pytorch代码实现之动态蛇形卷积模块DySnakeConv

动态蛇形卷积模块DySnakeConv

血管、道路等拓扑管状结构的精确分割在各个领域都至关重要，确保下游任务的准确性和效率。 然而，许多因素使任务变得复杂，包括薄的局部结构和可变的全局形态。在这项工作中，我们注意到管状结构的特殊性，并利用这些知识来指导我们的 DSCNet 在三个阶段同时增强感知：特征提取、特征融合、 和损失约束。 首先，我们提出了一种动态蛇卷积，通过自适应地关注细长和曲折的局部结构来准确捕获管状结构的特征。 随后，我们提出了一种多视图特征融合策略，以补充特征融合过程中多角度对特征的关注，确保保留来自不同全局形态的重要信息。 最后，提出了一种基于持久同源性的连续性约束损失函数，以更好地约束分割的拓扑连续性。 2D 和 3D 数据集上的实验表明，与多种方法相比，我们的 DSCNet 在管状结构分割任务上提供了更好的准确性和连续性。 我们的代码是公开的。

主要的挑战源于细长微弱的局部结构特征与复杂多变的全局形态特征。本文关注到管状结构细长连续的特点，并利用这一信息在神经网络以下三个阶段同时增强感知：特征提取、特征融合和损失约束。分别设计了动态蛇形卷积（Dynamic Snake Convolution），多视角特征融合策略与连续性拓扑约束损失。

