ProGamerGov / multimodal

TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale.

TorchMultimodal (Alpha Release)

Introduction

TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale. It provides:

• A repository of modular and composable building blocks (models, fusion layers, loss functions, datasets and utilities).
• A repository of examples that show how to combine these building blocks with components and common infrastructure from across the PyTorch Ecosystem to replicate state-of-the-art models published in the literature. These examples should serve as baselines for ongoing research in the field, as well as a starting point for future work.

As a first open source example, researchers will be able to train and extend FLAVA using TorchMultimodal.

Installation

TorchMultimodal requires Python >= 3.8. The library can be installed with or without CUDA support.

Building from Source

1. Create conda environment

   conda create -n torch-multimodal python=<python_version>
   conda activate torch-multimodal
   

2. Install pytorch, torchvision, and torchtext. See PyTorch documentation. For now we only support Linux platform.

   conda install pytorch torchvision torchtext cudatoolkit=11.3 -c pytorch-nightly -c nvidia
   
   # For CPU-only install
   conda install pytorch torchvision torchtext cpuonly -c pytorch-nightly
   

3. Download and install TorchMultimodal and remaining requirements.

   git clone --recursive https://github.com/facebookresearch/multimodal.git torchmultimodal
   cd torchmultimodal
   
   pip install -e .
   

   For developers please follow the development installation.

Documentation

The library builds on the following concepts:

• Architectures: These are general and composable classes that capture the core logic associated with a family of models. In most cases these take modules as inputs instead of flat arguments (see Models below). Examples include the LateFusionArchitecture, FLAVA and CLIPArchitecture. Users should either reuse an existing architecture or a contribute a new one. We avoid inheritance as much as possible.

• Models: These are specific instantiations of a given architecture implemented using builder functions. The builder functions take as input all of the parameters for constructing the modules needed to instantiate the architecture. See cnn_lstm.py for an example.

• Modules: These are self-contained components that can be stitched up in various ways to build an architecture. See lstm_encoder.py as an example.

Contributing

See the CONTRIBUTING file for how to help out.

License

TorchMultimodal is BSD licensed, as found in the LICENSE file.