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

Related tags

Deep Learningdeep-learningartificial-intelligencesegmentationimage-segmentationattention-mechanismmultilayer-perceptron
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
Implementation of gMLP, an all-MLP replacement for Transformers, in Pytorch

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

Phil Wang 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

Qiushi Yang 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

Haoyu Chen 71 Dec 30, 2022
MLP-Like Vision Permutator for Visual Recognition (PyTorch)
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

Qibin (Andrew) Hou 162 Nov 28, 2022
Pytorch implementation of
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"

Kim SungDong 194 Dec 28, 2022
Unofficial Implementation of MLP-Mixer in TensorFlow
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

Rishabh Anand 24 Mar 23, 2022
Implementation of
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

Shoufa Chen 244 Dec 27, 2022
Unofficial Implementation of MLP-Mixer, Image Classification Model
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

Oğuzhan Ercan 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

Soroush Omranpour 1 Jan 1, 2022
Comments
  • Something is wrong with your implementation.

    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

    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

    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

    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)
Owner
Phil Wang
Working with Attention
Phil Wang
GitHub Repository
PyTorch implementation of DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images

DARDet PyTorch implementation of "DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images", [pdf]. Highlights: 1. We develop a new dense

41 Oct 23, 2022
This project is a re-implementation of MASTER: Multi-Aspect Non-local Network for Scene Text Recognition by MMOCR

This project is a re-implementation of MASTER: Multi-Aspect Non-local Network for Scene Text Recognition by MMOCR，which is an open-source toolbox based on PyTorch. The overall architecture will be sh

Jianquan Ye 82 Nov 17, 2022
Speech Recognition using DeepSpeech2.

deepspeech.pytorch Implementation of DeepSpeech2 for PyTorch using PyTorch Lightning. The repo supports training/testing and inference using the DeepS

Sean Naren 2k Jan 04, 2023
Residual Pathway Priors for Soft Equivariance Constraints

Residual Pathway Priors for Soft Equivariance Constraints This repo contains the implementation and the experiments for the paper Residual Pathway Pri

Marc Finzi 13 Oct 12, 2022
CROSS-LINGUAL ABILITY OF MULTILINGUAL BERT: AN EMPIRICAL STUDY

M-BERT-Study CROSS-LINGUAL ABILITY OF MULTILINGUAL BERT: AN EMPIRICAL STUDY Motivation Multilingual BERT (M-BERT) has shown surprising cross lingual a

CogComp 1 Feb 28, 2022
[arXiv] What-If Motion Prediction for Autonomous Driving ❓🚗💨

WIMP - What If Motion Predictor Reference PyTorch Implementation for What If Motion Prediction [PDF] [Dynamic Visualizations] Setup Requirements The W

William Qi 96 Dec 29, 2022
Open source repository for the code accompanying the paper 'Non-Rigid Neural Radiance Fields Reconstruction and Novel View Synthesis of a Deforming Scene from Monocular Video'.

Non-Rigid Neural Radiance Fields This is the official repository for the project "Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synt

Facebook Research 296 Dec 29, 2022
Exploring Simple Siamese Representation Learning

G-SimSiam A PyTorch implementation which refers to repo for the paper Exploring Simple Siamese Representation Learning by Xinlei Chen & Kaiming He Add

zhuyun 1 Dec 19, 2021
A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling"

SelfGNN A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which will appear in Th

Zekarias Tilahun 24 Jun 21, 2022
Official code of the paper "Expanding Low-Density Latent Regions for Open-Set Object Detection" (CVPR 2022)

OpenDet Expanding Low-Density Latent Regions for Open-Set Object Detection (CVPR2022) Jiaming Han, Yuqiang Ren, Jian Ding, Xingjia Pan, Ke Yan, Gui-So

csuhan 64 Jan 07, 2023
Code for our paper "Interactive Analysis of CNN Robustness"

Perturber Code for our paper "Interactive Analysis of CNN Robustness" Datasets Feature visualizations: Google Drive Fine-tuning checkpoints as saved m

Stefan Sietzen 0 Aug 17, 2021
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand

Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand Introduction We propose a generalization of leaderboards, bidimensional leader

4 Dec 03, 2022
DECAF: Deep Extreme Classification with Label Features

DECAF DECAF: Deep Extreme Classification with Label Features @InProceedings{Mittal21, author = "Mittal, A. and Dahiya, K. and Agrawal, S. and Sain

46 Nov 06, 2022
Code for NAACL 2021 full paper "Efficient Attentions for Long Document Summarization"

LongDocSum Code for NAACL 2021 paper "Efficient Attentions for Long Document Summarization" This repository contains data and models needed to reprodu

56 Jan 02, 2023
UPSNet: A Unified Panoptic Segmentation Network

UPSNet: A Unified Panoptic Segmentation Network Introduction UPSNet is initially described in a CVPR 2019 oral paper. Disclaimer This repository is te

Uber Research 622 Dec 26, 2022
Code for "Human Pose Regression with Residual Log-likelihood Estimation", ICCV 2021 Oral

Human Pose Regression with Residual Log-likelihood Estimation [Paper] [arXiv] [Project Page] Human Pose Regression with Residual Log-likelihood Estima

JeffLi 347 Dec 24, 2022
A curated list of awesome deep long-tailed learning resources.

A curated list of awesome deep long-tailed learning resources.

vanint 210 Dec 25, 2022
Multi Agent Path Finding Algorithms

MATP-solver Simulator collision check path step random initial states or given states Traditional method Seperate A* algorithem Confict-based Search S

30 Dec 12, 2022
Simple streamlit app to demonstrate HERE Tour Planning

Table of Contents About the Project Built With Getting Started Prerequisites Installation Usage Roadmap Contributing License Acknowledgements About Th

Amol 8 Sep 05, 2022
A PyTorch implementation for V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

A PyTorch implementation of V-Net Vnet is a PyTorch implementation of the paper V-Net: Fully Convolutional Neural Networks for Volumetric Medical Imag

Matthew Macy 606 Dec 21, 2022