ReplicationPad2d — PyTorch 2.7 documentation (original) (raw)

class torch.nn.ReplicationPad2d(padding)[source][source]¶

Pads the input tensor using replication 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 (padding_left\text{padding\_left},padding_right\text{padding\_right}, padding_top\text{padding\_top}, padding_bottom\text{padding\_bottom})

Shape:

• Input: (N,C,Hin,Win)(N, C, H_{in}, W_{in}) or (C,Hin,Win)(C, H_{in}, W_{in}).
• Output: (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) or (C,Hout,Wout)(C, H_{out}, W_{out}), where
  Hout=Hin+padding_top+padding_bottomH_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}
  Wout=Win+padding_left+padding_rightW_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}

Examples:

m = nn.ReplicationPad2d(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([[[[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.]]]])

using different paddings for different sides

m = nn.ReplicationPad2d((1, 1, 2, 0)) m(input) tensor([[[[0., 0., 1., 2., 2.], [0., 0., 1., 2., 2.], [0., 0., 1., 2., 2.], [3., 3., 4., 5., 5.], [6., 6., 7., 8., 8.]]]])