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MINet.py
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MINet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import kaiming_normal_,constant_
from math import sqrt
class MeanShift(nn.Conv2d):
def __init__(
self, rgb_range,
rgb_mean=(0.4488, 0.4371, 0.4040), rgb_std=(1.0, 1.0, 1.0), sign=-1):
super(MeanShift, self).__init__(3, 3, kernel_size=1)
std = torch.Tensor(rgb_std)
self.weight.data = torch.eye(3).view(3, 3, 1, 1) / std.view(3, 1, 1, 1)
self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) / std
for p in self.parameters():
p.requires_grad = False
class ShareSepConv(nn.Module):
def __init__(self, kernel_size):
super(ShareSepConv, self).__init__()
assert kernel_size % 2 == 1, 'kernel size should be odd'
self.padding = (kernel_size - 1) // 2
weight_tensor = torch.zeros(1, 1, kernel_size, kernel_size)
weight_tensor[0, 0, (kernel_size - 1) // 2, (kernel_size - 1) // 2] = 1
self.weight = nn.Parameter(weight_tensor)
self.kernel_size = kernel_size
def forward(self, x):
inc = x.size(1)
expand_weight = self.weight.expand(inc, 1, self.kernel_size, self.kernel_size).contiguous()
return F.conv2d(x, expand_weight,
None, 1, self.padding, 1, inc)
class SmoothDilatedResidualBlock(nn.Module):
def __init__(self, channel_num, dilation=1, group=1):
super(SmoothDilatedResidualBlock, self).__init__()
self.pre_conv1 = ShareSepConv(dilation * 2 - 1)
self.conv1 = nn.Conv2d(channel_num, channel_num, 3, 1, padding=dilation, dilation=dilation, groups=group,
bias=False)
self.norm1 = nn.InstanceNorm2d(channel_num, affine=True)
self.pre_conv2 = ShareSepConv(dilation * 2 - 1)
self.conv2 = nn.Conv2d(channel_num, channel_num, 3, 1, padding=dilation, dilation=dilation, groups=group,
bias=False)
self.norm2 = nn.InstanceNorm2d(channel_num, affine=True)
def forward(self, x):
y = F.relu(self.norm1(self.conv1(self.pre_conv1(x))))
y = self.norm2(self.conv2(self.pre_conv2(y)))
return F.relu(x + y)
class SmoothDilatedResBlock(nn.Module):
def __init__(self, channel_num, dilation=1, group=1):
super(SmoothDilatedResBlock, self).__init__()
self.pre_conv1 = ShareSepConv(dilation * 2 - 1)
self.conv1 = nn.Conv2d(channel_num, channel_num, 3, 1, padding=dilation, dilation=dilation, groups=group,
bias=False)
self.norm1 = nn.InstanceNorm2d(channel_num, affine=True)
self.pre_conv2 = ShareSepConv(dilation * 2 - 1)
self.conv2 = nn.Conv2d(channel_num, channel_num, 3, 1, padding=1, bias=False)
self.norm2 = nn.InstanceNorm2d(channel_num, affine=True)
def forward(self, x):
y = F.relu(self.norm1(self.conv1(self.pre_conv1(x))))
y = self.norm2(self.conv2(self.pre_conv2(y)))
return F.relu(y)
class ResidualBlock(nn.Module):
def __init__(self, channel_num, dilation=1, group=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(channel_num, channel_num, 3, 1, padding=dilation, dilation=dilation, groups=group,
bias=False)
self.norm1 = nn.InstanceNorm2d(channel_num, affine=True)
