ReflectionPad3d — PyTorch 2.7 documentation (original) (raw)

class torch.nn.ReflectionPad3d(padding)[source][source]¶

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 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.ReflectionPad3d(1) input = torch.arange(8, dtype=torch.float).reshape(1, 1, 2, 2, 2) m(input) tensor([[[[[7., 6., 7., 6.], [5., 4., 5., 4.], [7., 6., 7., 6.], [5., 4., 5., 4.]], [[3., 2., 3., 2.], [1., 0., 1., 0.], [3., 2., 3., 2.], [1., 0., 1., 0.]], [[7., 6., 7., 6.], [5., 4., 5., 4.], [7., 6., 7., 6.], [5., 4., 5., 4.]], [[3., 2., 3., 2.], [1., 0., 1., 0.], [3., 2., 3., 2.], [1., 0., 1., 0.]]]]])