upload the model and the archs and images

archs/NAFBlock.py

@@ -0,0 +1,172 @@
+import torch
+import torch.nn as nn
+from arch_utilNAFNET import LayerNorm2d
+import torch.nn.functional as F
+
+# Modules from model
+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

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+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

archs/model.py

@@ -0,0 +1,174 @@
+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
+
+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

models/NAFourNet16_LOLv2Real.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e6a451ba6d06e262b8f7c4e4336b87b4448c1516f377af725cb1171e8e478a0d
+size 1605726

requirements.txt

@@ -0,0 +1,183 @@
+absl-py==2.1.0
+anyio==4.2.0
+appdirs==1.4.4
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asttokens==2.4.1
+async-lru==2.0.4
+attrs==23.2.0
+Babel==2.14.0
+backcall==0.2.0
+beautifulsoup4==4.12.3
+bleach==6.1.0
+certifi==2023.7.22
+cffi==1.16.0
+charset-normalizer==3.3.0
+click==8.1.7
+colorama==0.4.6
+coloredlogs==15.0.1
+comm==0.2.1
+contourpy==1.1.1
+cycler==0.12.0
+Cython==3.0.6
+debugpy==1.8.0
+decorator==4.4.2
+defusedxml==0.7.1
+docker-pycreds==0.4.0
+exceptiongroup==1.2.0
+executing==2.0.1
+fastjsonschema==2.19.1
+filelock==3.12.4
+flatbuffers==23.5.26
+fonttools==4.43.0
+fqdn==1.5.1
+fsspec==2023.9.2
+gitdb==4.0.11
+GitPython==3.1.42
+humanfriendly==10.0
+idna==3.4
+imageio==2.33.0
+imageio-ffmpeg==0.4.9
+importlib-metadata==7.0.1
+importlib-resources==6.1.0
+ipykernel==6.29.0
+ipython==8.12.3
+ipywidgets==8.1.1
+isoduration==20.11.0
+jax==0.4.25
+jedi==0.19.1
+Jinja2==3.1.2
+json5==0.9.14
+jsonpointer==2.4
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+jupyter==1.0.0
+jupyter-console==6.6.3
+jupyter-events==0.9.0
+jupyter-lsp==2.2.2
+jupyter_client==8.6.0
+jupyter_core==5.7.1
+jupyter_server==2.12.5
+jupyter_server_terminals==0.5.2
+jupyterlab==4.0.11
+jupyterlab-widgets==3.0.9
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.25.2
+kiwisolver==1.4.5
+kornia==0.7.2
+kornia_rs==0.1.3
+lazy_loader==0.3
+lightning-utilities==0.11.2
+lpips==0.1.4
+MarkupSafe==2.1.3
+matplotlib==3.7.3
+matplotlib-inline==0.1.6
+mediapipe==0.10.10
+mistune==3.0.2
+ml-dtypes==0.3.2
+moviepy==1.0.3
+mpmath==1.3.0
+nbclient==0.9.0
+nbconvert==7.14.2
+nbformat==5.9.2
+nest-asyncio==1.6.0
+netron==7.5.3
+networkx==3.1
+notebook==7.0.7
+notebook_shim==0.2.3
+numpy==1.24.4
+nvidia-cublas-cu12==12.1.3.1
+nvidia-cuda-cupti-cu12==12.1.105
+nvidia-cuda-nvrtc-cu12==12.1.105
+nvidia-cuda-runtime-cu12==12.1.105
+nvidia-cudnn-cu12==8.9.2.26
+nvidia-cufft-cu12==11.0.2.54
+nvidia-curand-cu12==10.3.2.106
+nvidia-cusolver-cu12==11.4.5.107
+nvidia-cusparse-cu12==12.1.0.106
+nvidia-nccl-cu12==2.18.1
+nvidia-nvjitlink-cu12==12.3.101
+nvidia-nvtx-cu12==12.1.105
+onnx==1.14.1
+onnxruntime==1.16.0
+opencv-contrib-python==4.9.0.80
+opencv-python==4.8.1.78
+opt-einsum==3.3.0
+overrides==7.7.0
+packaging==23.2
+pandas==2.0.3
+pandocfilters==1.5.1
+parso==0.8.3
+pexpect==4.9.0
+pickleshare==0.7.5
+Pillow==10.0.1
+pkgutil_resolve_name==1.3.10
+platformdirs==4.1.0
+proglog==0.1.10
+prometheus-client==0.19.0
+prompt-toolkit==3.0.43
+protobuf==3.20.3
+psutil==5.9.5
+ptflops==0.7.3
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py-cpuinfo==9.0.0
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+Pygments==2.17.2
+pyparsing==3.1.1
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+python-dateutil==2.8.2
+python-json-logger==2.0.7
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+qtconsole==5.5.1
+QtPy==2.4.1
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+referencing==0.33.0
+requests==2.31.0
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rpds-py==0.17.1
+scikit-image==0.21.0
+scipy==1.10.1
+seaborn==0.13.0
+Send2Trash==1.8.2
+sentry-sdk==1.40.5
+setproctitle==1.3.3
+shapely==2.0.2
+six==1.16.0
+smmap==5.0.1
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+soupsieve==2.5
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+thop==0.1.1.post2209072238
+tifffile==2023.7.10
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+urllib3==2.0.6
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+webencodings==0.5.1
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