paddle 图像分割学习总结

2024-02-27 01:58
文章标签 学习 总结 图像 分割 paddle

本文主要是介绍paddle 图像分割学习总结，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

提示：文章写完后，目录可以自动生成，如何生成可参考右边的帮助文档

文章目录

  • 前言
  • 一、图像分割综述
  • 二、FCN
  • U-Net
  • PSPNet
  • DeepLab

前言

课程链接：https://aistudio.baidu.com/aistudio/course/introduce/1767

提示：以下是本篇文章正文内容

一、图像分割综述

根据 不同的任务和数据类型：

-图像分割（image segmentation）（像素级分类）

  • 图像语义分割（image semantic segmentation）
  • 图像实例分割（image instance segmentation）
  • 图像全景分割（image panoptic segmentation）
  • 视频目标分割（video object segmentation）
  • 视频实例分割（video instance segmentation）

示例：

  • 语义分割：给每个pixel分类
  • 实例分割：给每个筐里的object分mask
  • 全景分割：背景pixel分类+框里mask
  • VOS ：通常会给定目标的mask，求特定目标的mask
  • VIS ：根据目标检测的框，求目标的mask
    在这里插入图片描述

分割网络的评价指标：mIoU 和 mAcc

  • mean intersection-over-unio
    分割每一类别的交并比
  • mean accuracy
    分对的像素 / 所有像素

二、FCN

网络结构：
在这里插入图片描述

代码如下（示例）：

import numpy as np
import paddle.fluid as fluid
from paddle.fluid.dygraph import to_variable
from paddle.fluid.dygraph import Conv2D
from paddle.fluid.dygraph import Conv2DTranspose
from paddle.fluid.dygraph import Dropout
from paddle.fluid.dygraph import BatchNorm
from paddle.fluid.dygraph import Pool2D
from paddle.fluid.dygraph import Linear
# from vgg import VGG16BNclass VGG(fluid.dygraph.Layer):def __init__(self, layers=16, use_bn=False, num_classes=1000):super(VGG, self).__init__()self.layers = layersself.use_bn = use_bnsupported_layers = [16, 19]assert layers in supported_layersif layers == 16:depth = [2, 2, 3, 3, 3]elif layers == 19:depth = [2, 2, 4, 4, 4]num_channels = [3, 64, 128, 256, 512]num_filters = [64, 128, 256, 512, 512]self.layer1 = fluid.dygraph.Sequential(*self.make_layer(num_channels[0], num_filters[0], depth[0], use_bn, name='layer1'))self.layer2 = fluid.dygraph.Sequential(*self.make_layer(num_channels[1], num_filters[1], depth[1], use_bn, name='layer2'))self.layer3 = fluid.dygraph.Sequential(*self.make_layer(num_channels[2], num_filters[2], depth[2], use_bn, name='layer3'))self.layer4 = fluid.dygraph.Sequential(*self.make_layer(num_channels[3], num_filters[3], depth[3], use_bn, name='layer4'))self.layer5 = fluid.dygraph.Sequential(*self.make_layer(num_channels[4], num_filters[4], depth[4], use_bn, name='layer5'))self.classifier = fluid.dygraph.Sequential(Linear(input_dim=512 * 7 * 7, output_dim=4096, act='relu'),Dropout(),Linear(input_dim=4096, output_dim=4096, act='relu'),Dropout(),Linear(input_dim=4096, output_dim=num_classes))self.out_dim = 512 * 7 * 7def forward(self, inputs):x = self.layer1(inputs)x = fluid.layers.pool2d(x, pool_size=2, pool_stride=2)x = self.layer2(x)x = fluid.layers.pool2d(x, pool_size=2, pool_stride=2)x = self.layer3(x)x = fluid.layers.pool2d(x, pool_size=2, pool_stride=2)x = self.layer4(x)x = fluid.layers.pool2d(x, pool_size=2, pool_stride=2)x = self.layer5(x)x = fluid.layers.pool2d(x, pool_size=2, pool_stride=2)x = fluid.layers.adaptive_pool2d(x, pool_size=(7,7), pool_type='avg')x = fluid.layers.reshape(x, shape=[-1, self.out_dim])x = self.classifier(x)return xclass FCN8s(fluid.dygraph.Layer):# TODO: create fcn8s modeldef __init__(self, num_classes=59):super(FCN8s, self).