def forward(self, inputs):
output = inputs
#print(output.shape)
x_0 = inputs
conv = getattr(self, 'conv1')
x_1 = conv(x_0)
conv = getattr(self, 'conv2')
x_2 = conv(x_1)
conv = getattr(self, 'conv3')
x_3 = conv(x_2)
conv = getattr(self, 'conv4')
x_4 = conv(x_3)
return x_0 + x_1 + x_2 + x_3 + x_4
def forward(self, inputs):
x = inputs
for i in range(1, self.n + 1):
conv = getattr(self, 'conv%d' % i)
x = conv(x)
return x
def forward(self, inputs):
inputs = self.keep_dim(inputs)
x = inputs
for i in range(1, self.n + 1):
conv = getattr(self, 'conv%d' % i)
x = conv(x)
out = self.relu(x + inputs)
return out
def conv_nbn(in_channels, out_channels, conv, *args, **kwargs):
return nn.Sequential(conv(in_channels, out_channels, *args, **kwargs))
def conv_bn(in_channels, out_channels, conv, *args, **kwargs):
return nn.Sequential(conv(in_channels, out_channels, *args, **kwargs),
nn.BatchNorm2d(out_channels))