linmult 1.2.0

General-purpose Multimodal Transformer with Linear Complexity Attention Mechanism.

LinMulT

General-purpose Multimodal Transformer with Linear Complexity Attention Mechanism.

Setup

Install package from PyPi

pip install linmult

Install package from repository root

git clone https://github.com/fodorad/LinMulT
cd LinMulT
pip install -e .
pip install -U -r requirements.txt
python -m unittest

Quick start

Example 1:

Simple transformer encoder with linear attention. The forward pass is performed using an input sequence.

import torch
from linmult import LinT

# input shape: (batch_size, time_dimension, feature_dimension)
x = torch.rand((32, 15, 1024), device='cuda')
model = LinT(input_modality_channels=1024, output_dim=5).cuda()
y_pred_seq = model(x)

# output shape: (batch_size, time_dimension, output_dimension)
assert y_pred_seq.size() == torch.Size([32, 15, 5])

Example 2:

Multimodal Transformer with Linear Attention. The forward pass is performed using 2 input sequences. Both input sequences have the same time dimension.

import torch
from linmult import LinMulT

# input shape: (batch_size, time_dimension, feature_dimension)
x_1 = torch.rand((32, 15, 1024), device='cuda')
x_2 = torch.rand((32, 15, 160), device='cuda')
model = LinMulT(input_modality_channels=[1024, 160], output_dim=5).cuda()
y_pred_cls, y_pred_seq = model([x_1, x_2])

# 1. output shape: (batch_size, output_dimension)
assert y_pred_cls.size() == torch.Size([32, 5])

# 2. output shape: (batch_size, time_dimension, output_dimension)
assert y_pred_seq.size() == torch.Size([32, 15, 5])

Example 3:

Multimodal Transformer with Linear Attention. The forward pass is performed using 3 input sequences with different time dimensions.

import torch
from linmult import LinMulT

# input shape: (batch_size, time_dimension, feature_dimension)
x_1 = torch.rand((16, 1500, 25), device='cuda')
x_2 = torch.rand((16, 450, 35), device='cuda')
x_3 = torch.rand((16, 120, 768), device='cuda')
model = LinMulT(input_modality_channels=[25, 35, 768],
                output_dim=5,
                add_time_collapse=True,
                add_self_attention_fusion=False).cuda()
y_pred_cls = model([x_1, x_2, x_3])

# output shape: (batch_size, output_dimension)
assert y_pred_cls.size() == torch.Size([16, 5])

Similar projects using LinMulT

(2023) BlinkLinMulT

LinMulT is trained for blink presence detection and eye state recognition tasks. Our results demonstrate comparable or superior performance compared to state-of-the-art models on 2 tasks, using 7 public benchmark databases.

• paper: BlinkLinMulT: Transformer-based Eye Blink Detection (accepted, available soon)
• code: https://github.com/fodorad/BlinkLinMulT

(2022) PersonalityLinMulT

LinMulT is trained for Big Five personality trait estimation using the First Impressions V2 dataset and sentiment estimation using the MOSI and MOSEI datasets.

• paper: Multimodal Sentiment and Personality Perception Under Speech: A Comparison of Transformer-based Architectures (pdf, website)
• code: https://github.com/fodorad/PersonalityLinMulT

Citation - BibTex

If you found our research helpful or influential please consider citing:

(2023) LinMulT for blink presence detection and eye state recognition:

@article{blinklinmult-fodor23,
  title = {BlinkLinMulT: Transformer-based Eye Blink Detection},
  author = {Fodor, {\'A}d{\'a}m and Fenech, Kristian and L{\H{o}}rincz, Andr{\'a}s},
  journal = {...}
  pages = {1--19},
  year = {2023}
}

(2022) LinMulT for personality trait and sentiment estimation:

@InProceedings{pmlr-v173-fodor22a,
  title = {Multimodal Sentiment and Personality Perception Under Speech: A Comparison of Transformer-based Architectures},
  author = {Fodor, {\'A}d{\'a}m and Saboundji, Rachid R. and Jacques Junior, Julio C. S. and Escalera, Sergio and Gallardo-Pujol, David and L{\H{o}}rincz, Andr{\'a}s},
  booktitle = {Understanding Social Behavior in Dyadic and Small Group Interactions},
  pages = {218--241},
  year = {2022},
  editor = {Palmero, Cristina and Jacques Junior, Julio C. S. and Clapés, Albert and Guyon, Isabelle and Tu, Wei-Wei and Moeslund, Thomas B. and Escalera, Sergio},
  volume = {173},
  series = {Proceedings of Machine Learning Research},
  month = {16 Oct},
  publisher = {PMLR},
  pdf = {https://proceedings.mlr.press/v173/fodor22a/fodor22a.pdf},
  url = {https://proceedings.mlr.press/v173/fodor22a.html}
}

Acknowledgement

The code is inspired by the following two materials:

Multimodal Transformer:

• paper: Multimodal Transformer for Unaligned Multimodal Language Sequences (1906.00295)
• code: https://github.com/yaohungt/Multimodal-Transformer

Linear Attention:

• paper: Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention (2006.16236)
• code: https://github.com/idiap/fast-transformers

Contact

• Ádám Fodor ([e-mail])