Implementation of Segformer, Attention + MLP neural network for segmentation, in Pytorch

Last update: Dec 25, 2022

Overview

Segformer - Pytorch

Implementation of Segformer, Attention + MLP neural network for segmentation, in Pytorch.

Install

$ pip install segformer-pytorch

Usage

For example, MiT-B0

import torch
from segformer_pytorch import Segformer

model = Segformer(
    patch_size = 4,                 # patch size
    dims = (32, 64, 160, 256),      # dimensions of each stage
    heads = (1, 2, 5, 8),           # heads of each stage
    ff_expansion = (8, 8, 4, 4),    # feedforward expansion factor of each stage
    reduction_ratio = (8, 4, 2, 1), # reduction ratio of each stage for efficient attention
    num_layers = 2,                 # num layers of each stage
    decoder_dim = 256,              # decoder dimension
    num_classes = 4                 # number of segmentation classes
)

x = torch.randn(1, 3, 256, 256)
pred = model(x) # (1, 4, 64, 64)  # output is (H/4, W/4) map of the number of segmentation classes

Make sure the keywords are at most a tuple of 4, as this repository is hard-coded to give the MiT 4 stages as done in the paper.

Citations

@misc{xie2021segformer,
    title   = {SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers}, 
    author  = {Enze Xie and Wenhai Wang and Zhiding Yu and Anima Anandkumar and Jose M. Alvarez and Ping Luo},
    year    = {2021},
    eprint  = {2105.15203},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

You might also like...

Implementation of gMLP, an all-MLP replacement for Transformers, in Pytorch

383 Jan 2, 2023

Pytorch implementation of MLP-Mixer with loading pre-trained models.

MLP-Mixer-Pytorch PyTorch implementation of MLP-Mixer: An all-MLP Architecture for Vision with the function of loading official ImageNet pre-trained p

2 Sep 29, 2022

PyTorch code for our paper "Attention in Attention Network for Image Super-Resolution"

Under construction... Attention in Attention Network for Image Super-Resolution (A2N) This repository is an PyTorch implementation of the paper "Atten

71 Dec 30, 2022

MLP-Like Vision Permutator for Visual Recognition (PyTorch)

Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition (arxiv) This is a Pytorch implementation of our paper. We present Vision

162 Nov 28, 2022

Pytorch implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling"

RNN-for-Joint-NLU Pytorch implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling"

194 Dec 28, 2022

Unofficial Implementation of MLP-Mixer in TensorFlow

mlp-mixer-tf Unofficial Implementation of MLP-Mixer [abs, pdf] in TensorFlow. Note: This project may have some bugs in it. I'm still learning how to i

24 Mar 23, 2022

Implementation of "A MLP-like Architecture for Dense Prediction"

A MLP-like Architecture for Dense Prediction (arXiv) Updates (22/07/2021) Initial release. Model Zoo We provide CycleMLP models pretrained on ImageNet

244 Dec 27, 2022

Unofficial Implementation of MLP-Mixer, Image Classification Model

MLP-Mixer Unoffical Implementation of MLP-Mixer, easy to use with terminal. Train and test easly. https://arxiv.org/abs/2105.01601 MLP-Mixer is an arc

6 Dec 5, 2022

MLP-Numpy - A simple modular implementation of Multi Layer Perceptron in pure Numpy.

MLP-Numpy A simple modular implementation of Multi Layer Perceptron in pure Numpy. I used the Iris dataset from scikit-learn library for the experimen

1 Jan 1, 2022

Comments

Something is wrong with your implementation.

Hello!

First of all, I really like the repo. The implementation is clean and so much easier to understand than the official repo. But after doing some digging, I realized that the number of parameters and layers (especially conv2d) is quite different from the official implementation. This is the case for all variants I have tested (B0 and B5).

Check out the README in my repo here, and you'll see what I mean. I also included images of the execution graphs of the two different implementations in the 'src' folder, which could help to debug.

I don't quite have time to dig into the source of the problem, but I just thought I'd share my observations with you.

opened by camlaedtke 0
Models weights + model output HxW

Hi,

Could you please add the models weights so we can start training from them?

Also, why you choose to train models with an output of size (H/4,W/4) and not the original (HxW) size?

Great job for the paper, very interesting :)

opened by isega24 2
The model configurations for all the SegFormer B0 ~ B5

Hello How are you? Thanks for contributing to this project. Is the model configuration in README MiT-B0 correctly? That's because the total number of params for the model is 36M. Could u provide all the model configurations for SegFormer B0 ~ B5?

opened by rose-jinyang 5

a question about kv reshape in Efficient Self-Attention

Thanks for sharing your work, your code is so elegant, and inspired me a lot. Here is a question about the implementation of Efficient Self-Attention

It seems you use a "mean op" to reshape k,v. and the official implementation uses a (learnable) linear mapping to reshape k,v

may I ask, whether this difference significantly matters in your experiment ?

in your code:

k, v = map(lambda t: reduce(t, 'b c (h r1) (w r2) -> b c h w', 'mean', r1 = r, r2 = r), (k, v))

the original implementation uses:

self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
self.norm = nn.LayerNorm(dim)

x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
x_ = self.norm(x_)
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
k, v = kv[0], kv[1]

opened by masszhou 1

Releases(0.0.6)

0.0.6(Aug 24, 2021)

