model.py

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


class CBDNet(nn.Module):
    def __init__(self):
        super(CBDNet, self).__init__()
        self.fcn = FCN()
        self.unet = UNet()
    
    def forward(self, x):
        noise_level = self.fcn(x)
        concat_img = torch.cat([x, noise_level], dim=1)
        out = self.unet(concat_img) + x
        return noise_level, out


class FCN(nn.Module):
    def __init__(self):
        super(FCN, self).__init__()
        self.inc = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.conv = nn.Sequential(
            nn.Conv2d(32, 32, 3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.outc = nn.Sequential(
            nn.Conv2d(32, 3, 3, padding=1),
            nn.ReLU(inplace=True)
        )
    
    def forward(self, x):
        conv1 = self.inc(x)
        conv2 = self.conv(conv1)
        conv3 = self.conv(conv2)
        conv4 = self.conv(conv3)
        conv5 = self.outc(conv4)
        return conv5


class UNet(nn.Module):
    def __init__(self):
        super(UNet, self).__init__()
        
        self.inc = nn.Sequential(
            single_conv(6, 64),
            single_conv(64, 64)
        )

        self.down1 = nn.AvgPool2d(2)
        self.conv1 = nn.Sequential(
            single_conv(64, 128),
            single_conv(128, 128),
            single_conv(128, 128)
        )

        self.down2 = nn.AvgPool2d(2)
        self.conv2 = nn.Sequential(
            single_conv(128, 256),
            single_conv(256, 256),
            single_conv(256, 256),
            single_conv(256, 256),
            single_conv(256, 256),
            single_conv(256, 256)
        )

        self.up1 = up(256)
        self.conv3 = nn.Sequential(
            single_conv(128, 128),
            single_conv(128, 128),
            single_conv(128, 128)
        )

        self.up2 = up(128)
        self.conv4 = nn.Sequential(
            single_conv(64, 64),
            single_conv(64, 64)
        )

        self.outc = outconv(64, 3)

    def forward(self, x):
        inx = self.inc(x)

        down1 = self.down1(inx)
        conv1 = self.conv1(down1)

        down2 = self.down2(conv1)
        conv2 = self.conv2(down2)

        up1 = self.up1(conv2, conv1)
        conv3 = self.conv3(up1)

        up2 = self.up2(conv3, inx)
        conv4 = self.conv4(up2)

        out = self.outc(conv4)
        return out


class single_conv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(single_conv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, 3, padding=1),
            nn.ReLU(inplace=True)
        )

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


class up(nn.Module):
    def __init__(self, in_ch):
        super(up, self).__init__()
        self.up = nn.ConvTranspose2d(in_ch, in_ch//2, 2, stride=2)

    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 = x2 + x1
        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 fixed_loss(nn.Module):
    def __init__(self):
        super().__init__()
        
    def forward(self, out_image, gt_image, est_noise, gt_noise, if_asym):
        h_x = est_noise.size()[2]
        w_x = est_noise.size()[3]
        count_h = self._tensor_size(est_noise[:, :, 1:, :])
        count_w = self._tensor_size(est_noise[:, :, : ,1:])
        h_tv = torch.pow((est_noise[:, :, 1:, :] - est_noise[:, :, :h_x-1, :]), 2).sum()
        w_tv = torch.pow((est_noise[:, :, :, 1:] - est_noise[:, :, :, :w_x-1]), 2).sum()
        tvloss = h_tv / count_h + w_tv / count_w

        loss = torch.mean(torch.pow((out_image - gt_image), 2)) + \
                if_asym * 0.5 * torch.mean(torch.mul(torch.abs(0.3 - F.relu(gt_noise - est_noise)), torch.pow(est_noise - gt_noise, 2))) + \
                0.05 * tvloss
        return loss

    def _tensor_size(self,t):
        return t.size()[1]*t.size()[2]*t.size()[3]