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Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens

This repository is a re-implementation of the paper Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens. It includes PyTorch code for pretraining and zero-shot evaluation.

@inproceedings{chen2023revisiting,
  title={Revisiting multimodal representation in contrastive learning: from patch and token embeddings to finite discrete tokens},
  author={Chen, Yuxiao and Yuan, Jianbo and Tian, Yu and Geng, Shijie and Li, Xinyu and Zhou, Ding and Metaxas, Dimitris N and Yang, Hongxia},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={15095--15104},
  year={2023}
}

Installation

Please run the following commands in your shell:

conda create -n fdt python=3.8
conda activate fdt
pip install -r requirements.txt

Dataset Preparation

Pre-training

This paper uses four publicly available datasets for pre-training, including YFCC-15M V2, Conceptual Captions (CC3M), Conceptual 12M (CC12M), and LAION115M.

In this repository, we pre-train the model using the CC3M dataset. The data preparation process is outlined as follows:

1. Download the dataset using the script provided by img2dataset. The downloaded dataset is saved as the webdataset format. There are a series of .tar files. Each file is a ``shard'', and it has two files for each training sample, one for the image and one for the corresponding text.
2. We further process the shards using tarproc to make that each shard contains 1,000 samples:

git clone https://github.com/tmbdev-archive/tarproc #install tarproc
python data_process/wds/process_download_data.py \
    --src_fold ${path/to/downloaded/data} \
    --res_fold ${path/to/save/results} \
    --tarproc_fold ${path/to/tarpoc/folder}

Evaluation

ImageNet

1. Create a folder named ImageNet-1K/ under $DATA.
2. Download the validation set from the official website and extract it.
3. Download the label file from DeClip - val_official.json
4. Download the list of image paths - val_datalist.pkl

ImageNet-1K/
|–– val/ # contains 1,000 folders like n04357314, n03976657, etc.
|-- val_official.json 
|-- val_datalist.pkl

MSCOCO

1. Create a folder named coco/ under $DATA.
2. Download the validation set from official website and extract it under '$DATA/coco/'.
3. Download the list of image names and their captions- testall.pkl, and place it under '$DATA/coco/', resulting in the following directory structure:

coco/
|–– val2014/ #
|-- testall.pkl

Pre-training

1. Modify the below parameters in example/clip_fdt/config_cc3m.yaml:

data.train.data_path ---> the path of the preprocessed CC3M dataset.
data.train.num_samples --->  the total number of samples.
data.train.num_shards --> the number of shards.
saver.save_freq --> the iteration times for saving checkpoints.
lr_scheduler.max_iter --> the total number of iterations for pre-training. It is the product of data.train.data.epoch and the number of iterations for an epoch.

2. Run the following command to start pre-training:

bash run.sh example/clip_fdt/train_solver.py \
--config example/clip_fdt/config_cc3m.yaml \
--output_path ${path/to/save/log_and_ckpt} \
--batch_size 128 # batch size for each GPU

Testing

1. Change the below parameters in example/clip_fdt/test.sh :

MODEL_FOLD: the path to the folder where the pre-trained models and config files are saved.
DATA_FOLD: the path to the folder where the downstream datasets are saved.

2. Run the following command:

bash example/clip_fdt/test.sh

Our pre-trained models

Acknowledgement

Part of our code is borrowed from the following repositories/sources. We express our gratitude to the authors for releasing their codes.

• https://github.com/Sense-GVT/DeCLIP/tree/main
• https://github.com/openai/CLIP
• https://github.com/mlfoundations/open_clip
• https://github.com/KaiyangZhou/CoOp