unet模型学习笔记_down(nn.module):

unet模型学习笔记

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前言

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提示：以下是本篇文章正文内容，下面案例可供参考

一、模型图

二、依据模型图搭建模型

1.图中可以看出每层有两次卷积核，将图像输入后，进行两次卷积，每次卷积跟一个批归一化和激活函数激活

class DoubleConv(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),#padding为1，卷积后图片尺寸大小不变
            nn.BatchNorm2d(out_channels),#批归一化
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )##经过两次卷积，每次卷积后跟着批归一化和relu激活函数

    def forward(self, x):
        return self.double_conv(x)
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下采样：
下采样用最大池化，同时改变图像通道数

class Down(nn.Module):
    """Downscaling with maxpool then double conv"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            DoubleConv(in_channels, out_channels)
        )

    def forward(self, x):
        return self.maxpool_conv(x)
## Down:经过一次最大池化（图像缩小一倍）后再经过两次卷积
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上采样：
上采样有反卷积操作、双线性差值、最邻近插值等等，pytorch有封装好的API

class Up(nn.Module):
    """Upscaling then double conv"""

    def __init__(self, in_channels, out_channels, bilinear=True):
        super().__init__()

        # if bilinear, use the normal convolutions to reduce the number of channels
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)

        self.conv = DoubleConv(in_channels, out_channels)

    def forward(self, x1, x2):
        x1 = self.up(x1)
        # input is CHW
        diffY = torch.tensor([x2.size()[2] - x1.size()[2]])
        diffX = torch.tensor([x2.size()[3] - x1.size()[3]])

        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])

        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)

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双线性插值很好理解，pytorch中反卷积操作可以参考：反卷积和上采样

最后是结果输出，输出分类结果的特征图
class OutConv(nn.Module):
def init(self, in_channels, out_channels):
super(OutConv, self).init()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

def forward(self, x):
    return self.conv(x)
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最后再申明一个类，把上述模块按照模型图搭积木搭起来就可以了。