__init__()self.num_classes = num_classesself.layer1 = fluid.dygraph.Sequential(Conv2D(num_channels=3, num_filters=64, filter_size=3, padding=1),BatchNorm(num_channels=64, act='relu'),Conv2D(num_channels=64, num_filters=64, filter_size=3, padding=1),BatchNorm(num_channels=64, act='relu'),Pool2D(pool_size=2, pool_stride=2, pool_type='max')) # 1/ 2self.layer2 = fluid.dygraph.Sequential(Conv2D(num_channels=64, num_filters=128, filter_size=3, padding=1),BatchNorm(num_channels=128, act='relu'),Conv2D(num_channels=128, num_filters=128, filter_size=3, padding=1),BatchNorm(num_channels=128, act='relu'),Pool2D(pool_size=2, pool_stride=2, pool_type='max')) # 1/ 4self.layer3 = fluid.dygraph.Sequential(Conv2D(num_channels=128, num_filters=256, filter_size=3, padding=1),BatchNorm(num_channels=256, act='relu'),Conv2D(num_channels=256, num_filters=256, filter_size=3, padding=1),BatchNorm(num_channels=256, act='relu'),Conv2D(num_channels=256, num_filters=256, filter_size=3, padding=1),BatchNorm(num_channels=256, act='relu'),Pool2D(pool_size=2, pool_stride=2, pool_type='max')) # 1 / 8self.layer4 = fluid.dygraph.Sequential(Conv2D(num_channels=256, num_filters=512, filter_size=3, padding=1),BatchNorm(num_channels=512, act='relu'),Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1),BatchNorm(num_channels=512, act='relu'),Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1),BatchNorm(num_channels=512, act='relu'),Pool2D(pool_size=2, pool_stride=2, pool_type='max')) # 1 / 16self.layer5 = fluid.dygraph.Sequential(Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1),BatchNorm(num_channels=512, act='relu'),Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1),BatchNorm(num_channels=512, act='relu'),Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1),BatchNorm(num_channels=512, act='relu'),Pool2D(pool_size=2, pool_stride=2, pool_type='max')) # 1 / 32self.conv67 = fluid.dygraph.Sequential(Conv2D(num_channels=512, num_filters=512, filter_size=1),BatchNorm(num_channels=512, act='relu'),Conv2D(num_channels=512, num_filters=512, filter_size=1),BatchNorm(num_channels=512, act='relu'))self.conv = Conv2D(num_channels=512, num_filters=self.num_classes, filter_size=1)def forward(self, inputs):x = self.layer1(inputs)x = self.layer2(x)x = self.layer3(x)pool3 = x # 1/8 256x = self.layer4(x)pool4 = x # 1/16 512x = self.layer5(x)x = self.conv67(x)x = fluid.layers.interpolate(x, pool4.shape[2:])x = fluid.layers.elementwise_add(pool4, x)x = fluid.layers.interpolate(x, pool3.shape[2:])pool3 = Conv2D(num_channels= 256, num_filters=512, filter_size=1, act='relu')(pool3)x = fluid.layers.elementwise_add(pool3, x)x = fluid.layers.interpolate(x, inputs.shape[2:])x = self.conv(x)return xdef main():with fluid.dygraph.guard():x_data = np.random.rand(2, 3, 512, 512).astype(np.float32)x = to_variable(x_data)model = FCN8s(num_classes=59)model.eval()pred = model(x)print(pred.shape)if __name__ == '__main__':main()

