mindspore.ops.MirrorPad

class mindspore.ops.MirrorPad(*args, **kwargs)[source]

Pads the input tensor according to the paddings and mode.

Parameters

mode (str) – Specifies the padding mode. The optional values are “REFLECT” and “SYMMETRIC”. Default: “REFLECT”.

Inputs:

  • input_x (Tensor) - The input tensor.

  • paddings (Tensor) - The paddings tensor. The value of paddings is a matrix(list), and its shape is (N, 2). N is the rank of input data. All elements of paddings are int type. For the input in the D th dimension, paddings[D, 0] indicates how many sizes to be extended ahead of the input tensor in the D th dimension, and paddings[D, 1] indicates how many sizes to be extended behind the input tensor in the D th dimension.

Outputs:

Tensor, the tensor after padding.

  • 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 input_x or paddings is not a Tensor.

  • TypeError – If mode is not a str.

Supported Platforms:

Ascend GPU CPU

Examples

>>> from mindspore import Tensor
>>> from mindspore.ops import operations as ops
>>> import mindspore.nn as nn
>>> import numpy as np
>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.pad = ops.MirrorPad(mode="REFLECT")
...     def construct(self, x, paddings):
...         return self.pad(x, paddings)
...
>>> x = np.random.random(size=(2, 3)).astype(np.float32)
>>> paddings = Tensor([[1, 1], [2, 2]])
>>> pad = Net()
>>> output = pad(Tensor(x), paddings)
>>> print(output.shape)
(4, 7)