fpn与unet的区别
时间: 2023-05-16 12:07:09 浏览: 160
FPN(Feature Pyramid Network)和U-Net都是用于图像分割的深度学习模型,但它们的结构和应用场景有所不同。FPN是一种自顶向下的特征金字塔网络,它通过将不同层次的特征图进行融合,从而实现多尺度的目标检测和分割。而U-Net则是一种编码-解码结构的网络,它可以在保留高分辨率信息的同时,提取不同层次的特征,从而实现更加精细的分割效果。总的来说,FPN适用于需要检测不同尺度目标的场景,而U-Net适用于需要更加精细的分割效果的场景。
相关问题
用pytorch搭建UNet3Plus网络
下面是用PyTorch搭建UNet3Plus网络的代码示例:
```python
import torch
import torch.nn as nn
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
return x
class UNet3Plus(nn.Module):
def __init__(self, in_channels, out_channels, num_classes):
super().__init__()
self.conv_block1 = ConvBlock(in_channels, 64)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv_block2 = ConvBlock(64, 128)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv_block3 = ConvBlock(128, 256)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv_block4 = ConvBlock(256, 512)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.center = ConvBlock(512, 1024)
self.upsample4_1 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
self.conv_block4_1 = ConvBlock(1024, 512)
self.upsample3_1 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
self.conv_block3_1 = ConvBlock(512, 256)
self.upsample2_1 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
self.conv_block2_1 = ConvBlock(256, 128)
self.upsample1_1 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
self.conv_block1_1 = ConvBlock(128, 64)
self.seg_out_1 = nn.Conv2d(64, num_classes, kernel_size=1)
self.upsample4_2 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
self.conv_block4_2 = ConvBlock(1024, 512)
self.upsample3_2 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
self.conv_block3_2 = ConvBlock(512, 256)
self.upsample2_2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
self.conv_block2_2 = ConvBlock(256, 128)
self.seg_out_2 = nn.Conv2d(128, num_classes, kernel_size=1)
def forward(self, x):
conv1 = self.conv_block1(x)
pool1 = self.pool1(conv1)
conv2 = self.conv_block2(pool1)
pool2 = self.pool2(conv2)
conv3 = self.conv_block3(pool2)
pool3 = self.pool3(conv3)
conv4 = self.conv_block4(pool3)
pool4 = self.pool4(conv4)
center = self.center(pool4)
up4_1 = self.upsample4_1(center)
concat4_1 = torch.cat([up4_1, conv4], dim=1)
conv4_1 = self.conv_block4_1(concat4_1)
up3_1 = self.upsample3_1(conv4_1)
concat3_1 = torch.cat([up3_1, conv3], dim=1)
conv3_1 = self.conv_block3_1(concat3_1)
up2_1 = self.upsample2_1(conv3_1)
concat2_1 = torch.cat([up2_1, conv2], dim=1)
conv2_1 = self.conv_block2_1(concat2_1)
up1_1 = self.upsample1_1(conv2_1)
concat1_1 = torch.cat([up1_1, conv1], dim=1)
conv1_1 = self.conv_block1_1(concat1_1)
seg_out_1 = self.seg_out_1(conv1_1)
up4_2 = self.upsample4_2(center)
concat4_2 = torch.cat([up4_2, conv4_1], dim=1)
conv4_2 = self.conv_block4_2(concat4_2)
up3_2 = self.upsample3_2(conv4_2)
concat3_2 = torch.cat([up3_2, conv3_1], dim=1)
conv3_2 = self.conv_block3_2(concat3_2)
up2_2 = self.upsample2_2(conv3_2)
concat2_2 = torch.cat([up2_2, conv2_1], dim=1)
conv2_2 = self.conv_block2_2(concat2_2)
seg_out_2 = self.seg_out_2(conv2_2)
return seg_out_1, seg_out_2
```
UNet3Plus是一种基于UNet和FPN的语义分割网络,采用了多个分辨率的特征融合策略,能够较好地在医疗图像分割、自然图像分割等任务中发挥作用。这里定义了一个ConvBlock模块,用于构建卷积操作,并依次实现了各个模块的前向传递过程。在网络的前向传递中,先对输入进行卷积处理与池化操作,再进行多层级别的特征融合操作,最终输出分割结果。
Ma et al. (2021) used ResNet-50+FPN(He et al. 2016; Lin et al. 2017) to carry a semantic segmentation neural network, demonstrating the feasibility of deep learning in large-scale AGs mapping. Chen et al. (2021) successfully extracted AGs with the help of the classic semantic segmentation network UNet, and since then, some segmentation models specifically designed for AGs mapping tasks have been proposed(He et al. 2023; Liu et al. 2023). Although these models are based on classical convolutional neural networks (CNNs) and improved with the help of advanced components in CNNs to achieve better results, there are still three main problems in AGs mapping: difficult to extract spatially dense distribution, algorithm maladaptation, and lack of trainable data. On the other hand, the intrinsic relationship between the visual features of AGs and the network architecture has not been sufficiently explained. How to implement an efficient AGs segmentation model based on the unique or more niche characteristics of AGs still needs to be supplemented more.
Ma等人(2021)采用ResNet-50+FPN(He等人2016;Lin等人2017)构建了一个语义分割神经网络,展示了深度学习在大规模农田地块映射中的可行性。陈等人(2021)成功地利用经典的语义分割网络UNet提取了农田地块,并且此后还提出了一些专门针对农田地块映射任务设计的分割模型(He等人2023;Liu等人2023)。尽管这些模型以经典卷积神经网络(CNNs)为基础,并借助CNNs中的先进组件做出改进取得了更好的效果,但农田地块映射仍存在三个主要问题:难以提取空间密集分布、算法不适应性以及缺少可训练数据。另一方面,农田地块视觉特征与网络架构之间的内在关系还没有得到充分的解释。如何基于农田地块的独特或更专业的特性来实现高效的农田地块分割模型,仍需要更多的补充。