X-VLM: learning multi-grained vision language alignments
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts. Yan Zeng, Xinsong Zhang, Hang Li. arXiv 2021.
- Nov 2022: Release X2-VLM: All-In-One for Vision Language Tasks; All-In-One == Image + Video + Transfer to Other Languages / Domains
- May 2022: The paper has been accepted by ICML 2022
- Jan 2022: Release official PyTorch implementation and X-VLM checkpoints
- Nov 2021: Release preprint in arXiv
X-VLM (216M parameters: swin-base + 6L text + 6L cross):
Hiring
We are looking for interns / FTEs at ByteDance AI-LAB (in Beijing / Shanghai)! If you are interested in working with us on vision language models, please send your resume to zhangxinsong.0320@bytedance.com.
Features
- Support several backbones
- vision encoder: deit / clip-vit / swin-transformer
- text encoder: bert / roberta
- Support apex O1 / O2 for pre-training
- Read from and write to HDFS
- Distributed training across nodes for both pre-training and fine-tuning
Please read the code for more details.
Requirements
- Install python3 environment
pip3 install -r requirements.txt - Download raw images from corresponding websites
- Download the json files we provided, which contains image read paths and captions and/or bbox annotations
- If running pre-training scripts:
- install Apex
- download pre-trained models for parameter initialization
- image encoder: clip-vit-base / swin-transformer-base
- text encoder: bert-base
-
Organize these files like this (% is for pre-training only):
X-VLM/ data/ finetune/ refcoco+/*.json *.json %pretrain_4m/*.json %swin_base_patch4_window7_224_22k.pth %bert-base-uncased/ config.json pytorch_model.bin tokenizer_config.json tokenizer.json vocab.txt images/ coco/ train2014/*.jpg val2014/*.jpg test2015/*.jpg visualgenome/ image/*.jpg nlvr2/ images/ train/0-99/*.png dev/*.png test1/*.png %sbu/*.jpg %cc-3m/*.jpg
Pretrain
python3 run.py --task "pretrain_4m_base" --dist "1" --output_dir "output/pretrain_4m_base"For distributed training across nodes, see run.py for more details. To make a fair comparison of some recent works, we pre-trained X-VLM (4M/16M) for 200K steps.
Data
🌟UPDATE: our multi-lingual multi-modal project Cross-View Language Modeling released the text of COCO+VG+SBU+CC3M and Object And Region Annotations in six languages. You can use english text for X-VLM pre-training.
All datasets we utilized are publicly available. We cannot re-distribute the data. So, please prepare the pre-training data by yourself. Here, we provide some data examples. Read the code dataset/pretrain_dataset.py/ImageTextJsonDataset & RegionTextJsonDataset for details.
# image-captions pairs, providing 'binary' or 'image_rpath'
{'caption': 'dog on bike in harajuku',
'binary': binary_encoding_of_the_image,
'image_rpath': local_rpath_of_the_image
}
# object/region annotations, providing 'binary' or 'image_rpath'
{'elems': [{'caption': 'lady sitting at table that has pizza on it', # str or list of str
'bb': [155, 0, 205, 131] # (x, y, w, h)
},
{'caption': 'window',
'attributes': 'closed', # str or list of str
'bb': [20, 130, 335, 185]
},
]
'caption': if_exist, # str or list of str
'binary': binary_encoding_of_the_image,
'image_rpath': local_rpath_of_the_image
}Checkpoints
X-VLM (4M, 200K steps)
X-VLM (16M, 200K steps)
Finetune
Datasets for finetuning and checkpoints of X-VLM (4M/16M) can be downloaded in following links.
Data
Checkpoints and Logs (16M)
retrieval-mscoco
retrieval-flickr
vqa
nlvr2
refcoco
refcoco-weak
captioning-coco
Checkpoints and Logs (4M)
Examples
# train
python3 run.py --task "vqa" --dist "1" --output_dir "output/vqa" --checkpoint "4m_base_model_state_step_199999.th"
# train: if using >2 nodes for fine-tuning, specify --output_hdfs to save some tmp results; it is only required by vqa & refcoco
python3 run.py --task "vqa" --dist "all" --output_dir "output/vqa" --output_hdfs "hdfs://xxx/vqa_tmp" --checkpoint "4m_base_model_state_step_199999.th"
# evaluate
python3 run.py --task "vqa" --dist "1" --evaluate --output_dir "output/vqa_eval" --checkpoint "4m_base_finetune/vqa/model_state_epoch_9.th"Specify "--task" to finetune on image-text retrieval, nlvr2, visual grounding, or image captioning. See run.py for details.
More Examples of Captioning:
# adapt cross-modal encoder + MLM head -> lm decoder; subsequent fine-tuning is included
python3 run.py --task "coco_capt_domain" --dist "1" --output_dir "output/coco_capt_domain" --checkpoint "4m_base_model_state_step_199999.th"
# fine-tune only; evaluate is included
python3 run.py --task "coco_captioning" --dist "1" --output_dir "output/coco_captioning" --checkpoint "4m_base_finetune/coco_caption/lm_domain_pretrain.th"
# evaluate only
python3 run.py --task "coco_captioning" --dist "1" --output_dir "output/coco_captioning" --evaluate --checkpoint "4m_base_finetune/coco_caption/coco_capt_ft_epoch_4.th"
# further CIDEr optimization; evaluate is included
python3 run.py --task "coco_captioning_scst" --dist "1" --output_dir "output/coco_captioning_scst" --checkpoint "4m_base_finetune/coco_caption/coco_capt_ft_epoch_4.th"
# evaluate only
python3 run.py --task "coco_captioning" --dist "1" --output_dir "output/coco_captioning_scst" --evaluate --checkpoint "4m_base_finetune/coco_caption/coco_capt_cider_step_41000.th"To make a fair comparison, we follow the previous works for fine-tuning. So, some scripts are based on ALBEF, OSCAR, and BLIP. We thank the authors for opening source their code.
Evaluation on VLUE
VLUE is a new OOD benchmark to evaluate vision-language models, which has been accepted by ICML2022.
python3 run.py --task "eval_vlue_itr" --dist "1" --evaluate --output_dir "output/" --checkpoint "itr_coco/checkpoint_9.pth"
python3 run.py --task "eval_vlue_vqa" --dist "1" --evaluate --output_dir "output/" --checkpoint "vqa/model_state_epoch_9.th"
python3 run.py --task "eval_vlue_nlvr" --dist "1" --evaluate --output_dir "output/" --checkpoint "nlvr/nlvr_ft/checkpoint_best.pth"
python3 run.py --task "eval_vlue_refcoco" --dist "1" --evaluate --output_dir "output/" --checkpoint "refcoco_bbox/checkpoint_best.pth"
python3 run.py --task "eval_vlue_refcoco_weakly" --dist "1" --evaluate --output_dir "output/" --checkpoint "refcoco/checkpoint_best.pth"Citation
If you find this repository useful, please considering giving ⭐ or citing:
@article{xvlm,
title={Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts},
author={Zeng, Yan and Zhang, Xinsong and Li, Hang},
journal={arXiv preprint arXiv:2111.08276},
year={2021}
}Contact
For issues using this code, please submit a GitHub issue.