SATLossCustom/model.py at main · MASILab/SATLossCustom · GitHub

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

class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels, mid_channels=None):
        super().__init__()
        if not mid_channels:
            mid_channels = out_channels
        self.double_conv = nn.Sequential(
            nn.Conv3d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm3d(mid_channels),
            nn.ReLU(inplace=True),
            nn.Conv3d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm3d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.double_conv(x)


class Down(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool3d(2),
            DoubleConv(in_channels, out_channels)
        )

    def forward(self, x):
        return self.maxpool_conv(x)


class Up(nn.Module):
    def __init__(self, in_channels, out_channels, trilinear=True):
        super().__init__()

        if trilinear:
            self.up = nn.Upsample(scale_factor=2, mode='trilinear', align_corners=True)
            self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
        else:
            self.up = nn.ConvTranspose3d(
                in_channels,
                in_channels // 2,
                kernel_size=2,
                stride=2
            )
            self.conv = DoubleConv(in_channels, out_channels)

    def forward(self, x1, x2):
        x1 = self.up(x1)

        diffD = x2.size()[2] - x1.size()[2]
        diffH = x2.size()[3] - x1.size()[3]
        diffW = x2.size()[4] - x1.size()[4]

        x1 = F.pad(
            x1,
            [
                diffW // 2, diffW - diffW // 2,
                diffH // 2, diffH - diffH // 2,
                diffD // 2, diffD - diffD // 2
            ]
        )

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


class OutConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=1)

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


class UNet(nn.Module):
    def __init__(self, n_channels, n_classes, Hb=None, Wb=None, trilinear=False, bmodule=None):
        super().__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.trilinear = trilinear

        self.inc = DoubleConv(n_channels, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        factor = 2 if trilinear else 1
        self.down4 = Down(512, 1024 // factor)
        self.bmodule = bmodule

        self.up1 = Up(1024, 512 // factor, trilinear)
        self.up2 = Up(512, 256 // factor, trilinear)
        self.up3 = Up(256, 128 // factor, trilinear)
        self.up4 = Up(128, 64, trilinear)
        self.outc = OutConv(64, n_classes)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)

        if self.bmodule:
            x5 = self.bmodule(x5)

        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        logits = self.outc(x)
        return logits


class UNet_small(nn.Module):
    def __init__(self, n_channels, n_classes, Hb=None, Wb=None, trilinear=False, bmodule=None):
        super().__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.trilinear = trilinear

        self.inc = DoubleConv(n_channels, 16)
        self.down1 = Down(16, 32)
        self.down2 = Down(32, 64)
        self.down3 = Down(64, 128)
        factor = 2 if trilinear else 1
        self.down4 = Down(128, 256 // factor)
        self.bmodule = bmodule

        self.up1 = Up(256, 128 // factor, trilinear)
        self.up2 = Up(128, 64 // factor, trilinear)
        self.up3 = Up(64, 32 // factor, trilinear)
        self.up4 = Up(32, 16, trilinear)
        self.outc = OutConv(16, n_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)

        if self.bmodule:
            x5 = self.bmodule(x5)

        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        logits = self.outc(x)
        return logits