论文地址:https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28
1.是什么?
Unet是一种用于图像分割的深度学习网络模型,其结构由编码器和解码器组成,可以对图像进行像素级别的分割。具体来说,编码器部分采用卷积神经网络对图像进行特征提取和降维,解码器部分则采用反卷积神经网络对特征进行上采样和重建。Unet的特点是具有较少的参数和较好的泛化能力,适用于小样本和多类别的图像分割任务。
2.为什么?
U-Net和FCN非常的相似,U-Net比FCN稍晚提出来,但都发表在2015年,和FCN相比,U-Net的第一个特点是完全对称,也就是左边和右边是很类似的,而FCN的decoder相对简单。第二个区别就是skip connection,FCN用的是加操作(summation),U-Net用的是叠操作(concatenation)。这些都是细节,重点是它们的结构用了一个比较经典的思路,也就是编码和解码(encoder-decoder)结构。其实可以将图像->高语义feature map的过程看成编码器,高语义->像素级别的分类score map的过程看作解码器
此外, 由于UNet也和FCN一样, 是全卷积形式, 没有全连接层(即没有固定图的尺寸),所以容易适应很多输入尺寸大小,但并不是所有的尺寸都可以,需要根据网络结构决定
3.怎么样?
3.1网络结构
如上图,Unet 网络结构是对称的,形似英文字母 U 所以被称为 Unet。整张图都是由蓝/白色框与各种颜色的箭头组成,其中,蓝/白色框表示 feature map;蓝色箭头表示 3x3 卷积,用于特征提取;灰色箭头表示 skip-connection,用于特征融合;红色箭头表示池化 pooling,用于降低维度;绿色箭头表示上采样 upsample,用于恢复维度;青色箭头表示 1x1 卷积,用于输出结果 。其中灰色箭头copy and crop
中的copy
就是concatenate
而crop
是为了让两者的长宽一致 。
Encoder 由卷积操作和下采样操作组成,文中所用的卷积结构统一为 3x3 的卷积核,padding 为 0 ,striding 为 1。没有 padding 所以每次卷积之后 feature map 的 H 和 W 变小了,在 skip-connection 时要注意 feature map 的维度(其实也可以将 padding 设置为 1 避免维度不对应问题)
3.2 代码实现
python
from typing import Dict
import torch
import torch.nn as nn
import torch.nn.functional as F
class DoubleConv(nn.Sequential):
def __init__(self, in_channels, out_channels, mid_channels=None):
if mid_channels is None:
mid_channels = out_channels
super(DoubleConv, self).__init__(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
class Down(nn.Sequential):
def __init__(self, in_channels, out_channels):
super(Down, self).__init__(
nn.MaxPool2d(2, stride=2),
DoubleConv(in_channels, out_channels)
)
class Up(nn.Module):
def __init__(self, in_channels, out_channels, bilinear=True):
super(Up, self).__init__()
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
else:
self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor:
x1 = self.up(x1)
# [N, C, H, W]
diff_y = x2.size()[2] - x1.size()[2]
diff_x = x2.size()[3] - x1.size()[3]
# padding_left, padding_right, padding_top, padding_bottom
x1 = F.pad(x1, [diff_x // 2, diff_x - diff_x // 2,
diff_y // 2, diff_y - diff_y // 2])
x = torch.cat([x2, x1], dim=1)
x = self.conv(x)
return x
class OutConv(nn.Sequential):
def __init__(self, in_channels, num_classes):
super(OutConv, self).__init__(
nn.Conv2d(in_channels, num_classes, kernel_size=1)
)
class UNet(nn.Module):
def __init__(self,
in_channels: int = 1,
num_classes: int = 2,
bilinear: bool = True,
base_c: int = 64):
super(UNet, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
self.bilinear = bilinear
self.in_conv = DoubleConv(in_channels, base_c)
self.down1 = Down(base_c, base_c * 2)
self.down2 = Down(base_c * 2, base_c * 4)
self.down3 = Down(base_c * 4, base_c * 8)
factor = 2 if bilinear else 1
self.down4 = Down(base_c * 8, base_c * 16 // factor)
self.up1 = Up(base_c * 16, base_c * 8 // factor, bilinear)
self.up2 = Up(base_c * 8, base_c * 4 // factor, bilinear)
self.up3 = Up(base_c * 4, base_c * 2 // factor, bilinear)
self.up4 = Up(base_c * 2, base_c, bilinear)
self.out_conv = OutConv(base_c, num_classes)
def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
x1 = self.in_conv(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
logits = self.out_conv(x)
return {"out": logits}
参考: