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MBD-RDFF.py
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MBD-RDFF.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from networks.base_networks import Encoder_MDCBlock1, Decoder_MDCBlock1
def make_model(args, parent=False):
return Net()
class make_dense(nn.Module):
def __init__(self, nChannels, growthRate, kernel_size=3):
super(make_dense, self).__init__()
self.conv = nn.Conv2d(nChannels, growthRate, kernel_size=kernel_size, padding=(kernel_size-1)//2, bias=False)
def forward(self, x):
out = F.relu(self.conv(x))
out = torch.cat((x, out), 1)
return out
# Residual dense block (RDB) architecture
class RDB(nn.Module):
def __init__(self, nChannels, nDenselayer, growthRate, scale = 1.0):
super(RDB, self).__init__()
nChannels_ = nChannels
self.scale = scale
modules = []
for i in range(nDenselayer):
modules.append(make_dense(nChannels_, growthRate))
nChannels_ += growthRate
self.dense_layers = nn.Sequential(*modules)
self.conv_1x1 = nn.Conv2d(nChannels_, nChannels, kernel_size=1, padding=0, bias=False)
def forward(self, x):
out = self.dense_layers(x)
out = self.conv_1x1(out) * self.scale
out = out + x
return out
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(reflection_padding)
self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride)
def forward(self, x):
out = self.reflection_pad(x)
out = self.conv2d(out)
return out
class UpsampleConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(UpsampleConvLayer, self).__init__()
self.conv2d = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=stride)
def forward(self, x):
out = self.conv2d(x)
return out
class ResidualBlock(torch.nn.Module):
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
self.relu = nn.PReLU()
def forward(self, x):
residual = x
out = self.relu(self.conv1(x))
out = self.conv2(out) * 0.1
out = torch.add(out, residual)
return out
class Net(nn.Module):
def __init__(self, res_blocks=18):
super(Net, self).__init__()
self.conv_input = ConvLayer(3, 16, kernel_size=11, stride=1)
self.dense0 = nn.Sequential(
ResidualBlock(16),
ResidualBlock(16),
ResidualBlock(16)
)
self.conv2x = ConvLayer(16, 32, kernel_size=3, stride=2)
self.conv1 = RDB(16, 4, 16)
self.fusion1 = Encoder_MDCBlock1(16, 2, mode='iter2')
self.dense1 = nn.Sequential(
ResidualBlock(32),
ResidualBlock(32),
ResidualBlock(32)
)
self.conv4x = ConvLayer(32, 64, kernel_size=3, stride=2)
self.conv2 = RDB(32, 4, 32)
self.fusion2 = Encoder_MDCBlock1(32, 3, mode='iter2')
self.dense2 = nn.Sequential(
ResidualBlock(64),
ResidualBlock(64),
ResidualBlock(64)
)
self.conv8x = ConvLayer(64, 128, kernel_size=3, stride=2)
self.conv3 = RDB(64, 4, 64)
self.fusion3 = Encoder_MDCBlock1(64, 4, mode='iter2')
self.dense3 = nn.Sequential(
ResidualBlock(128),
ResidualBlock(128),
ResidualBlock(128)
)
self.conv16x = ConvLayer(128, 256, kernel_size=3, stride=2)
self.conv4 = RDB(128, 4, 128)
self.fusion4 = Encoder_MDCBlock1(128, 5, mode='iter2')
self.dehaze = nn.Sequential()
for i in range(0, res_blocks):
self.dehaze.add_module('res%d' % i, ResidualBlock(256))
self.convd16x = UpsampleConvLayer(256, 128, kernel_size=3, stride=2)
self.dense_4 = nn.Sequential(
ResidualBlock(128),
ResidualBlock(128),
ResidualBlock(128)
)
self.conv_4 = RDB(64, 4, 64)
self.fusion_4 = Decoder_MDCBlock1(64, 2, mode='iter2')
self.convd8x = UpsampleConvLayer(128, 64, kernel_size=3, stride=2)
self.dense_3 = nn.Sequential(
ResidualBlock(64),
ResidualBlock(64),
ResidualBlock(64)
)
self.conv_3 = RDB(32, 4, 32)
self.fusion_3 = Decoder_MDCBlock1(32, 3, mode='iter2')
self.convd4x = UpsampleConvLayer(64, 32, kernel_size=3, stride=2)
