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Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning

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# Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning

This is a PyTorch/GPU implementation of the paper [SCL](https://arxiv.org/pdf/2211.13437.pdf) and its extension version [GLSCL](https://arxiv.org/abs/2306.07096).

## Install

Our environment: CUDA11.3, torch1.11.0, torchvision0.12.0.

```bash
pip install -r requirements.txt
```

In our further experiments of the extension journal, we use a newer environment: CUDA11.7, torch2.0, Pytorch-Lightning2.0.

The pytorch and lightning version relation can be found at [here](https://lightning.ai/docs/pytorch/stable/versioning.html).

## Weights and ALIGN-BENCH

Our pre-trained and fine-tuned model weights can be downloaded at [huggingface/SCL](https://huggingface.co/jiyatai/SCL).

Our developed cross-modal alignment benchmark can be gained at [huggingface/ALIGN-BENCH](https://huggingface.co/datasets/jiyatai/ALIGN-BENCH).

## Dataset Preparation

We follow [ViLT](https://github.com/dandelin/ViLT) and use `pyarrow` to serialize the datasets. See [this link](https://github.com/dandelin/ViLT/blob/master/DATA.md) for details.

## Pre-training

```bash
python run.py --task pretrain
```

The detailed settings can be found in './scl/config.py', like pretraining datasets, optimation arguments, input size.

Note that 'plugins=[MyCluster(), MyDDPPlugin()]' of pl.Trainer(run.py) is used in multi-nodes ddp training.

## Downstream Tasks

```bash
python run.py --task vqa/nlvr2/f30k/coco/msrvtt/lsmdc
```

## Visualization and Quantify

```bash
python visualize_global.py # global-local cross-modal visualization
python visualize_local.py # local-local cross-modal visualization
python align_global.py # global-local cross-modal alignment quantify on ALIGN-BENCH
python align_local.py # local-local cross-modal alignment quantify on ALIGN-BENCH
```

## Acknowledgements

The code is based on [METER](https://github.com/zdou0830/METER) and [VLC](https://github.com/guilk/VLC).

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