原文地址：Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation

结构图

pytorch代码实现

import torch
import torch.nn as nnclass DySnakeConv(nn.Module):def __init__(self, inc, ouc, k=3, act=True) -> None:super().__init__()self.conv_0 = Conv(inc, ouc, k, act=act)self.conv_x = DSConv(inc, ouc, 0, k)self.conv_y = DSConv(inc, ouc, 1, k)self.conv_1x1 = Conv(ouc * 3, ouc, 1, act=act)def forward(self, x):return self.conv_1x1(torch.cat([self.conv_0(x), self.conv_x(x), self.conv_y(x)], dim=1))class DSConv(nn.Module):def __init__(self, in_ch, out_ch, morph, kernel_size=3, if_offset=True, extend_scope=1):"""The Dynamic Snake Convolution:param in_ch: input channel:param out_ch: output channel:param kernel_size: the size of kernel:param extend_scope: the range to expand (default 1 for this method):param morph: the morphology of the convolution kernel is mainly divided into two typesalong the x-axis (0) and the y-axis (1) (see the paper for details):param if_offset: whether deformation is required, if it is False, it is the standard convolution kernel"""super(DSConv, self).__init__()# use the <offset_conv> to learn the deformable offsetself.offset_conv = nn.Conv2d(in_ch, 2 * kernel_size, 3, padding=1)self.bn = nn.BatchNorm2d(2 * kernel_size)self.kernel_size = kernel_size# two types of the DSConv (along x-axis and y-axis)self.dsc_conv_x = nn.Conv2d(in_ch,out_ch,kernel_size=(kernel_size, 1),stride=(kernel_size, 1),padding=0,)self.dsc_conv_y = nn.Conv2d(in_ch,out_ch,kernel_size=(1, kernel_size),stride=(1, kernel_size),padding=0,)self.gn = nn.GroupNorm(out_ch // 4, out_ch)self.act = Conv.default_actself.extend_scope = extend_scopeself.morph = morphself.if_offset = if_offsetdef forward(self, f):offset = self.offset_conv(f)offset = self.bn(offset)# We need a range of deformation between -1 and 1 to mimic the snake's swingoffset = torch.tanh(offset)input_shape = f.shapedsc = DSC(input_shape, self.kernel_size, self.extend_scope, self.morph)deformed_feature = dsc.deform_conv(f, offset, self.if_offset)if self.morph == 0:x = self.dsc_conv_x(deformed_feature.type(f.dtype))x = self.gn(x)x = self.act(x)return xelse:x = self.dsc_conv_y(deformed_feature.type(f.dtype))x = self.gn(x)x = self.act(x)return x# Core code, for ease of understanding, we mark the dimensions of input and output next to the code
class DSC(object):def __init__(self, input_shape, kernel_size, extend_scope, morph):self.num_points = kernel_sizeself.width = input_shape[2]self.height = input_shape[3]self.morph = morphself.extend_scope = extend_scope  # offset (-1 ~ 1) * extend_scope# define feature map shape"""B: Batch size  C: Channel  W: Width  H: Height"""self.num_batch = input_shape[0]self.num_channels = input_shape[1]"""input: offset [B,2*K,W,H]  K: Kernel size (2*K: 2D image, deformation contains <x_offset> and <y_offset>)output_x: [B,1,W,K*H]   coordinate mapoutput_y: [B,1,K*W,H]   coordinate map"""def _coordinate_map_3D(self, offset, if_offset):device = offset.device# offsety_offset, x_offset = torch.split(offset, self.num_points, dim=1)y_center = torch.arange(0, self.width).repeat([self.height])y_center = y_center.reshape(self.height, self.width)y_center = y_center.permute(1, 0)y_center = y_center.reshape([-1, self.width, self.height])y_center = y_center.repeat([self.num_points, 1, 1]).float()y_center = y_center.unsqueeze(0)x_center = torch.arange(0, self.height).repeat([self.width])x_center = x_center.reshape(self.width, self.height)x_center = x_center.permute(0, 1)x_center = x_center.reshape([-1, self.width, self.height])x_center = x_center.repeat([self.num_points, 1, 1]).float()x_center = x_center.unsqueeze(0)if self.morph == 0:"""Initialize the kernel and flatten the kernely: only need 0x: -num_points//2 ~ num_points//2 (Determined by the kernel size)!!! The related PPT will be submitted later, and the PPT will contain the whole changes of each step"""y = torch.linspace(0, 0, 1)x = torch.linspace(-int(self.num_points // 2),int(self.num_points // 2),int(self.num_points),)y, x = torch.meshgrid(y, x)y_spread = y.reshape(-1, 1)x_spread = x.reshape(-1, 1)y_grid = y_spread.repeat([1, self.width * self.height])y_grid = y_grid.reshape([self.num_points, self.width, self.height])y_grid = y_grid.unsqueeze(0)  # [B*K*K, W,H]x_grid = x_spread.repeat([1, self.width * self.height])x_grid = x_grid.reshape([self.num_points, self.width, self.height])x_grid = x_grid.unsqueeze(0)  # [B*K*K, W,H]y_new = y_center + y_gridx_new = x_center + x_gridy_new = y_new.repeat(self.num_batch, 1, 1, 1).to(device)x_new = x_new.repeat(self.num_batch, 1, 1, 1).to(device)y_offset_new = y_offset.detach().clone()if if_offset:y_offset = y_offset.permute(1, 0, 2, 3)y_offset_new = y_offset_new.permute(1, 0, 2, 3)center = int(self.num_points // 2)# The center position remains unchanged and the rest of the positions begin to swing# This part is quite simple. The main idea is that "offset is an iterative process"y_offset_new[center] = 0for index in range(1, center):y_offset_new[center + index] = (y_offset_new[center + index - 1] + y_offset[center + index])y_offset_new[center - index] = (y_offset_new[center - index + 1] + y_offset[center - index])y_offset_new = y_offset_new.permute(1, 0, 2, 3).to(device)y_new = y_new.add(y_offset_new.mul(self.extend_scope))y_new = y_new.reshape([self.num_batch, self.num_points, 1, self.width, self.height])y_new = y_new.permute(0, 3, 1, 4, 2)y_new = y_new.reshape([self.num_batch, self.num_points * self.width, 1 * self.height])x_new = x_new.reshape([self.num_batch, self.num_points, 