Models | Example scripts | Getting started | Code overview | Installation | Contributing | License

TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale, including both content understanding and generative models. TorchMultimodal contains:

• A repository of modular and composable building blocks (fusion layers, loss functions, datasets and utilities).
• A collection of common multimodal model classes built up from said building blocks with pretrained weights for canonical configurations.
• A set 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.

TorchMultimodal contains a number of models, including

• ALBEF: model class, paper
• BLIP-2: model class, paper
• CLIP: model class, paper
• CoCa: model class, paper
• DALL-E 2: model, paper
• FLAVA: model class, paper
• MAE/Audio MAE: model class, MAE paper, Audio MAE paper
• MDETR: model class, paper

In addition to the above models, we provide example scripts for training, fine-tuning, and evaluation of models on popular multimodal tasks. Examples can be found under examples/ and include

Below we give minimal examples of how you can write a simple training or zero-shot evaluation script using components from TorchMultimodal.

import torch
from PIL import Image
from torchmultimodal.models.flava.model import flava_model
from torchmultimodal.transforms.bert_text_transform import BertTextTransform
from torchmultimodal.transforms.flava_transform import FLAVAImageTransform

# Define helper function for zero-shot prediction
def predict(zero_shot_model, image, labels):
  zero_shot_model.eval()
  with torch.no_grad():
      image = image_transform(img)["image"].unsqueeze(0)
      texts = text_transform(labels)
      _, image_features = zero_shot_model.encode_image(image, projection=True)
      _, text_features = zero_shot_model.encode_text(texts, projection=True)
      scores = image_features @ text_features.t()
      probs = torch.nn.Softmax(dim=-1)(scores)
      label = labels[torch.argmax(probs)]
      print(
          "Label probabilities: ",
          {labels[i]: probs[:, i] for i in range(len(labels))},
      )
      print(f"Predicted label: {label}")


image_transform = FLAVAImageTransform(is_train=False)
text_transform = BertTextTransform()
zero_shot_model = flava_model(pretrained=True)
img = Image.open("my_image.jpg")  # point to your own image
predict(zero_shot_model, img, ["dog", "cat", "house"])

# Example output:
# Label probabilities:  {'dog': tensor([0.80590]), 'cat': tensor([0.0971]), 'house': tensor([0.0970])}
# Predicted label: dog

import torch
from torch.utils.data import DataLoader
from torchmultimodal.models.masked_auto_encoder.model import vit_l_16_image_mae
from torchmultimodal.models.masked_auto_encoder.utils import (
  CosineWithWarmupAndLRScaling,
)
from torchmultimodal.modules.losses.reconstruction_loss import ReconstructionLoss
from torchmultimodal.transforms.mae_transform import ImagePretrainTransform

mae_transform = ImagePretrainTransform()
dataset = MyDatasetClass(transforms=mae_transform)  # you should define this
dataloader = DataLoader(dataset, batch_size=8)

# Instantiate model and loss
mae_model = vit_l_16_image_mae()
mae_loss = ReconstructionLoss()

# Define optimizer and lr scheduler
optimizer = torch.optim.AdamW(mae_model.parameters())
lr_scheduler = CosineWithWarmupAndLRScaling(
  optimizer, max_iters=1000, warmup_iters=100  # you should set these
)

# Train one epoch
for batch in dataloader:
  model_out = mae_model(batch["images"])
  loss = mae_loss(model_out.decoder_pred, model_out.label_patches, model_out.mask)
  loss.backward()
  optimizer.step()
  lr_scheduler.step()

diffusion_labs contains components for building diffusion models. For more details on these components, see diffusion_labs/README.md.

Look here for model classes as well as any other modeling code specific to a given architecture. E.g. the directory torchmultimodal/models/blip2 contains modeling components specific to BLIP-2.

Look here for common generic building blocks that can be stitched together to build a new architecture. This includes layers like codebooks, patch embeddings, or transformer encoder/decoders, losses like contrastive loss with temperature or reconstruction loss, encoders like ViT and BERT, and fusion modules like Deep Set fusion.

Look here for common data transforms from popular models, e.g. CLIP, FLAVA, and MAE.

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

1. Install conda environment

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

2. Install pytorch, torchvision, and torchaudio. See PyTorch documentation.

   # Use the current CUDA version as seen [here](https://pytorch.org/get-started/locally/)
   # Select the nightly Pytorch build, Linux as the OS, and conda. Pick the most recent CUDA version.
   conda install pytorch torchvision torchaudio pytorch-cuda=\<cuda_version\> -c pytorch-nightly -c nvidia
   
   # For CPU-only install
   conda install pytorch torchvision torchaudio cpuonly -c pytorch-nightly
   

Nightly binary on Linux for Python 3.8 and 3.9 can be installed via pip wheels. For now we only support Linux platform through PyPI.

python -m pip install torchmultimodal-nightly

Alternatively, you can also build from our source code and run our examples:

git clone --recursive https://github.com/facebookresearch/multimodal.git multimodal
cd multimodal

pip install -e .

For developers please follow the development installation.

We welcome any feature requests, bug reports, or pull requests from the community. See the CONTRIBUTING file for how to help out.

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