ReflectionPad3d¶
- class torch.nn.ReflectionPad3d(padding)[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 (\(\text{padding\_left}\), \(\text{padding\_right}\), \(\text{padding\_top}\), \(\text{padding\_bottom}\), \(\text{padding\_front}\), \(\text{padding\_back}\))
- Shape:
Input: \((N, C, D_{in}, H_{in}, W_{in})\) or \((C, D_{in}, H_{in}, W_{in})\).
Output: \((N, C, D_{out}, H_{out}, W_{out})\) or \((C, D_{out}, H_{out}, W_{out})\), 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.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.]]]]])