Source code(tar.gz)
Source code(zip)
0.0.5(Jun 30, 2021)

Source code(tar.gz)
Source code(zip)
0.0.4(Jun 23, 2021)

Source code(tar.gz)
Source code(zip)
0.0.3(Jun 19, 2021)

Source code(tar.gz)
Source code(zip)
0.0.2(Jun 11, 2021)

Source code(tar.gz)
Source code(zip)
0.0.1b(Jun 11, 2021)

Source code(tar.gz)
Source code(zip)

Owner

Phil Wang

Working with Attention

GitHub Repository

This project is based on RIFE and aims to make RIFE more practical for users by adding various features and design new models

CPM 项目描述 CPM（Chinese Pretrained Models）模型是北京智源人工智能研究院和清华大学发布的中文大规模预训练模型。官方发布了三种规模的模型，参数量分别为109M、334M、2.6B，用户需申请与通过审核，方可下载。由于原项目需要考虑大模型的训练和使用，需要安装较为复杂

190 Jan 08, 2023

Curvlearn, a Tensorflow based non-Euclidean deep learning framework.

English | 简体中文 Why Non-Euclidean Geometry Considering these simple graph structures shown below. Nodes with same color has 2-hop distance whereas 1-ho

123 Dec 12, 2022

Contextualized Perturbation for Textual Adversarial Attack, NAACL 2021

Contextualized Perturbation for Textual Adversarial Attack Introduction This is a PyTorch implementation of Contextualized Perturbation for Textual Ad

30 Jan 01, 2023

Very Deep Convolutional Networks for Large-Scale Image Recognition

pytorch-vgg Some scripts to convert the VGG-16 and VGG-19 models [1] from Caffe to PyTorch. The converted models can be used with the PyTorch model zo

217 Dec 05, 2022

Kaggle DSTL Satellite Imagery Feature Detection

206 Oct 29, 2022

Identifying a Training-Set Attack’s Target Using Renormalized Influence Estimation

Identifying a Training-Set Attack’s Target Using Renormalized Influence Estimation By: Zayd Hammoudeh and Daniel Lowd Paper: Arxiv Preprint Coming soo

2 Oct 08, 2022

Fairness Metrics: All you need to know

Fairness Metrics: All you need to know Testing machine learning software for ethical bias has become a pressing current concern. Recent research has p

1 Jan 17, 2022

GraphLily: A Graph Linear Algebra Overlay on HBM-Equipped FPGAs

GraphLily: A Graph Linear Algebra Overlay on HBM-Equipped FPGAs GraphLily is the first FPGA overlay for graph processing. GraphLily supports a rich se

39 Dec 13, 2022

"Learning and Analyzing Generation Order for Undirected Sequence Models" in Findings of EMNLP, 2021

undirected-generation-dev This repo contains the source code of the models described in the following paper "Learning and Analyzing Generation Order f

0 Mar 25, 2022

novel deep learning research works with PaddlePaddle

Research 发布基于飞桨的前沿研究工作，包括CV、NLP、KG、STDM等领域的顶会论文和比赛冠军模型。目录计算机视觉(Computer Vision) 自然语言处理(Natrual Language Processing) 知识图谱(Knowledge Graph) 时空数据挖掘(Spa

1.5k Dec 29, 2022

Official Pytorch implementation for video neural representation (NeRV)

NeRV: Neural Representations for Videos (NeurIPS 2021) Project Page | Paper | UVG Data Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav S

214 Dec 28, 2022

Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems

Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems This repository is the official implementation of Rever

6 Aug 25, 2022

Reference PyTorch implementation of "End-to-end optimized image compression with competition of prior distributions"

PyTorch reference implementation of "End-to-end optimized image compression with competition of prior distributions" by Benoit Brummer and Christophe

6 Jun 16, 2022

Weakly Supervised End-to-End Learning (NeurIPS 2021)

WeaSEL: Weakly Supervised End-to-end Learning This is a PyTorch-Lightning-based framework, based on our End-to-End Weak Supervision paper (NeurIPS 202

131 Jan 06, 2023

Pytorch implementation for DFN: Distributed Feedback Network for Single-Image Deraining.

DFN：Distributed Feedback Network for Single-Image Deraining Abstract Recently, deep convolutional neural networks have achieved great success for sing

6 Nov 05, 2022

for a paper about leveraging discourse markers for training new models

TSLM-DISCOURSE-MARKERS Scope This repository contains: (1) Code to extract discourse markers from wikipedia (TSA). (1) Code to extract significant dis

6 Nov 02, 2022

code for EMNLP 2019 paper Text Summarization with Pretrained Encoders

PreSumm This code is for EMNLP 2019 paper Text Summarization with Pretrained Encoders Updates Jan 22 2020: Now you can Summarize Raw Text Input!. Swit

1.2k Dec 28, 2022

Algorithmic encoding of protected characteristics and its implications on disparities across subgroups

Algorithmic encoding of protected characteristics and its implications on disparities across subgroups This repository contains the code for the paper

15 Oct 24, 2022

scAR (single-cell Ambient Remover) is a package for data denoising in single-cell omics.

scAR scAR (single cell Ambient Remover) is a package for denoising multiple single cell omics data. It can be used for multiple tasks, such as, sgRNA

19 Nov 28, 2022

[ICML 2020] Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

PG-MORL This repository contains the implementation for the paper Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Contro

65 Jan 07, 2023