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m3ae_public

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Multimodal Masked Autoencoders (M3AE): A JAX/Flax Implementation

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- __init__.py
- data.py
- finetune_main.py
- jax_utils.py
- linear_main.py
- m3ae_main.py
- mae_main.py
- model.py
- utils.py
- vqgan.py
.gitignore
LICENSE
README.md
requirements.txt

# Installation Guide

1. Get the code

git clone https://github.com/young-geng/m3ae_public

Downloads the entire project code from GitHub to your computer.

cd m3ae_public

Moves into the project folder you just downloaded.

2. Official Install Script

Easy Recommended

Prerequisites

Python 3 Python is required to use pip.

pip install requirements.txt

Installs the package published on PyPI directly — no need to clone the source.

✅ After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.

Pulled directly from this repo's README.

3. Python

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install requirements.txt

Installs the package published on PyPI directly — no need to clone the source.

python3 -m m3ae.mae_main \

Runs the Python script (or module).

python3 -m m3ae.m3ae_main \

Runs the Python script (or module).

python3 -m m3ae.linear_main \

Runs the Python script (or module).

python3 -m m3ae.finetune_main \

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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# Multimodal Masked Autoencoders (M3AE): A JAX/Flax Implementation

This is a JAX/Flax re-implementation for the paper [Multimodal Masked Autoencoders Learn Transferable Representations](https://arxiv.org/abs/2205.14204).

```
@article{geng2022multimodal,
  title={Multimodal Masked Autoencoders Learn Transferable Representations},
  author={Geng, Xinyang and Liu, Hao and Lee, Lisa and Schuurams, Dale and Levine, Sergey and Abbeel, Pieter},
  journal={arXiv preprint arXiv:2205.14204},
  year={2022}
}
```

This implementation has been tested on GPU and Google Cloud TPU and supports multi-host training with TPU Pods.
Unliked the original implementation used for the paper, this implementation also supports the following new
features:
* Predicting discretized image tokens from VQGAN as output (similar to BEiT).
* Training on a combination of paired image-text data (e.g. CC12M) and unpaired text data (e.g. Wikipedia).

## Installation
If this is on GPU, replace the following lines in `requirements.txt`
```
-f https://storage.googleapis.com/jax-releases/libtpu_releases.html
jax[tpu]==0.3.12
```
with
```
--f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
jax[cuda]==0.3.12
```
Install the dependencies with pip and add this repo directory to your `PYTHONPATH` environment variable.
```
pip install requirements.txt
export PYTHONPATH="$PYTHONPATH:$(pwd)"
```

## Running Experiments
Experiments can be launched via the following commands.

Pre-training MAE (image only model) on Conceptual 12M (CC12M)
```
python3 -m m3ae.mae_main \
    --mae.model_type='large' \
    --mae.use_type_embedding=False \
    --seed=42 \
    --epochs=100 \
    --lr_warmup_epochs=5 \
    --batch_size=4096 \
    --dataloader_n_workers=16 \
    --log_freq=500 \
    --plot_freq=2000 \
    --save_model_freq=10000 \
    --lr_peak_value=1.5e-4 \
    --weight_decay=0.05 \
    --discretized_image=False \
    --load_checkpoint='' \
    --dataset='cc12m' \
    --cc12m_data.path="<YOUR DATA HDF5 FILE PATH>" \
    --cc12m_data.image_normalization='cc12m'
```

Pre-training M3AE (image and text model) on Conceptual 12M (CC12M)
```
python3 -m m3ae.m3ae_main \
    --m3ae.model_type='large' \
    --m3ae.image_mask_ratio=0.75 \
    --m3ae.text_mask_ratio=0.75 \
    --seed=42 \
    --epochs=100 \
    --lr_warmup_epochs=5 \
    --batch_size=4096 \
    --discretized_image=False \
    --dataloader_n_workers=16 \
    --log_freq=500 \
    --plot_freq=2000 \
    --save_model_freq=10000 \
    --image_loss_weight=1.0 \
    --text_loss_weight=0.5 \
    --lr_peak_value=1.5e-4 \
    --weight_decay=0.05 \
    --load_checkpoint='' \
    --data.path="<YOUR DATA HDF5 FILE PATH>" \
    --data.transform_type='pretrain' \
    --data.image_normalization='cc12m'
```

