archs/model.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import functools
# import arch_util as arch_util
# from NAFBlock import *
import kornia
import torch.nn.functional as F
import torchvision.models

try:
    import archs.arch_util as arch_util
    from archs.NAFBlock import *

except:
    import arch_util as arch_util
    from NAFBlock import *
class VGG19(torch.nn.Module):

    def __init__(self, requires_grad=False):
        super().__init__()
        vgg_pretrained_features = torchvision.models.vgg19(pretrained=True).features
        self.slice1 = torch.nn.Sequential()
        self.slice2 = torch.nn.Sequential()
        self.slice3 = torch.nn.Sequential()
        self.slice4 = torch.nn.Sequential()
        self.slice5 = torch.nn.Sequential()
        for x in range(2):
            self.slice1.add_module(str(x), vgg_pretrained_features[x])
        for x in range(2, 7):
            self.slice2.add_module(str(x), vgg_pretrained_features[x])
        for x in range(7, 12):
            self.slice3.add_module(str(x), vgg_pretrained_features[x])
        for x in range(12, 21):
            self.slice4.add_module(str(x), vgg_pretrained_features[x])
        for x in range(21, 30):
            self.slice5.add_module(str(x), vgg_pretrained_features[x])
        if not requires_grad:
            for param in self.parameters():
                param.requires_grad = False

    def forward(self, X):
        h_relu1 = self.slice1(X)
        h_relu2 = self.slice2(h_relu1)
        h_relu3 = self.slice3(h_relu2)
        h_relu4 = self.slice4(h_relu3)
        h_relu5 = self.slice5(h_relu4)
        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
        return out
    
class VGGLoss(nn.Module):
    
    def __init__(self):

        super(VGGLoss, self).__init__()
        self.vgg = VGG19().cuda()
        # self.criterion = nn.L1Loss()
        self.criterion = nn.L1Loss(reduction='sum')
        self.criterion2 = nn.L1Loss()
        self.weights = [1.0 / 32, 1.0 / 16, 1.0 / 8, 1.0 / 4, 1.0]
    
    def forward(self, x, y):

        x_vgg, y_vgg = self.vgg(x), self.vgg(y)
        # print(x_vgg.shape, x_vgg.dtype, torch.max(x_vgg), torch.min(x_vgg), y_vgg.shape, y_vgg.dtype, torch.max(y_vgg), torch.min(y_vgg))
        loss = 0
        for i in range(len(x_vgg)):
            # print(x_vgg[i].shape, y_vgg[i].shape, 'hey')
            loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())
            # print(loss, i, 'hey')
            
        return loss


class FourNet(nn.Module):
    def __init__(self, nf=64):
        super(FourNet, self).__init__()

        # AMPLITUDE ENHANCEMENT
        self.AmpNet = nn.Sequential(
            AmplitudeNet_skip(8),
            nn.Sigmoid()
        )

        self.nf = nf
        ResidualBlock_noBN_f = functools.partial(arch_util.ResidualBlock_noBN, nf=nf)

        self.conv_first_1 = nn.Conv2d(3 * 2, nf, 3, 1, 1, bias=True)
        self.conv_first_2 = nn.Conv2d(nf, nf, 3, 2, 1, bias=True)
        self.conv_first_3 = nn.Conv2d(nf, nf, 3, 2, 1, bias=True)

        self.feature_extraction = arch_util.make_layer(ResidualBlock_noBN_f, 1)
        self.recon_trunk = arch_util.make_layer(ResidualBlock_noBN_f, 1)

        self.upconv1 = nn.Conv2d(nf*2, nf * 4, 3, 1, 1, bias=True)
        self.upconv2 = nn.Conv2d(nf*2, nf * 4, 3, 1, 1, bias=True)
        self.pixel_shuffle = nn.PixelShuffle(2)
        self.HRconv = nn.Conv2d(nf*2, nf, 3, 1, 1, bias=True)
        self.conv_last = nn.Conv2d(nf, 3, 3, 1, 1, bias=True)

