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mindspore.nn.ReplicationPad2d

class mindspore.nn.ReplicationPad2d(padding)

Pad on HW dimension of input x according to padding.

Parameters

padding (union[int, tuple]) –

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

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

• If padding is a tuple, uses $(pa{d}_{left},pa{d}_{right},pa{d}_{up},pa{d}_{down})$ to pad.

Inputs:

• x (Tensor) - 3D or 4D, shape: $(C,H_{in},W_{in})$ or $(N,C,H_{in},W_{in})$.

Outputs:

Tensor, after padding. Shape: $(C,H_{out},W_{out})$ or $(N,C,H_{out},W_{out})$, where $H_{out}=H_{in}+pa{d}_{up}+pa{d}_{down}$, $W_{out}=W_{in}+pa{d}_{left}+pa{d}_{right}$.

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
>>> pad2d = ms.nn.ReplicationPad2d(2)
>>> input = ms.Tensor(np.arange(0, 9).reshape(1, 1, 3, 3), ms.float32)
>>> print(input)
[[[[0. 1. 2.]
   [3. 4. 5.]
   [6. 7. 8.]]]]
>>> out = pad2d(input)
>>> print(out)
[[[[0. 0. 0. 1. 2. 2. 2.]
   [0. 0. 0. 1. 2. 2. 2.]
   [0. 0. 0. 1. 2. 2. 2.]
   [3. 3. 3. 4. 5. 5. 5.]
   [6. 6. 6. 7. 8. 8. 8.]
   [6. 6. 6. 7. 8. 8. 8.]
   [6. 6. 6. 7. 8. 8. 8.]]]]
>>> pad2d = ms.nn.ReplicationPad2d((1, 1, 2, 0))
>>> out = pad2d(input)
>>> print(out)
[[[[0. 0. 1. 2. 2.]
   [0. 0. 1. 2. 2.]
   [0. 0. 1. 2. 2.]
   [3. 3. 4. 5. 5.]
   [6. 6. 7. 8. 8.]]]]