oneflow.nn.ReplicationPad1d¶
-
class
oneflow.nn.
ReplicationPad1d
(padding)¶ Pads the input tensor using replication of the input boundary.
The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.ReplicationPad1d.html.
For N-dimensional padding, use
oneflow.nn.functional.pad()
.- Parameters
padding (int, tuple) – the size of the padding. If is int, uses the same padding in all boundaries. If a 2-tuple, uses (\(\text{padding_left}\), \(\text{padding_right}\))
- Shape:
Input: \((C, W_{in})\) or \((N, C, W_{in})\).
Output: \((C, W_{out})\) or \((N, C, W_{out})\), where
\(W_{out} = W_{in} + \text{padding_left} + \text{padding_right}\)
For example:
>>> import numpy as np >>> import oneflow as flow >>> m = flow.nn.ReplicationPad1d((2, 2)) >>> input = flow.tensor(np.arange(18).reshape((2, 3, 3)).astype(np.float32)) >>> out = m(input) >>> out tensor([[[ 0., 0., 0., 1., 2., 2., 2.], [ 3., 3., 3., 4., 5., 5., 5.], [ 6., 6., 6., 7., 8., 8., 8.]], [[ 9., 9., 9., 10., 11., 11., 11.], [12., 12., 12., 13., 14., 14., 14.], [15., 15., 15., 16., 17., 17., 17.]]], dtype=oneflow.float32)