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import torch
import torch.nn as nn
import torch.nn.functional as F
class PALayer(nn.Module):
def __init__(self, channel):
super(PALayer, self).__init__()
self.pa = nn.Sequential(
nn.Conv2d(channel, channel // 8, 1, padding=0, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(channel // 8, 1, 1, padding=0, bias=True),
nn.Sigmoid()
)
def forward(self, x):
y = self.pa(x)
return x * y
class CALayer(nn.Module):
def __init__(self, channel):
super(CALayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.ca = nn.Sequential(
nn.Conv2d(channel, channel // 8, 1, padding=0, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(channel // 8, channel, 1, padding=0, bias=True),
nn.Sigmoid()
)
def forward(self, x):
y = self.avg_pool(x)
y = self.ca(y)
return x * y
class DoubleConv(nn.Module):
def __init__(self, in_channels, out_channels, norm=False, leaky=True):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels) if norm else nn.Identity(),
nn.LeakyReLU(0.2, inplace=True) if leaky else nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels) if norm else nn.Identity(),
nn.LeakyReLU(0.2, inplace=True) if leaky else nn.ReLU(inplace=True)
)
def forward(self, x):
return self.conv(x)
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels, act=True):
super(OutConv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.Sigmoid() if act else nn.Identity()
)
def forward(self, x):
return self.conv(x)
class Down(nn.Module):
"""Downscaling with maxpool then double conv"""
def __init__(self, in_channels, out_channels, norm=True, leaky=True):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_channels, out_channels, norm=norm, leaky=leaky)
)
def forward(self, x):
return self.maxpool_conv(x)
class Up(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, bilinear=True, norm=True, leaky=True):
super().__init__()
# if bilinear, use the normal convolutions to reduce the number of channels
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels, norm=norm, leaky=leaky)
else:
self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels, norm=norm, leaky=leaky)
def forward(self, x1, x2):
x1 = self.up(x1)
# input is CHW
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
x = torch.cat([x2, x1], dim=1)
return self.conv(x)
class AttentiveDown(nn.Module):
def __init__(self, in_channels, out_channels, norm=False, leaky=True):
super().__init__()
self.down = Down(in_channels, out_channels, norm=norm, leaky=leaky)
self.attention = nn.Sequential(
CALayer(out_channels),
PALayer(out_channels)
)
def forward(self, x):
return self.attention(self.down(x))
class AttentiveUp(nn.Module):
def __init__(self, in_channels, out_channels, bilinear=True, norm=False, leaky=True):
super().__init__()
self.up = Up(in_channels, out_channels, bilinear, norm=norm, leaky=leaky)
self.attention = nn.Sequential(
CALayer(out_channels),
PALayer(out_channels)
)
def forward(self, x1, x2):
return self.attention(self.up(x1, x2))
class AttentiveDoubleConv(nn.Module):
def __init__(self, in_channels, out_channels, norm=False, leaky=False):
super().__init__()
self.conv = DoubleConv(in_channels, out_channels, norm=norm, leaky=leaky)
self.attention = nn.Sequential(
CALayer(out_channels),
PALayer(out_channels)
)
def forward(self, x):
return self.attention(self.conv(x))
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