ReflectionPad2d

class torch.nn.ReflectionPad2d(padding)

Pads the input tensor using the reflection of the input boundary.

For N-dimensional padding, use torch.nn.functional.pad().

Parameters:

padding (int, tuple) – the size of the padding. If is int, uses the same padding in all boundaries. If a 4-tuple, uses (\(\text{padding\_left}\), \(\text{padding\_right}\), \(\text{padding\_top}\), \(\text{padding\_bottom}\)) Note that padding size should be less than the corresponding input dimension.

Shape:

• Input: \((N, C, H_{in}, W_{in})\) or \((C, H_{in}, W_{in})\).

• Output: \((N, C, H_{out}, W_{out})\) or \((C, H_{out}, W_{out})\) where

  \(H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}\)

  \(W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}\)

Examples:

>>> m = nn.ReflectionPad2d(2)
>>> input = torch.arange(9, dtype=torch.float).reshape(1, 1, 3, 3)
>>> input
tensor([[[[0., 1., 2.],
          [3., 4., 5.],
          [6., 7., 8.]]]])
>>> m(input)
tensor([[[[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.]]]])
>>> # using different paddings for different sides
>>> m = nn.ReflectionPad2d((1, 1, 2, 0))
>>> m(input)
tensor([[[[7., 6., 7., 8., 7.],
          [4., 3., 4., 5., 4.],
          [1., 0., 1., 2., 1.],
          [4., 3., 4., 5., 4.],
          [7., 6., 7., 8., 7.]]]])