Coding-MLP-UNET-RESNET-2D OR 3D

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MLP

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import torch.nn as nn

class FCModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(FCModel, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        batch_size, seq_length, input_size = x.size()
        x = x.view(batch_size * seq_length, input_size)  # Reshape input to (batch_size * seq_length, input_size)
        h = self.fc1(x)
        out = self.fc2(h)
        out = out.view(batch_size, seq_length, -1)  # Reshape output back to (batch_size, seq_length, num_classes)
        out = out[:, -1, :]  # Take the last time step's output
        return out


# 设置超参数
input_size = 13456
hidden_size = 64
num_classes = 2

# 创建模型实例
model = FCModel(input_size, hidden_size, num_classes)

2D-Unet

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import torch
import torch.nn as nn
import torch.nn.functional as F

# UNet的一大层，包含了两层小的卷积
class DoubleConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, 3, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_ch, out_ch, 3, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        x = self.conv(x)
        return x

# 定义输入进来的第一层
class InConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(InConv, self).__init__()
        self.conv = DoubleConv(in_ch, out_ch)

    def forward(self, x):
        x = self.conv(x)
        return x

# 定义encoder中的向下传播，包括一个maxpool和一大层    
class Down(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(Down, self).__init__()
        self.mpconv = nn.Sequential(
            nn.MaxPool2d(2),
            DoubleConv(in_ch, out_ch)
        )

    def forward(self, x):
        x = self.mpconv(x)
        return x

# 定义decoder中的向上传播
class Up(nn.Module):
    def __init__(self, in_ch, out_ch, bilinear=True):
        super(Up, self).__init__()
        # 定义了self.up的方法
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.up = nn.ConvTranspose2d(in_ch // 2, in_ch // 2, 2, stride=2) # // 除以的结果向下取整

        self.conv = DoubleConv(in_ch, out_ch)

    def forward(self, x1, x2):  # x2是左侧的输出，x1是上一大层来的输出
        x1 = self.up(x1)

        diffY = x2.size()[2] - x1.size()[2]
        diffX = 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) # 将两个tensor拼接在一起 dim=1：在通道数（C）上进行拼接
        x = self.conv(x)
        return x

# 定义最终的输出
class OutConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(OutConv, self).__init__()
        self.conv = nn.Conv2d(in_ch, out_ch, 1)

    def forward(self, x):
        x = self.conv(x)
        return x

class Unet(nn.Module):
    def __init__(self, in_channels, classes): # in_channels 图片的通道数，1为灰度图，3为彩色图
        super(Unet, self).__init__()
        self.n_channels = in_channels
        self.n_classes = classes

        self.inc = InConv(in_channels, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        self.down4 = Down(512, 512)
        self.up1 = Up(1024, 256)
        self.up2 = Up(512, 128)
        self.up3 = Up(256, 64)
        self.up4 = Up(128, 64)
        self.outc = OutConv(64, classes)

    def forward(self, x):
        x1 = self.inc(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)
        x = self.outc(x)

        return x

2D-Resnet

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import torch
import torch.nn as nn
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super(BasicBlock, self).__init__()

        # 第一个卷积层
        self.conv1 = nn.Conv2d(
            in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False
        )
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

        # 第二个卷积层
        self.conv2 = nn.Conv2d(
            out_channels,
            out_channels * self.expansion,
            kernel_size=3,
            stride=1,
            padding=1,
            bias=False,
        )
        self.bn2 = nn.BatchNorm2d(out_channels * self.expansion)

        # 残差连接（shortcut connection）
        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels * self.expansion:
            self.shortcut = nn.Sequential(
                nn.Conv2d(
                    in_channels,
                    out_channels * self.expansion,
                    kernel_size=1,
                    stride=stride,
                    bias=False,
                ),
                nn.BatchNorm2d(out_channels * self.expansion),
            )

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out += self.shortcut(residual)
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()

        self.in_channels = 64

        # 第一个卷积层
        self.conv1 = nn.Conv2d(
            3, 64, kernel_size=3, stride=1, padding=1, bias=False
        )
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)

        # ResNet的四个阶段
        self.layer1 = self.make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self.make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self.make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self.make_layer(block, 512, num_blocks[3], stride=2)

        # 全局平均池化层和全连接层
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def make_layer(self, block, out_channels, num_blocks, stride):
        layers = []
        layers.append(block(self.in_channels, out_channels, stride))
        self.in_channels = out_channels * block.expansion
        for _ in range(1, num_blocks):
            layers.append(block(self.in_channels, out_channels))
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)

        out = self.avg_pool(out)
        out = torch.flatten(out, 1)
        out = self.fc(out)
        
        return out

def ResNet18(num_classes=10):
    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes)

3D-Resnet

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class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super(BasicBlock, self).__init__()

        # 第一个卷积层
        self.conv1 = nn.Conv3d(
            in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False
        )
        self.bn1 = nn.BatchNorm3d(out_channels)
        self.relu = nn.ReLU(inplace=True)

        # 第二个卷积层
        self.conv2 = nn.Conv3d(
            out_channels,
            out_channels * self.expansion,
            kernel_size=3,
            stride=1,
            padding=1,
            bias=False,
        )
        self.bn2 = nn.BatchNorm3d(out_channels * self.expansion)

        # 残差连接（shortcut connection）
        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels * self.expansion:
            self.shortcut = nn.Sequential(
                nn.Conv3d(
                    in_channels,
                    out_channels * self.expansion,
                    kernel_size=1,
                    stride=stride,
                    bias=False,
                ),
                nn.BatchNorm3d(out_channels * self.expansion),
            )

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out += self.shortcut(residual)
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()

        self.in_channels = 64

        # 第一个卷积层
        self.conv1 = nn.Conv3d(
            1, 64, kernel_size=3, stride=1, padding=1, bias=False
        )
        self.bn1 = nn.BatchNorm3d(64)
        self.relu = nn.ReLU(inplace=True)

        # ResNet的四个阶段
        self.layer1 = self.make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self.make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self.make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self.make_layer(block, 512, num_blocks[3], stride=2)

        # 全局平均池化层和全连接层
        self.avg_pool = nn.AdaptiveAvgPool3d((1, 1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def make_layer(self, block, out_channels, num_blocks, stride):
        layers = []
        layers.append(block(self.in_channels, out_channels, stride))
        self.in_channels = out_channels * block.expansion
        for _ in range(1, num_blocks):
            layers.append(block(self.in_channels, out_channels))
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)

        out = self.avg_pool(out)
        out = torch.flatten(out, 1)
        out = self.fc(out)
        
        return out

def ResNet18_3D(num_classes=10):
    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes)

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