1, self.width, self.height])x_new = x_new.permute(0, 3, 1, 4, 2)x_new = x_new.reshape([self.num_batch, self.num_points * self.width, 1 * self.height])return y_new, x_newelse:"""Initialize the kernel and flatten the kernely: -num_points//2 ~ num_points//2 (Determined by the kernel size)x: only need 0"""y = torch.linspace(-int(self.num_points // 2),int(self.num_points // 2),int(self.num_points),)x = torch.linspace(0, 0, 1)y, x = torch.meshgrid(y, x)y_spread = y.reshape(-1, 1)x_spread = x.reshape(-1, 1)y_grid = y_spread.repeat([1, self.width * self.height])y_grid = y_grid.reshape([self.num_points, self.width, self.height])y_grid = y_grid.unsqueeze(0)x_grid = x_spread.repeat([1, self.width * self.height])x_grid = x_grid.reshape([self.num_points, self.width, self.height])x_grid = x_grid.unsqueeze(0)y_new = y_center + y_gridx_new = x_center + x_gridy_new = y_new.repeat(self.num_batch, 1, 1, 1)x_new = x_new.repeat(self.num_batch, 1, 1, 1)y_new = y_new.to(device)x_new = x_new.to(device)x_offset_new = x_offset.detach().clone()if if_offset:x_offset = x_offset.permute(1, 0, 2, 3)x_offset_new = x_offset_new.permute(1, 0, 2, 3)center = int(self.num_points // 2)x_offset_new[center] = 0for index in range(1, center):x_offset_new[center + index] = (x_offset_new[center + index - 1] + x_offset[center + index])x_offset_new[center - index] = (x_offset_new[center - index + 1] + x_offset[center - index])x_offset_new = x_offset_new.permute(1, 0, 2, 3).to(device)x_new = x_new.add(x_offset_new.mul(self.extend_scope))y_new = y_new.reshape([self.num_batch, 1, self.num_points, self.width, self.height])y_new = y_new.permute(0, 3, 1, 4, 2)y_new = y_new.reshape([self.num_batch, 1 * self.width, self.num_points * self.height])x_new = x_new.reshape([self.num_batch, 1, self.num_points, self.width, self.height])x_new = x_new.permute(0, 3, 1, 4, 2)x_new = x_new.reshape([self.num_batch, 1 * self.width, self.num_points * self.height])return y_new, x_new"""input: input feature map [N,C,D,W,H]；coordinate map [N,K*D,K*W,K*H] output: [N,1,K*D,K*W,K*H]  deformed feature map"""def _bilinear_interpolate_3D(self, input_feature, y, x):device = input_feature.devicey = y.reshape([-1]).float()x = x.reshape([-1]).float()zero = torch.zeros([]).int()max_y = self.width - 1max_x = self.height - 1# find 8 grid locationsy0 = torch.floor(y).int()y1 = y0 + 1x0 = torch.floor(x).int()x1 = x0 + 1# clip out coordinates exceeding feature map volumey0 = torch.clamp(y0, zero, max_y)y1 = torch.clamp(y1, zero, max_y)x0 = torch.clamp(x0, zero, max_x)x1 = torch.clamp(x1, zero, max_x)input_feature_flat = input_feature.flatten()input_feature_flat = input_feature_flat.reshape(self.num_batch, self.num_channels, self.width, self.height)input_feature_flat = input_feature_flat.permute(0, 2, 3, 1)input_feature_flat = input_feature_flat.reshape(-1, self.num_channels)dimension = self.height * self.widthbase = torch.arange(self.num_batch) * dimensionbase = base.reshape([-1, 1]).float()repeat = torch.ones([self.num_points * self.width * self.height]).unsqueeze(0)repeat = repeat.float()base = torch.matmul(base, repeat)base = base.reshape([-1])base = base.to(device)base_y0 = base + y0 * self.heightbase_y1 = base + y1 * self.height# top rectangle of the neighbourhood volumeindex_a0 = base_y0 - base + x0index_c0 = base_y0 - base + x1# bottom rectangle of the neighbourhood volumeindex_a1 = base_y1 - base + x0index_c1 = base_y1 - base + x1# get 8 grid valuesvalue_a0 = input_feature_flat[index_a0.type(torch.int64)].to(device)value_c0 = input_feature_flat[index_c0.type(torch.int64)].to(device)value_a1 = input_feature_flat[index_a1.type(torch.int64)].to(device)value_c1 = input_feature_flat[index_c1.type(torch.int64)].to(device)# find 8 grid locationsy0 = torch.floor(y).int()y1 = y0 + 1x0 = torch.floor(x).int()x1 = x0 + 1# clip out coordinates exceeding feature map volumey0 = torch.clamp(y0, zero, max_y + 1)y1 = torch.clamp(y1, zero, max_y + 1)x0 = torch.clamp(x0, zero, max_x + 1)x1 = torch.clamp(x1, zero, max_x + 1)x0_float = x0.float()x1_float = x1.float()y0_float = y0.float()y1_float = y1.float()vol_a0 = ((y1_float - y) * (x1_float - x)).unsqueeze(-1).to(device)vol_c0 = ((y1_float - y) * (x - x0_float)).unsqueeze(-1).to(device)vol_a1 = ((y - y0_float) * (x1_float - x)).unsqueeze(-1).to(device)vol_c1 = ((y - y0_float) * (x - x0_float)).unsqueeze(-1).to(device)outputs = (value_a0 * vol_a0 + value_c0 * vol_c0 + value_a1 * vol_a1 +value_c1 * vol_c1)if self.morph == 0:outputs = outputs.reshape([self.num_batch,self.num_points * self.width,1 * self.height,self.num_channels,])outputs = outputs.permute(0, 3, 1, 2)else:outputs = outputs.reshape([self.num_batch,1 * self.width,self.num_points * self.height,self.num_channels,])outputs = outputs.permute(0, 3, 1, 2)return outputsdef deform_conv(self, input, offset, if_offset):y, x = self._coordinate_map_3D(offset, if_offset)deformed_feature = self._bilinear_interpolate_3D(input, y, x)return deformed_feature#### YOLOV5
class Bottleneck_DySnake(nn.Module):# Standard bottleneckdef __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansionsuper().__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = DySnakeConv(c_, c2, 3)self.add = shortcut and c1 == c2def forward(self, x):return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))class C3_DySnake(C3):# C3 module with DySnakeConvdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):super().__init__(c1, c2, n, shortcut, g, e)c_ = int(c2 * e)self.m = nn.Sequential(*(Bottleneck_DySnake(c_, c_, shortcut, g, e=1.0) for _ in range(n)))

