https://github.com/lucidrains/uformer-pytorch

Implementation of Uformer, Attention-based Unet, in Pytorch
https://github.com/lucidrains/uformer-pytorch

artificial-intelligence deep-learning image-segmentation transformer unet

Last synced: about 11 hours ago
JSON representation

Implementation of Uformer, Attention-based Unet, in Pytorch

• Host: GitHub
• URL: https://github.com/lucidrains/uformer-pytorch
• Owner: lucidrains
• License: mit
• Created: 2021-06-17T00:56:03.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-10-26T15:28:20.000Z (almost 3 years ago)
• Last Synced: 2024-09-07T03:56:40.893Z (26 days ago)
• Topics: artificial-intelligence, deep-learning, image-segmentation, transformer, unet
• Language: Python
• Homepage:
• Size: 86.9 KB
• Stars: 92
• Watchers: 2
• Forks: 16
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        


## Uformer - Pytorch

Implementation of Uformer, Attention-based Unet, in Pytorch. It will only offer the concat-cross-skip connection.

This repository will be geared towards use in a project for learning protein structures. Specifically, it will include the ability to condition on time steps (needed for DDPM), as well as 2d relative positional encoding using rotary embeddings (instead of the bias on the attention matrix in the paper).

## Install

```bash

$ pip install uformer-pytorch

```

## Usage

```python

import torch

from uformer_pytorch import Uformer

model = Uformer(

    dim = 64,           # initial dimensions after input projection, which increases by 2x each stage

    stages = 4,         # number of stages

    num_blocks = 2,     # number of transformer blocks per stage

    window_size = 16,   # set window size (along one side) for which to do the attention within

    dim_head = 64,

    heads = 8,

    ff_mult = 4

)

x = torch.randn(1, 3, 256, 256)

pred = model(x) # (1, 3, 256, 256)

```

To condition on time for DDPM training

```python

import torch

from uformer_pytorch import Uformer

model = Uformer(

    dim = 64,

    stages = 4,

    num_blocks = 2,

    window_size = 16,

    dim_head = 64,

    heads = 8,

    ff_mult = 4,

    time_emb = True    # set this to true

)

x = torch.randn(1, 3, 256, 256)

time = torch.arange(1)

pred = model(x, time = time) # (1, 3, 256, 256)

```

## Citations

```bibtex

@misc{wang2021uformer,

    title   = {Uformer: A General U-Shaped Transformer for Image Restoration}, 

    author  = {Zhendong Wang and Xiaodong Cun and Jianmin Bao and Jianzhuang Liu},

    year    = {2021},

    eprint  = {2106.03106},

    archivePrefix = {arXiv},

    primaryClass = {cs.CV}

}

```