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
Medical Insurance Cost Prediction using Machine earning

Medical-Insurance-Cost-Prediction-using-Machine-learning - Here in this project, I will use regression analysis to predict medical insurance cost for people in different regions, and based on several

1 Dec 27, 2021
Implémentation en pyhton de l'article Depixelizing pixel art de Johannes Kopf et Dani Lischinski

Implémentation en pyhton de l'article Depixelizing pixel art de Johannes Kopf et Dani Lischinski

TableauBits 3 May 29, 2022
DeepOBS: A Deep Learning Optimizer Benchmark Suite

DeepOBS - A Deep Learning Optimizer Benchmark Suite DeepOBS is a benchmarking suite that drastically simplifies, automates and improves the evaluation

Aaron Bahde 7 May 12, 2020
Official code for NeurIPS 2021 paper "Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN"

Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN Official code for NeurIPS 2021 paper "Towards Scalable Unpaired Virtu

68 Dec 21, 2022
Securetar - A streaming wrapper around python tarfile and allow secure handling files and support encryption

Secure Tar Secure Tarfile library It's a streaming wrapper around python tarfile

Pascal Vizeli 2 Dec 09, 2022
PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.

Unsupervised_IEPGAN This is the PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer. Ha

25 Oct 26, 2022
Transformer in Computer Vision

Transformer-in-Vision A paper list of some recent Transformer-based CV works. If you find some ignored papers, please open issues or pull requests. **

506 Dec 26, 2022
CompilerGym is a library of easy to use and performant reinforcement learning environments for compiler tasks

CompilerGym is a library of easy to use and performant reinforcement learning environments for compiler tasks

Facebook Research 721 Jan 03, 2023
ConvMixer unofficial implementation

ConvMixer ConvMixer 非官方实现 pytorch 版本已经实现。 nets 是重构版本 ,test 是官方代码 感兴趣小伙伴可以对照看一下。 keras 已经实现 tf2.x 中 是tensorflow 2 版本 gelu 激活函数要求 tf=2.4 否则使用入下代码代替gelu

Jian Tengfei 8 Jul 11, 2022
Official Repo for Ground-aware Monocular 3D Object Detection for Autonomous Driving

Visual 3D Detection Package: This repo aims to provide flexible and reproducible visual 3D detection on KITTI dataset. We expect scripts starting from

Yuxuan Liu 305 Dec 19, 2022
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision

SANDS This is an annonymous repository containing code and data necessary to reproduce the results published in "Semi-supervised Stance Detection of T

2 Sep 22, 2022
《Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement》(ECCV 2020) GitHub: [fig9]

Unsupervised 3D Human Pose Representation [Paper] The implementation of our paper Unsupervised 3D Human Pose Representation with Viewpoint and Pose Di

42 Nov 24, 2022
Bottleneck Transformers for Visual Recognition

Bottleneck Transformers for Visual Recognition Experiments Model Params (M) Acc (%) ResNet50 baseline (ref) 23.5M 93.62 BoTNet-50 18.8M 95.11% BoTNet-

Myeongjun Kim 236 Jan 03, 2023
Plenoxels: Radiance Fields without Neural Networks

Plenoxels: Radiance Fields without Neural Networks Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa UC Be

Sara Fridovich-Keil 81 Dec 25, 2022
[ICRA 2022] CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation

This is the official implementation of our paper: Bowen Wen, Wenzhao Lian, Kostas Bekris, and Stefan Schaal. "CaTGrasp: Learning Category-Level Task-R

Bowen Wen 199 Jan 04, 2023
Official PyTorch Implementation of Rank & Sort Loss [ICCV2021]

Rank & Sort Loss for Object Detection and Instance Segmentation The official implementation of Rank & Sort Loss. Our implementation is based on mmdete

Kemal Oksuz 229 Dec 20, 2022
Object detection (YOLO) with pytorch, OpenCV and python

Real Time Object/Face Detection Using YOLO-v3 This project implements a real time object and face detection using YOLO algorithm. You only look once,

1 Aug 04, 2022
Rendering Point Clouds with Compute Shaders

Compute Shader Based Point Cloud Rendering This repository contains the source code to our techreport: Rendering Point Clouds with Compute Shaders and

Markus Schütz 460 Jan 05, 2023
Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection

An official implementation of paper Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection

11 Nov 23, 2022
Locally cache assets that are normally streamed in POPULATION: ONE

Population One Localizer This is no longer needed as of the build shipped on 03/03/22, thank you bigbox :) Locally cache assets that are normally stre

Ahman Woods 2 Mar 04, 2022