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attention_R2_Unet.py
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attention_R2_Unet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary
import os
from torch.utils.tensorboard import SummaryWriter
path = os.getcwd()+'\\runs'
writer = SummaryWriter(path+'\\experiment')
class up_conv(nn.Module):
def __init__(self,ch_in,ch_out):
super(up_conv,self).__init__()
self.up = nn.Sequential(
nn.Upsample(scale_factor=2),
nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True)
)
def forward(self,x):
x = self.up(x)
return x
class Recurrent_block(nn.Module):
def __init__(self, ch_out, t=2):
super(Recurrent_block, self).__init__()
self.t = t
self.ch_out = ch_out
self.conv = nn.Sequential(
nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True)
)
def forward(self, x):
for i in range(self.t):
if i == 0:
x1 = self.conv(x)
x1 = self.conv(x + x1)
return x1
class RRCNN_block(nn.Module):
def __init__(self,ch_in,ch_out,t=2):
super(RRCNN_block,self).__init__()
self.RCNN = nn.Sequential(
Recurrent_block(ch_out,t=t),
Recurrent_block(ch_out,t=t)
)
self.Conv_1x1 = nn.Conv2d(ch_in,ch_out,kernel_size=1,stride=1,padding=0)
def forward(self,x):
x = self.Conv_1x1(x)
x1 = self.RCNN(x)
return x+x1
class Attention_block(nn.Module):
def __init__(self, F_g, F_l, F_int):
super(Attention_block, self).__init__()
self.W_g = nn.Sequential(
nn.Conv2d(F_g, F_int, kernel_size=1, stride=1, padding=0, bias=True),
nn.BatchNorm2d(F_int)
)
self.W_x = nn.Sequential(
nn.Conv2d(F_l, F_int, kernel_size=1, stride=1, padding=0, bias=True),
nn.BatchNorm2d(F_int)
)
self.psi = nn.Sequential(
nn.Conv2d(F_int, 1, kernel_size=1, stride=1, padding=0, bias=True),
nn.BatchNorm2d(1),
nn.Sigmoid()
)
self.relu = nn.ReLU(inplace=True)
def forward(self, g, x):
# 下采样的gating signal 卷积
g1 = self.W_g(g)
# 上采样的 l 卷积
x1 = self.W_x(x)
# concat + relu
psi = self.relu(g1 + x1)
# channel 减为1，并Sigmoid,得到权重矩阵
psi = self.psi(psi)
# 返回加权的 x
return x * psi
y=torch.rand(1,512,224,224)
x = torch.rand(1, 512, 224, 224)
Atten=Attention_block(512,512,256)
print(Atten(x,y).shape)
class Att_R2U_Net(nn.Module):
#img_ch 表示输入图片的channel数 而output_ch表示输出图片的channel数
def __init__(self, img_ch=1, output_ch=1):
super(Att_R2U_Net, self).__init__()
times = 2 # 循环模块的次数
self.Maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
self.Upsample = nn.Upsample(scale_factor=2)
self.channel_1 = 8 ## R2U-net activation maps in first layer
self.channel_2 = 2 * self.channel_1
self.channel_3 = 2 * self.channel_2
self.channel_4 = 2 * self.channel_3
self.channel_5 = 2 * self.channel_4
self.channels = [self.channel_1, self.channel_2, self.channel_3, self.channel_4, self.channel_5]
self.RRCNN1 = RRCNN_block(ch_in=img_ch, ch_out=self.channel_1, t=times)#1-64
self.RRCNN2 = RRCNN_block(ch_in=self.channel_1, ch_out=self.channel_2, t=times)#64-128
self.RRCNN3 = RRCNN_block(ch_in=self.channel_2, ch_out=self.channel_3, t=times)#128-256
self.RRCNN4 = RRCNN_block(ch_in=self.channel_3, ch_out=self.channel_4, t=times)#256-512
self.RRCNN5 = RRCNN_block(ch_in=self.channel_4, ch_out=self.channel_5, t=times)#512-1024
self.Up5 = up_conv(ch_in=self.channel_5, ch_out=self.channel_4)#1024-512
self.Att5=Attention_block(self.channel_4,self.channel_4,self.channel_3)#512-512的变化
self.Up_RRCNN5 = RRCNN_block(ch_in=self.channel_5, ch_out=self.channel_4, t=times)#cat之后1024-512
self.Up4 = up_conv(ch_in=self.channel_4, ch_out=self.channel_3)#512-256
self.Att4 = Attention_block(self.channel_3, self.channel_3, self.channel_2) # 256-256的变化
self.Up_RRCNN4 = RRCNN_block(ch_in=self.channel_4, ch_out=self.channel_3, t=times)#cat之后512-256
self.Up3 = up_conv(ch_in=self.channel_3, ch_out=self.channel_2)#256-128
self.Att3= Attention_block(self.channel_2, self.channel_2, self.channel_1) # 128-128的变化
self.Up_RRCNN3 = RRCNN_block(ch_in=self.channel_3, ch_out=self.channel_2, t=times)#cat之后256-128
self.Up2 = up_conv(ch_in=self.channel_2, ch_out=self.channel_1)
self.Att2 = Attention_block(self.channel_1, self.channel_1, int(0.5*self.channel_1))#64-64 32为self.channel_1的一半
self.Up_RRCNN2 = RRCNN_block(ch_in=self.channel_2, ch_out=self.channel_1, t=times)
self.Conv_1x1 = nn.Conv2d(self.channel_1, output_ch, kernel_size=1, stride=1, padding=0)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
# encoding path
x1 = self.RRCNN1(x)
x2 = self.Maxpool(x1)
x2 = self.RRCNN2(x2)
x3 = self.Maxpool(x2)
x3 = self.RRCNN3(x3)
x4 = self.Maxpool(x3)
x4 = self.RRCNN4(x4)
x5 = self.Maxpool(x4)
x5 = self.RRCNN5(x5)
# decoding + concat path
d5 = self.Up5(x5)
d5=self.Att5(g=d5,x=x4)
d5 = torch.cat((x4, d5), dim=1)#1024*28*28
d5 = self.Up_RRCNN5(d5)#512*28*28
d4 = self.Up4(d5)#256*56*56
x3 = self.Att4(g=d4, x=x3)#256*56*56
d4 = torch.cat((x3, d4), dim=1)#512*56*56
d4 = self.Up_RRCNN4(d4)#256*56*56
d3 = self.Up3(d4) # 128*112*112
x2 = self.Att3(g=d3, x=x2) # 128*112*112
d3 = torch.cat((x2, d3), dim=1) # 256*112*112
d3 = self.Up_RRCNN3(d3) # 128*112*112
d2 = self.Up2(d3)#64*224*224
x1 = self.Att2(g=d2, x=x1) # 64*224*224
d2 = torch.cat((x1, d2), dim=1)#128*224*224
d2 = self.Up_RRCNN2(d2)# 64*224*224
d1 = self.Conv_1x1(d2)
d1 = self.sigmoid(d1)
return d1
model=Att_R2U_Net()
# x=torch.rand(1, 1, 224, 224)
# print(model(x).shape)
# ch=1
# h=224
# w=224
# summary(model, input_size=(ch, h, w),device='cpu')
dummy_input = torch.rand(10, 1, 224, 224)
with SummaryWriter(comment='Unet') as w:
w.add_graph(model, (dummy_input, ))
#tensorboard --logdir=D:\opencv-python\条纹投影\Unet_test\runs\Feb02_11-32-31_LAPTOP-85DL9FARUnet