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import torch as t
from torch import nn
## 下采样的基本结构
# 参考：https://github.com/JavisPeng/u_net_liver/blob/a1b9553d8ba8c6e5a3d4c5fabd387e130e60a072/dataset.py#L16
# 常用的两个卷积操作简单封装
class DoubleConv(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.conv_down = nn.Sequential(
# 主流实现方式
# - 在3x3的卷积层加上一个padding(也就是每次经过这个3x3的卷积层的时候不会去改变特征层的高和宽)
# - 在卷积核ReLU之间加BatchNormalization
nn.Conv2d(in_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
nn.Conv2d(in_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
)
def forward(self, input):
return self.conv_down(input)
# U-Net网络
class Unet(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
# 下采样
self.conv1 = DoubleConv(in_ch, 64)
self.pool1 = nn.MaxPool2d(2)
self.conv2 = DoubleConv(64, 128)
self.pool2 = nn.MaxPool2d(2)
self.conv3 = DoubleConv(128, 256)
self.pool3 = nn.MaxPool2d(2)
self.conv4 = DoubleConv(256, 512)
self.pool4 = nn.MaxPool2d(2)
self.conv5 = DoubleConv(512, 1024)
# 上采样
# 转置卷积(有参数可以训练)可以实现上采样，也可以使用Upsample上采样(通过插值完成，没有训练参数，速度更快)(保证k=stride,stride即上采样倍数)
self.up6 = nn.ConvTranspose2d(1024, 512, 2, stride=2)
self.conv6 = DoubleConv(1024, 512)
self.up7 = nn.ConvTranspose2d(512, 256, 2, stride=2)
self.conv7 = DoubleConv(512, 256)
self.up8 = nn.ConvTranspose2d(256, 128, 2, stride=2)
self.conv8 = DoubleConv(256, 128)
self.up9 = nn.ConvTranspose2d(128, 64, 2, stride=2)
self.conv9 = DoubleConv(128, 64)
self.conv10 = nn.Conv2d(64, out_ch, 1)
def forward(self, x):
c1 = self.conv1(x)
p1 = self.pool1(c1)
c2 = self.conv2(p1)
p2 = self.pool2(c2)
c3 = self.conv3(p2)
p3 = self.pool3(c3)
c4 = self.conv4(p3)
p4 = self.pool4(c4)
c5 = self.conv5(p4)
up_6 = self.up6(c5)
merge6 = t.cat([up_6, c4], dim=1) # cat 拼接
c6 = self.conv6(merge6) # 对拼接之后的继续向上进行卷积
up_7 = self.up7(c6)
merge7 = t.cat([up_7, c3], dim=1)
c7 = self.conv7(merge7)
up_8 = self.up8(c7)
merge8 = t.cat([up_8, c2], dim=1)
c8 = self.conv8(merge8)
up_9 = self.up9(c8)
merge9 = t.cat([up_9, c1], dim=1)
c9 = self.conv9(merge9)
c10 = self.conv10(c9)
out = nn.Sigmoid()(c10)
return out
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说明几点先：
