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imp_subnet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import modules.module_util as mutil
import modules.Unet_common as common
import functools
import config as c
from non_local_dot_product import NONLocalBlock2D
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y.expand_as(x)
# Dense connection
class ResidualDenseBlock_out(nn.Module):
def __init__(self, input, output, bias=True):
super(ResidualDenseBlock_out, self).__init__()
self.conv1 = nn.Conv2d(input, 32, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(input + 32, 32, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(input + 2 * 32, 32, 3, 1, 1, bias=bias)
self.conv4 = nn.Conv2d(input + 3 * 32, 32, 3, 1, 1, bias=bias)
self.conv5 = nn.Conv2d(input + 4 * 32, output, 3, 1, 1, bias=bias)
self.lrelu = nn.LeakyReLU(inplace=True)
self.senet = SELayer(channel=input + 4 * 32)
mutil.initialize_weights([self.conv5], 0.)
def forward(self, x):
x1 = self.lrelu(self.conv1(x))
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
x = torch.cat((x, x1, x2, x3, x4), 1)
x = self.senet(x)
x5 = self.conv5(x)
return x5
class ResidualDenseBlock_out2(nn.Module):
def __init__(self, input, output, bias=True):
super(ResidualDenseBlock_out2, self).__init__()
self.conv1 = nn.Conv2d(input, 32, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(input + 32, 32, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(input + 2 * 32, 32, 3, 1, 1, bias=bias)
self.conv4 = nn.Conv2d(input + 3 * 32, 32, 3, 1, 1, bias=bias)
self.conv5 = nn.Conv2d(input + 4 * 32, output, 3, 1, 1, bias=bias)
self.lrelu = nn.LeakyReLU(inplace=True)
mutil.initialize_weights([self.conv5], 0.)
def forward(self, x):
x1 = self.lrelu(self.conv1(x))
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
x = torch.cat((x, x1, x2, x3, x4), 1)
x5 = self.conv5(x)
return x5
# Dense connection
class PrepareBlock(nn.Module):
def __init__(self, input, bias=True):
super(PrepareBlock, self).__init__()
self.conv1 = nn.Conv2d(input, 64, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(input + 64, 64, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(input + 2 * 64, 64, 3, 1, 1, bias=bias)
self.lrelu = nn.LeakyReLU(inplace=True)
self.senet = SELayer(channel=input + 2 * 64)
mutil.initialize_weights([self.conv3], 0.)
def forward(self, x):
x1 = self.lrelu(self.conv1(x))
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
x = torch.cat((x, x1, x2), 1)
x = self.senet(x)
x3 = self.conv3(x)
return x3
class ImpMapBlock(nn.Module):
def __init__(self):
super(ImpMapBlock, self).__init__()
self.rrdb = ResidualDenseBlock_out(input=64 + 64 + 64, output=3)
self.s_cover = PrepareBlock(input=3)
self.s_secret = PrepareBlock(input=3)
self.s_steg = PrepareBlock(input=3)
self.nonlocal_block = NONLocalBlock2D(3, sub_sample=True, bn_layer=True)
def forward(self, cover, secret, steg):
x_cover = self.s_cover(cover)
x_steg = self.s_steg(steg)
x_secret = self.s_secret(secret)
x = torch.cat((x_cover, x_steg), 1)
x = torch.cat((x, x_secret), 1)
x = self.rrdb(x)
return x
class ImpMapBlock_Nonlocal(nn.Module):
def __init__(self):
super(ImpMapBlock_Nonlocal, self).__init__()
self.rrdb = ResidualDenseBlock_out2(input=64 + 64 + 64, output=3)
self.s_cover = PrepareBlock(input=3)
self.s_secret = PrepareBlock(input=3)
self.s_steg = PrepareBlock(input=3)
self.nonlocal_block = NONLocalBlock2D(3, sub_sample=True, bn_layer=True)
def forward(self, cover, secret, steg):
x_cover = self.s_cover(cover)
x_steg = self.s_steg(steg)
x_secret = self.s_secret(secret)
x = torch.cat((x_cover, x_steg), 1)
x = torch.cat((x, x_secret), 1)
x = self.rrdb(x)
x = self.nonlocal_block(x)
return x