My_Unet.py

# Code for different variants of U-Net
# Some parts taken from https://github.com/milesial/Pytorch-UNet
# Implements light (half feature channels) and lighter (quarter number of feature maps) U-Net


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

def count_trainable_parameters(model):  # to count trainable parameters
    return sum(p.numel() for p in model.parameters() if p.requires_grad)

class double_conv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(double_conv, 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


class inconv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(inconv, self).__init__()
        self.conv = double_conv(in_ch, out_ch)

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


class down(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(down, self).__init__()
        self.mpconv = nn.Sequential(
            nn.MaxPool2d(2),
            double_conv(in_ch, out_ch)
        )

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


class up(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(up, self).__init__()
        self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)

        self.conv = double_conv(in_ch, out_ch)

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

        # input is CHW
        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)
        x = self.conv(x)
        return x

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


number_classes = 3  # building, road, background

########
# U-Net
########
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()

        self.inc = inconv(3, 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, number_classes)

    def forward(self, x):
        x1 = self.inc(x.permute(0, 3, 1, 2))
        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 F.softmax(x, dim = 1)

########
# light U-Net, half the features maps than the original
########
class Net_light(nn.Module):
    def __init__(self):
        super(Net_light, self).__init__()

        self.inc = inconv(3, 32)
        self.down1 = down(32, 64)
        self.down2 = down(64, 128)
        self.down3 = down(128, 256)
        self.down4 = down(256, 256)
        self.up1 = up(512, 128)
        self.up2 = up(256, 64)
        self.up3 = up(128, 32)
        self.up4 = up(64, 32)
        self.outc = outconv(32, number_classes)

    def forward(self, x):
        x1 = self.inc(x.permute(0, 3, 1, 2))
        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 F.softmax(x, dim = 1)


########
# lighter U-Net, quarter the features maps than the original
########
class Net_lighter(nn.Module):
    def __init__(self):
        super(Net_lighter, self).__init__()

        self.inc = inconv(3, 16)
        self.down1 = down(16, 32)
        self.down2 = down(32, 64)
        self.down3 = down(64, 128)
        self.down4 = down(128, 128)
        self.up1 = up(256, 64)
        self.up2 = up(128, 32)
        self.up3 = up(64, 16)
        self.up4 = up(32, 16)
        self.outc = outconv(16, number_classes)

    def forward(self, x):
        x1 = self.inc(x.permute(0, 3, 1, 2))
        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 F.softmax(x, dim = 1)


class Q_Net_lighter(nn.Module):
    def __init__(self):
        super(Q_Net_lighter, self).__init__()

        self.inc = inconv(3, 16)
        self.down1 = down(16, 32)
        self.down2 = down(32, 64)
        self.down3 = down(64, 128)
        self.down4 = down(128, 128)
        self.up1 = up(256, 64)
        self.up2 = up(128, 32)
        self.up3 = up(64, 16)
        self.up4 = up(32, 16)
        self.outc = outconv(16, 1)

    def forward(self, x):
        x1 = self.inc(x.permute(0, 3, 1, 2))
        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