r2unet.py

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

class up_conv(nn.Module):
    def __init__(self,ch_in,ch_out):
        super(up_conv,self).__init__()
        self.up = nn.Sequential(
            nn.Upsample(scale_factor=2),
            nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=1,padding=1,bias=True),
		    nn.BatchNorm2d(ch_out),
			nn.ReLU(inplace=True)
        )

    def forward(self,x):
        x = self.up(x)
        return x
class Recurrent_block(nn.Module):
    def __init__(self, ch_out, t=2):
        super(Recurrent_block, self).__init__()
        self.t = t
        self.ch_out = ch_out
        self.conv = nn.Sequential(
            nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
            nn.BatchNorm2d(ch_out),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        for i in range(self.t):

            if i == 0:
                x1 = self.conv(x)

            x1 = self.conv(x + x1)
        return x1

class RRCNN_block(nn.Module):
    def __init__(self,ch_in,ch_out,t=2):
        super(RRCNN_block,self).__init__()
        self.RCNN = nn.Sequential(
            Recurrent_block(ch_out,t=t),
            Recurrent_block(ch_out,t=t)
        )
        self.Conv_1x1 = nn.Conv2d(ch_in,ch_out,kernel_size=1,stride=1,padding=0)

    def forward(self,x):
        x = self.Conv_1x1(x)
        x1 = self.RCNN(x)
        return x+x1
class R2U_Net(nn.Module):
    def __init__(self, img_ch=3, output_ch=1, t=2):
        super(R2U_Net, self).__init__()

        self.Maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.Upsample = nn.Upsample(scale_factor=2)

        self.RRCNN1 = RRCNN_block(ch_in=img_ch, ch_out=64, t=t)

        self.RRCNN2 = RRCNN_block(ch_in=64, ch_out=128, t=t)

        self.RRCNN3 = RRCNN_block(ch_in=128, ch_out=256, t=t)

        self.RRCNN4 = RRCNN_block(ch_in=256, ch_out=512, t=t)

        self.RRCNN5 = RRCNN_block(ch_in=512, ch_out=1024, t=t)

        self.Up5 = up_conv(ch_in=1024, ch_out=512)
        self.Up_RRCNN5 = RRCNN_block(ch_in=1024, ch_out=512, t=t)

        self.Up4 = up_conv(ch_in=512, ch_out=256)
        self.Up_RRCNN4 = RRCNN_block(ch_in=512, ch_out=256, t=t)

        self.Up3 = up_conv(ch_in=256, ch_out=128)
        self.Up_RRCNN3 = RRCNN_block(ch_in=256, ch_out=128, t=t)

        self.Up2 = up_conv(ch_in=128, ch_out=64)
        self.Up_RRCNN2 = RRCNN_block(ch_in=128, ch_out=64, t=t)

        self.Conv_1x1 = nn.Conv2d(64, output_ch, kernel_size=1, stride=1, padding=0)
        self.sigmoid = nn.Sigmoid()
    def forward(self, x):
        # encoding path
        x1 = self.RRCNN1(x)

        x2 = self.Maxpool(x1)
        x2 = self.RRCNN2(x2)

        x3 = self.Maxpool(x2)
        x3 = self.RRCNN3(x3)

        x4 = self.Maxpool(x3)
        x4 = self.RRCNN4(x4)

        x5 = self.Maxpool(x4)
        x5 = self.RRCNN5(x5)

        # decoding + concat path
        d5 = self.Up5(x5)
        d5 = torch.cat((x4, d5), dim=1)
        d5 = self.Up_RRCNN5(d5)

        d4 = self.Up4(d5)
        d4 = torch.cat((x3, d4), dim=1)
        d4 = self.Up_RRCNN4(d4)

        d3 = self.Up3(d4)
        d3 = torch.cat((x2, d3), dim=1)
        d3 = self.Up_RRCNN3(d3)

        d2 = self.Up2(d3)
        d2 = torch.cat((x1, d2), dim=1)
        d2 = self.Up_RRCNN2(d2)

        d1 = self.Conv_1x1(d2)
        d1 = self.sigmoid(d1)
        return d1