mindspore.ops.MirrorPad
- class mindspore.ops.MirrorPad(mode='REFLECT')[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) - Tensor of shape \((N, *)\), where \(*\) means, any number of additional dimensions.
paddings (Tensor) - Paddings requires constant 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. Both paddings[D, 0] and paddings[D, 1] must be no greater than input_x.dim_size(D) (or input_x.dim_size(D) - 1) if mode is SYMMETRIC (if REFLECT, respectively).
- 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]]. For a more intuitive understanding, please see the example below.
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]]. For a more intuitive understanding, please see the example below.
- Raises
TypeError – If input_x or paddings is not a Tensor.
TypeError – If mode is not a str.
ValueError – If paddings.size is not equal to 2 * rank of input_x.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> from mindspore import Tensor, nn, ops >>> # case1: mode="REFLECT" >>> class Net(nn.Cell): ... def __init__(self, mode): ... super(Net, self).__init__() ... self.pad = ops.MirrorPad(mode=mode) ... self.paddings = Tensor([[1, 1], [2, 2]]) ... def construct(self, input_x): ... return self.pad(input_x, self.paddings) ... >>> input_x = Tensor([[1,2,3], [4,5,6], [7,8,9]]) >>> pad = Net("REFLECT") >>> output = pad(input_x) >>> print(output) [[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]] >>> # case2: mode="SYMMETRIC" >>> pad = Net("SYMMETRIC") >>> output = pad(input_x) >>> print(output) [[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]]