self.dense_2 = nn.Sequential(
ResidualBlock(32),
ResidualBlock(32),
ResidualBlock(32)
)
self.conv_2 = RDB(16, 4, 16)
self.fusion_2 = Decoder_MDCBlock1(16, 4, mode='iter2')
self.convd2x = UpsampleConvLayer(32, 16, kernel_size=3, stride=2)
self.dense_1 = nn.Sequential(
ResidualBlock(16),
ResidualBlock(16),
ResidualBlock(16)
)
self.conv_1 = RDB(8, 4, 8)
self.fusion_1 = Decoder_MDCBlock1(8, 5, mode='iter2')
self.conv_output = ConvLayer(16, 3, kernel_size=3, stride=1)
def forward(self, x):
res1x = self.conv_input(x)
res1x_1, res1x_2 = res1x.split([(res1x.size()[1] // 2), (res1x.size()[1] // 2)], dim=1)
feature_mem = [res1x_1]
x = self.dense0(res1x) + res1x
res2x = self.conv2x(x)
res2x_1, res2x_2 = res2x.split([(res2x.size()[1] // 2), (res2x.size()[1] // 2)], dim=1)
res2x_1 = self.fusion1(res2x_1, feature_mem)
res2x_2 = self.conv1(res2x_2)
feature_mem.append(res2x_1)
res2x = torch.cat((res2x_1, res2x_2), dim=1)
res2x =self.dense1(res2x) + res2x
res4x =self.conv4x(res2x)
res4x_1, res4x_2 = res4x.split([(res4x.size()[1] // 2), (res4x.size()[1] // 2)], dim=1)
res4x_1 = self.fusion2(res4x_1, feature_mem)
res4x_2 = self.conv2(res4x_2)
feature_mem.append(res4x_1)
res4x = torch.cat((res4x_1, res4x_2), dim=1)
res4x = self.dense2(res4x) + res4x
res8x = self.conv8x(res4x)
res8x_1, res8x_2 = res8x.split([(res8x.size()[1] // 2), (res8x.size()[1] // 2)], dim=1)
res8x_1 = self.fusion3(res8x_1, feature_mem)
res8x_2 = self.conv3(res8x_2)
feature_mem.append(res8x_1)
res8x = torch.cat((res8x_1, res8x_2), dim=1)
res8x = self.dense3(res8x) + res8x
res16x = self.conv16x(res8x)
res16x_1, res16x_2 = res16x.split([(res16x.size()[1] // 2), (res16x.size()[1] // 2)], dim=1)
res16x_1 = self.fusion4(res16x_1, feature_mem)
res16x_2 = self.conv4(res16x_2)
res16x = torch.cat((res16x_1, res16x_2), dim=1)
res_dehaze = res16x
in_ft = res16x*2
res16x = self.dehaze(in_ft) + in_ft - res_dehaze
res16x_1, res16x_2 = res16x.split([(res16x.size()[1] // 2), (res16x.size()[1] // 2)], dim=1)
feature_mem_up = [res16x_1]
res16x = self.convd16x(res16x)
res16x = F.upsample(res16x, res8x.size()[2:], mode='bilinear')
res8x = torch.add(res16x, res8x)
res8x = self.dense_4(res8x) + res8x - res16x
res8x_1, res8x_2 = res8x.split([(res8x.size()[1] // 2), (res8x.size()[1] // 2)], dim=1)
res8x_1 = self.fusion_4(res8x_1, feature_mem_up)
res8x_2 = self.conv_4(res8x_2)
feature_mem_up.append(res8x_1)
res8x = torch.cat((res8x_1, res8x_2), dim=1)
res8x = self.convd8x(res8x)
res8x = F.upsample(res8x, res4x.size()[2:], mode='bilinear')
res4x = torch.add(res8x, res4x)
res4x = self.dense_3(res4x) + res4x - res8x
res4x_1, res4x_2 = res4x.split([(res4x.size()[1] // 2), (res4x.size()[1] // 2)], dim=1)
res4x_1 = self.fusion_3(res4x_1, feature_mem_up)
res4x_2 = self.conv_3(res4x_2)
feature_mem_up.append(res4x_1)
res4x = torch.cat((res4x_1, res4x_2), dim=1)
res4x = self.convd4x(res4x)
res4x = F.upsample(res4x, res2x.size()[2:], mode='bilinear')
res2x = torch.add(res4x, res2x)
res2x = self.dense_2(res2x) + res2x - res4x
res2x_1, res2x_2 = res2x.split([(res2x.size()[1] // 2), (res2x.size()[1] // 2)], dim=1)
res2x_1 = self.fusion_2(res2x_1, feature_mem_up)
res2x_2 = self.conv_2(res2x_2)
feature_mem_up.append(res2x_1)
res2x = torch.cat((res2x_1, res2x_2), dim=1)
res2x = self.convd2x(res2x)
res2x = F.upsample(res2x, x.size()[2:], mode='bilinear')
x = torch.add(res2x, x)
x = self.dense_1(x) + x - res2x
x_1, x_2 = x.split([(x.size()[1] // 2), (x.size()[1] // 2)], dim=1)
x_1 = self.fusion_1(x_1, feature_mem_up)
x_2 = self.conv_1(x_2)
x = torch.cat((x_1, x_2), dim=1)
x = self.conv_output(x)
return x