self.conv = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=7, padding=3)

class ZeroPad2d(ConstantPad2d):
 # Pads the input tensor boundaries with zero.
 def __init__(self, padding):
  super(ZeroPad2d, self).__init__(padding, 0)

class ConstantPad2d(_ConstantPadNd):
 # Pads the input tensor boundaries with a constant value.
 def __init__(self, padding, value):
  super(ConstantPad2d, self).__init__(value)
  self.padding = _quadruple(padding)

> m = nn.ReflectionPad2d(2)
> input = torch.arange(9).reshape(1, 1, 3, 3)
> input
 
(0 ,0 ,.,.) =
 0 1 2
 3 4 5
 6 7 8
[torch.FloatTensor of size (1,1,3,3)]
 
> m(input)
 
(0 ,0 ,.,.) =
 8 7 6 7 8 7 6
 5 4 3 4 5 4 3
 2 1 0 1 2 1 0
 5 4 3 4 5 4 3
 8 7 6 7 8 7 6
 5 4 3 4 5 4 3
 2 1 0 1 2 1 0

class ReflectionPad2d(_ReflectionPadNd):
 # Pads the input tensor using the reflection of the input boundary.
 
 def __init__(self, padding):
  super(ReflectionPad2d, self).__init__()
  self.padding = _quadruple(padding)

> m = nn.ReplicationPad2d(2)
> input = torch.arange(9).reshape(1, 1, 3, 3)
> input
 
(0 ,0 ,.,.) =
 0 1 2
 3 4 5
 6 7 8
[torch.FloatTensor of size (1,1,3,3)]
 
> m(input)
 
(0 ,0 ,.,.) =
 0 0 0 1 2 2 2
 0 0 0 1 2 2 2
 0 0 0 1 2 2 2
 3 3 3 4 5 5 5
 6 6 6 7 8 8 8
 6 6 6 7 8 8 8
 6 6 6 7 8 8 8
[torch.FloatTensor of size (1,1,7,7)]

class ReplicationPad2d(_ReplicationPadNd):
 # Pads the input tensor using replication of the input boundary.
 
 def __init__(self, padding):
  super(ReplicationPad2d, self).__init__()
  self.padding = _quadruple(padding)

class DEMO(nn.Module):
 
 def __init__(self):
  super(DEMO, self).__init__()
  self.pad = nn.ReflectionPad2d(1)
  self.conv = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, padding=0)
 
 def forward(self, x):
  x = self.pad(x)
  x = self.conv(x)
  return F.relu(x)