GCN模块的实现比较简单，在giuhub上看到两种实现，轻微不同
实现一：https://github.com/ycszen/pytorch-segmentation/blob/master/gcn.py
class GCN(nn.Module):
def __init__(self, inplanes, planes, ks=7):
super(GCN, self).__init__()
self.conv_l1 = nn.Conv2d(inplanes, planes, kernel_size=(ks, 1),
padding=(ks/2, 0))
self.conv_l2 = nn.Conv2d(planes, planes, kernel_size=(1, ks),
padding=(0, ks/2))
self.conv_r1 = nn.Conv2d(inplanes, planes, kernel_size=(1, ks),
padding=(0, ks/2))
self.conv_r2 = nn.Conv2d(planes, planes, kernel_size=(ks, 1),
padding=(ks/2, 0))
def forward(self, x):
x_l = self.conv_l1(x)
x_l = self.conv_l2(x_l)
x_r = self.conv_r1(x)
x_r = self.conv_r2(x_r)
x = x_l + x_r
return x
实现二：https://github.com/ogvalt/large_kernel_matters/blob/master/scripts/model.py
class GCN(nn.Module):
def __init__(self, inchannels, channels=21, k=3):
super(GCN, self).__init__()
self.conv_l1 = Conv2D(in_channels=inchannels, out_channels=channels, kernel_size=(k, 1), padding='same')
self.conv_l2 = Conv2D(in_channels=channels, out_channels=channels, kernel_size=(1, k), padding='same')
self.conv_r1 = Conv2D(in_channels=inchannels, out_channels=channels, kernel_size=(1, k), padding='same')
self.conv_r2 = Conv2D(in_channels=channels, out_channels=channels, kernel_size=(k, 1), padding='same')
def forward(self, x):
x1 = self.conv_l1(x)
x1 = self.conv_l2(x1)
x2 = self.conv_r1(x)
x2 = self.conv_r2(x2)
out = x1 + x2
return out
两种实现不同之处在padding的方式，一种是设定值，一种是自动的。