class ChannelAttentionModule():
def __init__(self, c1, reduction=16):
super(ChannelAttentionModule, self).__init__()
mid_channel = c1 // reduction
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.shared_MLP = (
(in_features=c1, out_features=mid_channel),
(),
(in_features=mid_channel, out_features=c1)
)
= ()
#=SiLU()
def forward(self, x):
avgout = self.shared_MLP(self.avg_pool(x).view((0),-1)).unsqueeze(2).unsqueeze(3)
maxout = self.shared_MLP(self.max_pool(x).view((0),-1)).unsqueeze(2).unsqueeze(3)
return (avgout + maxout)
class SpatialAttentionModule():
def __init__(self):
super(SpatialAttentionModule, self).__init__()
self.conv2d = nn.Conv2d(in_channels=2, out_channels=1, kernel_size=7, stride=1, padding=3)
#=SiLU()
= ()
def forward(self, x):
avgout = (x, dim=1, keepdim=True)
maxout, _ = (x, dim=1, keepdim=True)
out = ([avgout, maxout], dim=1)
out = (self.conv2d(out))
return out
class CBAM():
def __init__(self, c1,c2):
super(CBAM, self).__init__()
self.channel_attention = ChannelAttentionModule(c1)
self.spatial_attention = SpatialAttentionModule()
def forward(self, x):
out = self.channel_attention(x) * x
out = self.spatial_attention(out) * out
return out
