用pytorch搭建UNet3Plus网络

下面是用PyTorch搭建UNet3Plus网络的代码示例：

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。