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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
class DropBlock(nn.Module):
def __init__(self, block_size: int = 5, p: float = 0.1):
super().__init__()
self.block_size = block_size
self.p = p
def calculate_gamma(self, x: Tensor) -> float:
invalid = (1 - self.p) / (self.block_size ** 2)
valid = (x.shape[-1] ** 2) / ((x.shape[-1] - self.block_size + 1) ** 2)
return invalid * valid
def forward(self, x: Tensor) -> Tensor:
N, C, H, W = x.size()
if self.training:
gamma = self.calculate_gamma(x)
mask_shape = (N, C, H - self.block_size + 1, W - self.block_size + 1)
mask = torch.bernoulli(torch.full(mask_shape, gamma, device=x.device))
mask = F.pad(mask, [self.block_size // 2] * 4, value=0)
mask_block = 1 - F.max_pool2d(
mask,
kernel_size=(self.block_size, self.block_size),
stride=(1, 1),
padding=(self.block_size // 2, self.block_size // 2),
)
x = mask_block * x * (mask_block.numel() / mask_block.sum())
return x
class double_conv(nn.Module):
def __init__(self,intc,outc):
super().__init__()
self.conv1=nn.Conv2d(intc,outc,kernel_size=3,padding=1,stride=1)
self.drop1= DropBlock(7, 0.9)
self.bn1=nn.BatchNorm2d(outc)
self.relu1=nn.ReLU()
self.conv2=nn.Conv2d(outc,outc,kernel_size=3,padding=1,stride=1)
self.drop2=DropBlock(7, 0.9)
self.bn2=nn.BatchNorm2d(outc)
self.relu2=nn.ReLU()
def forward(self,input):
x=self.relu1(self.bn1(self.drop1(self.conv1(input))))
x=self.relu2(self.bn2(self.drop2(self.conv2(x))))
return x
class upconv(nn.Module):
def __init__(self,intc,outc) -> None:
super().__init__()
self.up=nn.ConvTranspose2d(intc, outc, kernel_size=2, stride=2, padding=0)
# self.relu=nn.ReLU()
def forward(self,input):
x=self.up(input)
#x=self.relu(self.up(input))
return x
class unet(nn.Module):
def __init__(self,int,out) -> None:
'int: represent the number of image channels'
'out: number of the desired final channels'
super().__init__()
'encoder'
self.convlayer1=double_conv(int,64)
self.down1=nn.MaxPool2d((2, 2))
self.convlayer2=double_conv(64,128)
self.down2=nn.MaxPool2d((2, 2))
self.convlayer3=double_conv(128,256)
self.down3=nn.MaxPool2d((2, 2))
self.convlayer4=double_conv(256,512)
self.down4=nn.MaxPool2d((2, 2))
'bridge'
self.bridge=double_conv(512,1024)
'decoder'
self.up1=upconv(1024,512)
self.convlayer5=double_conv(1024,512)
self.up2=upconv(512,256)
self.convlayer6=double_conv(512,256)
self.up3=upconv(256,128)
self.convlayer7=double_conv(256,128)
self.up4=upconv(128,64)
self.convlayer8=double_conv(128,64)
'output'
self.outputs = nn.Conv2d(64, out, kernel_size=1, padding=0)
self.sig=nn.Sigmoid()
def forward(self,input):
'encoder'
l1=self.convlayer1(input)
d1=self.down1(l1)
l2=self.convlayer2(d1)
d2=self.down2(l2)
l3=self.convlayer3(d2)
d3=self.down3(l3)
l4=self.convlayer4(d3)
d4=self.down4(l4)
'bridge'
bridge=self.bridge(d4)
'decoder'
up1=self.up1(bridge)
up1 = torch.cat([up1, l4], axis=1)
l5=self.convlayer5(up1)
up2=self.up2(l5)
up2 = torch.cat([up2, l3], axis=1)
l6=self.convlayer6(up2)
up3=self.up3(l6)
up3= torch.cat([up3, l2], axis=1)
l7=self.convlayer7(up3)
up4=self.up4(l7)
up4 = torch.cat([up4, l1], axis=1)
l8=self.convlayer8(up4)
out=self.outputs(l8)
#out=self.sig(self.outputs(l8))
return out
class spatialAttention(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv77=nn.Conv2d(2,1,kernel_size=7,padding=3)
self.sig=nn.Sigmoid()
def forward(self,input):
x=torch.cat( (torch.max(input,1)[0].unsqueeze(1), torch.mean(input,1).unsqueeze(1)), dim=1 )
x=self.sig(self.conv77(x))
x=input*x
return x
class SAunet(nn.Module):
def __init__(self,int,out) -> None:
'int: represent the number of image channels'
'out: number of the desired final channels'
super().__init__()
'encoder'
self.convlayer1=double_conv(int,64)
self.down1=nn.MaxPool2d((2, 2))
self.convlayer2=double_conv(64,128)
self.down2=nn.MaxPool2d((2, 2))
self.convlayer3=double_conv(128,256)
self.down3=nn.MaxPool2d((2, 2))
self.convlayer4=double_conv(256,512)
self.down4=nn.MaxPool2d((2, 2))
'bridge'
self.attmodule=spatialAttention()
self.bridge1=nn.Conv2d(512,1024,kernel_size=3,stride=1,padding=1)
self.bn1=nn.BatchNorm2d(1024)
self.act1=nn.ReLU()
self.bridge2=nn.Conv2d(1024,1024,kernel_size=3,stride=1,padding=1)
self.bn2=nn.BatchNorm2d(1024)
self.act2=nn.ReLU()
'decoder'
self.up1=upconv(1024,512)
self.convlayer5=double_conv(1024,512)
self.up2=upconv(512,256)
self.convlayer6=double_conv(512,256)
self.up3=upconv(256,128)
self.convlayer7=double_conv(256,128)
self.up4=upconv(128,64)
self.convlayer8=double_conv(128,64)
'output'
self.outputs = nn.Conv2d(64, out, kernel_size=1, padding=0)
self.sig=nn.Sigmoid()
def forward(self,input):
'encoder'
l1=self.convlayer1(input)
d1=self.down1(l1)
l2=self.convlayer2(d1)
d2=self.down2(l2)
l3=self.convlayer3(d2)
d3=self.down3(l3)
l4=self.convlayer4(d3)
d4=self.down4(l4)
'bridge'
b1=self.act1(self.bn1(self.bridge1(d4)))
att=self.attmodule(b1)
b2=self.act2(self.bn2(self.bridge2(att)))
'decoder'
up1=self.up1(b2)
up1 = torch.cat([up1, l4], axis=1)
l5=self.convlayer5(up1)
up2=self.up2(l5)
up2 = torch.cat([up2, l3], axis=1)
l6=self.convlayer6(up2)
up3=self.up3(l6)
up3= torch.cat([up3, l2], axis=1)
l7=self.convlayer7(up3)
up4=self.up4(l7)
up4 = torch.cat([up4, l1], axis=1)
l8=self.convlayer8(up4)
out=self.outputs(l8)
#out=self.sig(self.outputs(l8))
return out
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