mindspore.nn.ReplicationPad3d

class mindspore.nn.ReplicationPad3d(padding)[source]

Pad on DHW dimension of input x according to padding.

Parameters

padding (union[int, tuple]) –

The padding size to pad the last three dimension of x .

If padding is an integer, all directions will be padded with the same size.
If padding is a tuple, uses \((pad_{left}, pad_{right}, pad_{up}, pad_{down}, pad_{front}, pad_{back})\) to pad.

Inputs:

x (Tensor) - 4D or 5D, shape: \((C, D_{in}, H_{in}, W_{in})\) or \((N, C, D_{in}, H_{in}, W_{in})\).

Outputs:

Tensor, after padding, shape: \((C, D_{out}, H_{out}, W_{out})\) or \((N, C, D_{out}, H_{out}, W_{out})\), where \(D_{out} = D_{in} + pad_{front} + pad_{back}\), \(H_{out} = H_{in} + pad_{up} + pad_{down}\), \(W_{out} = W_{in} + pad_{left} + pad_{right}\).

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 as ms
>>> pad3d = ms.nn.ReplicationPad3d(1)
>>> input = ms.Tensor(np.arange(0, 9).reshape(1, 1, 1, 3, 3), ms.float32)
>>> out = pad3d(input)
>>> print(out)
[[[[[0. 0. 1. 2. 2.]
    [0. 0. 1. 2. 2.]
    [3. 3. 4. 5. 5.]
    [6. 6. 7. 8. 8.]
    [6. 6. 7. 8. 8.]]
   [[0. 0. 1. 2. 2.]
    [0. 0. 1. 2. 2.]
    [3. 3. 4. 5. 5.]
    [6. 6. 7. 8. 8.]
    [6. 6. 7. 8. 8.]]
   [[0. 0. 1. 2. 2.]
    [0. 0. 1. 2. 2.]
    [3. 3. 4. 5. 5.]
    [6. 6. 7. 8. 8.]
    [6. 6. 7. 8. 8.]]]]]