unet.py

# From https://github.com/milesial/Pytorch-UNet
import torch
import torch.nn as nn
import torch.nn.functional as F
#from l2h.main5_config import num_slice as num_slice
#changed to res block
class double_conv(nn.Module):
    '''(conv => BN => ReLU) * 2'''
    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):
        #input=x
        x = self.conv(x)
        #x=torch.add(x,input)
        return x


class inconv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(inconv, 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 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, tr_ch,in_ch, out_ch, bilinear=True):
        super(up, self).__init__()

        #  would be a nice idea if the upsampling could be learned too,
        #  but my machine do not have enough memory to handle all those weights
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.up = nn.ConvTranspose2d(tr_ch, tr_ch, 2, stride=2)

        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, x1, x2):
        x1 = self.up(x1)
        
        # input is CHW
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]
        #print("diffY",diffY)
        #print("diffx",diffX)

        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

class UNet(nn.Module):
    def __init__(self, n_channels, n_classes,act,num_slice):
    #parameters:
    #n_channels:input channels
    #n_classes:output channels
    #act: activation method in the last layer. I do this because jiarong suggested that different activation may have impact
    #num_slice: num of slices to use as the input, normally n_channels=2*num_slice+1
    
        super(UNet, self).__init__()
        self.inc = inconv(n_channels, 32)       
        self.down1 = down(32, 32)
        self.down2 = down(32, 64)
        self.down3 = down(64, 128)
        #self.btm= conv_twice(128, 128)
        self.up1 = up(128,192, 64)
        self.up2 = up(64,96, 32)
        self.up3 = up(32,64, 32)
        #self.up4 = up(128, 64)
        self.outc = outconv(32, n_classes)
        self.act=act
        self.num_slice=num_slice
    def forward(self, x):
        input = x[:,self.num_slice,:,:].view(x.shape[0],1,x.shape[2],x.shape[3])
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x = self.up1(x4, x3)
        x = self.up2(x, x2)
        x = self.up3(x, x1)
        x = self.outc(x)
        x = self.act(x)
        x=torch.add(x,input)
        return x,input