This is a file about Unet implemented in Pytorch

Related tags

Deep LearningUnet
Overview

Unet

this is an implemetion of Unet in Pytorch and it's architecture is as follows which is the same with paper of Unet

architecture of Unet

component of Unet

Convolution and downsampling

two layers of convolution which consists of BatchNorm and Relu and then downsampling

class TwoConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(TwoConv, self).__init__()
        self.twoconv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
        )

    def forward(self, x):
        return self.twoconv(x)

class TwoConvDown(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(TwoConvDown, self).__init__()
        self.twoconvdown = nn.Sequential(
            nn.MaxPool2d(2),
            TwoConv(in_channels, out_channels),
        )

    def forward(self, x):
        return self.twoconvdown(x)

Upsampling and Concatation

there are two modes, "pad" and "crop" to deal with the different size of two parts in Unet. "crop" is the same operation with paper of Unet.

class UpCat(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UpCat, self).__init__()
        self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
        self.twoconv = TwoConv(in_channels=in_channels, out_channels=out_channels)

    def forward(self, x1, x2, mode="pad"):
        '''
        :param x1: Unet right part, size is samller
        :param x2: Unet left part,size is larger
        :return:
        '''
        x1 = self.up(x1)
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]

        if mode == "pad":
            x1 = nn.functional.pad(x1, (diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2))
        elif mode == "crop":
            left = diffX // 2
            right = diffX - left
            up = diffY // 2
            down = diffY - up

            x2 = x2[:, :, left:x2.size()[2]-right, up:x2.size()[3]-down]

        x = torch.cat([x2, x1], dim=1)
        x = self.twoconv(x)
        return x

main part of Unet

class Unet(nn.Module):
    def __init__(self, in_channels,
                 channel_list: list = [64, 128, 256, 512, 1024],
                 length = 5,
                 mode = "pad")
Owner
Dragon
Dragon
Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

pytorch Implementation of U-Net, R2U-Net, Attention U-Net, Attention R2U-Net U-Net: Convolutional Networks for Biomedical Image Segmentation https://a

leejunhyun 2k Jan 02, 2023
Official code release for "GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis"

GRAF This repository contains official code for the paper GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis. You can find detailed usage i

349 Dec 29, 2022
Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch

Bootstrap Your Own Latent (BYOL), in Pytorch Practical implementation of an astoundingly simple method for self-supervised learning that achieves a ne

Phil Wang 1.4k Dec 29, 2022
An official implementation of the Anchor DETR.

Anchor DETR: Query Design for Transformer-Based Detector Introduction This repository is an official implementation of the Anchor DETR. We encode the

MEGVII Research 276 Dec 28, 2022
Let's Git - Versionsverwaltung & Open Source Hausaufgabe

Let's Git - Versionsverwaltung & Open Source Hausaufgabe Herzlich Willkommen zu dieser Hausaufgabe für unseren MOOC: Let's Git! Wir hoffen, dass Du vi

1 Dec 13, 2021
PyTorch Lightning implementation of Automatic Speech Recognition

lasr Lightening Automatic Speech Recognition An MIT License ASR research library, built on PyTorch-Lightning, for developing end-to-end ASR models. In

Soohwan Kim 40 Sep 19, 2022
基于tensorflow 2.x的图片识别工具集

Classification.tf2 基于tensorflow 2.x的图片识别工具集 功能 粗粒度场景图片分类 细粒度场景图片分类 其他场景图片分类 模型部署 tensorflow serving本地推理和docker部署 tensorRT onnx ... 数据集 https://hyper.a

Wei Qi 1 Nov 03, 2021
This is the repo for the paper `SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization'. (published in Bioinformatics'21)

SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization This is the code for our paper ``SumGNN: Multi-typed Drug

Yue Yu 58 Dec 21, 2022
A quantum game modeling of pandemic (QHack 2022)

Contributors: @JongheumJung, @YoonjaeChung, @GyunghunKim Abstract In the regime of a global pandemic, leaders around the world need to consider variou

Yoonjae Chung 8 Apr 03, 2022
Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification tasks

Uniformer - Pytorch Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification ta

Phil Wang 90 Nov 24, 2022
Deep learning models for classification of 15 common weeds in the southern U.S. cotton production systems.

CottonWeeds Deep learning models for classification of 15 common weeds in the southern U.S. cotton production systems. requirements pytorch torchsumma

Dong Chen 8 Jun 07, 2022
This project aims to be a handler for input creation and running of multiple RICEWQ simulations.

What is autoRICEWQ? This project aims to be a handler for input creation and running of multiple RICEWQ simulations. What is RICEWQ? From the descript

Yass Fuentes 1 Feb 01, 2022
COLMAP - Structure-from-Motion and Multi-View Stereo

COLMAP About COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface.

4.7k Jan 07, 2023
code for "Feature Importance-aware Transferable Adversarial Attacks"

Feature Importance-aware Attack(FIA) This repository contains the code for the paper: Feature Importance-aware Transferable Adversarial Attacks (ICCV

Hengchang Guo 44 Nov 24, 2022
Code for the Population-Based Bandits Algorithm, presented at NeurIPS 2020.

Population-Based Bandits (PB2) Code for the Population-Based Bandits (PB2) Algorithm, from the paper Provably Efficient Online Hyperparameter Optimiza

Jack Parker-Holder 22 Nov 16, 2022
(Preprint) Official PyTorch implementation of "How Do Vision Transformers Work?"

(Preprint) Official PyTorch implementation of "How Do Vision Transformers Work?"

xxxnell 656 Dec 30, 2022
An end-to-end library for editing and rendering motion of 3D characters with deep learning [SIGGRAPH 2020]

Deep-motion-editing This library provides fundamental and advanced functions to work with 3D character animation in deep learning with Pytorch. The co

1.2k Dec 29, 2022
OpenCVのGrabCut()を利用したセマンティックセグメンテーション向けアノテーションツール(Annotation tool using GrabCut() of OpenCV. It can be used to create datasets for semantic segmentation.)

[Japanese/English] GrabCut-Annotation-Tool GrabCut-Annotation-Tool.mp4 OpenCVのGrabCut()を利用したアノテーションツールです。 セマンティックセグメンテーション向けのデータセット作成にご使用いただけます。 ※Grab

KazuhitoTakahashi 30 Nov 18, 2022
NeROIC: Neural Object Capture and Rendering from Online Image Collections

NeROIC: Neural Object Capture and Rendering from Online Image Collections This repository is for the source code for the paper NeROIC: Neural Object C

Snap Research 647 Dec 27, 2022
Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch

SRDenseNet-pytorch Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch (http://openaccess.thecvf.com/content_ICC

wxy 114 Nov 26, 2022