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.hqwc.cn/news/138728.html

如若内容造成侵权/违法违规/事实不符，请联系编程知识网进行投诉反馈email:809451989@qq.com，一经查实，立即删除！

相关文章

Linux性能优化--性能追踪：受CPU限制的应用程序（GIMP）

Linux性能优化--性能追踪：受CPU限制的应用程序（GIMP）

10.0 概述 本章包含了一个例子&#xff1a;如何用Linux性能工具在受CPU限制的应用程序中寻找并修复性能问题。 阅读本章后&#xff0c;你将能够&#xff1a; 在受CPU限制的应用程序中明确所有的CPU被哪些源代码行使用。用1trace和oprofile弄清楚应用程序调用各种内部与外部函…
阅读更多...
Leetcode刷题详解——长度最小的子数组

Leetcode刷题详解——长度最小的子数组

1. 题目链接&#xff1a;209. 长度最小的子数组 2. 题目描述&#xff1a; 给定一个含有 n 个正整数的数组和一个正整数 target 。 找出该数组中满足其总和大于等于 target 的长度最小的 连续子数组 [numsl, numsl1, ..., numsr-1, numsr] &#xff0c;并返回其长度**。**如果不…
阅读更多...
【打印机故障】打印时一直显示任务“排队中”无法打印或取消

