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import torch
import torch.nn as nn
from loss import LossFunction, TextureDifference
from utils import blur, pair_downsampler
class Denoise_1(nn.Module):
def __init__(self, chan_embed=48):
super(Denoise_1, self).__init__()
self.act = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.conv1 = nn.Conv2d(3, chan_embed, 3, padding=1)
self.conv2 = nn.Conv2d(chan_embed, chan_embed, 3, padding=1)
self.conv3 = nn.Conv2d(chan_embed, 3, 1)
def forward(self, x):
x = self.act(self.conv1(x))
x = self.act(self.conv2(x))
x = self.conv3(x)
return x
class Denoise_2(nn.Module):
def __init__(self, chan_embed=96):
super(Denoise_2, self).__init__()
self.act = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.conv1 = nn.Conv2d(6, chan_embed, 3, padding=1)
self.conv2 = nn.Conv2d(chan_embed, chan_embed, 3, padding=1)
self.conv3 = nn.Conv2d(chan_embed, 6, 1)
def forward(self, x):
x = self.act(self.conv1(x))
x = self.act(self.conv2(x))
x = self.conv3(x)
return x
class Enhancer(nn.Module):
def __init__(self, layers, channels):
super(Enhancer, self).__init__()
kernel_size = 3
dilation = 1
padding = int((kernel_size - 1) / 2) * dilation
self.in_conv = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=channels, kernel_size=kernel_size, stride=1, padding=padding),
nn.ReLU()
)
self.conv = nn.Sequential(
nn.Conv2d(in_channels=channels, out_channels=channels, kernel_size=kernel_size, stride=1, padding=padding),
nn.BatchNorm2d(channels),
nn.ReLU()
)
self.blocks = nn.ModuleList()
for i in range(layers):
self.blocks.append(self.conv)
self.out_conv = nn.Sequential(
nn.Conv2d(in_channels=channels, out_channels=3, kernel_size=3, stride=1, padding=1),
nn.Sigmoid()
)
def forward(self, input):
fea = self.in_conv(input)
for conv in self.blocks:
fea = fea + conv(fea)
fea = self.out_conv(fea)
fea = torch.clamp(fea, 0.0001, 1)
return fea
class Network(nn.Module):
def __init__(self):
super(Network, self).__init__()
self.enhance = Enhancer(layers=3, channels=64)
self.denoise_1 = Denoise_1(chan_embed=48)
self.denoise_2 = Denoise_2(chan_embed=48)
self._l2_loss = nn.MSELoss()
self._l1_loss = nn.L1Loss()
self._criterion = LossFunction()
self.avgpool = nn.AvgPool2d(kernel_size=3, stride=1, padding=1)
self.TextureDifference = TextureDifference()
def enhance_weights_init(self, m):
if isinstance(m, nn.Conv2d):
m.weight.data.normal_(0.0, 0.02)
if m.bias != None:
m.bias.data.zero_()
if isinstance(m, nn.BatchNorm2d):
m.weight.data.normal_(1., 0.02)
def denoise_weights_init(self, m):
if isinstance(m, nn.Conv2d):
m.weight.data.normal_(0, 0.02)
if m.bias != None:
m.bias.data.zero_()
if isinstance(m, nn.BatchNorm2d):
m.weight.data.normal_(1., 0.02)
def forward(self, input):
eps = 1e-4
input = input + eps
L11, L12 = pair_downsampler(input)
L_pred1 = L11 - self.denoise_1(L11)
L_pred2 = L12 - self.denoise_1(L12)
L2 = input - self.denoise_1(input)
L2 = torch.clamp(L2, eps, 1)
s2 = self.enhance(L2.detach())
s21, s22 = pair_downsampler(s2)
H2 = input / s2
H2 = torch.clamp(H2, eps, 1)
H11 = L11 / s21
H11 = torch.clamp(H11, eps, 1)
H12 = L12 / s22
H12 = torch.clamp(H12, eps, 1)
H3_pred = torch.cat([H11, s21], 1).detach() - self.denoise_2(torch.cat([H11, s21], 1))
H3_pred = torch.clamp(H3_pred, eps, 1)
H13 = H3_pred[:, :3, :, :]
s13 = H3_pred[:, 3:, :, :]
H4_pred = torch.cat([H12, s22], 1).detach() - self.denoise_2(torch.cat([H12, s22], 1))
H4_pred = torch.clamp(H4_pred, eps, 1)
H14 = H4_pred[:, :3, :, :]
s14 = H4_pred[:, 3:, :, :]
H5_pred = torch.cat([H2, s2], 1).detach() - self.denoise_2(torch.cat([H2, s2], 1))
H5_pred = torch.clamp(H5_pred, eps, 1)
H3 = H5_pred[:, :3, :, :]
s3 = H5_pred[:, 3:, :, :]
L_pred1_L_pred2_diff = self.TextureDifference(L_pred1, L_pred2)
H3_denoised1, H3_denoised2 = pair_downsampler(H3)
H3_denoised1_H3_denoised2_diff= self.TextureDifference(H3_denoised1, H3_denoised2)
H1 = L2 / s2
H1 = torch.clamp(H1, 0, 1)
H2_blur = blur(H1)
H3_blur = blur(H3)
return L_pred1, L_pred2, L2, s2, s21, s22, H2, H11, H12, H13, s13, H14, s14, H3, s3, H3_pred, H4_pred, L_pred1_L_pred2_diff, H3_denoised1_H3_denoised2_diff, H2_blur, H3_blur
def _loss(self, input):
L_pred1, L_pred2, L2, s2, s21, s22, H2, H11, H12, H13, s13, H14, s14, H3, s3, H3_pred, H4_pred, L_pred1_L_pred2_diff, H3_denoised1_H3_denoised2_diff, H2_blur, H3_blur = self(
input)
loss = 0
loss += self._criterion(input, L_pred1, L_pred2, L2, s2, s21, s22, H2, H11, H12, H13, s13, H14, s14, H3, s3,
H3_pred, H4_pred, L_pred1_L_pred2_diff, H3_denoised1_H3_denoised2_diff, H2_blur,
H3_blur)
return loss
class Finetunemodel(nn.Module):
def __init__(self, weights):
super(Finetunemodel, self).__init__()
self.enhance = Enhancer(layers=3, channels=64)
self.denoise_1 = Denoise_1(chan_embed=48)
self.denoise_2 = Denoise_2(chan_embed=48)
# CPU/GPU compatible loading
device = 'cuda' if torch.cuda.is_available() else 'cpu'
try:
base_weights = torch.load(weights, map_location=device)
pretrained_dict = base_weights
model_dict = self.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
self.load_state_dict(model_dict)
print(f"✅ Loaded weights from {weights} on {device}")
except Exception as e:
print(f"⚠️ Could not load weights: {e}")
print("Using random initialization")
def weights_init(self, m):
if isinstance(m, nn.Conv2d):
m.weight.data.normal_(0, 0.02)
if m.bias is not None:
m.bias.data.zero_()
if isinstance(m, nn.BatchNorm2d):
m.weight.data.normal_(1., 0.02)
def forward(self, input):
eps = 1e-4
input = input + eps
L2 = input - self.denoise_1(input)
L2 = torch.clamp(L2, eps, 1)
s2 = self.enhance(L2)
H2 = input / s2
H2 = torch.clamp(H2, eps, 1)
H5_pred = torch.cat([H2, s2], 1).detach() - self.denoise_2(torch.cat([H2, s2], 1))
H5_pred = torch.clamp(H5_pred, eps, 1)
H3 = H5_pred[:, :3, :, :]
return H2, H3 |