Delete archs

archs/NAFBlock.py

@@ -1,176 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-# Modules from model
-try:
-    from archs.arch_util import LayerNorm2d
-    import archs.arch_util as arch_util
-except:
-    from arch_util import LayerNorm2d
-    import arch_util as arch_util
-
-# Process Block 4 en SFNet y 5 bloques en AmpNet, con el spatial block aplicado en AmpNet (frequency stage) 
-# tal y como lo tienen ellos en su github (aunque en el paper es al revés) y no lo aplican el space stage
-
-
-class SimpleGate(nn.Module):
-    def forward(self, x):
-        x1, x2 = x.chunk(2, dim=1)
-        return x1 * x2
-
-class SpaBlock(nn.Module):
-    def __init__(self, nc, DW_Expand = 2,  FFN_Expand=2, drop_out_rate=0.):
-        super(SpaBlock, self).__init__()
-        dw_channel = nc * DW_Expand
-        self.conv1 = nn.Conv2d(in_channels=nc, out_channels=dw_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
-        self.conv2 = nn.Conv2d(in_channels=dw_channel, out_channels=dw_channel, kernel_size=3, padding=1, stride=1, groups=dw_channel,
-                               bias=True) # the dconv
-        self.conv3 = nn.Conv2d(in_channels=dw_channel // 2, out_channels=nc, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
-        
-        # Simplified Channel Attention
-        self.sca = nn.Sequential(
-            nn.AdaptiveAvgPool2d(1),
-            nn.Conv2d(in_channels=dw_channel // 2, out_channels=dw_channel // 2, kernel_size=1, padding=0, stride=1,
-                      groups=1, bias=True),
-        )
-
-        # SimpleGate
-        self.sg = SimpleGate()
-
-        ffn_channel = FFN_Expand * nc
-        self.conv4 = nn.Conv2d(in_channels=nc, out_channels=ffn_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
-        self.conv5 = nn.Conv2d(in_channels=ffn_channel // 2, out_channels=nc, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
-
-        self.norm1 = LayerNorm2d(nc)
-        self.norm2 = LayerNorm2d(nc)
-
-        self.dropout1 = nn.Dropout(drop_out_rate) if drop_out_rate > 0. else nn.Identity()
-        self.dropout2 = nn.Dropout(drop_out_rate) if drop_out_rate > 0. else nn.Identity()
-
-        self.beta = nn.Parameter(torch.zeros((1, nc, 1, 1)), requires_grad=True)
-        self.gamma = nn.Parameter(torch.zeros((1, nc, 1, 1)), requires_grad=True)
-
-    def forward(self, x):
-
-        x = self.norm1(x) # size [B, C, H, W]
-
-        x = self.conv1(x) # size [B, 2*C, H, W]
-        x = self.conv2(x) # size [B, 2*C, H, W]
-        x = self.sg(x)    # size [B, C, H, W]
-        x = x * self.sca(x) # size [B, C, H, W]
-        x = self.conv3(x) # size [B, C, H, W]
-
-        x = self.dropout1(x)
-
-        y = x + x * self.beta # size [B, C, H, W]
-
-        x = self.conv4(self.norm2(y)) # size [B, 2*C, H, W]
-        x = self.sg(x)  # size [B, C, H, W]
-        x = self.conv5(x) # size [B, C, H, W]
-
-        x = self.dropout2(x)
-
-        return y + x * self.gamma
-
-class FreBlock(nn.Module):
-    def __init__(self, nc):
-        super(FreBlock, self).__init__()
-        self.fpre = nn.Conv2d(nc, nc, 1, 1, 0)
-        self.process1 = nn.Sequential(
-            nn.Conv2d(nc, nc, 1, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-            nn.Conv2d(nc, nc, 1, 1, 0))
-        self.process2 = nn.Sequential(
-            nn.Conv2d(nc, nc, 1, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-            nn.Conv2d(nc, nc, 1, 1, 0))
-
-    def forward(self, x):
-        _, _, H, W = x.shape
-        x_freq = torch.fft.rfft2(self.fpre(x), norm='backward')
-        mag = torch.abs(x_freq)
-        pha = torch.angle(x_freq)
-        mag = self.process1(mag)
-        pha = self.process2(pha)
-        real = mag * torch.cos(pha)
-        imag = mag * torch.sin(pha)
-        x_out = torch.complex(real, imag)
-        x_out = torch.fft.irfft2(x_out, s=(H, W), norm='backward')
-
-        return x_out+x
-
-class ProcessBlock(nn.Module):
-    def __init__(self, in_nc, spatial = True):
-        super(ProcessBlock,self).__init__()
-        self.spatial = spatial
-        self.spatial_process = SpaBlock(in_nc) if spatial else nn.Identity()
-        self.frequency_process = FreBlock(in_nc)
-        self.cat = nn.Conv2d(2*in_nc,in_nc,1,1,0) if spatial else nn.Conv2d(in_nc,in_nc,1,1,0)
-
-    def forward(self, x):
-        xori = x
-        x_freq = self.frequency_process(x)
-        x_spatial = self.spatial_process(x)
-        xcat = torch.cat([x_spatial,x_freq],1)
-        x_out = self.cat(xcat) if self.spatial else self.cat(x_freq)
-
-        return x_out+xori
-
-class SFNet(nn.Module):
-
-    def __init__(self, nc,n=5):
-        super(SFNet,self).__init__()
-
-        self.list_block = list()
-        for index in range(n):
-
-            self.list_block.append(ProcessBlock(nc,spatial=False))
-  
-        self.block = nn.Sequential(*self.list_block)
-
-    def forward(self, x):
-
-        x_ori = x
-        x_out = self.block(x_ori)
-        xout = x_ori + x_out
-
-        return xout
-
-class AmplitudeNet_skip(nn.Module):
-    def __init__(self, nc,n=1):
-        super(AmplitudeNet_skip,self).__init__()
-
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(3, nc, 1, 1, 0),
-            ProcessBlock(nc),
-        )
-        self.conv2 = ProcessBlock(nc)
-        self.conv3 = ProcessBlock(nc)
-        self.conv4 = nn.Sequential(
-            ProcessBlock(nc * 2),
-            nn.Conv2d(nc * 2, nc, 1, 1, 0),
-        )
-
-        self.conv5 = nn.Sequential(
-            ProcessBlock(nc * 2),
-            nn.Conv2d(nc * 2, nc, 1, 1, 0),
-        )
-
-        self.convout = nn.Sequential(
-            ProcessBlock(nc * 2),
-            nn.Conv2d(nc * 2, 3, 1, 1, 0),
-        )
-
-    def forward(self, x):
-
-        x1 = self.conv1(x)
-        x2 = self.conv2(x1)
-        x3 = self.conv3(x2)
-        x4 = self.conv5(torch.cat((x2, x3), dim=1))
-        xout = self.convout(torch.cat((x1, x4), dim=1))
-
-        return xout
-
-
-

