
+Train on PathQuestions (PQ) dataset
+```
+# PQ-2H 
+python main.py --data_name pq --model_name ht --cfg ht_pq2h --n_hop 2 --num_workers 2 --lr 0.0001 --abl_ans_fc --exp_name ht_pq2h
+# PQ-3H 
+python main.py --data_name pq --model_name ht --cfg ht_pq3h --n_hop 3 --num_workers 2 --lr 0.0001 --abl_ans_fc --exp_name ht_pq3h
+# PQ-M (a mixture of the PQ-2H and PQ-3H questions)
+python main.py --data_name pq --model_name ht --cfg ht_pqM --n_hop 3 --num_workers 2 --lr 0.0001 --abl_ans_fc --exp_name ht_pqM
+```
+
+Train on PathQuestions-Large (PQL) dataset
+```
+# PQL-2H
+python main.py --data_name pql --model_name ht --cfg ht_pql2h --n_hop 2 --num_workers 2 --lr 0.0001 --abl_ans_fc --exp_name ht_pql2h --split_seed 789
+# PQL-3H-More
+python main.py --data_name pql --model_name ht --cfg ht_pql3h_more --n_hop 3 --num_workers 2 --lr 0.0001 --abl_ans_fc --exp_name ht_pql3h_more --split_seed 789
+# PQL-M (a mixture of the PQL-2H and PQL-3H questions)
+python main.py --data_name pql --model_name ht --cfg ht_pqlM --n_hop 3 --num_workers 2 --lr 0.0001 --abl_ans_fc --exp_name ht_pqlM --split_seed 789
+```
+
+## Evaluation
+We release the trained model checkpoints (1-hop, 2-hop, 3-hop on ORG and PRP questions) that we have used for our experiments on KVQA dataset.
+```
+bash download_kvqa_checkpoints.sh
+
+# for original (ORG) questions on oracle setting in Table 1
+python main.py --model_name ht --cfg ht_kvqa --n_hop 1 --q_opt org --exp_name ht_kvqa_org_1hop_dist --inference
+python main.py --model_name ht --cfg ht_kvqa --n_hop 2 --q_opt org --exp_name ht_kvqa_org_2hop_dist --inference
+python main.py --model_name ht --cfg ht_kvqa --n_hop 3 --q_opt org --exp_name ht_kvqa_org_3hop_dist --inference
+
+# for phraphrased (PRP) questions on oracle setting in Table 1
+python main.py --model_name ht --cfg ht_kvqa --n_hop 1 --q_opt prp --exp_name ht_kvqa_prp_1hop_dist --inference
+python main.py --model_name ht --cfg ht_kvqa --n_hop 2 --q_opt prp --exp_name ht_kvqa_prp_2hop_dist --inference
+python main.py --model_name ht --cfg ht_kvqa --n_hop 3 --q_opt prp --exp_name ht_kvqa_prp_3hop_dist --inference
+```
+
+We also release the trained model checkpoints that we have achieved best performance on the five repeated runs of different data splits in PQ and PQL dataset.
+```
+# for PQ dataset
+bash download_pq_checkpoints.sh
+python main.py --data_name pq --model_name ht --cfg ht_pq2h --n_hop 2 --num_workers 2 --abl_ans_fc --inference --exp_name ht_pq2h_dist
+python main.py --data_name pq --model_name ht --cfg ht_pq3h --n_hop 3 --num_workers 2 --abl_ans_fc --inference --exp_name ht_pq3h_dist
+python main.py --data_name pq --model_name ht --cfg ht_pqM --n_hop 3 --num_workers 2 --abl_ans_fc --inference --exp_name ht_pqM_dist
+
+# for PQL dataset
+bash download_pql_checkpoints.sh
+python main.py --data_name pql --model_name ht --cfg ht_pql2h --n_hop 2 --num_workers 2 --abl_ans_fc --inference --exp_name ht_pql2h_dist --split_seed 789
+python main.py --data_name pql --model_name ht --cfg ht_pql3h --n_hop 3 --num_workers 2 --abl_ans_fc --inference --exp_name ht_pql3h_dist --split_seed 789
+python main.py --data_name pql --model_name ht --cfg ht_pql3h_more --n_hop 3 --num_workers 2 --abl_ans_fc --inference --exp_name ht_pql3h_more_dist --split_seed 789
+python main.py --data_name pql --model_name ht --cfg ht_pqlM --n_hop 3 --num_workers 2 --abl_ans_fc --inference --exp_name ht_pqlM_dist --split_seed 789
+```
+
+## Credits
+* Parts of the code were adapted from [Multimodal Transformer](https://github.com/yaohungt/Multimodal-Transformer) by Yao-Hung Hubert Tsai.
+
+## Citation
+```
+@inproceedings{heo-etal-2022-hypergraph,
+    title = "Hypergraph {T}ransformer: {W}eakly-Supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering",
+    author = "Heo, Yu-Jung and Kim, Eun-Sol and Choi, Woo Suk and Zhang, Byoung-Tak",
+    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = may,
+    year = "2022",
+    address = "Dublin, Ireland",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.acl-long.29",
+    pages = "373--390"
+}
+```

