mindspore.ops.pad

mindspore.ops.pad(input_x, paddings)[source]

Pads the input tensor according to the paddings.

The formula to calculate the shape of the output tensor is as follows,

\begin{array}{r} \begin{aligned} input_x_shape = (N_{1}, N_{2}, . . ., N_{n}) \\ \begin{aligned} output_shape = ( & N_{1} + p a d d i n g s [0, 0] + p a d d i n g s [0, 1], \\ N_{2} + p a d d i n g s [1, 0] + p a d d i n g s [1, 1], \\ . . ., \\ N_{n} + p a d d i n g s [n - 1, 0] + p a d d i n g s [n - 1, 1]) \end{aligned} \end{aligned} \end{array}

Note

Negative paddings value is only supported when input_x is not dynamic shape.

Parameters

input_x (Tensor) – Tensor of shape $(N, *)$ , where $*$ means, any number of additional dimensions.
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 the input in D th dimension, paddings[D, 0] indicates how many sizes to be extended(if this value > 0) or clipped(if this value < 0) ahead of the input tensor in the D th dimension, and paddings[D, 1] indicates how many sizes to be extended(if this value > 0) or clipped(if this value < 0) behind the input tensor in the D th dimension.

Returns

Tensor, the tensor after padding.

Raises

TypeError – If paddings is not a tuple.
TypeError – If input_x is not a Tensor.
ValueError – If shape of paddings is not $(N, 2)$ .
ValueError – If paddings.size is not equal to 2 * len(input_x).
ValueError – If the calculated output shape contains zero or negative dimension.
ValueError – If paddings contains negative value and input_x is dynamic shape.

Supported Platforms:: Ascend GPU CPU

Examples

>>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
>>> paddings = ((1, 2), (2, 1))
>>> output = ops.pad(input_x, paddings)
>>> print(output)
[[ 0.   0.   0.   0.   0.   0. ]
 [ 0.   0.  -0.1  0.3  3.6  0. ]
 [ 0.   0.   0.4  0.5 -3.2  0. ]
 [ 0.   0.   0.   0.   0.   0. ]
 [ 0.   0.   0.   0.   0.   0. ]]