【打印机故障】打印时一直显示任务“排队中”无法打印或取消

解决办法 一、关闭打印服务 如果打印任务无法取消或者删除&#xff0c;可以先暂停打印机的“服务&#xff0c;关闭”后台处理程序。具体方法如下&#xff1a; 1.在开始菜单搜索框中输入 services.msc或者中文输入“服务”&#xff0c;然后按下 enter 键。 2.从服务列表中选择打…
阅读更多...
程序员大厂之鹅厂探秘

程序员大厂之鹅厂探秘

阅读更多...
stm32备份

stm32备份

存储器的分类&#xff1a; 存储器首先根据断电后存储的数据是否会丢失&#xff0c;可以分为易失存储器和非易失存储器&#xff0c;易失存储器主要应用于内存&#xff0c;非易失存储器主要用于外存。 易失存储器以RAM随机存储器为代表&#xff0c;随机的含义是存储器中的数据读取…
阅读更多...
数据结构 排序

数据结构 排序

数据结构 排序 文章目录 数据结构 排序1. 排序的概念及引用1.1 排序的概念1.2 常见的排序算法 2.常见排序算法的实现2.1 插入排序2.1.1 基本思想2.1.2 直接插入排序2.1.3 希尔排序(缩小增量排序) 2.2 选择排序2.2.1 基本思想2.2.2 直接选择排序2.2.3 堆排序 2.3 交换排序2.3.1 …
阅读更多...
Echarts(1)

Echarts(1)

Echarts官方文档----快速上手 - Handbook - Apache ECharts 1.1ECharts的快速上手 ECharts 的入门使用特别简单, 5分钟就能够上手. 他大体分为这几个步骤 步骤1&#xff1a;引入 echarts.js 文件 该文件获取方式可通过&#xff1a;在 https://www.jsdelivr.com/package/npm…
阅读更多...
软件测试用例设计方法-因果图法

软件测试用例设计方法-因果图法

边界值法是等价类划分法的补充&#xff0c;所以&#xff0c;它们是一对搭档。 那么&#xff0c;判定表法有没有它的搭档呢&#xff1f; 答案是&#xff0c;有的。那就是本篇文章分享的用例设计方法—— 因果图法 。 定义 因果图法&#xff1a; 用来处理等价类划分和边界值考…
阅读更多...
JNDI-Injection-Exploit工具安装

JNDI-Injection-Exploit工具安装

从github上下载安装 git clone https://github.com/welk1n/JNDI-Injection-Exploit.git 打开 cd JNDI-Injection-Exploit 编译安装&#xff0c;Maven入门百科_maven中quickstart是什么意思-CSDN博客 mvn clean package -DskipTests 因为提示mvn错误&#xff0c;解决下…
阅读更多...
IDEA中点击New没有Java Class

IDEA中点击New没有Java Class

解决办法&#xff1a;右键src&#xff0c;也可以是其他文件名&#xff0c;点击Mark Directory as 点击Sources Root即可
阅读更多...
VMware 配置记录

VMware 配置记录

VMware 配置笔记 CentOS 7.9 镜像下载 官网太慢&#xff0c;建议在阿里云镜像站去CentOS配置页找标准版下载。 选标准版即可&#xff0c;各版本区别&#xff1a; DVD&#xff1a;标准版&#xff0c;包含常用软件&#xff0c;体积为 4.4 G&#xff1b;Everything&#xff1a…
阅读更多...
AFL模糊测试+GCOV覆盖率分析

AFL模糊测试+GCOV覆盖率分析

安全之安全(security)博客目录导读 覆盖率分析汇总 目录 一、代码示例 二、afl-cov工具下载 三、编译带覆盖率的版本并启动afl-cov 四、AFL编译插桩并运行afl-fuzz 五、结果查看 AFL相关详见AFL安全漏洞挖掘 GCOV相关详见GCOV覆盖率分析 现将两者结合&#xff0c;即进…
阅读更多...
推荐文章
最新文章

Copyright @ 2022~2023