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https://github.com/amazon-science/mm-cot

Official implementation for "Multimodal Chain-of-Thought Reasoning in Language Models" (stay tuned and more will be updated)
https://github.com/amazon-science/mm-cot

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Official implementation for "Multimodal Chain-of-Thought Reasoning in Language Models" (stay tuned and more will be updated)

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README 

        
# Multimodal Chain-of-Thought Reasoning in Language Models



"Imagine learning a textbook without figures or tables."



Multimodal-CoT incorporates vision features in a decoupled training framework. The framework consists of two training stages: (i) rationale generation and (ii) answer inference. Both stages share the same model architecture but differ in the input and output.



![](vision_features/mm-cot.png)



## Requirements



Install all required python dependencies:



```

pip install -r requirements.txt

```



## Datasets



Download the dataset from the following repository:



```

https://github.com/lupantech/ScienceQA/tree/main/data

```

The vision features (detr, resnet, clip, vit) are available at https://huggingface.co/cooelf/vision_features/tree/main



Alternatively, you may download the extracted vision features (detr, resnet, clip) from [vision_features](https://drive.google.com/file/d/13B0hc_F_45-UlqPLKSgRz-ALtFQ8kIJr/view?usp=share_link) and unzip the files under `vision_features`



## Extract Features (optional)



The processed vision features for ScienceQA are available at https://huggingface.co/cooelf/vision_features/tree/main. 



The following instructions show how we obtain those features.



Download the image files from [Google Drive](https://drive.google.com/drive/folders/1w8imCXWYn2LxajmGeGH_g5DaL2rabHev?usp=sharing) and unzip all the images (train, dev, test) in the same folder (). The structure should be:



```

images

├── 1

│   └── image.png

├── 2

│   └── image.png

├── 3

│   └── image.png

├── 5

│   └── image.png

├── 7

│   └── image.png

```



Run ```extract_features.py --data_root images --output_dir vision_features --img_type vit```



If you hope to use your own images, please structure those images in the way above, or modify the script ```extract_features.py```.



## Extract Captions (optional)



The processed captions for ScienceQA are available at ```data/instruct_captions.json```. 



The following instructions show how we obtain those features.



Intall lavis and prepare Vicuna weights to use InstructBLIP for caption extraction.



https://github.com/salesforce/LAVIS/tree/f982acc73288408bceda2d35471a8fcf55aa04ca/projects/instructblip



Assume that the images are stored in the ```images``` folder. 



```

python extract_caption.py

```



## Instructions



### Training 



```

# rationale generation

CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py \

    --data_root data/ScienceQA/data \

    --caption_file data/instruct_captions.json \

    --model declare-lab/flan-alpaca-large \

    --user_msg rationale --img_type vit \

    --bs 2 --eval_bs 4 --epoch 50 --lr 5e-5 --output_len 512 \

    --use_caption --use_generate --prompt_format QCM-E \

    --output_dir experiments



# answer inference

CUDA_VISIBLE_DEVICES=0,1,2,3 python main_central.py \

    --data_root data/ScienceQA/data \

    --caption_file data/instruct_captions.json \

    --model declare-lab/flan-alpaca-large \

    --user_msg answer --img_type vit \

    --bs 4 --eval_bs 8 --epoch 50 --lr 5e-5 --output_len 64 \

    --use_caption --use_generate --prompt_format QCMG-A \

    --output_dir experiments \

    --eval_le experiments/rationale_declare-lab-flan-alpaca-large_vit_QCM-E_lr5e-05_bs8_op512_ep50/predictions_ans_eval.json \

    --test_le experiments/rationale_declare-lab-flan-alpaca-large_vit_QCM-E_lr5e-05_bs8_op512_ep50/predictions_ans_test.json



```



### Inference 



Our trained models are available at https://huggingface.co/cooelf/mm-cot/tree/main. To use our trained models, please put the them under the ```models``` folder.



```

# rationale generation

CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py \

    --data_root data/ScienceQA/data \

    --caption_file data/instruct_captions.json \

    --model declare-lab/flan-alpaca-large \

    --user_msg rationale --img_type vit \

    --bs 2 --eval_bs 4  --epoch 50 --lr 5e-5 --output_len 512 \

    --use_caption --use_generate --prompt_format QCM-E \

    --output_dir experiments

    --evaluate_dir models/mm-cot-large-rationale



# answer inference

CUDA_VISIBLE_DEVICES=0,1,2,3 python main_central.py \

    --data_root data/ScienceQA/data \

    --caption_file data/instruct_captions.json \

    --model declare-lab/flan-alpaca-large \

    --user_msg answer --img_type vit \

    --bs 4 --eval_bs 8 --epoch 50 --lr 5e-5 --output_len 64  \

    --use_caption --use_generate --prompt_format QCMG-A \

    --output_dir experiments \

    --eval_le experiments/rationale_declare-lab-flan-alpaca-large_vit_QCM-E_lr5e-05_bs8_op512_ep50/predictions_ans_eval.json \

    --test_le experiments/rationale_declare-lab-flan-alpaca-large_vit_QCM-E_lr5e-05_bs8_op512_ep50/predictions_ans_test.json \

    --evaluate_dir models/mm-cot-large-answer

```



## Citing MM-CoT



```

@article{zhang2023multicot,

  title={Multimodal Chain-of-Thought Reasoning in Language Models},

  author={Zhang, Zhuosheng and Zhang, Aston and Li, Mu and Zhao, Hai and Karypis, George and Smola, Alex},

  journal={arXiv preprint arXiv:2302.00923},

  year={2023}

}

```



## License



This project is licensed under the Apache-2.0 License.



## Acknowledgement



Part of our codes are adapted from [ScienceQA](https://github.com/lupantech/ScienceQA), [Transformers](https://github.com/huggingface/transformers), [pytorch-image-models](https://github.com/huggingface/pytorch-image-models).



We thank [Pan Lu](https://lupantech.github.io/) for providing parameter size for ScienceQA baselines.