Linear classification on ImageNet for both pre-trained MAE and M3AE
```
python3 -m m3ae.linear_main \
    --mae.model_type="large" \
    --mae.use_type_embedding=True \
    --seed=42 \
    --epochs=90 \
    --batch_size=2048 \
    --lr_warmup_epochs=10 \
    --discretized_image=False \
    --dataloader_n_workers=16 \
    --dataloader_shuffle=False \
    --log_freq=500 \
    --save_model_freq=10000 \
    --lr_peak_value=1e-1 \
    --weight_decay=0 \
    --momentum=0.9 \
    --train_data.partition="train" \
    --val_data.partition="val" \
    --train_data.path="<YOUR DATA HDF5 FILE PATH>" \
    --val_data.path="<YOUR DATA HDF5 FILE PATH>" \
    --train_data.transform_type="linear_prob" \
    --val_data.transform_type="test" \
    --load_checkpoint='' \
    --load_pretrained="<YOUR PRE-TRAINED MODEL PATH>"
```

Finetuning on ImageNet for both pre-trained MAE and M3AE
```
python3 -m m3ae.finetune_main \
    --seed=42 \
    --mae.model_type=large \
    --mae.drop_path=0.1 \
    --weight_decay=0.05 \
    --mixup_alpha=0.8 \
    --cutmix_alpha=1.0 \
    --switch_prob=0.5 \
    --label_smoothing=0.1 \
    --layer_decay=0.60 \
    --clip_gradient=1e9 \
    --batch_size=1024 \
    --warmup_epochs=5 \
    --epochs=100 \
    --dataloader_n_workers=16 \
    --dataloader_shuffle=False \
    --log_freq=500 \
    --save_model_freq=10000 \
    --lr_peak_value=1e-3 \
    --train_data.partition="train" \
    --val_data.partition="val" \
    --train_data.path="<YOUR DATA HDF5 FILE PATH>" \
    --val_data.path="<YOUR DATA HDF5 FILE PATH>" \
    --train_data.transform_type="finetune" \
    --val_data.transform_type="test" \
    --load_pretrained="<YOUR PRE-TRAINED MODEL PATH>"
```

## HDF5 Data Format
In order to facilitate training on cloud, we store all the dataset
as HDF5 files and read them from cloud storage buckets. For paired image and text
dataset, the HDF5 data contains two field, `jpg` and `caption`. The `jpg` field
is an 1D array containing the raw bytes of JPEG encoded images. The `caption`
field is an 1D array of utf-8 encoded text. For ImageNet dataset, the image JPEG
bytes are stored in field `train_jpg` and `val_jpg`, and the integer labels are
stored in field `train_labels` and `val_labels`. For unpaired text only dataset,
the utf-8 encoded text is stored in field `text`.

## Pre-trained Model Weights
[Pre-trained model weights can be downloaded here](https://www.dropbox.com/scl/fo/hnw0qtdfntl9mxls2xb8g/h?rlkey=vyzg1fm71prznifv6ijjy4t6m&dl=0).
The M3AE and MAE models here are trained for 50 epochs on the CC12M dataset using
the hyperparameters specified in the paper.

For converting the pre-trained Jax weights to PyTorch, please refer to [this colab](https://colab.research.google.com/drive/1L6EiUUSkeTzWtB3_K4fR0evn-SDqKWME).

## Credits
* The MAE is heavily inspired by the [original MAE implementation](https://github.com/facebookresearch/mae).

* The VQGAN image tokenizers are from [MaskGiT](https://github.com/google-research/maskgit)
and [dalle-mini](https://huggingface.co/dalle-mini/vqgan_imagenet_f16_16384).

* The transformer implementation is heavily inspired by [jax-models](https://github.com/DarshanDeshpande/jax-models).

* Some utilities are borrowed from [JaxCQL](https://github.com/young-geng/JaxCQL).

## Contact
If you have any questions, please open an issue or contact young.geng@berkeley.edu and hao.liu@berkely.edu.