mindspore.nn.ReplicationPad1d

class mindspore.nn.ReplicationPad1d(padding)[source]

Pad on W dimension of input x according to padding.

Parameters

padding (union[int, tuple]) –

The padding size to pad the last dimension of x .

  • If padding is an integer, all directions will be padded with the same size.

  • If padding is a tuple, uses \((pad_{left}, pad_{right})\) to pad.

Inputs:

  • x (Tensor) - 2D or 3D, shape: \((C, W_{in})\) or \((N, C, W_{in})\).

Outputs:

Tensor, after padding. Shape: \((C, W_{out})\) or \((N, C, W_{out})\), where \(W_{out} = W_{in} + pad_{left} + pad_{right}\)

Raises

  • TypeError – If padding is neither a tuple nor an int.

  • TypeError – If there is an element in padding that is not int.

  • ValueError – If padding is tuple and the length of padding is not divisible by 2.

  • ValueError – If padding is tuple and there is a dimension mismatch between the padding and the tensor.

Supported Platforms:

GPU

Examples

>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore.nn import ReplicationPad1d
>>> pad1d = ReplicationPad1d(2)
>>> input = Tensor(np.arange(0, 8).reshape(1, 2, 4), mindspore.float32)
>>> print(input)
[[[0. 1. 2. 3.]
  [4. 5. 6. 7.]]]
>>> out = pad1d(input)
>>> print(out)
[[[0. 0. 0. 1. 2. 3. 3. 3.]
  [4. 4. 4. 5. 6. 7. 7. 7.]]]
>>> pad1d = ReplicationPad1d((3, 1))
>>> out = pad1d(input)
>>> print(out)
[[[0. 0. 0. 0. 1. 2. 3. 3.]
  [4. 4. 4. 4. 5. 6. 7. 7.]]]