Socratic models (SMs) [1] is a modular framework in which multiple pre-trained models are composed zeroshot via multimodal informed prompting. This is done to exchange information between models and capture new multimodal capabilities, without requiring finetuning. As a proof of concept, we modify the Socratic models framework such that it is entirely open-source and attempt to achieve the same results as the original version. Additionally, we investigate the capabilities of Socratic models on multimodal reasoning tasks such as chain-of-thought reasoning and visual question-answering in zeroshot and few-shot settings.

To install the environment, run:

conda env create -f environment.yml
conda activate socratic
python -m spacy download en

This repository provides scripts for CLIP with GPT-3, FLAN-T5, GitVision, BLIP and BLIP2 prompting, and self-contained ipython notebooks with prototype implementations of Socratic Models for image captioning geenration, chain-of-thought and visual question answering.The project was organised such that the downloading, caching and organisation of files is managed by the code. The classes were built in a modular fashion such that they could be adapted to different use-cases.

• scripts

  • coco_caption_base.py - Run a train/valid/test dataset on the Baseline Image Captioner.
  • coco_caption_base_hp_tune.py - Run a parameter search on the Baseline Image Captioner.
  • coco_caption_imp.py - Run a train/valid/test dataset on the Improved Image Captioner.
  • coco_caption_imp_hp_tune.py - Run a parameter search on the Improved Image Captioner.
  • coco_caption_gpt.py - Run a train/valid/test dataset on the Original Socratic Captioner.
  • coco_caption_git.py - Run a train/valid/test dataset using GIT.
  • coco_caption_blip.py - Run a train/valid/test dataset using BLIP.
  • coco_caption_blip2.py - Run a train/valid/test dataset using BLIP2.
  • image_captioning.py - Contains the functionality relating to the image captioning.
  • mm_reasoning.py - Contains the functionality relating to the multimodal reasoning.
  • generate_reasoning.py - Run a reasoning task.
  • utils.py - Contains utilities functions.
  • coco_evaluation.py - Run the evalutaion of the captions that were generated using different approaches.
  • reasoning_evaluation.py - Run the multimodal reasoning evalutation.

• notebooks

  • demo_baseline.ipynb - A demo of the Baseline Image Captioner in action.
  • demo_improved.ipynb - A demo of the Improved Image Captioner in action.
  • demo_gpt.ipynb - A demo of the Original Socratic Image Captioner in action.
  • demo_gitvision.ipynb - A demo of GIT in action.
  • demo_blip.ipynb - A demo of BLIP in action.
  • demo_blip2.ipynb - A demo of BLIP2 in action.
  • display_images_captions.ipynb - A display of a selection of captions that were obtained with the captioners.
  • visualise_CLIP.ipynb - Visualisations of the embedding space of CLIP.
  • socratic_mm_reasoning.ipynb - A showcase of the multimodal reasoning tasks.

• data

  • The data directory stores the input and generated data. It is automatically created when the code is run.

[1] Zeng, A. et al. Socratic models: Composing zero-shot multimodal reasoning with language. arXiv preprint arXiv:2204.00598 (2022).

This project is licensed under the terms of the MIT License, allowing free use of the code.