configs/ht_kvqa.yaml

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+DATASET:
+  NAME: "kvqa"
+  RAW_DATA: "data/kvqa/raw/dataset.json"
+  PROC_DATA: "data/kvqa/processed/proc_data.pkl"
+  FACT: "data/kvqa/processed/fact_with_index.pkl"
+  REL2IDX: "data/kvqa/processed/rel_index.pkl"
+  AVOCAB2IDX: "data/kvqa/processed/ans_vocab2idx.pkl"
+  IDX2VOCAB: "data/kvqa/processed/kg_qc_ans_idx2vocab.pkl"
+  VOCAB2IDX: "data/kvqa/processed/kg_qc_ans_vocab2idx.pkl"
+  NE2QID: "data/kvqa/processed/ne2qid.pkl"
+  QID2NE: "data/kvqa/processed/qid2ne.pkl"
+  IDX2QTYPE: "data/kvqa/processed/idx2qtype.pkl"
+  KG_1hop: "data/kvqa/processed/kg_1hop.pkl"
+  KG_2hop: "data/kvqa/processed/kg_2hop.pkl"
+  KG_3hop: "data/kvqa/processed/kg_3hop.pkl"
+  KG_spat: "data/kvqa/processed/kg_spatial.pkl"
+  GLOVE: "data/kvqa/processed/glove_embs_kvqa.pkl"
+  GLOVE_ANS_CAND: "data/kvqa/processed/glove_embs_kvqa_ans.pkl"
+RES:
+  TB: "tb/"
+  CKPT: "ckpt/"
+  LOG: "log/"
+MODEL:
+  SEED: 1234
+  NUM_EDGE: 19
+  NUM_MAX_Q: 15
+  NUM_MAX_C: 20
+  NUM_MAX_QNODE: 3
+  NUM_MAX_HK_1H: 50
+  NUM_MAX_HK_2H: 100
+  NUM_MAX_HK_3H: 150
+  NUM_MAX_KNODE_1H: 6
+  NUM_MAX_KNODE_2H: 10
+  NUM_MAX_KNODE_3H: 12
+  FC_HID_COEFF: 4
+  NUM_OUT: 300
+  NUM_ANS: 19360
+  NUM_WORD_EMB : 300
+  NUM_HIDDEN: 256
+  NUM_HEAD: 4
+  NUM_LAYER: 2
+  INP_DROPOUT: 0.0
+  ATTN_DROPOUT_K: 0.0
+  ATTN_DROPOUT_Q: 0.0
+  RELU_DROPOUT: 0.0
+  RES_DROPOUT: 0.0
+  EMB_DROPOUT: 0.0
+  ATTN_MASK: True
+  BATCH_SIZE: 256

configs/ht_pq2h.yaml

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+DATASET:
+  NAME: "pq2h"
+  PROC_DATA: "data/PathQuestion/processed/pq2h/proc_data.pkl"
+  AVOCAB2IDX: "data/PathQuestion/processed/pq2h/ans_vocab2idx.pkl"
+  IDX2VOCAB: "data/PathQuestion/processed/pq2h/idx2vocab.pkl"
+  VOCAB2IDX: "data/PathQuestion/processed/pq2h/vocab2idx.pkl"
+  KG_2hop: "data/PathQuestion/processed/pq2h/kg_2hop.pkl"
+  GLOVE: "data/PathQuestion/processed/pq2h/glove_embs.pkl"
+  GLOVE_ANS_CAND: "data/PathQuestion/processed/pq2h/glove_embs_ans.pkl"
+RES:
+  TB: "tb/"
+  CKPT: "ckpt/"
+  LOG: "log/"
+MODEL:
+  SEED: 1234
+  NUM_EDGE: 13
+  NUM_MAX_Q: 11
+  NUM_MAX_ASET: 2
+  NUM_MAX_QNODE: 3
+  NUM_MAX_HK_2H: 10
+  NUM_MAX_KNODE_2H: 5
+  FC_HID_COEFF: 1
+  NUM_OUT: 300
+  NUM_ANS: 305
+  NUM_WORD_EMB : 300
+  NUM_HIDDEN: 256 
+  NUM_HEAD: 4
+  NUM_LAYER: 2
+  INP_DROPOUT: 0.2
+  ATTN_DROPOUT_K: 0.1
+  ATTN_DROPOUT_Q: 0.1
+  RELU_DROPOUT: 0.1
+  RES_DROPOUT: 0.1
+  EMB_DROPOUT: 0.1
+  ATTN_MASK: True
+  BATCH_SIZE: 128

configs/ht_pq3h.yaml

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+DATASET:
+  NAME: "pq3h"
+  PROC_DATA: "data/PathQuestion/processed/pq3h/proc_data.pkl"
+  AVOCAB2IDX: "data/PathQuestion/processed/pq3h/ans_vocab2idx.pkl"
+  IDX2VOCAB: "data/PathQuestion/processed/pq3h/idx2vocab.pkl"
+  VOCAB2IDX: "data/PathQuestion/processed/pq3h/vocab2idx.pkl"
+  KG_3hop: "data/PathQuestion/processed/pq3h/kg_3hop.pkl"
+  GLOVE: "data/PathQuestion/processed/pq3h/glove_embs.pkl"
+  GLOVE_ANS_CAND: "data/PathQuestion/processed/pq3h/glove_embs_ans.pkl"
+RES:
+  TB: "tb/"
+  CKPT: "ckpt/"
+  LOG: "log/"
+MODEL:
+  SEED: 1234
+  NUM_EDGE: 13
+  NUM_MAX_Q: 15
+  NUM_MAX_ASET: 5
+  NUM_MAX_QNODE: 3
+  NUM_MAX_HK_3H: 45
+  NUM_MAX_KNODE_3H: 7
+  FC_HID_COEFF: 1
+  NUM_OUT: 300
+  NUM_ANS: 1009
+  NUM_WORD_EMB : 300
+  NUM_HIDDEN: 256 
+  NUM_HEAD: 4
+  NUM_LAYER: 2
+  INP_DROPOUT: 0.2
+  ATTN_DROPOUT_K: 0.1
+  ATTN_DROPOUT_Q: 0.1
+  RELU_DROPOUT: 0.1
+  RES_DROPOUT: 0.1
+  EMB_DROPOUT: 0.1
+  ATTN_MASK: True
+  BATCH_SIZE: 128