U-Net

网络结构：

  • 采用编码器和解码器的U形结构
  • 输入输出大小不变
  • skip结合方式：concatenation
    在这里插入图片描述

Paper Title: U-net:Convolutional networks for biomedical image segmentaion

在这里插入图片描述

PSPNet

psp模块
在这里插入图片描述

import numpy as np
import paddle.fluid as fluid
from paddle.fluid.dygraph import to_variable
from paddle.fluid.dygraph import Layer
from paddle.fluid.dygraph import Conv2D
from paddle.fluid.dygraph import BatchNorm
from paddle.fluid.dygraph import Dropout
from resnet_dilated import ResNet50# pool with different bin_size
# interpolate back to input size
# concat
class PSPModule(Layer):def __init__(self, num_channels, bin_size_list):super(PSPModule, self).__init__()self.bin_size_list = bin_size_listnum_filters = num_channels // len(bin_size_list)self.features = []for i in range(len(bin_size_list)):self.features.append(fluid.dygraph.Sequential(Conv2D(num_channels, num_filters, 1),BatchNorm(num_filters, act='relu')))def forward(self, inputs):out = [inputs]for idx, f in enumerate(self.features):x = fluid.layers.adaptive_pool2d(inputs, self.bin_size_list[idx])x = f(x)x = fluid.layers.interpolate(x, inputs.shape[2:], align_corners=True)out.append(x)out = fluid.layers.concat(out, axis=1)return outclass PSPNet(Layer):def __init__(self, num_classes=59, backbone='resnet50'):super(PSPNet, self).__init__()res = ResNet50()# stem: res.conv, res.pool2d_maxself.layer0 = fluid.dygraph.Sequential(res.conv,res.pool2d_max)self.layer1 = res.layer1self.layer2 = res.layer2self.layer3 = res.layer3self.layer4 = res.layer4num_channels = 2048# psp: 2048 -> 2048*2self.pspmodule = PSPModule(num_channels, [1,2,3,6])num_channels *= 2# cls: 2048*2 -> 512 -> num_classesself.classifier = fluid.dygraph.Sequential(Conv2D(num_channels = num_channels, num_filters=512, filter_size=3, padding=1),BatchNorm(512, act = 'relu'),Dropout(0.1),Conv2D(num_channels=512, num_filters=num_classes, filter_size=1))# aux: 1024 -> 256 -> num_classesdef forward(self, inputs):# aux: tmp_x = layer3x = self.layer0(inputs)x = self.layer1(x)x = self.layer2(x)x = self.layer3(x)aux = xx = self.layer4(x)x = self.pspmodule(x)x = self.classifier(x)x = fluid.layers.interpolate(x, inputs.shape[2:])return x, auxdef main():with fluid.dygraph.guard(fluid.CPUPlace()):x_data=np.random.rand(2,3, 473, 473).astype(np.float32)x = to_variable(x_data)model = PSPNet(num_classes=59)model.train()pred, aux = model(x)print(pred.shape, aux.shape)if __name__ =="__main__":main()

DeepLab

aspp模块升级版
在这里插入图片描述

class ASPPPooling(Layer):# TODO:def __init__(self, num_channels, num_filters):super(ASPPPooling, self).__init__()self.features = fluid.dygraph.Sequential(Conv2D(num_channels, num_filters, 1),BatchNorm(num_filters, act='relu'))def forward(self, inputs):n, c, h, w = inputs.shapex = fluid.layers.adaptive_pool2d(inputs, 1)x = self.features(x)x = fluid.layers.interpolate(x, [h, w])return xclass ASPPConv(fluid.dygraph.Sequential):# TODO:def __init__(self, num_channels, num_filters, dilation):super(ASPPConv, self).__init__(Conv2D(num_channels=num_channels, num_filters=num_filters, filter_size=3, padding=dilation, dilation=dilation),BatchNorm(num_filters, act='relu'))class ASPPModule(Layer):# TODO: def __init__(self, num_channels, num_filters, rates):super(ASPPModule, self).__init__()self.features = []self.features.append(fluid.dygraph.Sequential(Conv2D(num_channels, num_filters, 1),BatchNorm(num_filters, act='relu')))self.features.append(ASPPPooling(num_channels, num_filters))    for r in rates:self.features.append(ASPPConv(num_channels, num_filters, r))self.project = fluid.dygraph.Sequential(Conv2D(1280, 256, 1),BatchNorm(256, act='relu'))def forward(self, inputs):res = []for f in self.features:res.append(f(inputs))x = fluid.layers.concat(res, axis=1)x = self.project(x)return xclass DeepLabHead(fluid.dygraph.Sequential):def __init__(self, num_channels, num_classes):super(DeepLabHead, self).__init__(ASPPModule(num_channels, 256, [12, 24, 36]),Conv2D(256, 256, 3, padding=1),BatchNorm(256, act='relu'),Conv2D(256, num_classes, 1))class DeepLab(Layer):# TODO:def __init__(self, num_classes=59):super(DeepLab, self).__init__()bone = ResNet50(pretrained=False, duplicate_blocks=True)self.layer0 = fluid.dygraph.Sequential(bone.conv,bone.pool2d_max)self.layer1 = bone.layer1self.layer2 = bone.layer2self.layer3 = bone.layer3self.layer4 = bone.layer4#multi gridself.layer5 = bone.layer5self.layer6 = bone.layer6self.layer7 = bone.layer7# feature_dim = 2048self.classifier = DeepLabHead(2048, 59)def forward(self, inputs):n, c, h, w = inputs.shapex = self.layer0(inputs)x = self.layer1(x)x = self.layer2(x)x = self.layer3(x)x = self.layer4(x)x = self.layer5(x)x = self.layer6(x)x = self.layer7(x)x = self.classifier(x)x = fluid.layers.interpolate(x, [h, w], align_corners=False)return x

这篇关于paddle 图像分割学习总结的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！

原文地址:
本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如若转载，请注明出处：http://www.chinasem.cn/article/750881。

