In the ever-evolving world of AI, Multimodal Masked Autoencoders (M3AE) stand out as a powerful method for learning transferable representations across various data types. In this article, we will delve into how to implement M3AE using JAXFlax, explore some of its features, and provide essential troubleshooting tips along the way.

Understanding M3AE: An Analogy

Imagine you have a versatile chef who can prepare at least two types of cuisine: Italian and Japanese. Each time the chef tries a new dish, they adapt their techniques to combine flavors and learn from different cooking styles. This chef symbolizes the M3AE, which learns representations from both image and text data effectively, allowing for a fusion of knowledge and practices—much like how our chef blends their culinary skills.

Installation

Before we get cooking, make sure you have the necessary ingredients for our recipe. Follow these steps to install Multimodal Masked Autoencoders.

• If you are running on a GPU, replace the following line in requirements.txt:

-f https://storage.googleapis.com/jax-releases/libtpu_releases.htmljax[tpu]==0.3.12

• with:

--f https://storage.googleapis.com/jax-releases/jax_cuda_releases.htmljax[cuda]==0.3.12

• Install the dependencies by running:

pip install requirements.txt

• Add this repo directory to your PYTHONPATH environment variable:

export PYTHONPATH=$PYTHONPATH:$(pwd)

Running Experiments

Now that you have your installation ready, it’s time to run some experiments! Below are different commands you can use to pre-train, classify, and fine-tune your models.

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

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

Fine-tuning on ImageNet

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

Understanding HDF5 Data Format

Using HDF5 files allows for efficient data storage. Here’s how the data is structured:

• For a paired image and text dataset, you have two fields: jpg for the JPEG images and caption for UTF-8 text.
• For the ImageNet dataset, images are divided into fields train_jpg and val_jpg, with labels in train_labels and val_labels.
• For unpaired text datasets, the text is stored under the field text.

Pre-trained Model Weights

You can download the pre-trained model weights here. The models have been trained for 50 epochs on the CC12M dataset using specified hyperparameters.

For converting the pre-trained JAX weights to PyTorch, please refer to this colab.

Troubleshooting

If you encounter any issues during implementation, here are a few troubleshooting tips:

• Installation problems: Double-check that you have installed the correct packages as per the instructions.
• Data path errors: Ensure that the paths to your HDF5 files are correctly specified. Typos can be a common culprit.
• Performance issues: If training is slow, consider optimizing your batch size or the number of worker threads.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.