self.conv2 = nn.Conv2d(channel_num, channel_num, 3, 1, padding=dilation, dilation=dilation, groups=group,
bias=False)
self.norm2 = nn.InstanceNorm2d(channel_num, affine=True)
def forward(self, x):
y = F.relu(self.norm1(self.conv1(x)))
y = self.norm2(self.conv2(y))
return F.relu(x + y)
class ResBlock(nn.Module):
def __init__(self, channel_num, dilation=1, group=1):
super(ResBlock, self).__init__()
self.conv1 = nn.Conv2d(channel_num, channel_num, 3, 1, padding=dilation, dilation=dilation, groups=group,
bias=False)
self.norm1 = nn.InstanceNorm2d(channel_num, affine=True)
self.conv2 = nn.Conv2d(channel_num, channel_num, 3, 1, padding=1, groups=group, bias=False)
self.norm2 = nn.InstanceNorm2d(channel_num, affine=True)
def forward(self, x):
y = F.relu(self.norm1(self.conv1(x)))
y = self.norm2(self.conv2(y))
return F.relu(y)
class MI_Net(nn.Module):
def __init__(self, in_c=3, out_c=3, only_residual=True):
super(MI_Net, self).__init__()
self.conv1 = nn.Conv2d(in_c, 64, 3, 1, 1, bias=False)
self.norm1 = nn.InstanceNorm2d(64, affine=True)
self.conv2 = nn.Conv2d(64, 64, 3, 1, 1, bias=False)
self.norm2 = nn.InstanceNorm2d(64, affine=True)
self.conv3 = nn.Conv2d(64, 64, 3, 2, 1, bias=False)
self.norm3 = nn.InstanceNorm2d(64, affine=True)
self.res2 = SmoothDilatedResBlock(64, dilation=2)
self.res4 = SmoothDilatedResBlock(64, dilation=4)
self.res1 = ResBlock(64, dilation=1)
self.gate = nn.Conv2d(64 * 3, 3, 3, 1, 1, bias=True)
self.deconv3 = nn.ConvTranspose2d(64, 64, 4, 2, 1)
self.norm4 = nn.InstanceNorm2d(64, affine=True)
self.deconv2 = nn.Conv2d(64, 64, 3, 1, 1)
self.norm5 = nn.InstanceNorm2d(64, affine=True)
self.deconv1 = nn.Conv2d(64, out_c, 1)
self.deconv = nn.Conv2d(out_c, out_c, 1)
self.pre_processing = nn.Sequential(
ResidualBlock(64, dilation=1),
ResidualBlock(64, dilation=1),
ResidualBlock(64, dilation=1),
)
self.only_residual = only_residual
self.layer1=nn.Sequential(nn.Conv2d(3,3,kernel_size=1,stride=1,padding=0),
nn.AdaptiveAvgPool2d(1),
nn.Linear(1,1),
nn.Linear(1,1),
nn.Sigmoid())
self.conv_su1=nn.Conv2d(3,3,1,1,0)
self.conv_su2=nn.Conv2d(3,3,1,1,0)
for m in self.modules():
if isinstance(m,nn.Conv2d):
kaiming_normal_(m.weight,0.1)
def forward(self, Ih):
x = F.relu(self.norm1(self.conv1(Ih)))
x = F.relu(self.norm2(self.conv2(x)))
x = F.relu(self.norm3(self.conv3(x)))
x = self.pre_processing(x)
I_f = x
for _ in range(8):
x = self.res1(x)
x = torch.add(x, I_f)
x1 = x
for _ in range(8):
x = self.res2(x)
x = torch.add(x, x1)
x2 = x
for _ in range(8):
x = self.res4(x)
x = torch.add(x, x2)
x3 = x
gates = self.gate(torch.cat((x1, x2, x3), dim=1))
gated_X = x1 * gates[:, [0], :, :] + x2 * gates[:, [1], :, :] + x3 * gates[:, [2], :, :]
x = F.relu(self.norm4(self.deconv3(gated_X)))
x = F.relu(self.norm5(self.deconv2(x)))
x = self.deconv1(x)
x=self.conv_su1(x)+self.conv_su2(Ih)
x=nn.LeakyReLU(0.1,inplace=False)(x)
x=x+torch.mul(x,self.layer1(x))
x = self.deconv(x)
return x