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class torch.nn.ZeroPad2d(padding: Union[T, Tuple[T, T, T, T]])[source]

Pads the input tensor boundaries with zero.

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

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

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

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

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

Examples:

>>> m = nn.ZeroPad2d(2)
>>> input = torch.randn(1, 1, 3, 3)
>>> input
tensor([[[[-0.1678, -0.4418,  1.9466],
[ 0.9604, -0.4219, -0.5241],
[-0.9162, -0.5436, -0.6446]]]])
>>> m(input)
tensor([[[[ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000],
[ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000],
[ 0.0000,  0.0000, -0.1678, -0.4418,  1.9466,  0.0000,  0.0000],
[ 0.0000,  0.0000,  0.9604, -0.4219, -0.5241,  0.0000,  0.0000],
[ 0.0000,  0.0000, -0.9162, -0.5436, -0.6446,  0.0000,  0.0000],
[ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000],
[ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000]]]])
>>> # using different paddings for different sides
>>> m = nn.ZeroPad2d((1, 1, 2, 0))
>>> m(input)
tensor([[[[ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000],
[ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000],
[ 0.0000, -0.1678, -0.4418,  1.9466,  0.0000],
[ 0.0000,  0.9604, -0.4219, -0.5241,  0.0000],
[ 0.0000, -0.9162, -0.5436, -0.6446,  0.0000]]]])


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