ConstantPad3d

class torch.nn.ConstantPad3d(padding: Union[int, Tuple[int, int, int, int, int, int]], value: float)

Pads the input tensor boundaries with a constant value.

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 ($\text{padding\_left}$ , $\text{padding\_right}$ , $\text{padding\_top}$ , $\text{padding\_bottom}$ , $\text{padding\_front}$ , $\text{padding\_back}$ )

Shape:

• Input: $(N, C, D_{in}, H_{in}, W_{in})$

• Output: $(N, C, D_{out}, H_{out}, W_{out})$ where

  $D_{out} = D_{in} + \text{padding\_front} + \text{padding\_back}$

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

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

Examples:

>>> m = nn.ConstantPad3d(3, 3.5)
>>> input = torch.randn(16, 3, 10, 20, 30)
>>> output = m(input)
>>> # using different paddings for different sides
>>> m = nn.ConstantPad3d((3, 3, 6, 6, 0, 1), 3.5)
>>> output = m(input)