mindspore.nn.Pad

class mindspore.nn.Pad(paddings, mode='CONSTANT')[source]

Pads the input tensor according to the paddings and mode.

Parameters

  • paddings (tuple) – The shape of parameter paddings is (N, 2). N is the rank of input data. All elements of paddings are int type. For D th dimension of input, paddings[D, 0] indicates how many sizes to be extended ahead of the D th dimension of the input tensor, and paddings[D, 1] indicates how many sizes to be extended behind of the D th dimension of the input tensor. The padded size of each dimension D of the output is: \(paddings[D, 0] + input\_x.dim\_size(D) + paddings[D, 1]\)

  • mode (str) – Specifies padding mode. The optional values are “CONSTANT”, “REFLECT”, “SYMMETRIC”. Default: “CONSTANT”.

Inputs:

  • input_x (Tensor) - The input tensor.

Outputs:

Tensor, the tensor after padding.

  • If mode is “CONSTANT”, it fills the edge with 0, regardless of the values of the input_x. If the input_x is [[1,2,3], [4,5,6], [7,8,9]] and paddings is [[1,1], [2,2]], then the Outputs is [[0,0,0,0,0,0,0], [0,0,1,2,3,0,0], [0,0,4,5,6,0,0], [0,0,7,8,9,0,0], [0,0,0,0,0,0,0]].

  • If mode is “REFLECT”, it uses a way of symmetrical copying through the axis of symmetry to fill in. If the input_x is [[1,2,3], [4,5,6], [7,8,9]] and paddings is [[1,1], [2,2]], then the Outputs is [[6,5,4,5,6,5,4], [3,2,1,2,3,2,1], [6,5,4,5,6,5,4], [9,8,7,8,9,8,7], [6,5,4,5,6,5,4]].

  • If mode is “SYMMETRIC”, the filling method is similar to the “REFLECT”. It is also copied according to the symmetry axis, except that it includes the symmetry axis. If the input_x is [[1,2,3], [4,5,6], [7,8,9]] and paddings is [[1,1], [2,2]], then the Outputs is [[2,1,1,2,3,3,2], [2,1,1,2,3,3,2], [5,4,4,5,6,6,5], [8,7,7,8,9,9,8], [8,7,7,8,9,9,8]].

Raises

  • TypeError – If paddings is not a tuple.

  • ValueError – If length of paddings is more than 4 or its shape is not (n, 2).

  • ValueError – If mode is not one of ‘CONSTANT’, ‘REFLECT’, ‘SYMMETRIC’.

Supported Platforms:

Ascend GPU CPU

Examples

>>> from mindspore import Tensor
>>> from mindspore.ops import operations as P
>>> import mindspore.nn as nn
>>> import numpy as np
>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.pad = nn.Pad(paddings=((1, 1), (2, 2)), mode="CONSTANT")
...     def construct(self, x):
...         return self.pad(x)
>>> x = np.array([[0.3, 0.5, 0.2], [0.5, 0.7, 0.3]], dtype=np.float32)
>>> pad = Net()
>>> output = pad(Tensor(x))
>>> print(output)
[[0.         0.         0.         0.         0.         0.        0.         ]
 [0.         0.         0.3        0.5        0.2        0.        0.         ]
 [0.         0.         0.5        0.7        0.3        0.        0.         ]
 [0.         0.         0.         0.         0.         0.        0.         ]]