Dual Attention Network for Scene
2019-01-08 本文已影响38人
yanghedada
这是一篇arXiv上的文章，也是关于注意力机制的工作，不过是在图像分割使用通道注意力机制。
paper
这是一篇使用两种注意力机制的文章：
-
通道注意力机制
文中提到通道其实在分类时会起到不同作用，而使用通道注意力机制可以挖掘不用之间的相互依赖性 -
空间注意力机制
空间注意力机制就是使模型更加关注图像中实体区域的贡献，减弱背景的影响。
主干网络以dilated FCN为主要结构：
在网络上使用如下两个注意力机制：
发现有大神进行了PPT讲解
一下类容来自冯爽朗。
以上内容都是截图。。。主要是为了偷懒。。。
源码：github
本人比较关注注意力机制。。。
在源码的encoding/nn/attention.py中发现注意力机制模块：
如下：
class PAM_Module(Module):
""" Position attention module"""
#Ref from SAGAN
def __init__(self, in_dim):
super(PAM_Module, self).__init__()
self.chanel_in = in_dim
self.query_conv = Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
self.key_conv = Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
self.value_conv = Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
self.gamma = Parameter(torch.zeros(1))
self.softmax = Softmax(dim=-1)
def forward(self, x):
"""
inputs :
x : input feature maps( B X C X H X W)
returns :
out : attention value + input feature
attention: B X (HxW) X (HxW)
"""
m_batchsize, C, height, width = x.size()
proj_query = self.query_conv(x).view(m_batchsize, -1, width*height).permute(0, 2, 1) # reshape to (m_batchsize, width*height, c)
proj_key = self.key_conv(x).view(m_batchsize, -1, width*height)
energy = torch.bmm(proj_query, proj_key) # 矩阵乘法
attention = self.softmax(energy) # 添加非线性函数
proj_value = self.value_conv(x).view(m_batchsize, -1, width*height)
out = torch.bmm(proj_value, attention.permute(0, 2, 1))
out = out.view(m_batchsize, C, height, width) # reshape到原图
out = self.gamma*out + x # 相加
return out
class CAM_Module(Module):
""" Channel attention module"""
def __init__(self, in_dim):
super(CAM_Module, self).__init__()
self.chanel_in = in_dim
self.gamma = Parameter(torch.zeros(1))
self.softmax = Softmax(dim=-1)
def forward(self,x):
"""
inputs :
x : input feature maps( B X C X H X W)
returns :
out : attention value + input feature
attention: B X C X C
"""
m_batchsize, C, height, width = x.size()
proj_query = x.view(m_batchsize, C, -1)
proj_key = x.view(m_batchsize, C, -1).permute(0, 2, 1)
energy = torch.bmm(proj_query, proj_key)
energy_new = torch.max(energy, -1, keepdim=True)[0].expand_as(energy)-energy
attention = self.softmax(energy_new)
proj_value = x.view(m_batchsize, C, -1)
out = torch.bmm(attention, proj_value)
out = out.view(m_batchsize, C, height, width)
out = self.gamma*out + x
return out
两个模块的融合在encoding/models/danet.py
中如下：
class DANetHead(nn.Module):
def __init__(self, in_channels, out_channels, norm_layer):
super(DANetHead, self).__init__()
inter_channels = in_channels // 4
self.conv5a = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False),
norm_layer(inter_channels),
nn.ReLU())
self.conv5c = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False),
norm_layer(inter_channels),
nn.ReLU())
self.sa = PAM_Module(inter_channels)
self.sc = CAM_Module(inter_channels)
self.conv51 = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, padding=1, bias=False),
norm_layer(inter_channels),
nn.ReLU())
self.conv52 = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, padding=1, bias=False),
norm_layer(inter_channels),
nn.ReLU())
self.conv6 = nn.Sequential(nn.Dropout2d(0.1, False), nn.Conv2d(512, out_channels, 1))
self.conv7 = nn.Sequential(nn.Dropout2d(0.1, False), nn.Conv2d(512, out_channels, 1))
self.conv8 = nn.Sequential(nn.Dropout2d(0.1, False), nn.Conv2d(512, out_channels, 1))
def forward(self, x):
feat1 = self.conv5a(x)
sa_feat = self.sa(feat1)
sa_conv = self.conv51(sa_feat)
sa_output = self.conv6(sa_conv)
feat2 = self.conv5c(x)
sc_feat = self.sc(feat2)
sc_conv = self.conv52(sc_feat)
sc_output = self.conv7(sc_conv)
feat_sum = sa_conv+sc_conv
sasc_output = self.conv8(feat_sum)
output = [sasc_output]
output.append(sa_output)
output.append(sc_output)
return tuple(output)
上面就是模型的融合，可以看到就是把两个模型进行逐元素相加操作。。
参考:
冯爽朗
