ReplicationPad3d — PyTorch 2.7 documentation (original) (raw)

class torch.nn.ReplicationPad3d(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 6-tuple, uses (padding_left\text{padding\_left}, padding_right\text{padding\_right},padding_top\text{padding\_top}, padding_bottom\text{padding\_bottom},padding_front\text{padding\_front}, padding_back\text{padding\_back})

Shape:

• Input: (N,C,Din,Hin,Win)(N, C, D_{in}, H_{in}, W_{in}) or (C,Din,Hin,Win)(C, D_{in}, H_{in}, W_{in}).
• Output: (N,C,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out}) or (C,Dout,Hout,Wout)(C, D_{out}, H_{out}, W_{out}), where
  Dout=Din+padding_front+padding_backD_{out} = D_{in} + \text{padding\_front} + \text{padding\_back}
  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.ReplicationPad3d(3) input = torch.randn(16, 3, 8, 320, 480) output = m(input)

using different paddings for different sides

m = nn.ReplicationPad3d((3, 3, 6, 6, 1, 1)) output = m(input)