        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
        self.transformer = SFNet(nf, n = 4)
        self.recon_trunk_light = arch_util.make_layer(ResidualBlock_noBN_f, 6)

    def get_mask(self,dark):   # SNR map

        light = kornia.filters.gaussian_blur2d(dark, (5, 5), (1.5, 1.5))
        dark = dark[:, 0:1, :, :] * 0.299 + dark[:, 1:2, :, :] * 0.587 + dark[:, 2:3, :, :] * 0.114
        light = light[:, 0:1, :, :] * 0.299 + light[:, 1:2, :, :] * 0.587 + light[:, 2:3, :, :] * 0.114
        noise = torch.abs(dark - light)

        mask = torch.div(light, noise + 0.0001)

        batch_size = mask.shape[0]
        height = mask.shape[2]
        width = mask.shape[3]
        mask_max = torch.max(mask.view(batch_size, -1), dim=1)[0]
        mask_max = mask_max.view(batch_size, 1, 1, 1)
        mask_max = mask_max.repeat(1, 1, height, width)
        mask = mask * 1.0 / (mask_max + 0.0001)

        mask = torch.clamp(mask, min=0, max=1.0)
        return mask.float()

    def forward(self, x):

        # AMPLITUDE ENHANCEMENT
        #--------------------------------------------------------Frequency Stage---------------------------------------------------
        _, _, H, W = x.shape
        image_fft = torch.fft.fft2(x, norm='backward')
        mag_image = torch.abs(image_fft)
        pha_image = torch.angle(image_fft)
        curve_amps = self.AmpNet(x)
        mag_image = mag_image / (curve_amps + 0.00000001)  # * d4
        real_image_enhanced = mag_image * torch.cos(pha_image)
        imag_image_enhanced = mag_image * torch.sin(pha_image)
        img_amp_enhanced = torch.fft.ifft2(torch.complex(real_image_enhanced, imag_image_enhanced), s=(H, W),
                                           norm='backward').real

        x_center = img_amp_enhanced

        rate = 2 ** 3
        pad_h = (rate - H % rate) % rate
        pad_w = (rate - W % rate) % rate
        if pad_h != 0 or pad_w != 0:
            x_center = F.pad(x_center, (0, pad_w, 0, pad_h), "reflect")
            x = F.pad(x, (0, pad_w, 0, pad_h), "reflect")

        #------------------------------------------Spatial Stage---------------------------------------------------------------------

        L1_fea_1 = self.lrelu(self.conv_first_1(torch.cat((x_center,x),dim=1)))
        L1_fea_2 = self.lrelu(self.conv_first_2(L1_fea_1))   # Encoder
        L1_fea_3 = self.lrelu(self.conv_first_3(L1_fea_2))

        fea = self.feature_extraction(L1_fea_3)
        fea_light = self.recon_trunk_light(fea)

        h_feature = fea.shape[2]
        w_feature = fea.shape[3]
        mask_image = self.get_mask(x_center) # SNR Map
        mask = F.interpolate(mask_image, size=[h_feature, w_feature], mode='nearest') # Resize and Normalize SNR map

        fea_unfold = self.transformer(fea)

        channel = fea.shape[1]
        mask = mask.repeat(1, channel, 1, 1)
        fea = fea_unfold * (1 - mask) + fea_light * mask  # SNR-based Interaction

        out_noise = self.recon_trunk(fea)
        out_noise = torch.cat([out_noise, L1_fea_3], dim=1)
        out_noise = self.lrelu(self.pixel_shuffle(self.upconv1(out_noise)))
        out_noise = torch.cat([out_noise, L1_fea_2], dim=1)                   # Decoder
        out_noise = self.lrelu(self.pixel_shuffle(self.upconv2(out_noise)))
        out_noise = torch.cat([out_noise, L1_fea_1], dim=1)
        out_noise = self.lrelu(self.HRconv(out_noise))
        out_noise = self.conv_last(out_noise)
        out_noise = out_noise + x
        out_noise = out_noise[:, :, :H, :W]


        return out_noise, mag_image, x_center, mask_image