pytorch复现_UNet

什么是UNet

U-Net由收缩路径和扩张路径组成。收缩路径是一系列卷积层和汇集层，其中要素地图的分辨率逐渐降低。扩展路径是一系列上采样层和卷积层，其中特征地图的分辨率逐渐增加。

在扩展路径中的每一步，来自收缩路径的对应特征地图与当前特征地图级联。

主干结构解析

左边为特征提取网络（编码器），右边为特征融合网络（解码器）

高分辨率---编码---低分辨率---解码---高分辨率

特征提取网络

高分辨率---编码---低分辨率

前半部分是编码, 它的作用是特征提取(获取局部特征,并做图片级分类)，得到抽象语义特征

由两个3x3的卷积层（RELU）再加上一个2x2的maxpooling层组成一个下采样的模块，一共经过4次这样的操作

特征融合网络

低分辨率---解码---高分辨率

利用前面编码的抽象特征来恢复到原图尺寸的过程, 最终得到分割结果(掩码图片)

代码：

import torch.nn as nn
import torch

# 编码器(论文中称之为收缩路径)的基本单元
def contracting_block(in_channels, out_channels):
    block = torch.nn.Sequential(
        # 这里的卷积操作没有使用padding,所以每次卷积后图像的尺寸都会减少2个像素大小
        nn.Conv2d(kernel_size=(3, 3), in_channels=in_channels, out_channels=out_channels),
        nn.BatchNorm2d(out_channels),
        nn.ReLU(),
        nn.Conv2d(kernel_size=(3, 3), in_channels=out_channels, out_channels=out_channels),
        nn.BatchNorm2d(out_channels),
        nn.ReLU()
    )
    return block


# 解码器（论文中称之为扩张路径）的基本单元
class expansive_block(nn.Module):
    def __init__(self, in_channels, mid_channels, out_channels):
        super(expansive_block, self).__init__()

        # 每进行一次反卷积，通道数减半，尺寸扩大2倍
        self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=(3, 3), stride=2, padding=1,
                                     output_padding=1)
        self.block = nn.Sequential(
            # 这里的卷积操作没有使用padding,所以每次卷积后图像的尺寸都会减少2个像素大小
            nn.Conv2d(kernel_size=(3, 3), in_channels=in_channels, out_channels=mid_channels),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(),
            nn.Conv2d(kernel_size=(3, 3), in_channels=mid_channels, out_channels=out_channels),
            nn.BatchNorm2d(out_channels),
            nn.ReLU()
        )

    def forward(self, e, d):
        d = self.up(d)
        # concat
        # e是来自编码器部分的特征图，d是来自解码器部分的特征图，它们的形状都是[B,C,H,W]
        diffY = e.size()[2] - d.size()[2]
        diffX = e.size()[3] - d.size()[3]
        # 裁剪时，先计算e与d在高和宽方向的差距diffY和diffX，然后对e高方向进行裁剪，具体方法是两边分别裁剪diffY的一半，
        # 最后对e宽方向进行裁剪，具体方法是两边分别裁剪diffX的一半，
        # 具体的裁剪过程见下图一
        e = e[:, :, diffY // 2:e.size()[2] - diffY // 2, diffX // 2:e.size()[3] - diffX // 2]
        cat = torch.cat([e, d], dim=1)  # 在特征通道上进行拼接
        out = self.block(cat)
        return out


# 最后的输出卷积层
def final_block(in_channels, out_channels):
    block = nn.Conv2d(kernel_size=(1, 1), in_channels=in_channels, out_channels=out_channels)
    return block


class UNet(nn.Module):

    def __init__(self, in_channel, out_channel):
        super(UNet, self).__init__()

        # 编码器 (Encode)
        self.conv_encode1 = contracting_block(in_channels=in_channel, out_channels=64)
        self.conv_pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv_encode2 = contracting_block(in_channels=64, out_channels=128)
        self.conv_pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv_encode3 = contracting_block(in_channels=128, out_channels=256)
        self.conv_pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv_encode4 = contracting_block(in_channels=256, out_channels=512)
        self.conv_pool4 = nn.MaxPool2d(kernel_size=2, stride=2)

        # 编码器与解码器之间的过渡部分(Bottleneck)
        self.bottleneck = nn.Sequential(
            nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=1024),
            nn.BatchNorm2d(1024),
            nn.ReLU(),
            nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024),
            nn.BatchNorm2d(1024),
            nn.ReLU()
        )

        # 解码器(Decode)
        self.conv_decode4 = expansive_block(1024, 512, 512)
        self.conv_decode3 = expansive_block(512, 256, 256)
        self.conv_decode2 = expansive_block(256, 128, 128)
        self.conv_decode1 = expansive_block(128, 64, 64)

        self.final_layer = final_block(64, out_channel)

    def forward(self, x):
        # Encode
        encode_block1 = self.conv_encode1(x)
        encode_pool1 = self.conv_pool1(encode_block1)
        encode_block2 = self.conv_encode2(encode_pool1)
        encode_pool2 = self.conv_pool2(encode_block2)
        encode_block3 = self.conv_encode3(encode_pool2)
        encode_pool3 = self.conv_pool3(encode_block3)
        encode_block4 = self.conv_encode4(encode_pool3)
        encode_pool4 = self.conv_pool4(encode_block4)

        # Bottleneck
        bottleneck = self.bottleneck(encode_pool4)

        # Decode
        decode_block4 = self.conv_decode4(encode_block4, bottleneck)
        decode_block3 = self.conv_decode3(encode_block3, decode_block4)
        decode_block2 = self.conv_decode2(encode_block2, decode_block3)
        decode_block1 = self.conv_decode1(encode_block1, decode_block2)

        final_layer = self.final_layer(decode_block1)
        return final_layer


if __name__ == '__main__':
    image = torch.rand((1, 3, 572, 572))
    unet = UNet(in_channel=3, out_channel=2)
    mask = unet(image)
    print(mask.shape)
    
    #输出结果：
    torch.Size([1, 2, 388, 388])