# hamburger-pytorch

**Repository Path**: Sept98/hamburger-pytorch

## Basic Information

- **Project Name**: hamburger-pytorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-05-17
- **Last Updated**: 2022-09-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<img src="./hamburger.png" width="500px"></img>

<img src="./mu.png"></img>

## 🍔 - Pytorch

Pytorch implementation of the hamburger module from the ICLR 2021 paper <a href="https://openreview.net/forum?id=1FvkSpWosOl">Is Attention Better Than Matrix Decomposition?</a>. Following Betteridge's law, the answer according to the paper is "No" for segmentation and GANs.

This repository will contain the NMF-MU (nonnegative matrix factorization w/ multiplicative update) module sandwiched by linear projections.

## Install

```bash
$ pip install hamburger-pytorch
```

## Usage

```python
import torch
from hamburger_pytorch import Hamburger

hamburger = Hamburger(
    dim = 512,       # input dimension
    n = 32 * 32,     # n will be size of the sequence, in this case, height times width of the images
    ratio = 8,       # matrix factorization ratio, recommended to be at 8
    K = 6            # number of iterations, optimal at 6 as shown in paper
)

x = torch.randn(1, 512, 32, 32)
hamburger(x) + x # (1, 512, 32, 32)
```

## Citations

```bibtex
@inproceedings{
    anonymous2021is,
    title={Is Attention Better Than Matrix Decomposition?},
    author={Anonymous},
    booktitle={Submitted to International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=1FvkSpWosOl},
    note={under review}
}
```