相关文章

重新对Java的类加载器的学习方式

重新对Java的类加载器的学习方式

《重新对Java的类加载器的学习方式》：本文主要介绍重新对Java的类加载器的学习方式,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,望不吝赐教... 目录1、介绍1.1、简介1.2、符号引用和直接引用1、符号引用2、直接引用3、符号转直接的过程2、加载流程3、类加载的分类3.1、显示
阅读更多...
Python+wxPython构建图像编辑器

Python+wxPython构建图像编辑器

《Python+wxPython构建图像编辑器》图像编辑应用是学习GUI编程和图像处理的绝佳项目,本教程中,我们将使用wxPython,一个跨平台的PythonGUI工具包,构建一个简单的... 目录引言环境设置创建主窗口加载和显示图像实现绘制工具矩形绘制箭头绘制文字绘制临时绘制处理缩放和旋转缩放旋转保存编
阅读更多...
Linux区分SSD和机械硬盘的方法总结

Linux区分SSD和机械硬盘的方法总结

《Linux区分SSD和机械硬盘的方法总结》在Linux系统管理中,了解存储设备的类型和特性是至关重要的,不同的存储介质（如固态硬盘SSD和机械硬盘HDD）在性能、可靠性和适用场景上有着显著差异,本文... 目录一、lsblk 命令简介基本用法二、识别磁盘类型的关键参数：ROTA查询 ROTA 参数ROTA
阅读更多...
python+OpenCV反投影图像的实现示例详解

python+OpenCV反投影图像的实现示例详解

《python+OpenCV反投影图像的实现示例详解》：本文主要介绍python+OpenCV反投影图像的实现示例详解,本文通过实例代码图文并茂的形式给大家介绍的非常详细,感兴趣的朋友一起看看吧... 目录一、前言二、什么是反投影图像三、反投影图像的概念四、反向投影的工作原理一、利用反向投影backproj
阅读更多...
Qt实现网络数据解析的方法总结

Qt实现网络数据解析的方法总结

《Qt实现网络数据解析的方法总结》在Qt中解析网络数据通常涉及接收原始字节流,并将其转换为有意义的应用层数据,这篇文章为大家介绍了详细步骤和示例,感兴趣的小伙伴可以了解下... 目录1. 网络数据接收2. 缓冲区管理（处理粘包/拆包）3. 常见数据格式解析3.1 jsON解析3.2 XML解析3.3 自定义
阅读更多...
Java学习手册之Filter和Listener使用方法

Java学习手册之Filter和Listener使用方法

《Java学习手册之Filter和Listener使用方法》：本文主要介绍Java学习手册之Filter和Listener使用方法的相关资料,Filter是一种拦截器,可以在请求到达Servl... 目录一、Filter（过滤器）1. Filter 的工作原理2. Filter 的配置与使用二、Listen
阅读更多...
Python实现图片分割的多种方法总结

Python实现图片分割的多种方法总结

《Python实现图片分割的多种方法总结》图片分割是图像处理中的一个重要任务,它的目标是将图像划分为多个区域或者对象,本文为大家整理了一些常用的分割方法,大家可以根据需求自行选择... 目录1. 基于传统图像处理的分割方法(1) 使用固定阈值分割图片(2) 自适应阈值分割(3) 使用图像边缘检测分割(4)
阅读更多...
使用Python实现图像LBP特征提取的操作方法

使用Python实现图像LBP特征提取的操作方法

《使用Python实现图像LBP特征提取的操作方法》LBP特征叫做局部二值模式,常用于纹理特征提取,并在纹理分类中具有较强的区分能力,本文给大家介绍了如何使用Python实现图像LBP特征提取的操作方... 目录一、LBP特征介绍二、LBP特征描述三、一些改进版本的LBP1.圆形LBP算子2.旋转不变的LB
阅读更多...
Windows Docker端口占用错误及解决方案总结

Windows Docker端口占用错误及解决方案总结

《WindowsDocker端口占用错误及解决方案总结》在Windows环境下使用Docker容器时,端口占用错误是开发和运维中常见且棘手的问题,本文将深入剖析该问题的成因,介绍如何通过查看端口分配... 目录引言Windows docker 端口占用错误及解决方案汇总端口冲突形成原因解析诊断当前端口情况解
阅读更多...
Python如何将大TXT文件分割成4KB小文件

Python如何将大TXT文件分割成4KB小文件

《Python如何将大TXT文件分割成4KB小文件》处理大文本文件是程序员经常遇到的挑战,特别是当我们需要把一个几百MB甚至几个GB的TXT文件分割成小块时,下面我们来聊聊如何用Python自动完成这... 目录为什么需要分割TXT文件基础版：按行分割进阶版：精确控制文件大小完美解决方案：支持UTF-8编码
阅读更多...

Copyright China编程 23002807@qq.com

沪ICP备2023033072号-2