mindspore.nn.ReplicationPad3d

class mindspore.nn.ReplicationPad3d(padding)

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 $(pa{d}_{left},pa{d}_{right},pa{d}_{up},pa{d}_{down},pa{d}_{front},pa{d}_{back})$ 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 $D_{out}=D_{in}+pa{d}_{front}+pa{d}_{back}$, $H_{out}=H_{in}+pa{d}_{up}+pa{d}_{down}$, $W_{out}=W_{in}+pa{d}_{left}+pa{d}_{right}$.

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.]]]]]