Attention U-Net: 向U-Net引入注意力机制.
本文作者设计了Attention Unet，通过引入Attention gate模块，将注意力机制集成到Unet的跳跃连接和上采样模块中，实现空间上的注意力机制，来抑制图像中的无关信息，突出局部的重要特征。
Attention gate模块接收跳跃连接的输出和下一层的输出，通过空间注意力机制对特征进行重新加权。
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):
g1 = self.W_g(g)
x1 = self.W_x(x)
psi = self.relu(g1 + x1)
psi = self.psi(psi)
return x * torch.sigmoid(psi)
Attention Unet的完整构建如下：
class conv_block(nn.Module):
def __init__(self, ch_in, ch_out):
super(conv_block, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(ch_in,
ch_out,
kernel_size=3,
stride=1,
padding=1,
bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True),
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):
x = self.conv(x)
return x
class up_conv(nn.Module):
def __init__(self, ch_in, ch_out, convTranspose=True):
super(up_conv, self).__init__()
if convTranspose:
self.up = nn.ConvTranspose2d(in_channels=ch_in, out_channels=ch_in,kernel_size=4,stride=2, padding=1)
else:
self.up = nn.Upsample(scale_factor=2)
self.Conv = nn.Sequential(
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)
x = self.Conv(x)
return x
class single_conv(nn.Module):
def __init__(self, ch_in, ch_out):
super(single_conv, self).__init__()
self.conv = nn.Sequential(
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.conv(x)
return x
class AttU_Net(nn.Module):
"""
in_channel: input image channels
num_classes: output class number
channel_list: a channel list for adjust the model size
convTranspose: 是否使用反卷积上采样。True: use nn.convTranspose Flase: use nn.Upsample
"""
def __init__(self,
in_channel=3,
num_classes=1,
channel_list=[64, 128, 256, 512, 1024],
convTranspose=True):
super(AttU_Net, self).__init__()
self.Maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
self.Conv1 = conv_block(ch_in=in_channel, ch_out=channel_list[0])
self.Conv2 = conv_block(ch_in=channel_list[0], ch_out=channel_list[1])
self.Conv3 = conv_block(ch_in=channel_list[1], ch_out=channel_list[2])
self.Conv4 = conv_block(ch_in=channel_list[2], ch_out=channel_list[3])
self.Conv5 = conv_block(ch_in=channel_list[3], ch_out=channel_list[4])
self.Up5 = up_conv(ch_in=channel_list[4], ch_out=channel_list[3], convTranspose=convTranspose)
self.Att5 = Attention_block(F_g=channel_list[3],
F_l=channel_list[3],
F_int=channel_list[2])
self.Up_conv5 = conv_block(ch_in=channel_list[4],
ch_out=channel_list[3])
self.Up4 = up_conv(ch_in=channel_list[3], ch_out=channel_list[2], convTranspose=convTranspose)
self.Att4 = Attention_block(F_g=channel_list[2],
F_l=channel_list[2],
F_int=channel_list[1])
self.Up_conv4 = conv_block(ch_in=channel_list[3],
ch_out=channel_list[2])
self.Up3 = up_conv(ch_in=channel_list[2], ch_out=channel_list[1], convTranspose=convTranspose)
self.Att3 = Attention_block(F_g=channel_list[1],
F_l=channel_list[1],
F_int=64)
self.Up_conv3 = conv_block(ch_in=channel_list[2],
ch_out=channel_list[1])
self.Up2 = up_conv(ch_in=channel_list[1], ch_out=channel_list[0], convTranspose=convTranspose)
self.Att2 = Attention_block(F_g=channel_list[0],
F_l=channel_list[0],
F_int=channel_list[0] // 2)
self.Up_conv2 = conv_block(ch_in=channel_list[1],
ch_out=channel_list[0])
self.Conv_1x1 = nn.Conv2d(channel_list[0],
num_classes,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x):
# encoder
x1 = self.Conv1(x)
x2 = self.Maxpool(x1)
x2 = self.Conv2(x2)
x3 = self.Maxpool(x2)
x3 = self.Conv3(x3)
x4 = self.Maxpool(x3)
x4 = self.Conv4(x4)
x5 = self.Maxpool(x4)
x5 = self.Conv5(x5)
# decoder
d5 = self.Up5(x5)
x4 = self.Att5(g=d5, x=x4)
d5 = torch.cat((x4, d5), dim=1)
d5 = self.Up_conv5(d5)
d4 = self.Up4(d5)
x3 = self.Att4(g=d4, x=x3)
d4 = torch.cat((x3, d4), dim=1)
d4 = self.Up_conv4(d4)
d3 = self.Up3(d4)
x2 = self.Att3(g=d3, x=x2)
d3 = torch.cat((x2, d3), dim=1)
d3 = self.Up_conv3(d3)
d2 = self.Up2(d3)
x1 = self.Att2(g=d2, x=x1)
d2 = torch.cat((x1, d2), dim=1)
d2 = self.Up_conv2(d2)
d1 = self.Conv_1x1(d2)
return d1
