Pytorch implement of the paper "VLDeformer: Vision Language Decomposed Transformer for Fast Cross-modal Retrieval"
This is a pytorch implementation of the VLDeformer paper. Please remember to give a citation if this paper and codes benefits your research!
@article{zhang2022vldeformer,
title={VLDeformer: Vision--Language Decomposed Transformer for fast cross-modal retrieval},
author={Zhang, Lisai and Wu, Hongfa and Chen, Qingcai and Deng, Yimeng and Siebert, Joanna and Li, Zhonghua and Han, Yunpeng and Kong, Dejiang and Cao, Zhao},
journal={Knowledge-Based Systems},
volume={252},
pages={109316},
year={2022},
publisher={Elsevier}
}
Install the python packages following VinVL. To achieve the performance in the paper, at least 6 V100 GPU is suggested.
You need to download the dataset COCO and Flickr30k to reproduce the exprements, and also SBU for the large version.
Besides, we use the features extracted by VinVL, which are given in their download page. You can directly download these features from coco and flikckr30k.
If you want to run the model on your customed data, please refer to Scene Graph to extract the features, which is specified by the VinVL repo.
The decomposition is applied on the pre-trained one stream VinVL model, so you need to download it first.
path/to/azcopy copy 'https://biglmdiag.blob.core.windows.net/vinvl/model_ckpts/TASK_NAME' coco_ir --recursive
Afterwards, you can run our code to perform decomposition.
You can also directly use our pre-trained checkpoints for Flickr30k and COCO.
Run contrastive_learn.py using following args:
"program": "${workspaceFolder}/contrastive_learn.py",
"args": [
"--model_name_or_path",
"vinvl/coco_ir/base/checkpoint-1340000", // Your path to Vinvl checkpoint
"--data_dir",
"/raid/data_modal/coco_vinvL/coco_ir/", // Your path to Vinvl data
"--img_feat_file",
"/raid/data_modal/coco_vinvL/model_0060000/features.tsv", // Your path to Vinvl image feature tsv
"--eval_img_keys_file",
"test_img_keys_1k.tsv", // select the test file in ${data_dir}
"--do_train",
"--do_lower_case",
"--evaluate_during_training",
"--num_captions_per_img_val", "5",
"--per_gpu_train_batch_size", "300",
"--per_gpu_eval_batch_size", "300",
"--learning_rate", "7.5e-06",
"--warmup_steps", "200",
"--scheduler", "cos",
"--num_train_epochs", "200",
"--save_steps", "400",
"--add_od_labels",
"--od_label_type",
"vg",
"--max_seq_length",
"35",
"--max_img_seq_length",
"70",
"--output_dir",
"contrastive_checkpoint/output_infoNCE_coswarmup",
"--logit_gpu", "2,3,4,5,6,7", // Contrative learning requires large batch size
"--contrastive_gpu", "0",
"--temperature_t", "0.005",
"--temperature_i", "0.005",
]
For test, you can use the following args:
"program": "${workspaceFolder}/contrastive_learn.py",
"args": [
"--do_test",
"--do_eval",
"--test_split",
"test",
"--num_captions_per_img_val",
"5",
"--eval_img_keys_file",
"test_img_keys_1k.tsv",
"--per_gpu_eval_batch_size",
"400",
"--img_feat_file",
"/raid/data_modal/coco_vinvL/model_0060000/features.tsv",
"--eval_model_dir",
"contrastive_checkpoint/twodataset_flickr/checkpoint-93-7600",
"--logit_gpu", "0",
"--contrastive_gpu", "0",
"--max_seq_length", "70",
]
This repo is modified based on the VinVL, we thank the authors for sharing their project.
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