archs/arch_util.py

@@ -1,111 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.init as init
-import torch.nn.functional as F
-
-
-def initialize_weights(net_l, scale=1):
-    if not isinstance(net_l, list):
-        net_l = [net_l]
-    for net in net_l:
-        for m in net.modules():
-            if isinstance(m, nn.Conv2d):
-                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
-                m.weight.data *= scale  # for residual block
-                if m.bias is not None:
-                    m.bias.data.zero_()
-            elif isinstance(m, nn.Linear):
-                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.zero_()
-            elif isinstance(m, nn.BatchNorm2d):
-                init.constant_(m.weight, 1)
-                init.constant_(m.bias.data, 0.0)
-
-
-def make_layer(block, n_layers):
-    layers = []
-    for _ in range(n_layers):
-        layers.append(block())
-    return nn.Sequential(*layers)
-
-
-class ResidualBlock_noBN(nn.Module):
-    '''Residual block w/o BN
-    ---Conv-ReLU-Conv-+-
-     |________________|
-    '''
-
-    def __init__(self, nf=64):
-        super(ResidualBlock_noBN, self).__init__()
-        self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-
-        # initialization
-        initialize_weights([self.conv1, self.conv2], 0.1)
-
-    def forward(self, x):
-        identity = x
-        out = F.relu(self.conv1(x), inplace=True)
-        out = self.conv2(out)
-        return identity + out
-
-class ResidualBlock(nn.Module):
-    '''Residual block w/o BN
-    ---Conv-ReLU-Conv-+-
-     |________________|
-    '''
-
-    def __init__(self, nf=64):
-        super(ResidualBlock, self).__init__()
-        self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.bn = nn.BatchNorm2d(nf)
-        self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-
-        # initialization
-        initialize_weights([self.conv1, self.conv2], 0.1)
-
-    def forward(self, x):
-        identity = x
-        out = F.relu(self.bn(self.conv1(x)), inplace=True)
-        out = self.conv2(out)
-        return identity + out
-
-class LayerNormFunction(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, x, weight, bias, eps):
-        ctx.eps = eps
-        N, C, H, W = x.size()
-        mu = x.mean(1, keepdim=True)
-        var = (x - mu).pow(2).mean(1, keepdim=True)
-        y = (x - mu) / (var + eps).sqrt()
-        ctx.save_for_backward(y, var, weight)
-        y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1)
-        return y
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        eps = ctx.eps
-
-        N, C, H, W = grad_output.size()
-        y, var, weight = ctx.saved_variables
-        g = grad_output * weight.view(1, C, 1, 1)
-        mean_g = g.mean(dim=1, keepdim=True)
-
-        mean_gy = (g * y).mean(dim=1, keepdim=True)
-        gx = 1. / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g)
-        return gx, (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0), grad_output.sum(dim=3).sum(dim=2).sum(
-            dim=0), None
-
-class LayerNorm2d(nn.Module):
-
-    def __init__(self, channels, eps=1e-6):
-        super(LayerNorm2d, self).__init__()
-        self.register_parameter('weight', nn.Parameter(torch.ones(channels)))
-        self.register_parameter('bias', nn.Parameter(torch.zeros(channels)))
-        self.eps = eps
-
-    def forward(self, x):
-        return LayerNormFunction.apply(x, self.weight, self.bias, self.eps)

archs/model.py

@@ -1,181 +0,0 @@
-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